1. 线性模块，一阶特征(未经过显示特征交叉处理)，对应论文中的$l_z=(l_z^1,l_z^2, ..., l_z^{D_1})$
@@ -230,7 +230,7 @@ class ProductLayer(Layer):
下面是一个通过keras画的模型结构图，为了更好的显示，类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考题
diff --git a/docs/ch02/ch2.2/ch2.2.3/AFM.md b/docs/ch02/ch2.2/ch2.2.3/AFM.md
index 82164853d..1de9803ac 100644
--- a/docs/ch02/ch2.2/ch2.2.3/AFM.md
+++ b/docs/ch02/ch2.2/ch2.2.3/AFM.md
@@ -9,7 +9,7 @@ $$
## AFM模型原理
-
+
上图表示的就是AFM交叉特征部分的模型结构(非交叉部分与FM是一样的，图中并没有给出)。AFM最核心的两个点分别是Pair-wise Interaction Layer和Attention-based Pooling。前者将输入的非零特征的隐向量两两计算element-wise product(哈达玛积，两个向量对应元素相乘，得到的还是一个向量)，假如输入的特征中的非零向量的数量为m，那么经过Pair-wise Interaction Layer之后输出的就是$\frac{m(m-1)}{2}$个向量，再将前面得到的交叉特征向量组输入到Attention-based Pooling，该pooling层会先计算出每个特征组合的自适应权重(通过Attention Net进行计算)，通过加权求和的方式将向量组压缩成一个向量，由于最终需要输出的是一个数值，所以还需要将前一步得到的向量通过另外一个向量将其映射成一个值，得到最终的基于注意力加权的二阶交叉特征的输出。(对于这部分如果不是很清楚，可以先看下面对两个核心层的介绍)
@@ -109,13 +109,13 @@ def AFM(linear_feature_columns, dnn_feature_columns):
关于每一块的细节，这里就不解释了，在我们给出的GitHub代码中，我们已经加了非常详细的注释，大家看那个应该很容易看明白， 为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考
diff --git a/docs/ch02/ch2.2/ch2.2.3/DeepFM.md b/docs/ch02/ch2.2/ch2.2.3/DeepFM.md
index 93d532fab..c03efaf7a 100644
--- a/docs/ch02/ch2.2/ch2.2.3/DeepFM.md
+++ b/docs/ch02/ch2.2/ch2.2.3/DeepFM.md
@@ -7,17 +7,17 @@
- **DNN局限**
当我们使用DNN网络解决推荐问题的时候存在网络参数过于庞大的问题，这是因为在进行特征处理的时候我们需要使用one-hot编码来处理离散特征，这会导致输入的维度猛增。这里借用AI大会的一张图片：
-
+
这样庞大的参数量也是不实际的。为了解决DNN参数量过大的局限性，可以采用非常经典的Field思想，将OneHot特征转换为Dense Vector
-
+
此时通过增加全连接层就可以实现高阶的特征组合，如下图所示：
-
+
但是仍然缺少低阶的特征组合，于是增加FM来表示低阶的特征组合。
@@ -25,7 +25,7 @@
结合FM和DNN其实有两种方式，可以并行结合也可以串行结合。这两种方式各有几种代表模型。在DeepFM之前有FNN，虽然在影响力上可能并不如DeepFM，但是了解FNN的思想对我们理解DeepFM的特点和优点是很有帮助的。
-
+
FNN是使用预训练好的FM模块，得到隐向量，然后把隐向量作为DNN的输入，但是经过实验进一步发现，在Embedding layer和hidden layer1之间增加一个product层（如上图所示）可以提高模型的表现，所以提出了PNN，使用product layer替换FM预训练层。
@@ -33,7 +33,7 @@ FNN是使用预训练好的FM模块，得到隐向量，然后把隐向量作为
- **Wide&Deep**
FNN和PNN模型仍然有一个比较明显的尚未解决的缺点：对于低阶组合特征学习到的比较少，这一点主要是由于FM和DNN的串行方式导致的，也就是虽然FM学到了低阶特征组合，但是DNN的全连接结构导致低阶特征并不能在DNN的输出端较好的表现。看来我们已经找到问题了，将串行方式改进为并行方式能比较好的解决这个问题。于是Google提出了Wide&Deep模型（将前几章），但是如果深入探究Wide&Deep的构成方式，虽然将整个模型的结构调整为了并行结构，在实际的使用中Wide Module中的部分需要较为精巧的特征工程，换句话说人工处理对于模型的效果具有比较大的影响（这一点可以在Wide&Deep模型部分得到验证）。
-
+
如上图所示，该模型仍然存在问题：**在output Units阶段直接将低阶和高阶特征进行组合，很容易让模型最终偏向学习到低阶或者高阶的特征，而不能做到很好的结合。**
@@ -41,7 +41,7 @@ FNN和PNN模型仍然有一个比较明显的尚未解决的缺点：对于低
## 模型的结构与原理
-
+
前面的Field和Embedding处理是和前面的方法是相同的，如上图中的绿色部分；DeepFM将Wide部分替换为了FM layer如上图中的蓝色部分
@@ -58,12 +58,12 @@ $$
\hat{y}_{FM}(x) = w_0+\sum_{i=1}^N w_ix_i + \sum_{i=1}^N \sum_{j=i+1}^N v_i^T v_j x_ix_j
$$
-
+
### Deep
Deep架构图
-
+
Deep Module是为了学习高阶的特征组合，在上图中使用用全连接的方式将Dense Embedding输入到Hidden Layer，这里面Dense Embeddings就是为了解决DNN中的参数爆炸问题，这也是推荐模型中常用的处理方法。
@@ -130,13 +130,13 @@ def DeepFM(linear_feature_columns, dnn_feature_columns):
关于每一块的细节，这里就不解释了，在我们给出的GitHub代码中，我们已经加了非常详细的注释，大家看那个应该很容易看明白， 为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考
@@ -144,7 +144,7 @@ def DeepFM(linear_feature_columns, dnn_feature_columns):
2. 对于下图所示，根据你的理解Sparse Feature中的不同颜色节点分别表示什么意思
-
+
diff --git a/docs/ch02/ch2.2/ch2.2.3/NFM.md b/docs/ch02/ch2.2/ch2.2.3/NFM.md
index 0ccd2caa3..e7945ff00 100644
--- a/docs/ch02/ch2.2/ch2.2.3/NFM.md
+++ b/docs/ch02/ch2.2/ch2.2.3/NFM.md
@@ -10,11 +10,11 @@ $$
我们对比FM， 就会发现变化的是第三项，前两项还是原来的， 因为我们说FM的一个问题，就是只能到二阶交叉， 且是线性模型， 这是他本身的一个局限性， 而如果想突破这个局限性， 就需要从他的公式本身下点功夫， 于是乎，作者在这里改进的思路就是**用一个表达能力更强的函数来替代原FM中二阶隐向量内积的部分**。
-
+
而这个表达能力更强的函数呢， 我们很容易就可以想到神经网络来充当，因为神经网络理论上可以拟合任何复杂能力的函数， 所以作者真的就把这个$f(x)$换成了一个神经网络，当然不是一个简单的DNN， 而是依然底层考虑了交叉，然后高层使用的DNN网络， 这个也就是我们最终的NFM网络了：
-
+
这个结构，如果前面看过了PNN的伙伴会发现，这个结构和PNN非常像，只不过那里是一个product_layer， 而这里换成了Bi-Interaction Pooling了， 这个也是NFM的核心结构了。这里注意， 这个结构中，忽略了一阶部分，只可视化出来了$f(x)$， 我们还是下面从底层一点点的对这个网络进行剖析。
@@ -130,11 +130,11 @@ def NFM(linear_feature_columns, dnn_feature_columns):
有了上面的解释，这个模型的宏观层面相信就很容易理解了。关于这每一块的细节，这里就不解释了，在我们给出的GitHub代码中，我们已经加了非常详细的注释，大家看那个应该很容易看明白， 为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播。（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考题
diff --git a/docs/ch02/ch2.2/ch2.2.3/WideNDeep.md b/docs/ch02/ch2.2/ch2.2.3/WideNDeep.md
index a42f0872f..504ef9cb4 100644
--- a/docs/ch02/ch2.2/ch2.2.3/WideNDeep.md
+++ b/docs/ch02/ch2.2/ch2.2.3/WideNDeep.md
@@ -12,7 +12,7 @@ Wide&Deep模型就是围绕记忆性和泛化性进行讨论的，模型能够
## 模型结构及原理
-
+
其实wide&deep模型本身的结构是非常简单的，对于有点机器学习基础和深度学习基础的人来说都非常的容易看懂，但是如何根据自己的场景去选择那些特征放在Wide部分，哪些特征放在Deep部分就需要理解这篇论文提出者当时对于设计该模型不同结构时的意图了，所以这也是用好这个模型的一个前提。
@@ -88,13 +88,13 @@ def WideNDeep(linear_feature_columns, dnn_feature_columns):
关于每一块的细节，这里就不解释了，在我们给出的GitHub代码中，我们已经加了非常详细的注释，大家看那个应该很容易看明白， 为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播。（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考
diff --git a/docs/ch02/ch2.2/ch2.2.4/DIEN.md b/docs/ch02/ch2.2/ch2.2.4/DIEN.md
index 37a21e713..9be1b388d 100644
--- a/docs/ch02/ch2.2/ch2.2.4/DIEN.md
+++ b/docs/ch02/ch2.2/ch2.2.4/DIEN.md
@@ -6,7 +6,7 @@ DIN模型考虑了用户兴趣，并且强调用户兴趣是多样的，该模
## DIEN模型原理
-
+
模型的输入可以分成两大部分，一部分是用户的行为序列(这部分会通过兴趣提取层及兴趣演化层转换成与用户当前兴趣相关的embedding)，另一部分就是除了用户行为以外的其他所有特征，如Target id, Coontext Feature, UserProfile Feature，这些特征都转化成embedding的类型然后concat在一起（形成一个大的embedding）作为非行为相关的特征(这里可能也会存在一些非id类特征，应该可以直接进行concat)。最后DNN输入的部分由行为序列embedding和非行为特征embedding（多个特征concat到一起之后形成的一个大的向量）组成，将两者concat之后输入到DNN中。
@@ -23,13 +23,13 @@ DIN模型考虑了用户兴趣，并且强调用户兴趣是多样的，该模
首先需要明确的就是辅助损失是计算哪两个量的损失。计算的是用户每个时刻的兴趣表示（GRU每个时刻输出的隐藏状态形成的序列）与用户当前时刻实际点击的物品表示（输入的embedding序列）之间的损失，相当于是行为序列中的第t+1个物品与用户第t时刻的兴趣表示之间的损失**（为什么这里用户第t时刻的兴趣与第t+1时刻的真实点击做损失呢？我的理解是，只有知道了用户第t+1真实点击的商品，才能更好的确定用户第t时刻的兴趣）。**
-
+
当然，如果只计算用户点击物品与其点击前一次的兴趣之间的损失，只能认为是正样本之间的损失，那么用户第t时刻的兴趣其实还有很多其他的未点击的商品，这些未点击的商品就是负样本，负样本一般通过从用户点击序列中采样得到，这样一来辅助损失中就包含了用户某个时刻下的兴趣及与该时刻兴趣相关的正负物品。所以最终的损失函数表示如下。
-
+
其中$h_t^i$表示的是用户$i$第$t$时刻的隐藏状态，可以表示用户第$t$时刻的兴趣向量，$e_b^i，\hat{e_b^i}$分别表示的是正负样本，$e_b^i[t+1]$表示的是用户$i$第$t+1$时刻点击的物品向量。
@@ -56,7 +56,7 @@ $$
由于用户的兴趣是多样的，但是用户的每一种兴趣都有自己的发展过程，即使兴趣发生漂移我们可以只考虑用户与target item(广告或者商品)相关的兴趣演化过程，这样就不用考虑用户多样化的兴趣的问题了，而如何只获取与target item相关的信息，作者使用了与DIN模型中提取与target item相同的方法，来计算用户历史兴趣与target item之间的相似度，即这里也使用了DIN中介绍的局部激活单元(就是下图中的Attention模块)。
-
+
当得到了用户历史兴趣序列及兴趣序列与target item之间的相关性(注意力分数)之后，就需要再次对注意力序列进行建模得到用户注意力的演化过程，进一步表示用户最终的兴趣向量。此时的序列数据等同于有了一个序列及序列中每个向量的注意力权重，下面就是考虑如何使用这个注意力权重来一起优化序列建模的结果了。作者提出了三种注意力结合的GRU模型快：
diff --git a/docs/ch02/ch2.2/ch2.2.4/DIN.md b/docs/ch02/ch2.2/ch2.2.4/DIN.md
index f99a9c5fa..dc104de6f 100644
--- a/docs/ch02/ch2.2/ch2.2.4/DIN.md
+++ b/docs/ch02/ch2.2/ch2.2.4/DIN.md
@@ -158,13 +158,13 @@ def DIN(feature_columns, behavior_feature_list, behavior_seq_feature_list):
关于每一块的细节，这里就不解释了，在我们给出的GitHub代码中，我们已经加了非常详细的注释，大家看那个应该很容易看明白， 为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播。（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 思考
diff --git "a/docs/ch03/ch3.1/jupyter/\345\244\232\350\267\257\345\217\254\345\233\236.ipynb" "b/docs/ch03/ch3.1/jupyter/\345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
index 3a4bccd4e..08bc05222 100644
--- "a/docs/ch03/ch3.1/jupyter/\345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
+++ "b/docs/ch03/ch3.1/jupyter/\345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
@@ -1,2107 +1,2107 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 多路召回\n",
- "\n",
- "所谓的“多路召回”策略，就是指采用不同的策略、特征或简单模型，分别召回一部分候选集，然后把候选集混合在一起供后续排序模型使用，可以明显的看出，“多路召回策略”是在“计算速度”和“召回率”之间进行权衡的结果。其中，各种简单策略保证候选集的快速召回，从不同角度设计的策略保证召回率接近理想的状态，不至于损伤排序效果。如下图是多路召回的一个示意图，在多路召回中，每个策略之间毫不相关，所以一般可以写并发多线程同时进行，这样可以更加高效。\n",
- "\n",
- "
\n",
- "\n",
- "上图只是一个多路召回的例子，也就是说可以使用多种不同的策略来获取用户排序的候选商品集合，而具体使用哪些召回策略其实是与业务强相关的 ，针对不同的任务就会有对于该业务真实场景下需要考虑的召回规则。例如新闻推荐，召回规则可以是“热门新闻”、“作者召回”、“关键词召回”、“主题召回“、”协同过滤召回“等等。 \n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 导包"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:29.834662Z",
- "start_time": "2020-11-16T11:26:27.811511Z"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd \n",
- "import numpy as np\n",
- "from tqdm import tqdm \n",
- "from collections import defaultdict \n",
- "import os, math, warnings, math, pickle\n",
- "from tqdm import tqdm\n",
- "import faiss\n",
- "import collections\n",
- "import random\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from datetime import datetime\n",
- "from deepctr.feature_column import SparseFeat, VarLenSparseFeat\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from tensorflow.python.keras import backend as K\n",
- "from tensorflow.python.keras.models import Model\n",
- "from tensorflow.python.keras.preprocessing.sequence import pad_sequences\n",
- "\n",
- "from deepmatch.models import *\n",
- "from deepmatch.utils import sampledsoftmaxloss\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:31.831215Z",
- "start_time": "2020-11-16T11:26:31.826939Z"
- }
- },
- "outputs": [],
- "source": [
- "data_path = './data_raw/'\n",
- "save_path = './temp_results/'\n",
- "# 做召回评估的一个标志, 如果不进行评估就是直接使用全量数据进行召回\n",
- "metric_recall = False"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 读取数据\n",
- "在一般的rs比赛中读取数据部分主要分为三种模式， 不同的模式对应的不同的数据集：\n",
- "1. debug模式： 这个的目的是帮助我们基于数据先搭建一个简易的baseline并跑通， 保证写的baseline代码没有什么问题。 由于推荐比赛的数据往往非常巨大， 如果一上来直接采用全部的数据进行分析，搭建baseline框架， 往往会带来时间和设备上的损耗， **所以这时候我们往往需要从海量数据的训练集中随机抽取一部分样本来进行调试(train_click_log_sample)**， 先跑通一个baseline。\n",
- "2. 线下验证模式： 这个的目的是帮助我们在线下基于已有的训练集数据， 来选择好合适的模型和一些超参数。 **所以我们这一块只需要加载整个训练集(train_click_log)**， 然后把整个训练集再分成训练集和验证集。 训练集是模型的训练数据， 验证集部分帮助我们调整模型的参数和其他的一些超参数。\n",
- "3. 线上模式： 我们用debug模式搭建起一个推荐系统比赛的baseline， 用线下验证模式选择好了模型和一些超参数， 这一部分就是真正的对于给定的测试集进行预测， 提交到线上， **所以这一块使用的训练数据集是全量的数据集(train_click_log+test_click_log)**\n",
- "\n",
- "下面就分别对这三种不同的数据读取模式先建立不同的代导入函数， 方便后面针对不同的模式下导入数据。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:34.476240Z",
- "start_time": "2020-11-16T11:26:34.467352Z"
- }
- },
- "outputs": [],
- "source": [
- "# debug模式： 从训练集中划出一部分数据来调试代码\n",
- "def get_all_click_sample(data_path, sample_nums=10000):\n",
- " \"\"\"\n",
- " 训练集中采样一部分数据调试\n",
- " data_path: 原数据的存储路径\n",
- " sample_nums: 采样数目（这里由于机器的内存限制，可以采样用户做）\n",
- " \"\"\"\n",
- " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " all_user_ids = all_click.user_id.unique()\n",
- "\n",
- " sample_user_ids = np.random.choice(all_user_ids, size=sample_nums, replace=False) \n",
- " all_click = all_click[all_click['user_id'].isin(sample_user_ids)]\n",
- " \n",
- " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
- " return all_click\n",
- "\n",
- "# 读取点击数据，这里分成线上和线下，如果是为了获取线上提交结果应该讲测试集中的点击数据合并到总的数据中\n",
- "# 如果是为了线下验证模型的有效性或者特征的有效性，可以只使用训练集\n",
- "def get_all_click_df(data_path='./data_raw/', offline=True):\n",
- " if offline:\n",
- " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " else:\n",
- " trn_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " tst_click = pd.read_csv(data_path + 'testA_click_log.csv')\n",
- "\n",
- " all_click = trn_click.append(tst_click)\n",
- " \n",
- " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
- " return all_click"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:35.168738Z",
- "start_time": "2020-11-16T11:26:35.163210Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取文章的基本属性\n",
- "def get_item_info_df(data_path):\n",
- " item_info_df = pd.read_csv(data_path + 'articles.csv')\n",
- " \n",
- " # 为了方便与训练集中的click_article_id拼接，需要把article_id修改成click_article_id\n",
- " item_info_df = item_info_df.rename(columns={'article_id': 'click_article_id'})\n",
- " \n",
- " return item_info_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:36.152958Z",
- "start_time": "2020-11-16T11:26:36.146324Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取文章的Embedding数据\n",
- "def get_item_emb_dict(data_path):\n",
- " item_emb_df = pd.read_csv(data_path + 'articles_emb.csv')\n",
- " \n",
- " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
- " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols])\n",
- " # 进行归一化\n",
- " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
- "\n",
- " item_emb_dict = dict(zip(item_emb_df['article_id'], item_emb_np))\n",
- " pickle.dump(item_emb_dict, open(save_path + 'item_content_emb.pkl', 'wb'))\n",
- " \n",
- " return item_emb_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:37.333536Z",
- "start_time": "2020-11-16T11:26:37.329545Z"
- }
- },
- "outputs": [],
- "source": [
- "max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:42.163494Z",
- "start_time": "2020-11-16T11:26:38.018094Z"
- }
- },
- "outputs": [],
- "source": [
- "# 采样数据\n",
- "# all_click_df = get_all_click_sample(data_path)\n",
- "\n",
- "# 全量训练集\n",
- "all_click_df = get_all_click_df(offline=False)\n",
- "\n",
- "# 对时间戳进行归一化,用于在关联规则的时候计算权重\n",
- "all_click_df['click_timestamp'] = all_click_df[['click_timestamp']].apply(max_min_scaler)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:44.343500Z",
- "start_time": "2020-11-16T11:26:44.113891Z"
- }
- },
- "outputs": [],
- "source": [
- "item_info_df = get_item_info_df(data_path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:24.295343Z",
- "start_time": "2020-11-16T11:26:44.398007Z"
- }
- },
- "outputs": [],
- "source": [
- "item_emb_dict = get_item_emb_dict(data_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 工具函数"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取用户-文章-时间函数\n",
- "这个在基于关联规则的用户协同过滤的时候会用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:33.791656Z",
- "start_time": "2020-11-16T11:27:33.784305Z"
- }
- },
- "outputs": [],
- "source": [
- "# 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- "def get_user_item_time(click_df):\n",
- " \n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " \n",
- " def make_item_time_pair(df):\n",
- " return list(zip(df['click_article_id'], df['click_timestamp']))\n",
- " \n",
- " user_item_time_df = click_df.groupby('user_id')['click_article_id', 'click_timestamp'].apply(lambda x: make_item_time_pair(x))\\\n",
- " .reset_index().rename(columns={0: 'item_time_list'})\n",
- " user_item_time_dict = dict(zip(user_item_time_df['user_id'], user_item_time_df['item_time_list']))\n",
- " \n",
- " return user_item_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取文章-用户-时间函数\n",
- "这个在基于关联规则的文章协同过滤的时候会用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:38.327581Z",
- "start_time": "2020-11-16T11:27:38.321059Z"
- }
- },
- "outputs": [],
- "source": [
- "# 根据时间获取商品被点击的用户序列 {item1: [(user1, time1), (user2, time2)...]...}\n",
- "# 这里的时间是用户点击当前商品的时间，好像没有直接的关系。\n",
- "def get_item_user_time_dict(click_df):\n",
- " def make_user_time_pair(df):\n",
- " return list(zip(df['user_id'], df['click_timestamp']))\n",
- " \n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " item_user_time_df = click_df.groupby('click_article_id')['user_id', 'click_timestamp'].apply(lambda x: make_user_time_pair(x))\\\n",
- " .reset_index().rename(columns={0: 'user_time_list'})\n",
- " \n",
- " item_user_time_dict = dict(zip(item_user_time_df['click_article_id'], item_user_time_df['user_time_list']))\n",
- " return item_user_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取历史和最后一次点击\n",
- "这个在评估召回结果， 特征工程和制作标签转成监督学习测试集的时候回用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:50.894683Z",
- "start_time": "2020-11-16T11:27:50.888002Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取当前数据的历史点击和最后一次点击\n",
- "def get_hist_and_last_click(all_click):\n",
- " \n",
- " all_click = all_click.sort_values(by=['user_id', 'click_timestamp'])\n",
- " click_last_df = all_click.groupby('user_id').tail(1)\n",
- "\n",
- " # 如果用户只有一个点击，hist为空了，会导致训练的时候这个用户不可见，此时默认泄露一下\n",
- " def hist_func(user_df):\n",
- " if len(user_df) == 1:\n",
- " return user_df\n",
- " else:\n",
- " return user_df[:-1]\n",
- "\n",
- " click_hist_df = all_click.groupby('user_id').apply(hist_func).reset_index(drop=True)\n",
- "\n",
- " return click_hist_df, click_last_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取文章属性特征"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:55.893810Z",
- "start_time": "2020-11-16T11:27:55.887623Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取文章id对应的基本属性，保存成字典的形式，方便后面召回阶段，冷启动阶段直接使用\n",
- "def get_item_info_dict(item_info_df):\n",
- " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
- " item_info_df['created_at_ts'] = item_info_df[['created_at_ts']].apply(max_min_scaler)\n",
- " \n",
- " item_type_dict = dict(zip(item_info_df['click_article_id'], item_info_df['category_id']))\n",
- " item_words_dict = dict(zip(item_info_df['click_article_id'], item_info_df['words_count']))\n",
- " item_created_time_dict = dict(zip(item_info_df['click_article_id'], item_info_df['created_at_ts']))\n",
- " \n",
- " return item_type_dict, item_words_dict, item_created_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T06:42:38.730939Z",
- "start_time": "2020-11-13T06:42:38.728461Z"
- }
- },
- "source": [
- "### 获取用户历史点击的文章信息"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:59.650781Z",
- "start_time": "2020-11-16T11:27:59.640572Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_user_hist_item_info_dict(all_click):\n",
- " \n",
- " # 获取user_id对应的用户历史点击文章类型的集合字典\n",
- " user_hist_item_typs = all_click.groupby('user_id')['category_id'].agg(set).reset_index()\n",
- " user_hist_item_typs_dict = dict(zip(user_hist_item_typs['user_id'], user_hist_item_typs['category_id']))\n",
- " \n",
- " # 获取user_id对应的用户点击文章的集合\n",
- " user_hist_item_ids_dict = all_click.groupby('user_id')['click_article_id'].agg(set).reset_index()\n",
- " user_hist_item_ids_dict = dict(zip(user_hist_item_ids_dict['user_id'], user_hist_item_ids_dict['click_article_id']))\n",
- " \n",
- " # 获取user_id对应的用户历史点击的文章的平均字数字典\n",
- " user_hist_item_words = all_click.groupby('user_id')['words_count'].agg('mean').reset_index()\n",
- " user_hist_item_words_dict = dict(zip(user_hist_item_words['user_id'], user_hist_item_words['words_count']))\n",
- " \n",
- " # 获取user_id对应的用户最后一次点击的文章的创建时间\n",
- " all_click_ = all_click.sort_values('click_timestamp')\n",
- " user_last_item_created_time = all_click_.groupby('user_id')['created_at_ts'].apply(lambda x: x.iloc[-1]).reset_index()\n",
- " \n",
- " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
- " user_last_item_created_time['created_at_ts'] = user_last_item_created_time[['created_at_ts']].apply(max_min_scaler)\n",
- " \n",
- " user_last_item_created_time_dict = dict(zip(user_last_item_created_time['user_id'], \\\n",
- " user_last_item_created_time['created_at_ts']))\n",
- " \n",
- " return user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取点击次数最多的topk个文章"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:04.761105Z",
- "start_time": "2020-11-16T11:28:04.756419Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取近期点击最多的文章\n",
- "def get_item_topk_click(click_df, k):\n",
- " topk_click = click_df['click_article_id'].value_counts().index[:k]\n",
- " return topk_click"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 定义多路召回字典"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:08.321506Z",
- "start_time": "2020-11-16T11:28:07.623281Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取文章的属性信息，保存成字典的形式方便查询\n",
- "item_type_dict, item_words_dict, item_created_time_dict = get_item_info_dict(item_info_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:13.791569Z",
- "start_time": "2020-11-16T11:28:13.786522Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义一个多路召回的字典，将各路召回的结果都保存在这个字典当中\n",
- "user_multi_recall_dict = {'itemcf_sim_itemcf_recall': {},\n",
- " 'embedding_sim_item_recall': {},\n",
- " 'youtubednn_recall': {},\n",
- " 'youtubednn_usercf_recall': {}, \n",
- " 'cold_start_recall': {}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T05:41:12.710754Z",
- "start_time": "2020-11-16T05:40:57.842614Z"
- }
- },
- "outputs": [],
- "source": [
- "# 提取最后一次点击作为召回评估，如果不需要做召回评估直接使用全量的训练集进行召回(线下验证模型)\n",
- "# 如果不是召回评估，直接使用全量数据进行召回，不用将最后一次提取出来\n",
- "trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 召回效果评估函数\n",
- "做完了召回有时候也需要对当前的召回方法或者参数进行调整以达到更好的召回效果，因为召回的结果决定了最终排序的上限，下面也会提供一个召回评估的方法"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T05:41:18.579118Z",
- "start_time": "2020-11-16T05:41:18.571887Z"
- }
- },
- "outputs": [],
- "source": [
- "# 依次评估召回的前10, 20, 30, 40, 50个文章中的击中率\n",
- "def metrics_recall(user_recall_items_dict, trn_last_click_df, topk=5):\n",
- " last_click_item_dict = dict(zip(trn_last_click_df['user_id'], trn_last_click_df['click_article_id']))\n",
- " user_num = len(user_recall_items_dict)\n",
- " \n",
- " for k in range(10, topk+1, 10):\n",
- " hit_num = 0\n",
- " for user, item_list in user_recall_items_dict.items():\n",
- " # 获取前k个召回的结果\n",
- " tmp_recall_items = [x[0] for x in user_recall_items_dict[user][:k]]\n",
- " if last_click_item_dict[user] in set(tmp_recall_items):\n",
- " hit_num += 1\n",
- " \n",
- " hit_rate = round(hit_num * 1.0 / user_num, 5)\n",
- " print(' topk: ', k, ' : ', 'hit_num: ', hit_num, 'hit_rate: ', hit_rate, 'user_num : ', user_num)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 计算相似性矩阵\n",
- "\n",
- "这一部分主要是通过协同过滤以及向量检索得到相似性矩阵，相似性矩阵主要分为user2user和item2item，下面依次获取基于itemcf的item2item的相似性矩阵，"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### itemcf i2i_sim\n",
- "\n",
- "借鉴KDD2020的去偏商品推荐，在计算item2item相似性矩阵时，使用关联规则，使得计算的文章的相似性还考虑到了:\n",
- "1. 用户点击的时间权重\n",
- "2. 用户点击的顺序权重\n",
- "3. 文章创建的时间权重"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:30:51.872262Z",
- "start_time": "2020-11-16T11:30:51.860099Z"
- }
- },
- "outputs": [],
- "source": [
- "def itemcf_sim(df, item_created_time_dict):\n",
- " \"\"\"\n",
- " 文章与文章之间的相似性矩阵计算\n",
- " :param df: 数据表\n",
- " :item_created_time_dict: 文章创建时间的字典\n",
- " return : 文章与文章的相似性矩阵\n",
- " \n",
- " 思路: 基于物品的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
- " \"\"\"\n",
- " \n",
- " user_item_time_dict = get_user_item_time(df)\n",
- " \n",
- " # 计算物品相似度\n",
- " i2i_sim = {}\n",
- " item_cnt = defaultdict(int)\n",
- " for user, item_time_list in tqdm(user_item_time_dict.items()):\n",
- " # 在基于商品的协同过滤优化的时候可以考虑时间因素\n",
- " for loc1, (i, i_click_time) in enumerate(item_time_list):\n",
- " item_cnt[i] += 1\n",
- " i2i_sim.setdefault(i, {})\n",
- " for loc2, (j, j_click_time) in enumerate(item_time_list):\n",
- " if(i == j):\n",
- " continue\n",
- " \n",
- " # 考虑文章的正向顺序点击和反向顺序点击 \n",
- " loc_alpha = 1.0 if loc2 > loc1 else 0.7\n",
- " # 位置信息权重，其中的参数可以调节\n",
- " loc_weight = loc_alpha * (0.9 ** (np.abs(loc2 - loc1) - 1))\n",
- " # 点击时间权重，其中的参数可以调节\n",
- " click_time_weight = np.exp(0.7 ** np.abs(i_click_time - j_click_time))\n",
- " # 两篇文章创建时间的权重，其中的参数可以调节\n",
- " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " i2i_sim[i].setdefault(j, 0)\n",
- " # 考虑多种因素的权重计算最终的文章之间的相似度\n",
- " i2i_sim[i][j] += loc_weight * click_time_weight * created_time_weight / math.log(len(item_time_list) + 1)\n",
- " \n",
- " i2i_sim_ = i2i_sim.copy()\n",
- " for i, related_items in i2i_sim.items():\n",
- " for j, wij in related_items.items():\n",
- " i2i_sim_[i][j] = wij / math.sqrt(item_cnt[i] * item_cnt[j])\n",
- " \n",
- " # 将得到的相似性矩阵保存到本地\n",
- " pickle.dump(i2i_sim_, open(save_path + 'itemcf_i2i_sim.pkl', 'wb'))\n",
- " \n",
- " return i2i_sim_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:47:09.937002Z",
- "start_time": "2020-11-16T11:30:57.394334Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [14:20<00:00, 290.38it/s]\n"
- ]
- }
- ],
- "source": [
- "i2i_sim = itemcf_sim(all_click_df, item_created_time_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### usercf u2u_sim\n",
- "\n",
- "在计算用户之间的相似度的时候，也可以使用一些简单的关联规则，比如用户活跃度权重，这里将用户的点击次数作为用户活跃度的指标"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:14.951940Z",
- "start_time": "2020-11-16T09:11:14.945654Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_user_activate_degree_dict(all_click_df):\n",
- " all_click_df_ = all_click_df.groupby('user_id')['click_article_id'].count().reset_index()\n",
- " \n",
- " # 用户活跃度归一化\n",
- " mm = MinMaxScaler()\n",
- " all_click_df_['click_article_id'] = mm.fit_transform(all_click_df_[['click_article_id']])\n",
- " user_activate_degree_dict = dict(zip(all_click_df_['user_id'], all_click_df_['click_article_id']))\n",
- " \n",
- " return user_activate_degree_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:19.879276Z",
- "start_time": "2020-11-16T09:11:19.868808Z"
- }
- },
- "outputs": [],
- "source": [
- "def usercf_sim(all_click_df, user_activate_degree_dict):\n",
- " \"\"\"\n",
- " 用户相似性矩阵计算\n",
- " :param all_click_df: 数据表\n",
- " :param user_activate_degree_dict: 用户活跃度的字典\n",
- " return 用户相似性矩阵\n",
- " \n",
- " 思路: 基于用户的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
- " \"\"\"\n",
- " item_user_time_dict = get_item_user_time_dict(all_click_df)\n",
- " \n",
- " u2u_sim = {}\n",
- " user_cnt = defaultdict(int)\n",
- " for item, user_time_list in tqdm(item_user_time_dict.items()):\n",
- " for u, click_time in user_time_list:\n",
- " user_cnt[u] += 1\n",
- " u2u_sim.setdefault(u, {})\n",
- " for v, click_time in user_time_list:\n",
- " u2u_sim[u].setdefault(v, 0)\n",
- " if u == v:\n",
- " continue\n",
- " # 用户平均活跃度作为活跃度的权重，这里的式子也可以改善\n",
- " activate_weight = 100 * 0.5 * (user_activate_degree_dict[u] + user_activate_degree_dict[v]) \n",
- " u2u_sim[u][v] += activate_weight / math.log(len(user_time_list) + 1)\n",
- " \n",
- " u2u_sim_ = u2u_sim.copy()\n",
- " for u, related_users in u2u_sim.items():\n",
- " for v, wij in related_users.items():\n",
- " u2u_sim_[u][v] = wij / math.sqrt(user_cnt[u] * user_cnt[v])\n",
- " \n",
- " # 将得到的相似性矩阵保存到本地\n",
- " pickle.dump(u2u_sim_, open(save_path + 'usercf_u2u_sim.pkl', 'wb'))\n",
- "\n",
- " return u2u_sim_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T06:59:46.701572Z",
- "start_time": "2020-11-16T06:59:26.852246Z"
- }
- },
- "outputs": [],
- "source": [
- "# 由于usercf计算时候太耗费内存了，这里就不直接运行了\n",
- "# 如果是采样的话，是可以运行的\n",
- "user_activate_degree_dict = get_user_activate_degree_dict(all_click_df)\n",
- "u2u_sim = usercf_sim(all_click_df, user_activate_degree_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### item embedding sim\n",
- "\n",
- "使用Embedding计算item之间的相似度是为了后续冷启动的时候可以获取未出现在点击数据中的文章，后面有对冷启动专门的介绍，这里简单的说一下faiss。\n",
- "\n",
- "aiss是Facebook的AI团队开源的一套用于做聚类或者相似性搜索的软件库，底层是用C++实现。Faiss因为超级优越的性能，被广泛应用于推荐相关的业务当中.\n",
- "\n",
- "faiss工具包一般使用在推荐系统中的向量召回部分。在做向量召回的时候要么是u2u,u2i或者i2i，这里的u和i指的是user和item.我们知道在实际的场景中user和item的数量都是海量的，我们最容易想到的基于向量相似度的召回就是使用两层循环遍历user列表或者item列表计算两个向量的相似度，但是这样做在面对海量数据是不切实际的，faiss就是用来加速计算某个查询向量最相似的topk个索引向量。\n",
- "\n",
- "**faiss查询的原理：**\n",
- "\n",
- "faiss使用了PCA和PQ(Product quantization乘积量化)两种技术进行向量压缩和编码，当然还使用了其他的技术进行优化，但是PCA和PQ是其中最核心部分。\n",
- "\n",
- "1. PCA降维算法细节参考下面这个链接进行学习 \n",
- "[主成分分析（PCA）原理总结](https://www.cnblogs.com/pinard/p/6239403.html) \n",
- "\n",
- "2. PQ编码的细节下面这个链接进行学习 \n",
- "[实例理解product quantization算法](http://www.fabwrite.com/productquantization)\n",
- "\n",
- "**faiss使用**\n",
- "\n",
- "[faiss官方教程](https://github.com/facebookresearch/faiss/wiki/Getting-started)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:28.631803Z",
- "start_time": "2020-11-16T09:11:28.619926Z"
- }
- },
- "outputs": [],
- "source": [
- "# 向量检索相似度计算\n",
- "# topk指的是每个item, faiss搜索后返回最相似的topk个item\n",
- "def embdding_sim(click_df, item_emb_df, save_path, topk):\n",
- " \"\"\"\n",
- " 基于内容的文章embedding相似性矩阵计算\n",
- " :param click_df: 数据表\n",
- " :param item_emb_df: 文章的embedding\n",
- " :param save_path: 保存路径\n",
- " :patam topk: 找最相似的topk篇\n",
- " return 文章相似性矩阵\n",
- " \n",
- " 思路: 对于每一篇文章， 基于embedding的相似性返回topk个与其最相似的文章， 只不过由于文章数量太多，这里用了faiss进行加速\n",
- " \"\"\"\n",
- " \n",
- " # 文章索引与文章id的字典映射\n",
- " item_idx_2_rawid_dict = dict(zip(item_emb_df.index, item_emb_df['article_id']))\n",
- " \n",
- " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
- " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols].values, dtype=np.float32)\n",
- " # 向量进行单位化\n",
- " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
- " \n",
- " # 建立faiss索引\n",
- " item_index = faiss.IndexFlatIP(item_emb_np.shape[1])\n",
- " item_index.add(item_emb_np)\n",
- " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
- " sim, idx = item_index.search(item_emb_np, topk) # 返回的是列表\n",
- " \n",
- " # 将向量检索的结果保存成原始id的对应关系\n",
- " item_sim_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(item_emb_np)), sim, idx)):\n",
- " target_raw_id = item_idx_2_rawid_dict[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = item_idx_2_rawid_dict[rele_idx]\n",
- " item_sim_dict[target_raw_id][rele_raw_id] = item_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " # 保存i2i相似度矩阵\n",
- " pickle.dump(item_sim_dict, open(save_path + 'emb_i2i_sim.pkl', 'wb')) \n",
- " \n",
- " return item_sim_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:32:35.926116Z",
- "start_time": "2020-11-16T09:11:44.586967Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "364047it [00:23, 15292.14it/s]\n"
- ]
- }
- ],
- "source": [
- "item_emb_df = pd.read_csv(data_path + '/articles_emb.csv')\n",
- "emb_i2i_sim = embdding_sim(all_click_df, item_emb_df, save_path, topk=10) # topk可以自行设置"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 召回\n",
- "这个就是我们开篇提到的那个问题， 面的36万篇文章， 20多万用户的推荐， 我们又有哪些策略来缩减问题的规模？ 我们就可以再召回阶段筛选出用户对于点击文章的候选集合， 从而降低问题的规模。召回常用的策略：\n",
- "* Youtube DNN 召回\n",
- "* 基于文章的召回\n",
- " * 文章的协同过滤\n",
- " * 基于文章embedding的召回\n",
- "* 基于用户的召回\n",
- " * 用户的协同过滤\n",
- " * 用户embedding\n",
- "\n",
- "上面的各种召回方式一部分在基于用户已经看得文章的基础上去召回与这些文章相似的一些文章， 而这个相似性的计算方式不同， 就得到了不同的召回方式， 比如文章的协同过滤， 文章内容的embedding等。还有一部分是根据用户的相似性进行推荐，对于某用户推荐与其相似的其他用户看过的文章，比如用户的协同过滤和用户embedding。 还有一种思路是类似矩阵分解的思路，先计算出用户和文章的embedding之后，就可以直接算用户和文章的相似度， 根据这个相似度进行推荐， 比如YouTube DNN。 我们下面详细来看一下每一个召回方法："
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### YoutubeDNN召回\n",
- "**(这一步是直接获取用户召回的候选文章列表)**\n",
- "\n",
- "[论文下载地址](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)\n",
- "\n",
- "**Youtubednn召回架构**\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "关于YoutubeDNN原理和应用推荐看王喆的两篇博客：\n",
- "\n",
- "1. [重读Youtube深度学习推荐系统论文，字字珠玑，惊为神文](https://zhuanlan.zhihu.com/p/52169807)\n",
- "2. [YouTube深度学习推荐系统的十大工程问题](https://zhuanlan.zhihu.com/p/52504407)\n",
- "\n",
- "\n",
- "**参考文献:**\n",
- "1. https://zhuanlan.zhihu.com/p/52169807 (YouTubeDNN原理)\n",
- "2. https://zhuanlan.zhihu.com/p/26306795 (Word2Vec知乎众赞文章) --- word2vec放到排序中的w2v的介绍部分\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:13:11.058766Z",
- "start_time": "2020-11-16T10:13:11.041084Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取双塔召回时的训练验证数据\n",
- "# negsample指的是通过滑窗构建样本的时候，负样本的数量\n",
- "def gen_data_set(data, negsample=0):\n",
- " data.sort_values(\"click_timestamp\", inplace=True)\n",
- " item_ids = data['click_article_id'].unique()\n",
- "\n",
- " train_set = []\n",
- " test_set = []\n",
- " for reviewerID, hist in tqdm(data.groupby('user_id')):\n",
- " pos_list = hist['click_article_id'].tolist()\n",
- " \n",
- " if negsample > 0:\n",
- " candidate_set = list(set(item_ids) - set(pos_list)) # 用户没看过的文章里面选择负样本\n",
- " neg_list = np.random.choice(candidate_set,size=len(pos_list)*negsample,replace=True) # 对于每个正样本，选择n个负样本\n",
- " \n",
- " # 长度只有一个的时候，需要把这条数据也放到训练集中，不然的话最终学到的embedding就会有缺失\n",
- " if len(pos_list) == 1:\n",
- " train_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
- " test_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
- " \n",
- " # 滑窗构造正负样本\n",
- " for i in range(1, len(pos_list)):\n",
- " hist = pos_list[:i]\n",
- " \n",
- " if i != len(pos_list) - 1:\n",
- " train_set.append((reviewerID, hist[::-1], pos_list[i], 1, len(hist[::-1]))) # 正样本 [user_id, his_item, pos_item, label, len(his_item)]\n",
- " for negi in range(negsample):\n",
- " train_set.append((reviewerID, hist[::-1], neg_list[i*negsample+negi], 0,len(hist[::-1]))) # 负样本 [user_id, his_item, neg_item, label, len(his_item)]\n",
- " else:\n",
- " # 将最长的那一个序列长度作为测试数据\n",
- " test_set.append((reviewerID, hist[::-1], pos_list[i],1,len(hist[::-1])))\n",
- " \n",
- " random.shuffle(train_set)\n",
- " random.shuffle(test_set)\n",
- " \n",
- " return train_set, test_set\n",
- "\n",
- "# 将输入的数据进行padding，使得序列特征的长度都一致\n",
- "def gen_model_input(train_set,user_profile,seq_max_len):\n",
- "\n",
- " train_uid = np.array([line[0] for line in train_set])\n",
- " train_seq = [line[1] for line in train_set]\n",
- " train_iid = np.array([line[2] for line in train_set])\n",
- " train_label = np.array([line[3] for line in train_set])\n",
- " train_hist_len = np.array([line[4] for line in train_set])\n",
- "\n",
- " train_seq_pad = pad_sequences(train_seq, maxlen=seq_max_len, padding='post', truncating='post', value=0)\n",
- " train_model_input = {\"user_id\": train_uid, \"click_article_id\": train_iid, \"hist_article_id\": train_seq_pad,\n",
- " \"hist_len\": train_hist_len}\n",
- "\n",
- " return train_model_input, train_label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:13:18.124452Z",
- "start_time": "2020-11-16T10:13:18.098284Z"
- }
- },
- "outputs": [],
- "source": [
- "def youtubednn_u2i_dict(data, topk=20): \n",
- " sparse_features = [\"click_article_id\", \"user_id\"]\n",
- " SEQ_LEN = 30 # 用户点击序列的长度，短的填充，长的截断\n",
- " \n",
- " user_profile_ = data[[\"user_id\"]].drop_duplicates('user_id')\n",
- " item_profile_ = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
- " \n",
- " # 类别编码\n",
- " features = [\"click_article_id\", \"user_id\"]\n",
- " feature_max_idx = {}\n",
- " \n",
- " for feature in features:\n",
- " lbe = LabelEncoder()\n",
- " data[feature] = lbe.fit_transform(data[feature])\n",
- " feature_max_idx[feature] = data[feature].max() + 1\n",
- " \n",
- " # 提取user和item的画像，这里具体选择哪些特征还需要进一步的分析和考虑\n",
- " user_profile = data[[\"user_id\"]].drop_duplicates('user_id')\n",
- " item_profile = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
- " \n",
- " user_index_2_rawid = dict(zip(user_profile['user_id'], user_profile_['user_id']))\n",
- " item_index_2_rawid = dict(zip(item_profile['click_article_id'], item_profile_['click_article_id']))\n",
- " \n",
- " # 划分训练和测试集\n",
- " # 由于深度学习需要的数据量通常都是非常大的，所以为了保证召回的效果，往往会通过滑窗的形式扩充训练样本\n",
- " train_set, test_set = gen_data_set(data, 0)\n",
- " # 整理输入数据，具体的操作可以看上面的函数\n",
- " train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)\n",
- " test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)\n",
- " \n",
- " # 确定Embedding的维度\n",
- " embedding_dim = 16\n",
- " \n",
- " # 将数据整理成模型可以直接输入的形式\n",
- " user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),\n",
- " VarLenSparseFeat(SparseFeat('hist_article_id', feature_max_idx['click_article_id'], embedding_dim,\n",
- " embedding_name=\"click_article_id\"), SEQ_LEN, 'mean', 'hist_len'),]\n",
- " item_feature_columns = [SparseFeat('click_article_id', feature_max_idx['click_article_id'], embedding_dim)]\n",
- " \n",
- " # 模型的定义 \n",
- " # num_sampled: 负采样时的样本数量\n",
- " model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))\n",
- " # 模型编译\n",
- " model.compile(optimizer=\"adam\", loss=sampledsoftmaxloss) \n",
- " \n",
- " # 模型训练，这里可以定义验证集的比例，如果设置为0的话就是全量数据直接进行训练\n",
- " history = model.fit(train_model_input, train_label, batch_size=256, epochs=1, verbose=1, validation_split=0.0)\n",
- " \n",
- " # 训练完模型之后,提取训练的Embedding，包括user端和item端\n",
- " test_user_model_input = test_model_input\n",
- " all_item_model_input = {\"click_article_id\": item_profile['click_article_id'].values}\n",
- "\n",
- " user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)\n",
- " item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)\n",
- " \n",
- " # 保存当前的item_embedding 和 user_embedding 排序的时候可能能够用到，但是需要注意保存的时候需要和原始的id对应\n",
- " user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)\n",
- " item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)\n",
- " \n",
- " # embedding保存之前归一化一下\n",
- " user_embs = user_embs / np.linalg.norm(user_embs, axis=1, keepdims=True)\n",
- " item_embs = item_embs / np.linalg.norm(item_embs, axis=1, keepdims=True)\n",
- " \n",
- " # 将Embedding转换成字典的形式方便查询\n",
- " raw_user_id_emb_dict = {user_index_2_rawid[k]: \\\n",
- " v for k, v in zip(user_profile['user_id'], user_embs)}\n",
- " raw_item_id_emb_dict = {item_index_2_rawid[k]: \\\n",
- " v for k, v in zip(item_profile['click_article_id'], item_embs)}\n",
- " # 将Embedding保存到本地\n",
- " pickle.dump(raw_user_id_emb_dict, open(save_path + 'user_youtube_emb.pkl', 'wb'))\n",
- " pickle.dump(raw_item_id_emb_dict, open(save_path + 'item_youtube_emb.pkl', 'wb'))\n",
- " \n",
- " # faiss紧邻搜索，通过user_embedding 搜索与其相似性最高的topk个item\n",
- " index = faiss.IndexFlatIP(embedding_dim)\n",
- " # 上面已经进行了归一化，这里可以不进行归一化了\n",
- "# faiss.normalize_L2(user_embs)\n",
- "# faiss.normalize_L2(item_embs)\n",
- " index.add(item_embs) # 将item向量构建索引\n",
- " sim, idx = index.search(np.ascontiguousarray(user_embs), topk) # 通过user去查询最相似的topk个item\n",
- " \n",
- " user_recall_items_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(test_user_model_input['user_id'], sim, idx)):\n",
- " target_raw_id = user_index_2_rawid[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = item_index_2_rawid[rele_idx]\n",
- " user_recall_items_dict[target_raw_id][rele_raw_id] = user_recall_items_dict.get(target_raw_id, {})\\\n",
- " .get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " user_recall_items_dict = {k: sorted(v.items(), key=lambda x: x[1], reverse=True) for k, v in user_recall_items_dict.items()}\n",
- " # 将召回的结果进行排序\n",
- " \n",
- " # 保存召回的结果\n",
- " # 这里是直接通过向量的方式得到了召回结果，相比于上面的召回方法，上面的只是得到了i2i及u2u的相似性矩阵，还需要进行协同过滤召回才能得到召回结果\n",
- " # 可以直接对这个召回结果进行评估，为了方便可以统一写一个评估函数对所有的召回结果进行评估\n",
- " pickle.dump(user_recall_items_dict, open(save_path + 'youtube_u2i_dict.pkl', 'wb'))\n",
- " return user_recall_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:21:46.420014Z",
- "start_time": "2020-11-16T10:13:35.351131Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [02:02<00:00, 2038.57it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "keep_dims is deprecated, use keepdims instead\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Deprecated in favor of operator or tf.math.divide.\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "1149673/1149673 [==============================] - 216s 188us/sample - loss: 0.1326\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "250000it [00:32, 7720.75it/s]\n"
- ]
- }
- ],
- "source": [
- "# 由于这里需要做召回评估，所以讲训练集中的最后一次点击都提取了出来\n",
- "if not metric_recall:\n",
- " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(all_click_df, topk=20)\n",
- "else:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(trn_hist_click_df, topk=20)\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['youtubednn_recall'], trn_last_click_df, topk=20)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### itemcf recall\n",
- "\n",
- "上面已经通过协同过滤，Embedding检索的方式得到了文章的相似度矩阵，下面使用协同过滤的思想，给用户召回与其历史文章相似的文章。\n",
- "这里在召回的时候，也是用了关联规则的方式：\n",
- "1. 考虑相似文章与历史点击文章顺序的权重(细节看代码)\n",
- "2. 考虑文章创建时间的权重，也就是考虑相似文章与历史点击文章创建时间差的权重\n",
- "3. 考虑文章内容相似度权重(使用Embedding计算相似文章相似度，但是这里需要注意，在Embedding的时候并没有计算所有商品两两之间的相似度，所以相似的文章与历史点击文章不存在相似度，需要做特殊处理)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:48:40.580553Z",
- "start_time": "2020-11-16T11:48:40.567130Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于商品的召回i2i\n",
- "def item_based_recommend(user_id, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
- " \"\"\"\n",
- " 基于文章协同过滤的召回\n",
- " :param user_id: 用户id\n",
- " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- " :param i2i_sim: 字典，文章相似性矩阵\n",
- " :param sim_item_topk: 整数， 选择与当前文章最相似的前k篇文章\n",
- " :param recall_item_num: 整数， 最后的召回文章数量\n",
- " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
- " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
- " \n",
- " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
- " \"\"\"\n",
- " # 获取用户历史交互的文章\n",
- " user_hist_items = user_item_time_dict[user_id]\n",
- " user_hist_items_ = {user_id for user_id, _ in user_hist_items}\n",
- " \n",
- " item_rank = {}\n",
- " for loc, (i, click_time) in enumerate(user_hist_items):\n",
- " for j, wij in sorted(i2i_sim[i].items(), key=lambda x: x[1], reverse=True)[:sim_item_topk]:\n",
- " if j in user_hist_items_:\n",
- " continue\n",
- " \n",
- " # 文章创建时间差权重\n",
- " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " # 相似文章和历史点击文章序列中历史文章所在的位置权重\n",
- " loc_weight = (0.9 ** (len(user_hist_items) - loc))\n",
- " \n",
- " content_weight = 1.0\n",
- " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
- " content_weight += emb_i2i_sim[i][j]\n",
- " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
- " content_weight += emb_i2i_sim[j][i]\n",
- " \n",
- " item_rank.setdefault(j, 0)\n",
- " item_rank[j] += created_time_weight * loc_weight * content_weight * wij\n",
- " \n",
- " # 不足10个，用热门商品补全\n",
- " if len(item_rank) < recall_item_num:\n",
- " for i, item in enumerate(item_topk_click):\n",
- " if item in item_rank.items(): # 填充的item应该不在原来的列表中\n",
- " continue\n",
- " item_rank[item] = - i - 100 # 随便给个负数就行\n",
- " if len(item_rank) == recall_item_num:\n",
- " break\n",
- " \n",
- " item_rank = sorted(item_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num]\n",
- " \n",
- " return item_rank"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### itemcf sim召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T14:41:23.433038Z",
- "start_time": "2020-11-16T11:48:46.286350Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [2:51:13<00:00, 24.33it/s] \n"
- ]
- }
- ],
- "source": [
- "# 先进行itemcf召回, 为了召回评估，所以提取最后一次点击\n",
- "\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "\n",
- "i2i_sim = pickle.load(open(save_path + 'itemcf_i2i_sim.pkl', 'rb'))\n",
- "emb_i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl', 'rb'))\n",
- "\n",
- "sim_item_topk = 20\n",
- "recall_item_num = 10\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, \\\n",
- " i2i_sim, sim_item_topk, recall_item_num, \\\n",
- " item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- "\n",
- "user_multi_recall_dict['itemcf_sim_itemcf_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['itemcf_sim_itemcf_recall'], open(save_path + 'itemcf_recall_dict.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['itemcf_sim_itemcf_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### embedding sim 召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T15:04:51.527795Z",
- "start_time": "2020-11-16T14:59:03.907519Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [04:35<00:00, 905.85it/s] \n"
- ]
- }
- ],
- "source": [
- "# 这里是为了召回评估，所以提取最后一次点击\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
- "\n",
- "sim_item_topk = 20\n",
- "recall_item_num = 10\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- " \n",
- "user_multi_recall_dict['embedding_sim_item_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['embedding_sim_item_recall'], open(save_path + 'embedding_sim_item_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['embedding_sim_item_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### usercf召回\n",
- "\n",
- "基于用户协同过滤，核心思想是给用户推荐与其相似的用户历史点击文章，因为这里涉及到了相似用户的历史文章，这里仍然可以加上一些关联规则来给用户可能点击的文章进行加权，这里使用的关联规则主要是考虑相似用户的历史点击文章与被推荐用户历史点击商品的关系权重，而这里的关系就可以直接借鉴基于物品的协同过滤相似的做法，只不过这里是对被推荐物品关系的一个累加的过程，下面是使用的一些关系权重，及相关的代码：\n",
- "\n",
- "1. 计算被推荐用户历史点击文章与相似用户历史点击文章的相似度，文章创建时间差，相对位置的总和，作为各自的权重"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:09:32.293990Z",
- "start_time": "2020-11-17T02:09:32.278678Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于用户的召回 u2u2i\n",
- "def user_based_recommend(user_id, user_item_time_dict, u2u_sim, sim_user_topk, recall_item_num, \n",
- " item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
- " \"\"\"\n",
- " 基于文章协同过滤的召回\n",
- " :param user_id: 用户id\n",
- " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- " :param u2u_sim: 字典，文章相似性矩阵\n",
- " :param sim_user_topk: 整数， 选择与当前用户最相似的前k个用户\n",
- " :param recall_item_num: 整数， 最后的召回文章数量\n",
- " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
- " :param item_created_time_dict: 文章创建时间列表\n",
- " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
- " \n",
- " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
- " \"\"\"\n",
- " # 历史交互\n",
- " user_item_time_list = user_item_time_dict[user_id] # [(item1, time1), (item2, time2)..]\n",
- " user_hist_items = set([i for i, t in user_item_time_list]) # 存在一个用户与某篇文章的多次交互， 这里得去重\n",
- " \n",
- " items_rank = {}\n",
- " for sim_u, wuv in sorted(u2u_sim[user_id].items(), key=lambda x: x[1], reverse=True)[:sim_user_topk]:\n",
- " for i, click_time in user_item_time_dict[sim_u]:\n",
- " if i in user_hist_items:\n",
- " continue\n",
- " items_rank.setdefault(i, 0)\n",
- " \n",
- " loc_weight = 1.0\n",
- " content_weight = 1.0\n",
- " created_time_weight = 1.0\n",
- " \n",
- " # 当前文章与该用户看的历史文章进行一个权重交互\n",
- " for loc, (j, click_time) in enumerate(user_item_time_list):\n",
- " # 点击时的相对位置权重\n",
- " loc_weight += 0.9 ** (len(user_item_time_list) - loc)\n",
- " # 内容相似性权重\n",
- " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
- " content_weight += emb_i2i_sim[i][j]\n",
- " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
- " content_weight += emb_i2i_sim[j][i]\n",
- " \n",
- " # 创建时间差权重\n",
- " created_time_weight += np.exp(0.8 * np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " \n",
- " items_rank[i] += loc_weight * content_weight * created_time_weight * wuv\n",
- " \n",
- " # 热度补全\n",
- " if len(items_rank) < recall_item_num:\n",
- " for i, item in enumerate(item_topk_click):\n",
- " if item in items_rank.items(): # 填充的item应该不在原来的列表中\n",
- " continue\n",
- " items_rank[item] = - i - 100 # 随便给个复数就行\n",
- " if len(items_rank) == recall_item_num:\n",
- " break\n",
- " \n",
- " items_rank = sorted(items_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num] \n",
- " \n",
- " return items_rank"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### usercf sim召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:05:41.652501Z",
- "start_time": "2020-11-16T07:05:40.953871Z"
- }
- },
- "outputs": [],
- "source": [
- "# 这里是为了召回评估，所以提取最后一次点击\n",
- "# 由于usercf中计算user之间的相似度的过程太费内存了，全量数据这里就没有跑，跑了一个采样之后的数据\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- " \n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "\n",
- "u2u_sim = pickle.load(open(save_path + 'usercf_u2u_sim.pkl', 'rb'))\n",
- "\n",
- "sim_user_topk = 20\n",
- "recall_item_num = 10\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim) \n",
- "\n",
- "pickle.dump(user_recall_items_dict, open(save_path + 'usercf_u2u2i_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_recall_items_dict, trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T03:09:35.853516Z",
- "start_time": "2020-11-16T03:09:35.737625Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### user embedding sim召回\n",
- "\n",
- "虽然没有直接跑usercf的计算用户之间的相似度，为了验证上述基于用户的协同过滤的代码，下面使用了YoutubeDNN过程中产生的user embedding来进行向量检索每个user最相似的topk个user，在使用这里得到的u2u的相似性矩阵，使用usercf进行召回，具体代码如下"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:09:46.807811Z",
- "start_time": "2020-11-17T02:09:46.798033Z"
- }
- },
- "outputs": [],
- "source": [
- "# 使用Embedding的方式获取u2u的相似性矩阵\n",
- "# topk指的是每个user, faiss搜索后返回最相似的topk个user\n",
- "def u2u_embdding_sim(click_df, user_emb_dict, save_path, topk):\n",
- " \n",
- " user_list = []\n",
- " user_emb_list = []\n",
- " for user_id, user_emb in user_emb_dict.items():\n",
- " user_list.append(user_id)\n",
- " user_emb_list.append(user_emb)\n",
- " \n",
- " user_index_2_rawid_dict = {k: v for k, v in zip(range(len(user_list)), user_list)} \n",
- " \n",
- " user_emb_np = np.array(user_emb_list, dtype=np.float32)\n",
- " \n",
- " # 建立faiss索引\n",
- " user_index = faiss.IndexFlatIP(user_emb_np.shape[1])\n",
- " user_index.add(user_emb_np)\n",
- " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
- " sim, idx = user_index.search(user_emb_np, topk) # 返回的是列表\n",
- " \n",
- " # 将向量检索的结果保存成原始id的对应关系\n",
- " user_sim_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(user_emb_np)), sim, idx)):\n",
- " target_raw_id = user_index_2_rawid_dict[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = user_index_2_rawid_dict[rele_idx]\n",
- " user_sim_dict[target_raw_id][rele_raw_id] = user_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " # 保存i2i相似度矩阵\n",
- " pickle.dump(user_sim_dict, open(save_path + 'youtube_u2u_sim.pkl', 'wb')) \n",
- " return user_sim_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:14:31.355905Z",
- "start_time": "2020-11-17T02:09:53.236531Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "250000it [00:23, 10507.45it/s]\n"
- ]
- }
- ],
- "source": [
- "# 读取YoutubeDNN过程中产生的user embedding, 然后使用faiss计算用户之间的相似度\n",
- "# 这里需要注意，这里得到的user embedding其实并不是很好，因为YoutubeDNN中使用的是用户点击序列来训练的user embedding,\n",
- "# 如果序列普遍都比较短的话，其实效果并不是很好\n",
- "user_emb_dict = pickle.load(open(save_path + 'user_youtube_emb.pkl', 'rb'))\n",
- "u2u_sim = u2u_embdding_sim(all_click_df, user_emb_dict, save_path, topk=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "通过YoutubeDNN得到的user_embedding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:49:40.755431Z",
- "start_time": "2020-11-17T02:28:47.003514Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [19:43<00:00, 211.22it/s]\n"
- ]
- }
- ],
- "source": [
- "# 使用召回评估函数验证当前召回方式的效果\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "u2u_sim = pickle.load(open(save_path + 'youtube_u2u_sim.pkl', 'rb'))\n",
- "\n",
- "sim_user_topk = 20\n",
- "recall_item_num = 10\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- " \n",
- "user_multi_recall_dict['youtubednn_usercf_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['youtubednn_usercf_recall'], open(save_path + 'youtubednn_usercf_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['youtubednn_usercf_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:07:44.326253Z",
- "start_time": "2020-11-16T07:07:43.798931Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 冷启动问题"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**冷启动问题可以分成三类：文章冷启动，用户冷启动，系统冷启动。**\n",
- "\n",
- "- 文章冷启动：对于一个平台系统新加入的文章，该文章没有任何的交互记录，如何推荐给用户的问题。(对于我们场景可以认为是，日志数据中没有出现过的文章都可以认为是冷启动的文章)\n",
- "- 用户冷启动：对于一个平台系统新来的用户，该用户还没有文章的交互信息，如何给该用户进行推荐。(对于我们场景就是，测试集中的用户是否在测试集对应的log数据中出现过，如果没有出现过，那么可以认为该用户是冷启动用户。但是有时候并没有这么严格，我们也可以自己设定某些指标来判别哪些用户是冷启动用户，比如通过使用时长，点击率，留存率等等)\n",
- "- 系统冷启动：就是对于一个平台刚上线，还没有任何的相关历史数据，此时就是系统冷启动，其实也就是前面两种的一个综合。\n",
- "\n",
- "**当前场景下冷启动问题的分析：**\n",
- "\n",
- "对当前的数据进行分析会发现，日志中所有出现过的点击文章只有3w多个，而整个文章库中却有30多万，那么测试集中的用户最后一次点击是否会点击没有出现在日志中的文章呢？如果存在这种情况，说明用户点击的文章之前没有任何的交互信息，这也就是我们所说的文章冷启动。通过数据分析还可以发现，测试集用户只有一次点击的数据占得比例还不少，其实仅仅通过用户的一次点击就给用户推荐文章使用模型的方式也是比较难的，这里其实也可以考虑用户冷启动的问题，但是这里只给出物品冷启动的一些解决方案及代码，关于用户冷启动的话提一些可行性的做法。\n",
- "\n",
- "1. 文章冷启动(没有冷启动的探索问题) \n",
- " 其实我们这里不是为了做文章的冷启动而做冷启动，而是猜测用户可能会点击一些没有在log数据中出现的文章，我们要做的就是如何从将近27万的文章中选择一些文章作为用户冷启动的文章，这里其实也可以看成是一种召回策略，我们这里就采用简单的比较好理解的基于规则的召回策略来获取用户可能点击的未出现在log数据中的文章。\n",
- " 现在的问题变成了：如何给每个用户考虑从27万个商品中获取一小部分商品？随机选一些可能是一种方案。下面给出一些参考的方案。\n",
- " 1. 首先基于Embedding召回一部分与用户历史相似的文章\n",
- " 2. 从基于Embedding召回的文章中通过一些规则过滤掉一些文章，使得留下的文章用户更可能点击。我们这里的规则，可以是，留下那些与用户历史点击文章主题相同的文章，或者字数相差不大的文章。并且留下的文章尽量是与测试集用户最后一次点击时间更接近的文章，或者是当天的文章也行。\n",
- "2. 用户冷启动 \n",
- " 这里对测试集中的用户点击数据进行分析会发现，测试集中有百分之20的用户只有一次点击，那么这些点击特别少的用户的召回是不是可以单独做一些策略上的补充呢？或者是在排序后直接基于规则加上一些文章呢？这些都可以去尝试，这里没有提供具体的做法。\n",
- " \n",
- "**注意：** \n",
- "\n",
- "这里看似和基于embedding计算的item之间相似度然后做itemcf是一致的，但是现在我们的目的不一样，我们这里的目的是找到相似的向量，并且还没有出现在log日志中的商品，再加上一些其他的冷启动的策略，这里需要找回的数量会偏多一点，不然被筛选完之后可能都没有文章了"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T04:30:23.027164Z",
- "start_time": "2020-11-17T04:23:09.960235Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [05:01<00:00, 828.60it/s] \n"
- ]
- }
- ],
- "source": [
- "# 先进行itemcf召回，这里不需要做召回评估，这里只是一种策略\n",
- "trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
- "\n",
- "sim_item_topk = 150\n",
- "recall_item_num = 100 # 稍微召回多一点文章，便于后续的规则筛选\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
- " recall_item_num, item_topk_click,item_created_time_dict, emb_i2i_sim)\n",
- "pickle.dump(user_recall_items_dict, open(save_path + 'cold_start_items_raw_dict.pkl', 'wb'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T06:11:39.267581Z",
- "start_time": "2020-11-17T06:11:39.252563Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于规则进行文章过滤\n",
- "# 保留文章主题与用户历史浏览主题相似的文章\n",
- "# 保留文章字数与用户历史浏览文章字数相差不大的文章\n",
- "# 保留最后一次点击当天的文章\n",
- "# 按照相似度返回最终的结果\n",
- "\n",
- "def get_click_article_ids_set(all_click_df):\n",
- " return set(all_click_df.click_article_id.values)\n",
- "\n",
- "def cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
- " user_last_item_created_time_dict, item_type_dict, item_words_dict, \n",
- " item_created_time_dict, click_article_ids_set, recall_item_num):\n",
- " \"\"\"\n",
- " 冷启动的情况下召回一些文章\n",
- " :param user_recall_items_dict: 基于内容embedding相似性召回来的很多文章， 字典， {user1: [(item1, item2), ..], }\n",
- " :param user_hist_item_typs_dict: 字典， 用户点击的文章的主题映射\n",
- " :param user_hist_item_words_dict: 字典， 用户点击的历史文章的字数映射\n",
- " :param user_last_item_created_time_idct: 字典，用户点击的历史文章创建时间映射\n",
- " :param item_tpye_idct: 字典，文章主题映射\n",
- " :param item_words_dict: 字典，文章字数映射\n",
- " :param item_created_time_dict: 字典， 文章创建时间映射\n",
- " :param click_article_ids_set: 集合，用户点击过得文章, 也就是日志里面出现过的文章\n",
- " :param recall_item_num: 召回文章的数量， 这个指的是没有出现在日志里面的文章数量\n",
- " \"\"\"\n",
- " \n",
- " cold_start_user_items_dict = {}\n",
- " for user, item_list in tqdm(user_recall_items_dict.items()):\n",
- " cold_start_user_items_dict.setdefault(user, [])\n",
- " for item, score in item_list:\n",
- " # 获取历史文章信息\n",
- " hist_item_type_set = user_hist_item_typs_dict[user]\n",
- " hist_mean_words = user_hist_item_words_dict[user]\n",
- " hist_last_item_created_time = user_last_item_created_time_dict[user]\n",
- " hist_last_item_created_time = datetime.fromtimestamp(hist_last_item_created_time)\n",
- " \n",
- " # 获取当前召回文章的信息\n",
- " curr_item_type = item_type_dict[item]\n",
- " curr_item_words = item_words_dict[item]\n",
- " curr_item_created_time = item_created_time_dict[item]\n",
- " curr_item_created_time = datetime.fromtimestamp(curr_item_created_time)\n",
- "\n",
- " # 首先，文章不能出现在用户的历史点击中， 然后根据文章主题，文章单词数，文章创建时间进行筛选\n",
- " if curr_item_type not in hist_item_type_set or \\\n",
- " item in click_article_ids_set or \\\n",
- " abs(curr_item_words - hist_mean_words) > 200 or \\\n",
- " abs((curr_item_created_time - hist_last_item_created_time).days) > 90: \n",
- " continue\n",
- " \n",
- " cold_start_user_items_dict[user].append((item, score)) # {user1: [(item1, score1), (item2, score2)..]...}\n",
- " \n",
- " # 需要控制一下冷启动召回的数量\n",
- " cold_start_user_items_dict = {k: sorted(v, key=lambda x:x[1], reverse=True)[:recall_item_num] \\\n",
- " for k, v in cold_start_user_items_dict.items()}\n",
- " \n",
- " pickle.dump(cold_start_user_items_dict, open(save_path + 'cold_start_user_items_dict.pkl', 'wb'))\n",
- " \n",
- " return cold_start_user_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T06:35:38.758278Z",
- "start_time": "2020-11-17T06:31:40.164332Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [01:49<00:00, 2293.37it/s]\n"
- ]
- }
- ],
- "source": [
- "all_click_df_ = all_click_df.copy()\n",
- "all_click_df_ = all_click_df_.merge(item_info_df, how='left', on='click_article_id')\n",
- "user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict = get_user_hist_item_info_dict(all_click_df_)\n",
- "click_article_ids_set = get_click_article_ids_set(all_click_df)\n",
- "# 需要注意的是\n",
- "# 这里使用了很多规则来筛选冷启动的文章，所以前面再召回的阶段就应该尽可能的多召回一些文章，否则很容易被删掉\n",
- "cold_start_user_items_dict = cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
- " user_last_item_created_time_dict, item_type_dict, item_words_dict, \\\n",
- " item_created_time_dict, click_article_ids_set, recall_item_num)\n",
- "\n",
- "user_multi_recall_dict['cold_start_recall'] = cold_start_user_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:13:33.099298Z",
- "start_time": "2020-11-16T07:13:32.655036Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 多路召回合并\n",
- "多路召回合并就是将前面所有的召回策略得到的用户文章列表合并起来，下面是对前面所有召回结果的汇总\n",
- "1. 基于itemcf计算的item之间的相似度sim进行的召回 \n",
- "2. 基于embedding搜索得到的item之间的相似度进行的召回\n",
- "3. YoutubeDNN召回\n",
- "4. YoutubeDNN得到的user之间的相似度进行的召回\n",
- "5. 基于冷启动策略的召回\n",
- "\n",
- "**注意：** \n",
- "在做召回评估的时候就会发现有些召回的效果不错有些召回的效果很差，所以对每一路召回的结果，我们可以认为的定义一些权重，来做最终的相似度融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:02:16.033971Z",
- "start_time": "2020-11-17T07:02:16.019819Z"
- }
- },
- "outputs": [],
- "source": [
- "def combine_recall_results(user_multi_recall_dict, weight_dict=None, topk=25):\n",
- " final_recall_items_dict = {}\n",
- " \n",
- " # 对每一种召回结果按照用户进行归一化，方便后面多种召回结果，相同用户的物品之间权重相加\n",
- " def norm_user_recall_items_sim(sorted_item_list):\n",
- " # 如果冷启动中没有文章或者只有一篇文章，直接返回，出现这种情况的原因可能是冷启动召回的文章数量太少了，\n",
- " # 基于规则筛选之后就没有文章了, 这里还可以做一些其他的策略性的筛选\n",
- " if len(sorted_item_list) < 2:\n",
- " return sorted_item_list\n",
- " \n",
- " min_sim = sorted_item_list[-1][1]\n",
- " max_sim = sorted_item_list[0][1]\n",
- " \n",
- " norm_sorted_item_list = []\n",
- " for item, score in sorted_item_list:\n",
- " if max_sim > 0:\n",
- " norm_score = 1.0 * (score - min_sim) / (max_sim - min_sim) if max_sim > min_sim else 1.0\n",
- " else:\n",
- " norm_score = 0.0\n",
- " norm_sorted_item_list.append((item, norm_score))\n",
- " \n",
- " return norm_sorted_item_list\n",
- " \n",
- " print('多路召回合并...')\n",
- " for method, user_recall_items in tqdm(user_multi_recall_dict.items()):\n",
- " print(method + '...')\n",
- " # 在计算最终召回结果的时候，也可以为每一种召回结果设置一个权重\n",
- " if weight_dict == None:\n",
- " recall_method_weight = 1\n",
- " else:\n",
- " recall_method_weight = weight_dict[method]\n",
- " \n",
- " for user_id, sorted_item_list in user_recall_items.items(): # 进行归一化\n",
- " user_recall_items[user_id] = norm_user_recall_items_sim(sorted_item_list)\n",
- " \n",
- " for user_id, sorted_item_list in user_recall_items.items():\n",
- " # print('user_id')\n",
- " final_recall_items_dict.setdefault(user_id, {})\n",
- " for item, score in sorted_item_list:\n",
- " final_recall_items_dict[user_id].setdefault(item, 0)\n",
- " final_recall_items_dict[user_id][item] += recall_method_weight * score \n",
- " \n",
- " final_recall_items_dict_rank = {}\n",
- " # 多路召回时也可以控制最终的召回数量\n",
- " for user, recall_item_dict in final_recall_items_dict.items():\n",
- " final_recall_items_dict_rank[user] = sorted(recall_item_dict.items(), key=lambda x: x[1], reverse=True)[:topk]\n",
- "\n",
- " # 将多路召回后的最终结果字典保存到本地\n",
- " pickle.dump(final_recall_items_dict_rank, open(os.path.join(save_path, 'final_recall_items_dict.pkl'),'wb'))\n",
- "\n",
- " return final_recall_items_dict_rank"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:02:21.078455Z",
- "start_time": "2020-11-17T07:02:21.074060Z"
- }
- },
- "outputs": [],
- "source": [
- "# 这里直接对多路召回的权重给了一个相同的值，其实可以根据前面召回的情况来调整参数的值\n",
- "weight_dict = {'itemcf_sim_itemcf_recall': 1.0,\n",
- " 'embedding_sim_item_recall': 1.0,\n",
- " 'youtubednn_recall': 1.0,\n",
- " 'youtubednn_usercf_recall': 1.0, \n",
- " 'cold_start_recall': 1.0}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:04:35.747924Z",
- "start_time": "2020-11-17T07:02:26.889573Z"
- }
- },
- "outputs": [
+ "cells": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/5 [00:00\n",
+ "\n",
+ "上图只是一个多路召回的例子，也就是说可以使用多种不同的策略来获取用户排序的候选商品集合，而具体使用哪些召回策略其实是与业务强相关的 ，针对不同的任务就会有对于该业务真实场景下需要考虑的召回规则。例如新闻推荐，召回规则可以是“热门新闻”、“作者召回”、“关键词召回”、“主题召回“、”协同过滤召回“等等。 \n",
+ "\n"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "多路召回合并...\n",
- "itemcf_sim_itemcf_recall...\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 导包"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 20%|██ | 1/5 [00:08<00:34, 8.66s/it]"
- ]
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:29.834662Z",
+ "start_time": "2020-11-16T11:26:27.811511Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np\n",
+ "from tqdm import tqdm \n",
+ "from collections import defaultdict \n",
+ "import os, math, warnings, math, pickle\n",
+ "from tqdm import tqdm\n",
+ "import faiss\n",
+ "import collections\n",
+ "import random\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from datetime import datetime\n",
+ "from deepctr.feature_column import SparseFeat, VarLenSparseFeat\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from tensorflow.python.keras import backend as K\n",
+ "from tensorflow.python.keras.models import Model\n",
+ "from tensorflow.python.keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "from deepmatch.models import *\n",
+ "from deepmatch.utils import sampledsoftmaxloss\n",
+ "warnings.filterwarnings('ignore')"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "embedding_sim_item_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:31.831215Z",
+ "start_time": "2020-11-16T11:26:31.826939Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = './data_raw/'\n",
+ "save_path = './temp_results/'\n",
+ "# 做召回评估的一个标志, 如果不进行评估就是直接使用全量数据进行召回\n",
+ "metric_recall = False"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 40%|████ | 2/5 [00:16<00:24, 8.29s/it]"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 读取数据\n",
+ "在一般的rs比赛中读取数据部分主要分为三种模式， 不同的模式对应的不同的数据集：\n",
+ "1. debug模式： 这个的目的是帮助我们基于数据先搭建一个简易的baseline并跑通， 保证写的baseline代码没有什么问题。 由于推荐比赛的数据往往非常巨大， 如果一上来直接采用全部的数据进行分析，搭建baseline框架， 往往会带来时间和设备上的损耗， **所以这时候我们往往需要从海量数据的训练集中随机抽取一部分样本来进行调试(train_click_log_sample)**， 先跑通一个baseline。\n",
+ "2. 线下验证模式： 这个的目的是帮助我们在线下基于已有的训练集数据， 来选择好合适的模型和一些超参数。 **所以我们这一块只需要加载整个训练集(train_click_log)**， 然后把整个训练集再分成训练集和验证集。 训练集是模型的训练数据， 验证集部分帮助我们调整模型的参数和其他的一些超参数。\n",
+ "3. 线上模式： 我们用debug模式搭建起一个推荐系统比赛的baseline， 用线下验证模式选择好了模型和一些超参数， 这一部分就是真正的对于给定的测试集进行预测， 提交到线上， **所以这一块使用的训练数据集是全量的数据集(train_click_log+test_click_log)**\n",
+ "\n",
+ "下面就分别对这三种不同的数据读取模式先建立不同的代导入函数， 方便后面针对不同的模式下导入数据。"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "youtubednn_recall...\n",
- "youtubednn_usercf_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:34.476240Z",
+ "start_time": "2020-11-16T11:26:34.467352Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# debug模式： 从训练集中划出一部分数据来调试代码\n",
+ "def get_all_click_sample(data_path, sample_nums=10000):\n",
+ " \"\"\"\n",
+ " 训练集中采样一部分数据调试\n",
+ " data_path: 原数据的存储路径\n",
+ " sample_nums: 采样数目（这里由于机器的内存限制，可以采样用户做）\n",
+ " \"\"\"\n",
+ " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " all_user_ids = all_click.user_id.unique()\n",
+ "\n",
+ " sample_user_ids = np.random.choice(all_user_ids, size=sample_nums, replace=False) \n",
+ " all_click = all_click[all_click['user_id'].isin(sample_user_ids)]\n",
+ " \n",
+ " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
+ " return all_click\n",
+ "\n",
+ "# 读取点击数据，这里分成线上和线下，如果是为了获取线上提交结果应该讲测试集中的点击数据合并到总的数据中\n",
+ "# 如果是为了线下验证模型的有效性或者特征的有效性，可以只使用训练集\n",
+ "def get_all_click_df(data_path='./data_raw/', offline=True):\n",
+ " if offline:\n",
+ " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " else:\n",
+ " trn_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " tst_click = pd.read_csv(data_path + 'testA_click_log.csv')\n",
+ "\n",
+ " all_click = trn_click.append(tst_click)\n",
+ " \n",
+ " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
+ " return all_click"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 80%|████████ | 4/5 [00:23<00:06, 6.98s/it]"
- ]
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:35.168738Z",
+ "start_time": "2020-11-16T11:26:35.163210Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取文章的基本属性\n",
+ "def get_item_info_df(data_path):\n",
+ " item_info_df = pd.read_csv(data_path + 'articles.csv')\n",
+ " \n",
+ " # 为了方便与训练集中的click_article_id拼接，需要把article_id修改成click_article_id\n",
+ " item_info_df = item_info_df.rename(columns={'article_id': 'click_article_id'})\n",
+ " \n",
+ " return item_info_df"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "cold_start_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:36.152958Z",
+ "start_time": "2020-11-16T11:26:36.146324Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取文章的Embedding数据\n",
+ "def get_item_emb_dict(data_path):\n",
+ " item_emb_df = pd.read_csv(data_path + 'articles_emb.csv')\n",
+ " \n",
+ " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
+ " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols])\n",
+ " # 进行归一化\n",
+ " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
+ "\n",
+ " item_emb_dict = dict(zip(item_emb_df['article_id'], item_emb_np))\n",
+ " pickle.dump(item_emb_dict, open(save_path + 'item_content_emb.pkl', 'wb'))\n",
+ " \n",
+ " return item_emb_dict"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 5/5 [00:42<00:00, 8.40s/it]\n"
- ]
- }
- ],
- "source": [
- "# 最终合并之后每个用户召回150个商品进行排序\n",
- "final_recall_items_dict_rank = combine_recall_results(user_multi_recall_dict, weight_dict, topk=150)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 总结\n",
- "\n",
- "上述实现了如下召回策略：\n",
- "\n",
- "1. 基于关联规则的itemcf\n",
- "2. 基于关联规则的usercf\n",
- "3. youtubednn召回\n",
- "4. 冷启动召回\n",
- "\n",
- "对于上述实现的召回策略其实都不是最优的结果，我们只是做了个简单的尝试，其中还有很多地方可以优化，包括已经实现的这些召回策略的参数或者新加一些，修改一些关联规则都可以。当然还可以尝试更多的召回策略，比如对新闻进行热度召回等等。\n",
- "\n",
- "\n",
- "\n",
- "**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autoclose": false,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 1,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "nbTranslate": {
- "displayLangs": [
- "*"
- ],
- "hotkey": "alt-t",
- "langInMainMenu": true,
- "sourceLang": "en",
- "targetLang": "fr",
- "useGoogleTranslate": true
- },
- "tianchi_metadata": {
- "competitions": [],
- "datasets": [
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:37.333536Z",
+ "start_time": "2020-11-16T11:26:37.329545Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:42.163494Z",
+ "start_time": "2020-11-16T11:26:38.018094Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 采样数据\n",
+ "# all_click_df = get_all_click_sample(data_path)\n",
+ "\n",
+ "# 全量训练集\n",
+ "all_click_df = get_all_click_df(offline=False)\n",
+ "\n",
+ "# 对时间戳进行归一化,用于在关联规则的时候计算权重\n",
+ "all_click_df['click_timestamp'] = all_click_df[['click_timestamp']].apply(max_min_scaler)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:44.343500Z",
+ "start_time": "2020-11-16T11:26:44.113891Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "item_info_df = get_item_info_df(data_path)"
+ ]
+ },
{
- "id": "83580",
- "title": "零基础入门推荐系统 - 新闻推荐"
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:24.295343Z",
+ "start_time": "2020-11-16T11:26:44.398007Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "item_emb_dict = get_item_emb_dict(data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 工具函数"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取用户-文章-时间函数\n",
+ "这个在基于关联规则的用户协同过滤的时候会用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:33.791656Z",
+ "start_time": "2020-11-16T11:27:33.784305Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ "def get_user_item_time(click_df):\n",
+ " \n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " \n",
+ " def make_item_time_pair(df):\n",
+ " return list(zip(df['click_article_id'], df['click_timestamp']))\n",
+ " \n",
+ " user_item_time_df = click_df.groupby('user_id')['click_article_id', 'click_timestamp'].apply(lambda x: make_item_time_pair(x))\\\n",
+ " .reset_index().rename(columns={0: 'item_time_list'})\n",
+ " user_item_time_dict = dict(zip(user_item_time_df['user_id'], user_item_time_df['item_time_list']))\n",
+ " \n",
+ " return user_item_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取文章-用户-时间函数\n",
+ "这个在基于关联规则的文章协同过滤的时候会用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:38.327581Z",
+ "start_time": "2020-11-16T11:27:38.321059Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 根据时间获取商品被点击的用户序列 {item1: [(user1, time1), (user2, time2)...]...}\n",
+ "# 这里的时间是用户点击当前商品的时间，好像没有直接的关系。\n",
+ "def get_item_user_time_dict(click_df):\n",
+ " def make_user_time_pair(df):\n",
+ " return list(zip(df['user_id'], df['click_timestamp']))\n",
+ " \n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " item_user_time_df = click_df.groupby('click_article_id')['user_id', 'click_timestamp'].apply(lambda x: make_user_time_pair(x))\\\n",
+ " .reset_index().rename(columns={0: 'user_time_list'})\n",
+ " \n",
+ " item_user_time_dict = dict(zip(item_user_time_df['click_article_id'], item_user_time_df['user_time_list']))\n",
+ " return item_user_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取历史和最后一次点击\n",
+ "这个在评估召回结果， 特征工程和制作标签转成监督学习测试集的时候回用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:50.894683Z",
+ "start_time": "2020-11-16T11:27:50.888002Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取当前数据的历史点击和最后一次点击\n",
+ "def get_hist_and_last_click(all_click):\n",
+ " \n",
+ " all_click = all_click.sort_values(by=['user_id', 'click_timestamp'])\n",
+ " click_last_df = all_click.groupby('user_id').tail(1)\n",
+ "\n",
+ " # 如果用户只有一个点击，hist为空了，会导致训练的时候这个用户不可见，此时默认泄露一下\n",
+ " def hist_func(user_df):\n",
+ " if len(user_df) == 1:\n",
+ " return user_df\n",
+ " else:\n",
+ " return user_df[:-1]\n",
+ "\n",
+ " click_hist_df = all_click.groupby('user_id').apply(hist_func).reset_index(drop=True)\n",
+ "\n",
+ " return click_hist_df, click_last_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取文章属性特征"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:55.893810Z",
+ "start_time": "2020-11-16T11:27:55.887623Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取文章id对应的基本属性，保存成字典的形式，方便后面召回阶段，冷启动阶段直接使用\n",
+ "def get_item_info_dict(item_info_df):\n",
+ " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
+ " item_info_df['created_at_ts'] = item_info_df[['created_at_ts']].apply(max_min_scaler)\n",
+ " \n",
+ " item_type_dict = dict(zip(item_info_df['click_article_id'], item_info_df['category_id']))\n",
+ " item_words_dict = dict(zip(item_info_df['click_article_id'], item_info_df['words_count']))\n",
+ " item_created_time_dict = dict(zip(item_info_df['click_article_id'], item_info_df['created_at_ts']))\n",
+ " \n",
+ " return item_type_dict, item_words_dict, item_created_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T06:42:38.730939Z",
+ "start_time": "2020-11-13T06:42:38.728461Z"
+ }
+ },
+ "source": [
+ "### 获取用户历史点击的文章信息"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:59.650781Z",
+ "start_time": "2020-11-16T11:27:59.640572Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_user_hist_item_info_dict(all_click):\n",
+ " \n",
+ " # 获取user_id对应的用户历史点击文章类型的集合字典\n",
+ " user_hist_item_typs = all_click.groupby('user_id')['category_id'].agg(set).reset_index()\n",
+ " user_hist_item_typs_dict = dict(zip(user_hist_item_typs['user_id'], user_hist_item_typs['category_id']))\n",
+ " \n",
+ " # 获取user_id对应的用户点击文章的集合\n",
+ " user_hist_item_ids_dict = all_click.groupby('user_id')['click_article_id'].agg(set).reset_index()\n",
+ " user_hist_item_ids_dict = dict(zip(user_hist_item_ids_dict['user_id'], user_hist_item_ids_dict['click_article_id']))\n",
+ " \n",
+ " # 获取user_id对应的用户历史点击的文章的平均字数字典\n",
+ " user_hist_item_words = all_click.groupby('user_id')['words_count'].agg('mean').reset_index()\n",
+ " user_hist_item_words_dict = dict(zip(user_hist_item_words['user_id'], user_hist_item_words['words_count']))\n",
+ " \n",
+ " # 获取user_id对应的用户最后一次点击的文章的创建时间\n",
+ " all_click_ = all_click.sort_values('click_timestamp')\n",
+ " user_last_item_created_time = all_click_.groupby('user_id')['created_at_ts'].apply(lambda x: x.iloc[-1]).reset_index()\n",
+ " \n",
+ " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
+ " user_last_item_created_time['created_at_ts'] = user_last_item_created_time[['created_at_ts']].apply(max_min_scaler)\n",
+ " \n",
+ " user_last_item_created_time_dict = dict(zip(user_last_item_created_time['user_id'], \\\n",
+ " user_last_item_created_time['created_at_ts']))\n",
+ " \n",
+ " return user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取点击次数最多的topk个文章"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:04.761105Z",
+ "start_time": "2020-11-16T11:28:04.756419Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取近期点击最多的文章\n",
+ "def get_item_topk_click(click_df, k):\n",
+ " topk_click = click_df['click_article_id'].value_counts().index[:k]\n",
+ " return topk_click"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 定义多路召回字典"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:08.321506Z",
+ "start_time": "2020-11-16T11:28:07.623281Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取文章的属性信息，保存成字典的形式方便查询\n",
+ "item_type_dict, item_words_dict, item_created_time_dict = get_item_info_dict(item_info_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:13.791569Z",
+ "start_time": "2020-11-16T11:28:13.786522Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义一个多路召回的字典，将各路召回的结果都保存在这个字典当中\n",
+ "user_multi_recall_dict = {'itemcf_sim_itemcf_recall': {},\n",
+ " 'embedding_sim_item_recall': {},\n",
+ " 'youtubednn_recall': {},\n",
+ " 'youtubednn_usercf_recall': {}, \n",
+ " 'cold_start_recall': {}}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T05:41:12.710754Z",
+ "start_time": "2020-11-16T05:40:57.842614Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 提取最后一次点击作为召回评估，如果不需要做召回评估直接使用全量的训练集进行召回(线下验证模型)\n",
+ "# 如果不是召回评估，直接使用全量数据进行召回，不用将最后一次提取出来\n",
+ "trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 召回效果评估函数\n",
+ "做完了召回有时候也需要对当前的召回方法或者参数进行调整以达到更好的召回效果，因为召回的结果决定了最终排序的上限，下面也会提供一个召回评估的方法"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T05:41:18.579118Z",
+ "start_time": "2020-11-16T05:41:18.571887Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 依次评估召回的前10, 20, 30, 40, 50个文章中的击中率\n",
+ "def metrics_recall(user_recall_items_dict, trn_last_click_df, topk=5):\n",
+ " last_click_item_dict = dict(zip(trn_last_click_df['user_id'], trn_last_click_df['click_article_id']))\n",
+ " user_num = len(user_recall_items_dict)\n",
+ " \n",
+ " for k in range(10, topk+1, 10):\n",
+ " hit_num = 0\n",
+ " for user, item_list in user_recall_items_dict.items():\n",
+ " # 获取前k个召回的结果\n",
+ " tmp_recall_items = [x[0] for x in user_recall_items_dict[user][:k]]\n",
+ " if last_click_item_dict[user] in set(tmp_recall_items):\n",
+ " hit_num += 1\n",
+ " \n",
+ " hit_rate = round(hit_num * 1.0 / user_num, 5)\n",
+ " print(' topk: ', k, ' : ', 'hit_num: ', hit_num, 'hit_rate: ', hit_rate, 'user_num : ', user_num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 计算相似性矩阵\n",
+ "\n",
+ "这一部分主要是通过协同过滤以及向量检索得到相似性矩阵，相似性矩阵主要分为user2user和item2item，下面依次获取基于itemcf的item2item的相似性矩阵，"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### itemcf i2i_sim\n",
+ "\n",
+ "借鉴KDD2020的去偏商品推荐，在计算item2item相似性矩阵时，使用关联规则，使得计算的文章的相似性还考虑到了:\n",
+ "1. 用户点击的时间权重\n",
+ "2. 用户点击的顺序权重\n",
+ "3. 文章创建的时间权重"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:30:51.872262Z",
+ "start_time": "2020-11-16T11:30:51.860099Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def itemcf_sim(df, item_created_time_dict):\n",
+ " \"\"\"\n",
+ " 文章与文章之间的相似性矩阵计算\n",
+ " :param df: 数据表\n",
+ " :item_created_time_dict: 文章创建时间的字典\n",
+ " return : 文章与文章的相似性矩阵\n",
+ " \n",
+ " 思路: 基于物品的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
+ " \"\"\"\n",
+ " \n",
+ " user_item_time_dict = get_user_item_time(df)\n",
+ " \n",
+ " # 计算物品相似度\n",
+ " i2i_sim = {}\n",
+ " item_cnt = defaultdict(int)\n",
+ " for user, item_time_list in tqdm(user_item_time_dict.items()):\n",
+ " # 在基于商品的协同过滤优化的时候可以考虑时间因素\n",
+ " for loc1, (i, i_click_time) in enumerate(item_time_list):\n",
+ " item_cnt[i] += 1\n",
+ " i2i_sim.setdefault(i, {})\n",
+ " for loc2, (j, j_click_time) in enumerate(item_time_list):\n",
+ " if(i == j):\n",
+ " continue\n",
+ " \n",
+ " # 考虑文章的正向顺序点击和反向顺序点击 \n",
+ " loc_alpha = 1.0 if loc2 > loc1 else 0.7\n",
+ " # 位置信息权重，其中的参数可以调节\n",
+ " loc_weight = loc_alpha * (0.9 ** (np.abs(loc2 - loc1) - 1))\n",
+ " # 点击时间权重，其中的参数可以调节\n",
+ " click_time_weight = np.exp(0.7 ** np.abs(i_click_time - j_click_time))\n",
+ " # 两篇文章创建时间的权重，其中的参数可以调节\n",
+ " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " i2i_sim[i].setdefault(j, 0)\n",
+ " # 考虑多种因素的权重计算最终的文章之间的相似度\n",
+ " i2i_sim[i][j] += loc_weight * click_time_weight * created_time_weight / math.log(len(item_time_list) + 1)\n",
+ " \n",
+ " i2i_sim_ = i2i_sim.copy()\n",
+ " for i, related_items in i2i_sim.items():\n",
+ " for j, wij in related_items.items():\n",
+ " i2i_sim_[i][j] = wij / math.sqrt(item_cnt[i] * item_cnt[j])\n",
+ " \n",
+ " # 将得到的相似性矩阵保存到本地\n",
+ " pickle.dump(i2i_sim_, open(save_path + 'itemcf_i2i_sim.pkl', 'wb'))\n",
+ " \n",
+ " return i2i_sim_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:47:09.937002Z",
+ "start_time": "2020-11-16T11:30:57.394334Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [14:20<00:00, 290.38it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "i2i_sim = itemcf_sim(all_click_df, item_created_time_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### usercf u2u_sim\n",
+ "\n",
+ "在计算用户之间的相似度的时候，也可以使用一些简单的关联规则，比如用户活跃度权重，这里将用户的点击次数作为用户活跃度的指标"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:14.951940Z",
+ "start_time": "2020-11-16T09:11:14.945654Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_user_activate_degree_dict(all_click_df):\n",
+ " all_click_df_ = all_click_df.groupby('user_id')['click_article_id'].count().reset_index()\n",
+ " \n",
+ " # 用户活跃度归一化\n",
+ " mm = MinMaxScaler()\n",
+ " all_click_df_['click_article_id'] = mm.fit_transform(all_click_df_[['click_article_id']])\n",
+ " user_activate_degree_dict = dict(zip(all_click_df_['user_id'], all_click_df_['click_article_id']))\n",
+ " \n",
+ " return user_activate_degree_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:19.879276Z",
+ "start_time": "2020-11-16T09:11:19.868808Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def usercf_sim(all_click_df, user_activate_degree_dict):\n",
+ " \"\"\"\n",
+ " 用户相似性矩阵计算\n",
+ " :param all_click_df: 数据表\n",
+ " :param user_activate_degree_dict: 用户活跃度的字典\n",
+ " return 用户相似性矩阵\n",
+ " \n",
+ " 思路: 基于用户的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
+ " \"\"\"\n",
+ " item_user_time_dict = get_item_user_time_dict(all_click_df)\n",
+ " \n",
+ " u2u_sim = {}\n",
+ " user_cnt = defaultdict(int)\n",
+ " for item, user_time_list in tqdm(item_user_time_dict.items()):\n",
+ " for u, click_time in user_time_list:\n",
+ " user_cnt[u] += 1\n",
+ " u2u_sim.setdefault(u, {})\n",
+ " for v, click_time in user_time_list:\n",
+ " u2u_sim[u].setdefault(v, 0)\n",
+ " if u == v:\n",
+ " continue\n",
+ " # 用户平均活跃度作为活跃度的权重，这里的式子也可以改善\n",
+ " activate_weight = 100 * 0.5 * (user_activate_degree_dict[u] + user_activate_degree_dict[v]) \n",
+ " u2u_sim[u][v] += activate_weight / math.log(len(user_time_list) + 1)\n",
+ " \n",
+ " u2u_sim_ = u2u_sim.copy()\n",
+ " for u, related_users in u2u_sim.items():\n",
+ " for v, wij in related_users.items():\n",
+ " u2u_sim_[u][v] = wij / math.sqrt(user_cnt[u] * user_cnt[v])\n",
+ " \n",
+ " # 将得到的相似性矩阵保存到本地\n",
+ " pickle.dump(u2u_sim_, open(save_path + 'usercf_u2u_sim.pkl', 'wb'))\n",
+ "\n",
+ " return u2u_sim_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T06:59:46.701572Z",
+ "start_time": "2020-11-16T06:59:26.852246Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 由于usercf计算时候太耗费内存了，这里就不直接运行了\n",
+ "# 如果是采样的话，是可以运行的\n",
+ "user_activate_degree_dict = get_user_activate_degree_dict(all_click_df)\n",
+ "u2u_sim = usercf_sim(all_click_df, user_activate_degree_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### item embedding sim\n",
+ "\n",
+ "使用Embedding计算item之间的相似度是为了后续冷启动的时候可以获取未出现在点击数据中的文章，后面有对冷启动专门的介绍，这里简单的说一下faiss。\n",
+ "\n",
+ "aiss是Facebook的AI团队开源的一套用于做聚类或者相似性搜索的软件库，底层是用C++实现。Faiss因为超级优越的性能，被广泛应用于推荐相关的业务当中.\n",
+ "\n",
+ "faiss工具包一般使用在推荐系统中的向量召回部分。在做向量召回的时候要么是u2u,u2i或者i2i，这里的u和i指的是user和item.我们知道在实际的场景中user和item的数量都是海量的，我们最容易想到的基于向量相似度的召回就是使用两层循环遍历user列表或者item列表计算两个向量的相似度，但是这样做在面对海量数据是不切实际的，faiss就是用来加速计算某个查询向量最相似的topk个索引向量。\n",
+ "\n",
+ "**faiss查询的原理：**\n",
+ "\n",
+ "faiss使用了PCA和PQ(Product quantization乘积量化)两种技术进行向量压缩和编码，当然还使用了其他的技术进行优化，但是PCA和PQ是其中最核心部分。\n",
+ "\n",
+ "1. PCA降维算法细节参考下面这个链接进行学习 \n",
+ "[主成分分析（PCA）原理总结](https://www.cnblogs.com/pinard/p/6239403.html) \n",
+ "\n",
+ "2. PQ编码的细节下面这个链接进行学习 \n",
+ "[实例理解product quantization算法](http://www.fabwrite.com/productquantization)\n",
+ "\n",
+ "**faiss使用**\n",
+ "\n",
+ "[faiss官方教程](https://github.com/facebookresearch/faiss/wiki/Getting-started)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:28.631803Z",
+ "start_time": "2020-11-16T09:11:28.619926Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 向量检索相似度计算\n",
+ "# topk指的是每个item, faiss搜索后返回最相似的topk个item\n",
+ "def embdding_sim(click_df, item_emb_df, save_path, topk):\n",
+ " \"\"\"\n",
+ " 基于内容的文章embedding相似性矩阵计算\n",
+ " :param click_df: 数据表\n",
+ " :param item_emb_df: 文章的embedding\n",
+ " :param save_path: 保存路径\n",
+ " :patam topk: 找最相似的topk篇\n",
+ " return 文章相似性矩阵\n",
+ " \n",
+ " 思路: 对于每一篇文章， 基于embedding的相似性返回topk个与其最相似的文章， 只不过由于文章数量太多，这里用了faiss进行加速\n",
+ " \"\"\"\n",
+ " \n",
+ " # 文章索引与文章id的字典映射\n",
+ " item_idx_2_rawid_dict = dict(zip(item_emb_df.index, item_emb_df['article_id']))\n",
+ " \n",
+ " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
+ " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols].values, dtype=np.float32)\n",
+ " # 向量进行单位化\n",
+ " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
+ " \n",
+ " # 建立faiss索引\n",
+ " item_index = faiss.IndexFlatIP(item_emb_np.shape[1])\n",
+ " item_index.add(item_emb_np)\n",
+ " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
+ " sim, idx = item_index.search(item_emb_np, topk) # 返回的是列表\n",
+ " \n",
+ " # 将向量检索的结果保存成原始id的对应关系\n",
+ " item_sim_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(item_emb_np)), sim, idx)):\n",
+ " target_raw_id = item_idx_2_rawid_dict[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = item_idx_2_rawid_dict[rele_idx]\n",
+ " item_sim_dict[target_raw_id][rele_raw_id] = item_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " # 保存i2i相似度矩阵\n",
+ " pickle.dump(item_sim_dict, open(save_path + 'emb_i2i_sim.pkl', 'wb')) \n",
+ " \n",
+ " return item_sim_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:32:35.926116Z",
+ "start_time": "2020-11-16T09:11:44.586967Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "364047it [00:23, 15292.14it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "item_emb_df = pd.read_csv(data_path + '/articles_emb.csv')\n",
+ "emb_i2i_sim = embdding_sim(all_click_df, item_emb_df, save_path, topk=10) # topk可以自行设置"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 召回\n",
+ "这个就是我们开篇提到的那个问题， 面的36万篇文章， 20多万用户的推荐， 我们又有哪些策略来缩减问题的规模？ 我们就可以再召回阶段筛选出用户对于点击文章的候选集合， 从而降低问题的规模。召回常用的策略：\n",
+ "* Youtube DNN 召回\n",
+ "* 基于文章的召回\n",
+ " * 文章的协同过滤\n",
+ " * 基于文章embedding的召回\n",
+ "* 基于用户的召回\n",
+ " * 用户的协同过滤\n",
+ " * 用户embedding\n",
+ "\n",
+ "上面的各种召回方式一部分在基于用户已经看得文章的基础上去召回与这些文章相似的一些文章， 而这个相似性的计算方式不同， 就得到了不同的召回方式， 比如文章的协同过滤， 文章内容的embedding等。还有一部分是根据用户的相似性进行推荐，对于某用户推荐与其相似的其他用户看过的文章，比如用户的协同过滤和用户embedding。 还有一种思路是类似矩阵分解的思路，先计算出用户和文章的embedding之后，就可以直接算用户和文章的相似度， 根据这个相似度进行推荐， 比如YouTube DNN。 我们下面详细来看一下每一个召回方法："
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### YoutubeDNN召回\n",
+ "**(这一步是直接获取用户召回的候选文章列表)**\n",
+ "\n",
+ "[论文下载地址](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)\n",
+ "\n",
+ "**Youtubednn召回架构**\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "关于YoutubeDNN原理和应用推荐看王喆的两篇博客：\n",
+ "\n",
+ "1. [重读Youtube深度学习推荐系统论文，字字珠玑，惊为神文](https://zhuanlan.zhihu.com/p/52169807)\n",
+ "2. [YouTube深度学习推荐系统的十大工程问题](https://zhuanlan.zhihu.com/p/52504407)\n",
+ "\n",
+ "\n",
+ "**参考文献:**\n",
+ "1. https://zhuanlan.zhihu.com/p/52169807 (YouTubeDNN原理)\n",
+ "2. https://zhuanlan.zhihu.com/p/26306795 (Word2Vec知乎众赞文章) --- word2vec放到排序中的w2v的介绍部分\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:13:11.058766Z",
+ "start_time": "2020-11-16T10:13:11.041084Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取双塔召回时的训练验证数据\n",
+ "# negsample指的是通过滑窗构建样本的时候，负样本的数量\n",
+ "def gen_data_set(data, negsample=0):\n",
+ " data.sort_values(\"click_timestamp\", inplace=True)\n",
+ " item_ids = data['click_article_id'].unique()\n",
+ "\n",
+ " train_set = []\n",
+ " test_set = []\n",
+ " for reviewerID, hist in tqdm(data.groupby('user_id')):\n",
+ " pos_list = hist['click_article_id'].tolist()\n",
+ " \n",
+ " if negsample > 0:\n",
+ " candidate_set = list(set(item_ids) - set(pos_list)) # 用户没看过的文章里面选择负样本\n",
+ " neg_list = np.random.choice(candidate_set,size=len(pos_list)*negsample,replace=True) # 对于每个正样本，选择n个负样本\n",
+ " \n",
+ " # 长度只有一个的时候，需要把这条数据也放到训练集中，不然的话最终学到的embedding就会有缺失\n",
+ " if len(pos_list) == 1:\n",
+ " train_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
+ " test_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
+ " \n",
+ " # 滑窗构造正负样本\n",
+ " for i in range(1, len(pos_list)):\n",
+ " hist = pos_list[:i]\n",
+ " \n",
+ " if i != len(pos_list) - 1:\n",
+ " train_set.append((reviewerID, hist[::-1], pos_list[i], 1, len(hist[::-1]))) # 正样本 [user_id, his_item, pos_item, label, len(his_item)]\n",
+ " for negi in range(negsample):\n",
+ " train_set.append((reviewerID, hist[::-1], neg_list[i*negsample+negi], 0,len(hist[::-1]))) # 负样本 [user_id, his_item, neg_item, label, len(his_item)]\n",
+ " else:\n",
+ " # 将最长的那一个序列长度作为测试数据\n",
+ " test_set.append((reviewerID, hist[::-1], pos_list[i],1,len(hist[::-1])))\n",
+ " \n",
+ " random.shuffle(train_set)\n",
+ " random.shuffle(test_set)\n",
+ " \n",
+ " return train_set, test_set\n",
+ "\n",
+ "# 将输入的数据进行padding，使得序列特征的长度都一致\n",
+ "def gen_model_input(train_set,user_profile,seq_max_len):\n",
+ "\n",
+ " train_uid = np.array([line[0] for line in train_set])\n",
+ " train_seq = [line[1] for line in train_set]\n",
+ " train_iid = np.array([line[2] for line in train_set])\n",
+ " train_label = np.array([line[3] for line in train_set])\n",
+ " train_hist_len = np.array([line[4] for line in train_set])\n",
+ "\n",
+ " train_seq_pad = pad_sequences(train_seq, maxlen=seq_max_len, padding='post', truncating='post', value=0)\n",
+ " train_model_input = {\"user_id\": train_uid, \"click_article_id\": train_iid, \"hist_article_id\": train_seq_pad,\n",
+ " \"hist_len\": train_hist_len}\n",
+ "\n",
+ " return train_model_input, train_label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:13:18.124452Z",
+ "start_time": "2020-11-16T10:13:18.098284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def youtubednn_u2i_dict(data, topk=20): \n",
+ " sparse_features = [\"click_article_id\", \"user_id\"]\n",
+ " SEQ_LEN = 30 # 用户点击序列的长度，短的填充，长的截断\n",
+ " \n",
+ " user_profile_ = data[[\"user_id\"]].drop_duplicates('user_id')\n",
+ " item_profile_ = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
+ " \n",
+ " # 类别编码\n",
+ " features = [\"click_article_id\", \"user_id\"]\n",
+ " feature_max_idx = {}\n",
+ " \n",
+ " for feature in features:\n",
+ " lbe = LabelEncoder()\n",
+ " data[feature] = lbe.fit_transform(data[feature])\n",
+ " feature_max_idx[feature] = data[feature].max() + 1\n",
+ " \n",
+ " # 提取user和item的画像，这里具体选择哪些特征还需要进一步的分析和考虑\n",
+ " user_profile = data[[\"user_id\"]].drop_duplicates('user_id')\n",
+ " item_profile = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
+ " \n",
+ " user_index_2_rawid = dict(zip(user_profile['user_id'], user_profile_['user_id']))\n",
+ " item_index_2_rawid = dict(zip(item_profile['click_article_id'], item_profile_['click_article_id']))\n",
+ " \n",
+ " # 划分训练和测试集\n",
+ " # 由于深度学习需要的数据量通常都是非常大的，所以为了保证召回的效果，往往会通过滑窗的形式扩充训练样本\n",
+ " train_set, test_set = gen_data_set(data, 0)\n",
+ " # 整理输入数据，具体的操作可以看上面的函数\n",
+ " train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)\n",
+ " test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)\n",
+ " \n",
+ " # 确定Embedding的维度\n",
+ " embedding_dim = 16\n",
+ " \n",
+ " # 将数据整理成模型可以直接输入的形式\n",
+ " user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),\n",
+ " VarLenSparseFeat(SparseFeat('hist_article_id', feature_max_idx['click_article_id'], embedding_dim,\n",
+ " embedding_name=\"click_article_id\"), SEQ_LEN, 'mean', 'hist_len'),]\n",
+ " item_feature_columns = [SparseFeat('click_article_id', feature_max_idx['click_article_id'], embedding_dim)]\n",
+ " \n",
+ " # 模型的定义 \n",
+ " # num_sampled: 负采样时的样本数量\n",
+ " model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))\n",
+ " # 模型编译\n",
+ " model.compile(optimizer=\"adam\", loss=sampledsoftmaxloss) \n",
+ " \n",
+ " # 模型训练，这里可以定义验证集的比例，如果设置为0的话就是全量数据直接进行训练\n",
+ " history = model.fit(train_model_input, train_label, batch_size=256, epochs=1, verbose=1, validation_split=0.0)\n",
+ " \n",
+ " # 训练完模型之后,提取训练的Embedding，包括user端和item端\n",
+ " test_user_model_input = test_model_input\n",
+ " all_item_model_input = {\"click_article_id\": item_profile['click_article_id'].values}\n",
+ "\n",
+ " user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)\n",
+ " item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)\n",
+ " \n",
+ " # 保存当前的item_embedding 和 user_embedding 排序的时候可能能够用到，但是需要注意保存的时候需要和原始的id对应\n",
+ " user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)\n",
+ " item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)\n",
+ " \n",
+ " # embedding保存之前归一化一下\n",
+ " user_embs = user_embs / np.linalg.norm(user_embs, axis=1, keepdims=True)\n",
+ " item_embs = item_embs / np.linalg.norm(item_embs, axis=1, keepdims=True)\n",
+ " \n",
+ " # 将Embedding转换成字典的形式方便查询\n",
+ " raw_user_id_emb_dict = {user_index_2_rawid[k]: \\\n",
+ " v for k, v in zip(user_profile['user_id'], user_embs)}\n",
+ " raw_item_id_emb_dict = {item_index_2_rawid[k]: \\\n",
+ " v for k, v in zip(item_profile['click_article_id'], item_embs)}\n",
+ " # 将Embedding保存到本地\n",
+ " pickle.dump(raw_user_id_emb_dict, open(save_path + 'user_youtube_emb.pkl', 'wb'))\n",
+ " pickle.dump(raw_item_id_emb_dict, open(save_path + 'item_youtube_emb.pkl', 'wb'))\n",
+ " \n",
+ " # faiss紧邻搜索，通过user_embedding 搜索与其相似性最高的topk个item\n",
+ " index = faiss.IndexFlatIP(embedding_dim)\n",
+ " # 上面已经进行了归一化，这里可以不进行归一化了\n",
+ "# faiss.normalize_L2(user_embs)\n",
+ "# faiss.normalize_L2(item_embs)\n",
+ " index.add(item_embs) # 将item向量构建索引\n",
+ " sim, idx = index.search(np.ascontiguousarray(user_embs), topk) # 通过user去查询最相似的topk个item\n",
+ " \n",
+ " user_recall_items_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(test_user_model_input['user_id'], sim, idx)):\n",
+ " target_raw_id = user_index_2_rawid[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = item_index_2_rawid[rele_idx]\n",
+ " user_recall_items_dict[target_raw_id][rele_raw_id] = user_recall_items_dict.get(target_raw_id, {})\\\n",
+ " .get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " user_recall_items_dict = {k: sorted(v.items(), key=lambda x: x[1], reverse=True) for k, v in user_recall_items_dict.items()}\n",
+ " # 将召回的结果进行排序\n",
+ " \n",
+ " # 保存召回的结果\n",
+ " # 这里是直接通过向量的方式得到了召回结果，相比于上面的召回方法，上面的只是得到了i2i及u2u的相似性矩阵，还需要进行协同过滤召回才能得到召回结果\n",
+ " # 可以直接对这个召回结果进行评估，为了方便可以统一写一个评估函数对所有的召回结果进行评估\n",
+ " pickle.dump(user_recall_items_dict, open(save_path + 'youtube_u2i_dict.pkl', 'wb'))\n",
+ " return user_recall_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:21:46.420014Z",
+ "start_time": "2020-11-16T10:13:35.351131Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [02:02<00:00, 2038.57it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "keep_dims is deprecated, use keepdims instead\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Deprecated in favor of operator or tf.math.divide.\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "1149673/1149673 [==============================] - 216s 188us/sample - loss: 0.1326\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "250000it [00:32, 7720.75it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 由于这里需要做召回评估，所以讲训练集中的最后一次点击都提取了出来\n",
+ "if not metric_recall:\n",
+ " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(all_click_df, topk=20)\n",
+ "else:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(trn_hist_click_df, topk=20)\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['youtubednn_recall'], trn_last_click_df, topk=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### itemcf recall\n",
+ "\n",
+ "上面已经通过协同过滤，Embedding检索的方式得到了文章的相似度矩阵，下面使用协同过滤的思想，给用户召回与其历史文章相似的文章。\n",
+ "这里在召回的时候，也是用了关联规则的方式：\n",
+ "1. 考虑相似文章与历史点击文章顺序的权重(细节看代码)\n",
+ "2. 考虑文章创建时间的权重，也就是考虑相似文章与历史点击文章创建时间差的权重\n",
+ "3. 考虑文章内容相似度权重(使用Embedding计算相似文章相似度，但是这里需要注意，在Embedding的时候并没有计算所有商品两两之间的相似度，所以相似的文章与历史点击文章不存在相似度，需要做特殊处理)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:48:40.580553Z",
+ "start_time": "2020-11-16T11:48:40.567130Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于商品的召回i2i\n",
+ "def item_based_recommend(user_id, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
+ " \"\"\"\n",
+ " 基于文章协同过滤的召回\n",
+ " :param user_id: 用户id\n",
+ " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ " :param i2i_sim: 字典，文章相似性矩阵\n",
+ " :param sim_item_topk: 整数， 选择与当前文章最相似的前k篇文章\n",
+ " :param recall_item_num: 整数， 最后的召回文章数量\n",
+ " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
+ " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
+ " \n",
+ " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
+ " \"\"\"\n",
+ " # 获取用户历史交互的文章\n",
+ " user_hist_items = user_item_time_dict[user_id]\n",
+ " user_hist_items_ = {user_id for user_id, _ in user_hist_items}\n",
+ " \n",
+ " item_rank = {}\n",
+ " for loc, (i, click_time) in enumerate(user_hist_items):\n",
+ " for j, wij in sorted(i2i_sim[i].items(), key=lambda x: x[1], reverse=True)[:sim_item_topk]:\n",
+ " if j in user_hist_items_:\n",
+ " continue\n",
+ " \n",
+ " # 文章创建时间差权重\n",
+ " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " # 相似文章和历史点击文章序列中历史文章所在的位置权重\n",
+ " loc_weight = (0.9 ** (len(user_hist_items) - loc))\n",
+ " \n",
+ " content_weight = 1.0\n",
+ " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
+ " content_weight += emb_i2i_sim[i][j]\n",
+ " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
+ " content_weight += emb_i2i_sim[j][i]\n",
+ " \n",
+ " item_rank.setdefault(j, 0)\n",
+ " item_rank[j] += created_time_weight * loc_weight * content_weight * wij\n",
+ " \n",
+ " # 不足10个，用热门商品补全\n",
+ " if len(item_rank) < recall_item_num:\n",
+ " for i, item in enumerate(item_topk_click):\n",
+ " if item in item_rank.items(): # 填充的item应该不在原来的列表中\n",
+ " continue\n",
+ " item_rank[item] = - i - 100 # 随便给个负数就行\n",
+ " if len(item_rank) == recall_item_num:\n",
+ " break\n",
+ " \n",
+ " item_rank = sorted(item_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num]\n",
+ " \n",
+ " return item_rank"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### itemcf sim召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T14:41:23.433038Z",
+ "start_time": "2020-11-16T11:48:46.286350Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [2:51:13<00:00, 24.33it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 先进行itemcf召回, 为了召回评估，所以提取最后一次点击\n",
+ "\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "\n",
+ "i2i_sim = pickle.load(open(save_path + 'itemcf_i2i_sim.pkl', 'rb'))\n",
+ "emb_i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_item_topk = 20\n",
+ "recall_item_num = 10\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, \\\n",
+ " i2i_sim, sim_item_topk, recall_item_num, \\\n",
+ " item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ "\n",
+ "user_multi_recall_dict['itemcf_sim_itemcf_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['itemcf_sim_itemcf_recall'], open(save_path + 'itemcf_recall_dict.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['itemcf_sim_itemcf_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### embedding sim 召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T15:04:51.527795Z",
+ "start_time": "2020-11-16T14:59:03.907519Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [04:35<00:00, 905.85it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 这里是为了召回评估，所以提取最后一次点击\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
+ "\n",
+ "sim_item_topk = 20\n",
+ "recall_item_num = 10\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ " \n",
+ "user_multi_recall_dict['embedding_sim_item_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['embedding_sim_item_recall'], open(save_path + 'embedding_sim_item_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['embedding_sim_item_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### usercf召回\n",
+ "\n",
+ "基于用户协同过滤，核心思想是给用户推荐与其相似的用户历史点击文章，因为这里涉及到了相似用户的历史文章，这里仍然可以加上一些关联规则来给用户可能点击的文章进行加权，这里使用的关联规则主要是考虑相似用户的历史点击文章与被推荐用户历史点击商品的关系权重，而这里的关系就可以直接借鉴基于物品的协同过滤相似的做法，只不过这里是对被推荐物品关系的一个累加的过程，下面是使用的一些关系权重，及相关的代码：\n",
+ "\n",
+ "1. 计算被推荐用户历史点击文章与相似用户历史点击文章的相似度，文章创建时间差，相对位置的总和，作为各自的权重"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:09:32.293990Z",
+ "start_time": "2020-11-17T02:09:32.278678Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于用户的召回 u2u2i\n",
+ "def user_based_recommend(user_id, user_item_time_dict, u2u_sim, sim_user_topk, recall_item_num, \n",
+ " item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
+ " \"\"\"\n",
+ " 基于文章协同过滤的召回\n",
+ " :param user_id: 用户id\n",
+ " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ " :param u2u_sim: 字典，文章相似性矩阵\n",
+ " :param sim_user_topk: 整数， 选择与当前用户最相似的前k个用户\n",
+ " :param recall_item_num: 整数， 最后的召回文章数量\n",
+ " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
+ " :param item_created_time_dict: 文章创建时间列表\n",
+ " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
+ " \n",
+ " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
+ " \"\"\"\n",
+ " # 历史交互\n",
+ " user_item_time_list = user_item_time_dict[user_id] # [(item1, time1), (item2, time2)..]\n",
+ " user_hist_items = set([i for i, t in user_item_time_list]) # 存在一个用户与某篇文章的多次交互， 这里得去重\n",
+ " \n",
+ " items_rank = {}\n",
+ " for sim_u, wuv in sorted(u2u_sim[user_id].items(), key=lambda x: x[1], reverse=True)[:sim_user_topk]:\n",
+ " for i, click_time in user_item_time_dict[sim_u]:\n",
+ " if i in user_hist_items:\n",
+ " continue\n",
+ " items_rank.setdefault(i, 0)\n",
+ " \n",
+ " loc_weight = 1.0\n",
+ " content_weight = 1.0\n",
+ " created_time_weight = 1.0\n",
+ " \n",
+ " # 当前文章与该用户看的历史文章进行一个权重交互\n",
+ " for loc, (j, click_time) in enumerate(user_item_time_list):\n",
+ " # 点击时的相对位置权重\n",
+ " loc_weight += 0.9 ** (len(user_item_time_list) - loc)\n",
+ " # 内容相似性权重\n",
+ " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
+ " content_weight += emb_i2i_sim[i][j]\n",
+ " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
+ " content_weight += emb_i2i_sim[j][i]\n",
+ " \n",
+ " # 创建时间差权重\n",
+ " created_time_weight += np.exp(0.8 * np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " \n",
+ " items_rank[i] += loc_weight * content_weight * created_time_weight * wuv\n",
+ " \n",
+ " # 热度补全\n",
+ " if len(items_rank) < recall_item_num:\n",
+ " for i, item in enumerate(item_topk_click):\n",
+ " if item in items_rank.items(): # 填充的item应该不在原来的列表中\n",
+ " continue\n",
+ " items_rank[item] = - i - 100 # 随便给个复数就行\n",
+ " if len(items_rank) == recall_item_num:\n",
+ " break\n",
+ " \n",
+ " items_rank = sorted(items_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num] \n",
+ " \n",
+ " return items_rank"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### usercf sim召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:05:41.652501Z",
+ "start_time": "2020-11-16T07:05:40.953871Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 这里是为了召回评估，所以提取最后一次点击\n",
+ "# 由于usercf中计算user之间的相似度的过程太费内存了，全量数据这里就没有跑，跑了一个采样之后的数据\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ " \n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "\n",
+ "u2u_sim = pickle.load(open(save_path + 'usercf_u2u_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_user_topk = 20\n",
+ "recall_item_num = 10\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim) \n",
+ "\n",
+ "pickle.dump(user_recall_items_dict, open(save_path + 'usercf_u2u2i_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_recall_items_dict, trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T03:09:35.853516Z",
+ "start_time": "2020-11-16T03:09:35.737625Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### user embedding sim召回\n",
+ "\n",
+ "虽然没有直接跑usercf的计算用户之间的相似度，为了验证上述基于用户的协同过滤的代码，下面使用了YoutubeDNN过程中产生的user embedding来进行向量检索每个user最相似的topk个user，在使用这里得到的u2u的相似性矩阵，使用usercf进行召回，具体代码如下"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:09:46.807811Z",
+ "start_time": "2020-11-17T02:09:46.798033Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 使用Embedding的方式获取u2u的相似性矩阵\n",
+ "# topk指的是每个user, faiss搜索后返回最相似的topk个user\n",
+ "def u2u_embdding_sim(click_df, user_emb_dict, save_path, topk):\n",
+ " \n",
+ " user_list = []\n",
+ " user_emb_list = []\n",
+ " for user_id, user_emb in user_emb_dict.items():\n",
+ " user_list.append(user_id)\n",
+ " user_emb_list.append(user_emb)\n",
+ " \n",
+ " user_index_2_rawid_dict = {k: v for k, v in zip(range(len(user_list)), user_list)} \n",
+ " \n",
+ " user_emb_np = np.array(user_emb_list, dtype=np.float32)\n",
+ " \n",
+ " # 建立faiss索引\n",
+ " user_index = faiss.IndexFlatIP(user_emb_np.shape[1])\n",
+ " user_index.add(user_emb_np)\n",
+ " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
+ " sim, idx = user_index.search(user_emb_np, topk) # 返回的是列表\n",
+ " \n",
+ " # 将向量检索的结果保存成原始id的对应关系\n",
+ " user_sim_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(user_emb_np)), sim, idx)):\n",
+ " target_raw_id = user_index_2_rawid_dict[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = user_index_2_rawid_dict[rele_idx]\n",
+ " user_sim_dict[target_raw_id][rele_raw_id] = user_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " # 保存i2i相似度矩阵\n",
+ " pickle.dump(user_sim_dict, open(save_path + 'youtube_u2u_sim.pkl', 'wb')) \n",
+ " return user_sim_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:14:31.355905Z",
+ "start_time": "2020-11-17T02:09:53.236531Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "250000it [00:23, 10507.45it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 读取YoutubeDNN过程中产生的user embedding, 然后使用faiss计算用户之间的相似度\n",
+ "# 这里需要注意，这里得到的user embedding其实并不是很好，因为YoutubeDNN中使用的是用户点击序列来训练的user embedding,\n",
+ "# 如果序列普遍都比较短的话，其实效果并不是很好\n",
+ "user_emb_dict = pickle.load(open(save_path + 'user_youtube_emb.pkl', 'rb'))\n",
+ "u2u_sim = u2u_embdding_sim(all_click_df, user_emb_dict, save_path, topk=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "通过YoutubeDNN得到的user_embedding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:49:40.755431Z",
+ "start_time": "2020-11-17T02:28:47.003514Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [19:43<00:00, 211.22it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 使用召回评估函数验证当前召回方式的效果\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "u2u_sim = pickle.load(open(save_path + 'youtube_u2u_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_user_topk = 20\n",
+ "recall_item_num = 10\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ " \n",
+ "user_multi_recall_dict['youtubednn_usercf_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['youtubednn_usercf_recall'], open(save_path + 'youtubednn_usercf_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['youtubednn_usercf_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:07:44.326253Z",
+ "start_time": "2020-11-16T07:07:43.798931Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 冷启动问题"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**冷启动问题可以分成三类：文章冷启动，用户冷启动，系统冷启动。**\n",
+ "\n",
+ "- 文章冷启动：对于一个平台系统新加入的文章，该文章没有任何的交互记录，如何推荐给用户的问题。(对于我们场景可以认为是，日志数据中没有出现过的文章都可以认为是冷启动的文章)\n",
+ "- 用户冷启动：对于一个平台系统新来的用户，该用户还没有文章的交互信息，如何给该用户进行推荐。(对于我们场景就是，测试集中的用户是否在测试集对应的log数据中出现过，如果没有出现过，那么可以认为该用户是冷启动用户。但是有时候并没有这么严格，我们也可以自己设定某些指标来判别哪些用户是冷启动用户，比如通过使用时长，点击率，留存率等等)\n",
+ "- 系统冷启动：就是对于一个平台刚上线，还没有任何的相关历史数据，此时就是系统冷启动，其实也就是前面两种的一个综合。\n",
+ "\n",
+ "**当前场景下冷启动问题的分析：**\n",
+ "\n",
+ "对当前的数据进行分析会发现，日志中所有出现过的点击文章只有3w多个，而整个文章库中却有30多万，那么测试集中的用户最后一次点击是否会点击没有出现在日志中的文章呢？如果存在这种情况，说明用户点击的文章之前没有任何的交互信息，这也就是我们所说的文章冷启动。通过数据分析还可以发现，测试集用户只有一次点击的数据占得比例还不少，其实仅仅通过用户的一次点击就给用户推荐文章使用模型的方式也是比较难的，这里其实也可以考虑用户冷启动的问题，但是这里只给出物品冷启动的一些解决方案及代码，关于用户冷启动的话提一些可行性的做法。\n",
+ "\n",
+ "1. 文章冷启动(没有冷启动的探索问题) \n",
+ " 其实我们这里不是为了做文章的冷启动而做冷启动，而是猜测用户可能会点击一些没有在log数据中出现的文章，我们要做的就是如何从将近27万的文章中选择一些文章作为用户冷启动的文章，这里其实也可以看成是一种召回策略，我们这里就采用简单的比较好理解的基于规则的召回策略来获取用户可能点击的未出现在log数据中的文章。\n",
+ " 现在的问题变成了：如何给每个用户考虑从27万个商品中获取一小部分商品？随机选一些可能是一种方案。下面给出一些参考的方案。\n",
+ " 1. 首先基于Embedding召回一部分与用户历史相似的文章\n",
+ " 2. 从基于Embedding召回的文章中通过一些规则过滤掉一些文章，使得留下的文章用户更可能点击。我们这里的规则，可以是，留下那些与用户历史点击文章主题相同的文章，或者字数相差不大的文章。并且留下的文章尽量是与测试集用户最后一次点击时间更接近的文章，或者是当天的文章也行。\n",
+ "2. 用户冷启动 \n",
+ " 这里对测试集中的用户点击数据进行分析会发现，测试集中有百分之20的用户只有一次点击，那么这些点击特别少的用户的召回是不是可以单独做一些策略上的补充呢？或者是在排序后直接基于规则加上一些文章呢？这些都可以去尝试，这里没有提供具体的做法。\n",
+ " \n",
+ "**注意：** \n",
+ "\n",
+ "这里看似和基于embedding计算的item之间相似度然后做itemcf是一致的，但是现在我们的目的不一样，我们这里的目的是找到相似的向量，并且还没有出现在log日志中的商品，再加上一些其他的冷启动的策略，这里需要找回的数量会偏多一点，不然被筛选完之后可能都没有文章了"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T04:30:23.027164Z",
+ "start_time": "2020-11-17T04:23:09.960235Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [05:01<00:00, 828.60it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 先进行itemcf召回，这里不需要做召回评估，这里只是一种策略\n",
+ "trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
+ "\n",
+ "sim_item_topk = 150\n",
+ "recall_item_num = 100 # 稍微召回多一点文章，便于后续的规则筛选\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
+ " recall_item_num, item_topk_click,item_created_time_dict, emb_i2i_sim)\n",
+ "pickle.dump(user_recall_items_dict, open(save_path + 'cold_start_items_raw_dict.pkl', 'wb'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T06:11:39.267581Z",
+ "start_time": "2020-11-17T06:11:39.252563Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于规则进行文章过滤\n",
+ "# 保留文章主题与用户历史浏览主题相似的文章\n",
+ "# 保留文章字数与用户历史浏览文章字数相差不大的文章\n",
+ "# 保留最后一次点击当天的文章\n",
+ "# 按照相似度返回最终的结果\n",
+ "\n",
+ "def get_click_article_ids_set(all_click_df):\n",
+ " return set(all_click_df.click_article_id.values)\n",
+ "\n",
+ "def cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
+ " user_last_item_created_time_dict, item_type_dict, item_words_dict, \n",
+ " item_created_time_dict, click_article_ids_set, recall_item_num):\n",
+ " \"\"\"\n",
+ " 冷启动的情况下召回一些文章\n",
+ " :param user_recall_items_dict: 基于内容embedding相似性召回来的很多文章， 字典， {user1: [(item1, item2), ..], }\n",
+ " :param user_hist_item_typs_dict: 字典， 用户点击的文章的主题映射\n",
+ " :param user_hist_item_words_dict: 字典， 用户点击的历史文章的字数映射\n",
+ " :param user_last_item_created_time_idct: 字典，用户点击的历史文章创建时间映射\n",
+ " :param item_tpye_idct: 字典，文章主题映射\n",
+ " :param item_words_dict: 字典，文章字数映射\n",
+ " :param item_created_time_dict: 字典， 文章创建时间映射\n",
+ " :param click_article_ids_set: 集合，用户点击过得文章, 也就是日志里面出现过的文章\n",
+ " :param recall_item_num: 召回文章的数量， 这个指的是没有出现在日志里面的文章数量\n",
+ " \"\"\"\n",
+ " \n",
+ " cold_start_user_items_dict = {}\n",
+ " for user, item_list in tqdm(user_recall_items_dict.items()):\n",
+ " cold_start_user_items_dict.setdefault(user, [])\n",
+ " for item, score in item_list:\n",
+ " # 获取历史文章信息\n",
+ " hist_item_type_set = user_hist_item_typs_dict[user]\n",
+ " hist_mean_words = user_hist_item_words_dict[user]\n",
+ " hist_last_item_created_time = user_last_item_created_time_dict[user]\n",
+ " hist_last_item_created_time = datetime.fromtimestamp(hist_last_item_created_time)\n",
+ " \n",
+ " # 获取当前召回文章的信息\n",
+ " curr_item_type = item_type_dict[item]\n",
+ " curr_item_words = item_words_dict[item]\n",
+ " curr_item_created_time = item_created_time_dict[item]\n",
+ " curr_item_created_time = datetime.fromtimestamp(curr_item_created_time)\n",
+ "\n",
+ " # 首先，文章不能出现在用户的历史点击中， 然后根据文章主题，文章单词数，文章创建时间进行筛选\n",
+ " if curr_item_type not in hist_item_type_set or \\\n",
+ " item in click_article_ids_set or \\\n",
+ " abs(curr_item_words - hist_mean_words) > 200 or \\\n",
+ " abs((curr_item_created_time - hist_last_item_created_time).days) > 90: \n",
+ " continue\n",
+ " \n",
+ " cold_start_user_items_dict[user].append((item, score)) # {user1: [(item1, score1), (item2, score2)..]...}\n",
+ " \n",
+ " # 需要控制一下冷启动召回的数量\n",
+ " cold_start_user_items_dict = {k: sorted(v, key=lambda x:x[1], reverse=True)[:recall_item_num] \\\n",
+ " for k, v in cold_start_user_items_dict.items()}\n",
+ " \n",
+ " pickle.dump(cold_start_user_items_dict, open(save_path + 'cold_start_user_items_dict.pkl', 'wb'))\n",
+ " \n",
+ " return cold_start_user_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T06:35:38.758278Z",
+ "start_time": "2020-11-17T06:31:40.164332Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [01:49<00:00, 2293.37it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "all_click_df_ = all_click_df.copy()\n",
+ "all_click_df_ = all_click_df_.merge(item_info_df, how='left', on='click_article_id')\n",
+ "user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict = get_user_hist_item_info_dict(all_click_df_)\n",
+ "click_article_ids_set = get_click_article_ids_set(all_click_df)\n",
+ "# 需要注意的是\n",
+ "# 这里使用了很多规则来筛选冷启动的文章，所以前面再召回的阶段就应该尽可能的多召回一些文章，否则很容易被删掉\n",
+ "cold_start_user_items_dict = cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
+ " user_last_item_created_time_dict, item_type_dict, item_words_dict, \\\n",
+ " item_created_time_dict, click_article_ids_set, recall_item_num)\n",
+ "\n",
+ "user_multi_recall_dict['cold_start_recall'] = cold_start_user_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:13:33.099298Z",
+ "start_time": "2020-11-16T07:13:32.655036Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 多路召回合并\n",
+ "多路召回合并就是将前面所有的召回策略得到的用户文章列表合并起来，下面是对前面所有召回结果的汇总\n",
+ "1. 基于itemcf计算的item之间的相似度sim进行的召回 \n",
+ "2. 基于embedding搜索得到的item之间的相似度进行的召回\n",
+ "3. YoutubeDNN召回\n",
+ "4. YoutubeDNN得到的user之间的相似度进行的召回\n",
+ "5. 基于冷启动策略的召回\n",
+ "\n",
+ "**注意：** \n",
+ "在做召回评估的时候就会发现有些召回的效果不错有些召回的效果很差，所以对每一路召回的结果，我们可以认为的定义一些权重，来做最终的相似度融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:02:16.033971Z",
+ "start_time": "2020-11-17T07:02:16.019819Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def combine_recall_results(user_multi_recall_dict, weight_dict=None, topk=25):\n",
+ " final_recall_items_dict = {}\n",
+ " \n",
+ " # 对每一种召回结果按照用户进行归一化，方便后面多种召回结果，相同用户的物品之间权重相加\n",
+ " def norm_user_recall_items_sim(sorted_item_list):\n",
+ " # 如果冷启动中没有文章或者只有一篇文章，直接返回，出现这种情况的原因可能是冷启动召回的文章数量太少了，\n",
+ " # 基于规则筛选之后就没有文章了, 这里还可以做一些其他的策略性的筛选\n",
+ " if len(sorted_item_list) < 2:\n",
+ " return sorted_item_list\n",
+ " \n",
+ " min_sim = sorted_item_list[-1][1]\n",
+ " max_sim = sorted_item_list[0][1]\n",
+ " \n",
+ " norm_sorted_item_list = []\n",
+ " for item, score in sorted_item_list:\n",
+ " if max_sim > 0:\n",
+ " norm_score = 1.0 * (score - min_sim) / (max_sim - min_sim) if max_sim > min_sim else 1.0\n",
+ " else:\n",
+ " norm_score = 0.0\n",
+ " norm_sorted_item_list.append((item, norm_score))\n",
+ " \n",
+ " return norm_sorted_item_list\n",
+ " \n",
+ " print('多路召回合并...')\n",
+ " for method, user_recall_items in tqdm(user_multi_recall_dict.items()):\n",
+ " print(method + '...')\n",
+ " # 在计算最终召回结果的时候，也可以为每一种召回结果设置一个权重\n",
+ " if weight_dict == None:\n",
+ " recall_method_weight = 1\n",
+ " else:\n",
+ " recall_method_weight = weight_dict[method]\n",
+ " \n",
+ " for user_id, sorted_item_list in user_recall_items.items(): # 进行归一化\n",
+ " user_recall_items[user_id] = norm_user_recall_items_sim(sorted_item_list)\n",
+ " \n",
+ " for user_id, sorted_item_list in user_recall_items.items():\n",
+ " # print('user_id')\n",
+ " final_recall_items_dict.setdefault(user_id, {})\n",
+ " for item, score in sorted_item_list:\n",
+ " final_recall_items_dict[user_id].setdefault(item, 0)\n",
+ " final_recall_items_dict[user_id][item] += recall_method_weight * score \n",
+ " \n",
+ " final_recall_items_dict_rank = {}\n",
+ " # 多路召回时也可以控制最终的召回数量\n",
+ " for user, recall_item_dict in final_recall_items_dict.items():\n",
+ " final_recall_items_dict_rank[user] = sorted(recall_item_dict.items(), key=lambda x: x[1], reverse=True)[:topk]\n",
+ "\n",
+ " # 将多路召回后的最终结果字典保存到本地\n",
+ " pickle.dump(final_recall_items_dict_rank, open(os.path.join(save_path, 'final_recall_items_dict.pkl'),'wb'))\n",
+ "\n",
+ " return final_recall_items_dict_rank"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:02:21.078455Z",
+ "start_time": "2020-11-17T07:02:21.074060Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 这里直接对多路召回的权重给了一个相同的值，其实可以根据前面召回的情况来调整参数的值\n",
+ "weight_dict = {'itemcf_sim_itemcf_recall': 1.0,\n",
+ " 'embedding_sim_item_recall': 1.0,\n",
+ " 'youtubednn_recall': 1.0,\n",
+ " 'youtubednn_usercf_recall': 1.0, \n",
+ " 'cold_start_recall': 1.0}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:04:35.747924Z",
+ "start_time": "2020-11-17T07:02:26.889573Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/5 [00:00= topk\n",
- " \n",
- " del recall_df['pred_score']\n",
- " submit = recall_df[recall_df['rank'] <= topk].set_index(['user_id', 'rank']).unstack(-1).reset_index()\n",
- " \n",
- " submit.columns = [int(col) if isinstance(col, int) else col for col in submit.columns.droplevel(0)]\n",
- " # 按照提交格式定义列名\n",
- " submit = submit.rename(columns={'': 'user_id', 1: 'article_1', 2: 'article_2', \n",
- " 3: 'article_3', 4: 'article_4', 5: 'article_5'})\n",
- " \n",
- " save_name = save_path + model_name + '_' + datetime.today().strftime('%m-%d') + '.csv'\n",
- " submit.to_csv(save_name, index=False, header=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:04.332198Z",
- "start_time": "2020-11-18T04:21:04.325020Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序结果归一化\n",
- "def norm_sim(sim_df, weight=0.0):\n",
- " # print(sim_df.head())\n",
- " min_sim = sim_df.min()\n",
- " max_sim = sim_df.max()\n",
- " if max_sim == min_sim:\n",
- " sim_df = sim_df.apply(lambda sim: 1.0)\n",
- " else:\n",
- " sim_df = sim_df.apply(lambda sim: 1.0 * (sim - min_sim) / (max_sim - min_sim))\n",
- "\n",
- " sim_df = sim_df.apply(lambda sim: sim + weight) # plus one\n",
- " return sim_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## LGB排序模型"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:07.787698Z",
- "start_time": "2020-11-18T04:21:07.536514Z"
- }
- },
- "outputs": [],
- "source": [
- "# 防止中间出错之后重新读取数据\n",
- "trn_user_item_feats_df_rank_model = trn_user_item_feats_df.copy()\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_rank_model = val_user_item_feats_df.copy()\n",
- " \n",
- "tst_user_item_feats_df_rank_model = tst_user_item_feats_df.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:10.839656Z",
- "start_time": "2020-11-18T04:21:10.833109Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义特征列\n",
- "lgb_cols = ['sim0', 'time_diff0', 'word_diff0','sim_max', 'sim_min', 'sim_sum', \n",
- " 'sim_mean', 'score','click_size', 'time_diff_mean', 'active_level',\n",
- " 'click_environment','click_deviceGroup', 'click_os', 'click_country', \n",
- " 'click_region','click_referrer_type', 'user_time_hob1', 'user_time_hob2',\n",
- " 'words_hbo', 'category_id', 'created_at_ts','words_count']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:14.126608Z",
- "start_time": "2020-11-18T04:21:13.493653Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型分组\n",
- "trn_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
- "g_train = trn_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
- " g_val = val_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:16.136151Z",
- "start_time": "2020-11-18T04:21:16.124444Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型定义\n",
- "lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:22.965433Z",
- "start_time": "2020-11-18T04:21:17.799127Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型训练\n",
- "if offline:\n",
- " lgb_ranker.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'], group=g_train,\n",
- " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
- " eval_group= [g_val], eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
- "else:\n",
- " lgb_ranker.fit(trn_user_item_feats_df[lgb_cols], trn_user_item_feats_df['label'], group=g_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:28.616665Z",
- "start_time": "2020-11-18T04:21:24.672280Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df['pred_score'] = lgb_ranker.predict(tst_user_item_feats_df[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
- "\n",
- "# 将这里的排序结果保存一份，用户后面的模型融合\n",
- "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_ranker_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:40.253692Z",
- "start_time": "2020-11-18T04:21:30.546587Z"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 排序模型\n",
+ "通过召回的操作， 我们已经进行了问题规模的缩减， 对于每个用户， 选择出了N篇文章作为了候选集，并基于召回的候选集构建了与用户历史相关的特征，以及用户本身的属性特征，文章本省的属性特征，以及用户与文章之间的特征，下面就是使用机器学习模型来对构造好的特征进行学习，然后对测试集进行预测，得到测试集中的每个候选集用户点击的概率，返回点击概率最大的topk个文章，作为最终的结果。\n",
+ "\n",
+ "排序阶段选择了三个比较有代表性的排序模型，它们分别是：\n",
+ "\n",
+ "1. LGB的排序模型\n",
+ "2. LGB的分类模型\n",
+ "3. 深度学习的分类模型DIN\n",
+ "\n",
+ "得到了最终的排序模型输出的结果之后，还选择了两种比较经典的模型集成的方法：\n",
+ "\n",
+ "1. 输出结果加权融合\n",
+ "2. Staking（将模型的输出结果再使用一个简单模型进行预测）"
+ ]
},
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_ranker')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:26.195838Z",
- "start_time": "2020-11-18T04:21:46.115002Z"
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:39.770642Z",
+ "start_time": "2020-11-18T04:20:38.500875Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pickle\n",
+ "from tqdm import tqdm\n",
+ "import gc, os\n",
+ "import time\n",
+ "from datetime import datetime\n",
+ "import lightgbm as lgb\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1]\tvalid_0's ndcg@1: 0.909975\tvalid_0's ndcg@2: 0.963068\tvalid_0's ndcg@3: 0.96533\tvalid_0's ndcg@4: 0.965729\tvalid_0's ndcg@5: 0.965864\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9143\tvalid_0's ndcg@2: 0.964711\tvalid_0's ndcg@3: 0.966961\tvalid_0's ndcg@4: 0.967338\tvalid_0's ndcg@5: 0.967483\n",
- "[3]\tvalid_0's ndcg@1: 0.9181\tvalid_0's ndcg@2: 0.966114\tvalid_0's ndcg@3: 0.968289\tvalid_0's ndcg@4: 0.968773\tvalid_0's ndcg@5: 0.96887\n",
- "[4]\tvalid_0's ndcg@1: 0.925575\tvalid_0's ndcg@2: 0.969093\tvalid_0's ndcg@3: 0.971193\tvalid_0's ndcg@4: 0.971603\tvalid_0's ndcg@5: 0.97169\n",
- "[5]\tvalid_0's ndcg@1: 0.9267\tvalid_0's ndcg@2: 0.969635\tvalid_0's ndcg@3: 0.97166\tvalid_0's ndcg@4: 0.972037\tvalid_0's ndcg@5: 0.972133\n",
- "[6]\tvalid_0's ndcg@1: 0.927\tvalid_0's ndcg@2: 0.969682\tvalid_0's ndcg@3: 0.971757\tvalid_0's ndcg@4: 0.972134\tvalid_0's ndcg@5: 0.972231\n",
- "[7]\tvalid_0's ndcg@1: 0.928825\tvalid_0's ndcg@2: 0.970451\tvalid_0's ndcg@3: 0.972476\tvalid_0's ndcg@4: 0.97282\tvalid_0's ndcg@5: 0.972927\n",
- "[8]\tvalid_0's ndcg@1: 0.930025\tvalid_0's ndcg@2: 0.970988\tvalid_0's ndcg@3: 0.972951\tvalid_0's ndcg@4: 0.973295\tvalid_0's ndcg@5: 0.973402\n",
- "[9]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971347\tvalid_0's ndcg@3: 0.973384\tvalid_0's ndcg@4: 0.973707\tvalid_0's ndcg@5: 0.973794\n",
- "[10]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.971385\tvalid_0's ndcg@3: 0.973372\tvalid_0's ndcg@4: 0.973717\tvalid_0's ndcg@5: 0.973794\n",
- "[11]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.971433\tvalid_0's ndcg@3: 0.973333\tvalid_0's ndcg@4: 0.973699\tvalid_0's ndcg@5: 0.973767\n",
- "[12]\tvalid_0's ndcg@1: 0.93145\tvalid_0's ndcg@2: 0.971656\tvalid_0's ndcg@3: 0.973493\tvalid_0's ndcg@4: 0.973881\tvalid_0's ndcg@5: 0.973949\n",
- "[13]\tvalid_0's ndcg@1: 0.932525\tvalid_0's ndcg@2: 0.971927\tvalid_0's ndcg@3: 0.973839\tvalid_0's ndcg@4: 0.974227\tvalid_0's ndcg@5: 0.974304\n",
- "[14]\tvalid_0's ndcg@1: 0.932575\tvalid_0's ndcg@2: 0.971898\tvalid_0's ndcg@3: 0.973823\tvalid_0's ndcg@4: 0.974243\tvalid_0's ndcg@5: 0.97432\n",
- "[15]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972239\tvalid_0's ndcg@3: 0.974189\tvalid_0's ndcg@4: 0.974587\tvalid_0's ndcg@5: 0.974665\n",
- "[16]\tvalid_0's ndcg@1: 0.933475\tvalid_0's ndcg@2: 0.972309\tvalid_0's ndcg@3: 0.974209\tvalid_0's ndcg@4: 0.974596\tvalid_0's ndcg@5: 0.974674\n",
- "[17]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972369\tvalid_0's ndcg@3: 0.974307\tvalid_0's ndcg@4: 0.974684\tvalid_0's ndcg@5: 0.974761\n",
- "[18]\tvalid_0's ndcg@1: 0.9339\tvalid_0's ndcg@2: 0.972497\tvalid_0's ndcg@3: 0.974372\tvalid_0's ndcg@4: 0.974749\tvalid_0's ndcg@5: 0.974836\n",
- "[19]\tvalid_0's ndcg@1: 0.9345\tvalid_0's ndcg@2: 0.972845\tvalid_0's ndcg@3: 0.974645\tvalid_0's ndcg@4: 0.974979\tvalid_0's ndcg@5: 0.975085\n",
- "[20]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.97484\tvalid_0's ndcg@4: 0.975174\tvalid_0's ndcg@5: 0.975271\n",
- "[21]\tvalid_0's ndcg@1: 0.935\tvalid_0's ndcg@2: 0.973092\tvalid_0's ndcg@3: 0.97488\tvalid_0's ndcg@4: 0.975192\tvalid_0's ndcg@5: 0.975289\n",
- "[22]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.974988\tvalid_0's ndcg@4: 0.975289\tvalid_0's ndcg@5: 0.975386\n",
- "[23]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974824\tvalid_0's ndcg@4: 0.975136\tvalid_0's ndcg@5: 0.975223\n",
- "[24]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973274\tvalid_0's ndcg@3: 0.975087\tvalid_0's ndcg@4: 0.975388\tvalid_0's ndcg@5: 0.975475\n",
- "[25]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973345\tvalid_0's ndcg@3: 0.97512\tvalid_0's ndcg@4: 0.975443\tvalid_0's ndcg@5: 0.97553\n",
- "[26]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.975\tvalid_0's ndcg@4: 0.975313\tvalid_0's ndcg@5: 0.9754\n",
- "[27]\tvalid_0's ndcg@1: 0.935175\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.974983\tvalid_0's ndcg@4: 0.975295\tvalid_0's ndcg@5: 0.975382\n",
- "[28]\tvalid_0's ndcg@1: 0.935425\tvalid_0's ndcg@2: 0.973328\tvalid_0's ndcg@3: 0.975041\tvalid_0's ndcg@4: 0.975374\tvalid_0's ndcg@5: 0.975471\n",
- "[29]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973225\tvalid_0's ndcg@3: 0.974963\tvalid_0's ndcg@4: 0.975297\tvalid_0's ndcg@5: 0.975403\n",
- "[30]\tvalid_0's ndcg@1: 0.9353\tvalid_0's ndcg@2: 0.973235\tvalid_0's ndcg@3: 0.97501\tvalid_0's ndcg@4: 0.975311\tvalid_0's ndcg@5: 0.975418\n",
- "[31]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973361\tvalid_0's ndcg@3: 0.975099\tvalid_0's ndcg@4: 0.975422\tvalid_0's ndcg@5: 0.975528\n",
- "[32]\tvalid_0's ndcg@1: 0.9364\tvalid_0's ndcg@2: 0.973641\tvalid_0's ndcg@3: 0.975391\tvalid_0's ndcg@4: 0.975714\tvalid_0's ndcg@5: 0.97582\n",
- "[33]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973751\tvalid_0's ndcg@3: 0.975501\tvalid_0's ndcg@4: 0.975824\tvalid_0's ndcg@5: 0.975931\n",
- "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.973902\tvalid_0's ndcg@3: 0.975677\tvalid_0's ndcg@4: 0.975989\tvalid_0's ndcg@5: 0.976095\n",
- "[35]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974105\tvalid_0's ndcg@3: 0.975892\tvalid_0's ndcg@4: 0.976194\tvalid_0's ndcg@5: 0.9763\n",
- "[36]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974184\tvalid_0's ndcg@3: 0.975984\tvalid_0's ndcg@4: 0.976296\tvalid_0's ndcg@5: 0.976402\n",
- "[37]\tvalid_0's ndcg@1: 0.93845\tvalid_0's ndcg@2: 0.974366\tvalid_0's ndcg@3: 0.976166\tvalid_0's ndcg@4: 0.976467\tvalid_0's ndcg@5: 0.976574\n",
- "[38]\tvalid_0's ndcg@1: 0.938925\tvalid_0's ndcg@2: 0.974557\tvalid_0's ndcg@3: 0.976332\tvalid_0's ndcg@4: 0.976655\tvalid_0's ndcg@5: 0.976751\n",
- "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974471\tvalid_0's ndcg@3: 0.976234\tvalid_0's ndcg@4: 0.976557\tvalid_0's ndcg@5: 0.976653\n",
- "[40]\tvalid_0's ndcg@1: 0.938325\tvalid_0's ndcg@2: 0.974335\tvalid_0's ndcg@3: 0.97611\tvalid_0's ndcg@4: 0.976433\tvalid_0's ndcg@5: 0.97653\n",
- "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.974669\tvalid_0's ndcg@3: 0.976431\tvalid_0's ndcg@4: 0.976743\tvalid_0's ndcg@5: 0.97683\n",
- "[42]\tvalid_0's ndcg@1: 0.939375\tvalid_0's ndcg@2: 0.974833\tvalid_0's ndcg@3: 0.976546\tvalid_0's ndcg@4: 0.976858\tvalid_0's ndcg@5: 0.976945\n",
- "[43]\tvalid_0's ndcg@1: 0.939625\tvalid_0's ndcg@2: 0.974878\tvalid_0's ndcg@3: 0.976628\tvalid_0's ndcg@4: 0.97694\tvalid_0's ndcg@5: 0.977027\n",
- "[44]\tvalid_0's ndcg@1: 0.9395\tvalid_0's ndcg@2: 0.974832\tvalid_0's ndcg@3: 0.97657\tvalid_0's ndcg@4: 0.976893\tvalid_0's ndcg@5: 0.97698\n",
- "[45]\tvalid_0's ndcg@1: 0.939775\tvalid_0's ndcg@2: 0.974949\tvalid_0's ndcg@3: 0.976674\tvalid_0's ndcg@4: 0.976997\tvalid_0's ndcg@5: 0.977084\n",
- "[46]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.974945\tvalid_0's ndcg@3: 0.976708\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977107\n",
- "[47]\tvalid_0's ndcg@1: 0.94005\tvalid_0's ndcg@2: 0.975004\tvalid_0's ndcg@3: 0.976766\tvalid_0's ndcg@4: 0.977078\tvalid_0's ndcg@5: 0.977175\n",
- "[48]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
- "[49]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
- "[50]\tvalid_0's ndcg@1: 0.9405\tvalid_0's ndcg@2: 0.975264\tvalid_0's ndcg@3: 0.976989\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
- "[51]\tvalid_0's ndcg@1: 0.941125\tvalid_0's ndcg@2: 0.975526\tvalid_0's ndcg@3: 0.977226\tvalid_0's ndcg@4: 0.977528\tvalid_0's ndcg@5: 0.977605\n",
- "[52]\tvalid_0's ndcg@1: 0.941\tvalid_0's ndcg@2: 0.97548\tvalid_0's ndcg@3: 0.977193\tvalid_0's ndcg@4: 0.977484\tvalid_0's ndcg@5: 0.977561\n",
- "[53]\tvalid_0's ndcg@1: 0.9411\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.977259\tvalid_0's ndcg@4: 0.977539\tvalid_0's ndcg@5: 0.977616\n",
- "[54]\tvalid_0's ndcg@1: 0.9412\tvalid_0's ndcg@2: 0.975712\tvalid_0's ndcg@3: 0.977299\tvalid_0's ndcg@4: 0.97759\tvalid_0's ndcg@5: 0.977667\n",
- "[55]\tvalid_0's ndcg@1: 0.94155\tvalid_0's ndcg@2: 0.975841\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977719\tvalid_0's ndcg@5: 0.977797\n",
- "[56]\tvalid_0's ndcg@1: 0.941825\tvalid_0's ndcg@2: 0.975943\tvalid_0's ndcg@3: 0.97753\tvalid_0's ndcg@4: 0.977821\tvalid_0's ndcg@5: 0.977898\n",
- "[57]\tvalid_0's ndcg@1: 0.9416\tvalid_0's ndcg@2: 0.975891\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977741\tvalid_0's ndcg@5: 0.977818\n",
- "[58]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977494\tvalid_0's ndcg@4: 0.977795\tvalid_0's ndcg@5: 0.977873\n",
- "[59]\tvalid_0's ndcg@1: 0.942025\tvalid_0's ndcg@2: 0.975985\tvalid_0's ndcg@3: 0.977547\tvalid_0's ndcg@4: 0.977881\tvalid_0's ndcg@5: 0.977958\n",
- "[60]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975994\tvalid_0's ndcg@3: 0.977569\tvalid_0's ndcg@4: 0.977892\tvalid_0's ndcg@5: 0.977969\n",
- "[61]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977559\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.97796\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 读取排序特征"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[62]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976027\tvalid_0's ndcg@3: 0.97764\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.978028\n",
- "[63]\tvalid_0's ndcg@1: 0.942125\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977622\tvalid_0's ndcg@4: 0.977912\tvalid_0's ndcg@5: 0.977999\n",
- "[64]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977793\tvalid_0's ndcg@4: 0.978105\tvalid_0's ndcg@5: 0.978192\n",
- "[65]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976227\tvalid_0's ndcg@3: 0.977802\tvalid_0's ndcg@4: 0.978125\tvalid_0's ndcg@5: 0.978212\n",
- "[66]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976132\tvalid_0's ndcg@3: 0.977695\tvalid_0's ndcg@4: 0.978018\tvalid_0's ndcg@5: 0.978105\n",
- "[67]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976092\tvalid_0's ndcg@3: 0.977679\tvalid_0's ndcg@4: 0.978002\tvalid_0's ndcg@5: 0.978089\n",
- "[68]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976148\tvalid_0's ndcg@3: 0.977698\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.978108\n",
- "[69]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976123\tvalid_0's ndcg@3: 0.977686\tvalid_0's ndcg@4: 0.978009\tvalid_0's ndcg@5: 0.978096\n",
- "[70]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976222\tvalid_0's ndcg@3: 0.977785\tvalid_0's ndcg@4: 0.978097\tvalid_0's ndcg@5: 0.978184\n",
- "[71]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976188\tvalid_0's ndcg@3: 0.977763\tvalid_0's ndcg@4: 0.978075\tvalid_0's ndcg@5: 0.978162\n",
- "[72]\tvalid_0's ndcg@1: 0.9427\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977809\tvalid_0's ndcg@4: 0.978121\tvalid_0's ndcg@5: 0.978208\n",
- "[73]\tvalid_0's ndcg@1: 0.9428\tvalid_0's ndcg@2: 0.976255\tvalid_0's ndcg@3: 0.977843\tvalid_0's ndcg@4: 0.978155\tvalid_0's ndcg@5: 0.978242\n",
- "[74]\tvalid_0's ndcg@1: 0.94295\tvalid_0's ndcg@2: 0.97631\tvalid_0's ndcg@3: 0.977898\tvalid_0's ndcg@4: 0.97821\tvalid_0's ndcg@5: 0.978297\n",
- "[75]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976329\tvalid_0's ndcg@3: 0.977941\tvalid_0's ndcg@4: 0.978232\tvalid_0's ndcg@5: 0.978319\n",
- "[76]\tvalid_0's ndcg@1: 0.9433\tvalid_0's ndcg@2: 0.976471\tvalid_0's ndcg@3: 0.978059\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978437\n",
- "[77]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976416\tvalid_0's ndcg@3: 0.977991\tvalid_0's ndcg@4: 0.978314\tvalid_0's ndcg@5: 0.978381\n",
- "[78]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976657\tvalid_0's ndcg@3: 0.978194\tvalid_0's ndcg@4: 0.978517\tvalid_0's ndcg@5: 0.978585\n",
- "[79]\tvalid_0's ndcg@1: 0.94365\tvalid_0's ndcg@2: 0.976663\tvalid_0's ndcg@3: 0.978188\tvalid_0's ndcg@4: 0.978501\tvalid_0's ndcg@5: 0.978578\n",
- "[80]\tvalid_0's ndcg@1: 0.943725\tvalid_0's ndcg@2: 0.976628\tvalid_0's ndcg@3: 0.978203\tvalid_0's ndcg@4: 0.978515\tvalid_0's ndcg@5: 0.978593\n",
- "[81]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97672\tvalid_0's ndcg@3: 0.978295\tvalid_0's ndcg@4: 0.978607\tvalid_0's ndcg@5: 0.978685\n",
- "[82]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978397\tvalid_0's ndcg@4: 0.97872\tvalid_0's ndcg@5: 0.978787\n",
- "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976788\tvalid_0's ndcg@3: 0.978375\tvalid_0's ndcg@4: 0.978698\tvalid_0's ndcg@5: 0.978766\n",
- "[84]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.97679\tvalid_0's ndcg@3: 0.97839\tvalid_0's ndcg@4: 0.978702\tvalid_0's ndcg@5: 0.97878\n",
- "[85]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.976809\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978723\tvalid_0's ndcg@5: 0.9788\n",
- "[86]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976939\tvalid_0's ndcg@3: 0.978502\tvalid_0's ndcg@4: 0.978814\tvalid_0's ndcg@5: 0.978891\n",
- "[87]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.976976\tvalid_0's ndcg@3: 0.978551\tvalid_0's ndcg@4: 0.978852\tvalid_0's ndcg@5: 0.97893\n",
- "[88]\tvalid_0's ndcg@1: 0.944925\tvalid_0's ndcg@2: 0.977102\tvalid_0's ndcg@3: 0.978677\tvalid_0's ndcg@4: 0.978968\tvalid_0's ndcg@5: 0.979045\n",
- "[89]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978758\tvalid_0's ndcg@4: 0.979048\tvalid_0's ndcg@5: 0.979126\n",
- "[90]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.978735\tvalid_0's ndcg@4: 0.979026\tvalid_0's ndcg@5: 0.979104\n",
- "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978858\tvalid_0's ndcg@4: 0.979138\tvalid_0's ndcg@5: 0.979215\n",
- "[92]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.977267\tvalid_0's ndcg@3: 0.978905\tvalid_0's ndcg@4: 0.979174\tvalid_0's ndcg@5: 0.979251\n",
- "[93]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977193\tvalid_0's ndcg@3: 0.978818\tvalid_0's ndcg@4: 0.979098\tvalid_0's ndcg@5: 0.979176\n",
- "[94]\tvalid_0's ndcg@1: 0.94545\tvalid_0's ndcg@2: 0.97728\tvalid_0's ndcg@3: 0.97888\tvalid_0's ndcg@4: 0.97916\tvalid_0's ndcg@5: 0.979238\n",
- "[95]\tvalid_0's ndcg@1: 0.9458\tvalid_0's ndcg@2: 0.977394\tvalid_0's ndcg@3: 0.979006\tvalid_0's ndcg@4: 0.979286\tvalid_0's ndcg@5: 0.979364\n",
- "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979114\tvalid_0's ndcg@4: 0.979394\tvalid_0's ndcg@5: 0.979472\n",
- "[97]\tvalid_0's ndcg@1: 0.946475\tvalid_0's ndcg@2: 0.977659\tvalid_0's ndcg@3: 0.979259\tvalid_0's ndcg@4: 0.979539\tvalid_0's ndcg@5: 0.979616\n",
- "[98]\tvalid_0's ndcg@1: 0.94675\tvalid_0's ndcg@2: 0.97776\tvalid_0's ndcg@3: 0.97936\tvalid_0's ndcg@4: 0.979651\tvalid_0's ndcg@5: 0.979719\n",
- "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
- "[100]\tvalid_0's ndcg@1: 0.9468\tvalid_0's ndcg@2: 0.977794\tvalid_0's ndcg@3: 0.979369\tvalid_0's ndcg@4: 0.979671\tvalid_0's ndcg@5: 0.979739\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
- "[1]\tvalid_0's ndcg@1: 0.909075\tvalid_0's ndcg@2: 0.963019\tvalid_0's ndcg@3: 0.965069\tvalid_0's ndcg@4: 0.965543\tvalid_0's ndcg@5: 0.965601\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9123\tvalid_0's ndcg@2: 0.964273\tvalid_0's ndcg@3: 0.966248\tvalid_0's ndcg@4: 0.966722\tvalid_0's ndcg@5: 0.966789\n",
- "[3]\tvalid_0's ndcg@1: 0.915075\tvalid_0's ndcg@2: 0.965691\tvalid_0's ndcg@3: 0.967466\tvalid_0's ndcg@4: 0.967854\tvalid_0's ndcg@5: 0.967922\n",
- "[4]\tvalid_0's ndcg@1: 0.91845\tvalid_0's ndcg@2: 0.967047\tvalid_0's ndcg@3: 0.968735\tvalid_0's ndcg@4: 0.969133\tvalid_0's ndcg@5: 0.969201\n",
- "[5]\tvalid_0's ndcg@1: 0.92355\tvalid_0's ndcg@2: 0.968961\tvalid_0's ndcg@3: 0.970674\tvalid_0's ndcg@4: 0.97104\tvalid_0's ndcg@5: 0.971098\n",
- "[6]\tvalid_0's ndcg@1: 0.9253\tvalid_0's ndcg@2: 0.969607\tvalid_0's ndcg@3: 0.971345\tvalid_0's ndcg@4: 0.971689\tvalid_0's ndcg@5: 0.971747\n",
- "[7]\tvalid_0's ndcg@1: 0.926225\tvalid_0's ndcg@2: 0.969933\tvalid_0's ndcg@3: 0.971708\tvalid_0's ndcg@4: 0.972031\tvalid_0's ndcg@5: 0.972079\n",
- "[8]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.970104\tvalid_0's ndcg@3: 0.971804\tvalid_0's ndcg@4: 0.972116\tvalid_0's ndcg@5: 0.972184\n",
- "[9]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970682\tvalid_0's ndcg@3: 0.972307\tvalid_0's ndcg@4: 0.972598\tvalid_0's ndcg@5: 0.972675\n",
- "[10]\tvalid_0's ndcg@1: 0.92775\tvalid_0's ndcg@2: 0.970653\tvalid_0's ndcg@3: 0.972316\tvalid_0's ndcg@4: 0.972617\tvalid_0's ndcg@5: 0.972685\n",
- "[11]\tvalid_0's ndcg@1: 0.9283\tvalid_0's ndcg@2: 0.97084\tvalid_0's ndcg@3: 0.97254\tvalid_0's ndcg@4: 0.97281\tvalid_0's ndcg@5: 0.972887\n",
- "[12]\tvalid_0's ndcg@1: 0.9287\tvalid_0's ndcg@2: 0.971051\tvalid_0's ndcg@3: 0.972701\tvalid_0's ndcg@4: 0.97297\tvalid_0's ndcg@5: 0.973048\n",
- "[13]\tvalid_0's ndcg@1: 0.9297\tvalid_0's ndcg@2: 0.971389\tvalid_0's ndcg@3: 0.973001\tvalid_0's ndcg@4: 0.973313\tvalid_0's ndcg@5: 0.9734\n",
- "[14]\tvalid_0's ndcg@1: 0.92955\tvalid_0's ndcg@2: 0.971444\tvalid_0's ndcg@3: 0.972994\tvalid_0's ndcg@4: 0.973284\tvalid_0's ndcg@5: 0.973371\n",
- "[15]\tvalid_0's ndcg@1: 0.930225\tvalid_0's ndcg@2: 0.97174\tvalid_0's ndcg@3: 0.973253\tvalid_0's ndcg@4: 0.973543\tvalid_0's ndcg@5: 0.97363\n",
- "[16]\tvalid_0's ndcg@1: 0.930425\tvalid_0's ndcg@2: 0.971798\tvalid_0's ndcg@3: 0.973298\tvalid_0's ndcg@4: 0.97361\tvalid_0's ndcg@5: 0.973698\n",
- "[17]\tvalid_0's ndcg@1: 0.93125\tvalid_0's ndcg@2: 0.971992\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973903\tvalid_0's ndcg@5: 0.97398\n",
- "[18]\tvalid_0's ndcg@1: 0.931925\tvalid_0's ndcg@2: 0.972257\tvalid_0's ndcg@3: 0.973845\tvalid_0's ndcg@4: 0.974146\tvalid_0's ndcg@5: 0.974224\n",
- "[19]\tvalid_0's ndcg@1: 0.932375\tvalid_0's ndcg@2: 0.972376\tvalid_0's ndcg@3: 0.974038\tvalid_0's ndcg@4: 0.974318\tvalid_0's ndcg@5: 0.974376\n",
- "[20]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.972269\tvalid_0's ndcg@3: 0.973907\tvalid_0's ndcg@4: 0.974187\tvalid_0's ndcg@5: 0.974245\n",
- "[21]\tvalid_0's ndcg@1: 0.932725\tvalid_0's ndcg@2: 0.972568\tvalid_0's ndcg@3: 0.974181\tvalid_0's ndcg@4: 0.974471\tvalid_0's ndcg@5: 0.974529\n",
- "[22]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972735\tvalid_0's ndcg@3: 0.974298\tvalid_0's ndcg@4: 0.974599\tvalid_0's ndcg@5: 0.974657\n",
- "[23]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972642\tvalid_0's ndcg@3: 0.974255\tvalid_0's ndcg@4: 0.974545\tvalid_0's ndcg@5: 0.974594\n",
- "[24]\tvalid_0's ndcg@1: 0.933175\tvalid_0's ndcg@2: 0.972734\tvalid_0's ndcg@3: 0.974347\tvalid_0's ndcg@4: 0.974638\tvalid_0's ndcg@5: 0.974686\n",
- "[25]\tvalid_0's ndcg@1: 0.9331\tvalid_0's ndcg@2: 0.972754\tvalid_0's ndcg@3: 0.974366\tvalid_0's ndcg@4: 0.974636\tvalid_0's ndcg@5: 0.974674\n"
- ]
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:41.843180Z",
+ "start_time": "2020-11-18T04:20:41.837287Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = './data_raw/'\n",
+ "save_path = './temp_results/'\n",
+ "offline = False"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[26]\tvalid_0's ndcg@1: 0.933275\tvalid_0's ndcg@2: 0.972787\tvalid_0's ndcg@3: 0.974424\tvalid_0's ndcg@4: 0.974694\tvalid_0's ndcg@5: 0.974732\n",
- "[27]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972809\tvalid_0's ndcg@3: 0.974434\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.974732\n",
- "[28]\tvalid_0's ndcg@1: 0.933625\tvalid_0's ndcg@2: 0.972932\tvalid_0's ndcg@3: 0.974557\tvalid_0's ndcg@4: 0.974826\tvalid_0's ndcg@5: 0.974855\n",
- "[29]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972937\tvalid_0's ndcg@3: 0.974587\tvalid_0's ndcg@4: 0.974856\tvalid_0's ndcg@5: 0.974885\n",
- "[30]\tvalid_0's ndcg@1: 0.93355\tvalid_0's ndcg@2: 0.972873\tvalid_0's ndcg@3: 0.974523\tvalid_0's ndcg@4: 0.974792\tvalid_0's ndcg@5: 0.974821\n",
- "[31]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973065\tvalid_0's ndcg@3: 0.974753\tvalid_0's ndcg@4: 0.975022\tvalid_0's ndcg@5: 0.975051\n",
- "[32]\tvalid_0's ndcg@1: 0.93435\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974815\tvalid_0's ndcg@4: 0.975084\tvalid_0's ndcg@5: 0.975113\n",
- "[33]\tvalid_0's ndcg@1: 0.934475\tvalid_0's ndcg@2: 0.97323\tvalid_0's ndcg@3: 0.974855\tvalid_0's ndcg@4: 0.975135\tvalid_0's ndcg@5: 0.975164\n",
- "[34]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973113\tvalid_0's ndcg@3: 0.974738\tvalid_0's ndcg@4: 0.975028\tvalid_0's ndcg@5: 0.975057\n",
- "[35]\tvalid_0's ndcg@1: 0.93455\tvalid_0's ndcg@2: 0.973258\tvalid_0's ndcg@3: 0.97487\tvalid_0's ndcg@4: 0.975172\tvalid_0's ndcg@5: 0.975201\n",
- "[36]\tvalid_0's ndcg@1: 0.9344\tvalid_0's ndcg@2: 0.973265\tvalid_0's ndcg@3: 0.974828\tvalid_0's ndcg@4: 0.975129\tvalid_0's ndcg@5: 0.975158\n",
- "[37]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973438\tvalid_0's ndcg@3: 0.975013\tvalid_0's ndcg@4: 0.975304\tvalid_0's ndcg@5: 0.975323\n",
- "[38]\tvalid_0's ndcg@1: 0.934975\tvalid_0's ndcg@2: 0.973541\tvalid_0's ndcg@3: 0.975066\tvalid_0's ndcg@4: 0.975367\tvalid_0's ndcg@5: 0.975386\n",
- "[39]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973667\tvalid_0's ndcg@3: 0.975192\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975502\n",
- "[40]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973624\tvalid_0's ndcg@3: 0.975174\tvalid_0's ndcg@4: 0.975454\tvalid_0's ndcg@5: 0.975473\n",
- "[41]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973686\tvalid_0's ndcg@3: 0.975223\tvalid_0's ndcg@4: 0.975503\tvalid_0's ndcg@5: 0.975522\n",
- "[42]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973716\tvalid_0's ndcg@3: 0.975266\tvalid_0's ndcg@4: 0.975546\tvalid_0's ndcg@5: 0.975565\n",
- "[43]\tvalid_0's ndcg@1: 0.93615\tvalid_0's ndcg@2: 0.974022\tvalid_0's ndcg@3: 0.975534\tvalid_0's ndcg@4: 0.975814\tvalid_0's ndcg@5: 0.975843\n",
- "[44]\tvalid_0's ndcg@1: 0.936225\tvalid_0's ndcg@2: 0.974112\tvalid_0's ndcg@3: 0.975562\tvalid_0's ndcg@4: 0.975853\tvalid_0's ndcg@5: 0.975882\n",
- "[45]\tvalid_0's ndcg@1: 0.9365\tvalid_0's ndcg@2: 0.974167\tvalid_0's ndcg@3: 0.975654\tvalid_0's ndcg@4: 0.975945\tvalid_0's ndcg@5: 0.975974\n",
- "[46]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974206\tvalid_0's ndcg@3: 0.975694\tvalid_0's ndcg@4: 0.975995\tvalid_0's ndcg@5: 0.976024\n",
- "[47]\tvalid_0's ndcg@1: 0.93685\tvalid_0's ndcg@2: 0.974311\tvalid_0's ndcg@3: 0.975786\tvalid_0's ndcg@4: 0.976077\tvalid_0's ndcg@5: 0.976106\n",
- "[48]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974408\tvalid_0's ndcg@3: 0.975845\tvalid_0's ndcg@4: 0.976147\tvalid_0's ndcg@5: 0.976185\n",
- "[49]\tvalid_0's ndcg@1: 0.936975\tvalid_0's ndcg@2: 0.974342\tvalid_0's ndcg@3: 0.975829\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.976159\n",
- "[50]\tvalid_0's ndcg@1: 0.9371\tvalid_0's ndcg@2: 0.974388\tvalid_0's ndcg@3: 0.97585\tvalid_0's ndcg@4: 0.976152\tvalid_0's ndcg@5: 0.976191\n",
- "[51]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974329\tvalid_0's ndcg@3: 0.975841\tvalid_0's ndcg@4: 0.976121\tvalid_0's ndcg@5: 0.97616\n",
- "[52]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974578\tvalid_0's ndcg@3: 0.976078\tvalid_0's ndcg@4: 0.976369\tvalid_0's ndcg@5: 0.976407\n",
- "[53]\tvalid_0's ndcg@1: 0.9378\tvalid_0's ndcg@2: 0.974615\tvalid_0's ndcg@3: 0.976115\tvalid_0's ndcg@4: 0.976405\tvalid_0's ndcg@5: 0.976444\n",
- "[54]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974689\tvalid_0's ndcg@3: 0.976214\tvalid_0's ndcg@4: 0.976483\tvalid_0's ndcg@5: 0.976521\n",
- "[55]\tvalid_0's ndcg@1: 0.938225\tvalid_0's ndcg@2: 0.974803\tvalid_0's ndcg@3: 0.976303\tvalid_0's ndcg@4: 0.976572\tvalid_0's ndcg@5: 0.976611\n",
- "[56]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.9748\tvalid_0's ndcg@3: 0.976275\tvalid_0's ndcg@4: 0.976555\tvalid_0's ndcg@5: 0.976594\n",
- "[57]\tvalid_0's ndcg@1: 0.938525\tvalid_0's ndcg@2: 0.974914\tvalid_0's ndcg@3: 0.976414\tvalid_0's ndcg@4: 0.976683\tvalid_0's ndcg@5: 0.976722\n",
- "[58]\tvalid_0's ndcg@1: 0.93875\tvalid_0's ndcg@2: 0.975028\tvalid_0's ndcg@3: 0.976503\tvalid_0's ndcg@4: 0.976773\tvalid_0's ndcg@5: 0.976811\n",
- "[59]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975198\tvalid_0's ndcg@3: 0.976648\tvalid_0's ndcg@4: 0.976918\tvalid_0's ndcg@5: 0.976956\n",
- "[60]\tvalid_0's ndcg@1: 0.939025\tvalid_0's ndcg@2: 0.975177\tvalid_0's ndcg@3: 0.976615\tvalid_0's ndcg@4: 0.976884\tvalid_0's ndcg@5: 0.976923\n",
- "[61]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975205\tvalid_0's ndcg@3: 0.976642\tvalid_0's ndcg@4: 0.976912\tvalid_0's ndcg@5: 0.97695\n",
- "[62]\tvalid_0's ndcg@1: 0.93965\tvalid_0's ndcg@2: 0.975424\tvalid_0's ndcg@3: 0.976836\tvalid_0's ndcg@4: 0.977116\tvalid_0's ndcg@5: 0.977155\n",
- "[63]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.976996\tvalid_0's ndcg@4: 0.977276\tvalid_0's ndcg@5: 0.977315\n",
- "[64]\tvalid_0's ndcg@1: 0.940375\tvalid_0's ndcg@2: 0.975723\tvalid_0's ndcg@3: 0.977123\tvalid_0's ndcg@4: 0.977392\tvalid_0's ndcg@5: 0.977431\n",
- "[65]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977154\tvalid_0's ndcg@4: 0.977423\tvalid_0's ndcg@5: 0.977462\n",
- "[66]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975744\tvalid_0's ndcg@3: 0.977156\tvalid_0's ndcg@4: 0.977426\tvalid_0's ndcg@5: 0.977464\n",
- "[67]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.97576\tvalid_0's ndcg@3: 0.977172\tvalid_0's ndcg@4: 0.977431\tvalid_0's ndcg@5: 0.977469\n",
- "[68]\tvalid_0's ndcg@1: 0.940675\tvalid_0's ndcg@2: 0.975849\tvalid_0's ndcg@3: 0.977249\tvalid_0's ndcg@4: 0.977508\tvalid_0's ndcg@5: 0.977546\n",
- "[69]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.976017\tvalid_0's ndcg@3: 0.977454\tvalid_0's ndcg@4: 0.977724\tvalid_0's ndcg@5: 0.977762\n",
- "[70]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.977362\tvalid_0's ndcg@4: 0.977631\tvalid_0's ndcg@5: 0.97767\n",
- "[71]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.97735\tvalid_0's ndcg@4: 0.97763\tvalid_0's ndcg@5: 0.977668\n",
- "[72]\tvalid_0's ndcg@1: 0.941325\tvalid_0's ndcg@2: 0.976058\tvalid_0's ndcg@3: 0.97747\tvalid_0's ndcg@4: 0.977739\tvalid_0's ndcg@5: 0.977778\n",
- "[73]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977476\tvalid_0's ndcg@4: 0.977756\tvalid_0's ndcg@5: 0.977795\n",
- "[74]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.97759\tvalid_0's ndcg@4: 0.97788\tvalid_0's ndcg@5: 0.977919\n",
- "[75]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.977602\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.977921\n",
- "[76]\tvalid_0's ndcg@1: 0.94195\tvalid_0's ndcg@2: 0.976273\tvalid_0's ndcg@3: 0.977685\tvalid_0's ndcg@4: 0.977965\tvalid_0's ndcg@5: 0.978004\n",
- "[77]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.97627\tvalid_0's ndcg@3: 0.97767\tvalid_0's ndcg@4: 0.97795\tvalid_0's ndcg@5: 0.977989\n",
- "[78]\tvalid_0's ndcg@1: 0.94235\tvalid_0's ndcg@2: 0.976452\tvalid_0's ndcg@3: 0.977839\tvalid_0's ndcg@4: 0.978119\tvalid_0's ndcg@5: 0.978158\n",
- "[79]\tvalid_0's ndcg@1: 0.94265\tvalid_0's ndcg@2: 0.976562\tvalid_0's ndcg@3: 0.977937\tvalid_0's ndcg@4: 0.978228\tvalid_0's ndcg@5: 0.978267\n",
- "[80]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976667\tvalid_0's ndcg@3: 0.978067\tvalid_0's ndcg@4: 0.978347\tvalid_0's ndcg@5: 0.978385\n",
- "[81]\tvalid_0's ndcg@1: 0.94305\tvalid_0's ndcg@2: 0.97671\tvalid_0's ndcg@3: 0.978098\tvalid_0's ndcg@4: 0.978378\tvalid_0's ndcg@5: 0.978416\n",
- "[82]\tvalid_0's ndcg@1: 0.943175\tvalid_0's ndcg@2: 0.97674\tvalid_0's ndcg@3: 0.978115\tvalid_0's ndcg@4: 0.978417\tvalid_0's ndcg@5: 0.978456\n",
- "[83]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976752\tvalid_0's ndcg@3: 0.97814\tvalid_0's ndcg@4: 0.978441\tvalid_0's ndcg@5: 0.97848\n",
- "[84]\tvalid_0's ndcg@1: 0.943375\tvalid_0's ndcg@2: 0.976767\tvalid_0's ndcg@3: 0.978179\tvalid_0's ndcg@4: 0.978481\tvalid_0's ndcg@5: 0.97852\n",
- "[85]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976721\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978437\tvalid_0's ndcg@5: 0.978475\n",
- "[86]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976792\tvalid_0's ndcg@3: 0.978204\tvalid_0's ndcg@4: 0.978506\tvalid_0's ndcg@5: 0.978535\n",
- "[87]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.976851\tvalid_0's ndcg@3: 0.978239\tvalid_0's ndcg@4: 0.97854\tvalid_0's ndcg@5: 0.978569\n",
- "[88]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976882\tvalid_0's ndcg@3: 0.978282\tvalid_0's ndcg@4: 0.978572\tvalid_0's ndcg@5: 0.978611\n",
- "[89]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.976915\tvalid_0's ndcg@3: 0.97834\tvalid_0's ndcg@4: 0.97863\tvalid_0's ndcg@5: 0.978669\n"
- ]
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:53.358138Z",
+ "start_time": "2020-11-18T04:20:44.232944Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 重新读取数据的时候，发现click_article_id是一个浮点数，所以将其转换成int类型\n",
+ "trn_user_item_feats_df = pd.read_csv(save_path + 'trn_user_item_feats_df.csv')\n",
+ "trn_user_item_feats_df['click_article_id'] = trn_user_item_feats_df['click_article_id'].astype(int)\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df = pd.read_csv(save_path + 'val_user_item_feats_df.csv')\n",
+ " val_user_item_feats_df['click_article_id'] = val_user_item_feats_df['click_article_id'].astype(int)\n",
+ "else:\n",
+ " val_user_item_feats_df = None\n",
+ " \n",
+ "tst_user_item_feats_df = pd.read_csv(save_path + 'tst_user_item_feats_df.csv')\n",
+ "tst_user_item_feats_df['click_article_id'] = tst_user_item_feats_df['click_article_id'].astype(int)\n",
+ "\n",
+ "# 做特征的时候为了方便，给测试集也打上了一个无效的标签，这里直接删掉就行\n",
+ "del tst_user_item_feats_df['label']"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[90]\tvalid_0's ndcg@1: 0.943925\tvalid_0's ndcg@2: 0.976986\tvalid_0's ndcg@3: 0.978398\tvalid_0's ndcg@4: 0.978689\tvalid_0's ndcg@5: 0.978728\n",
- "[91]\tvalid_0's ndcg@1: 0.943875\tvalid_0's ndcg@2: 0.976999\tvalid_0's ndcg@3: 0.978399\tvalid_0's ndcg@4: 0.978679\tvalid_0's ndcg@5: 0.978717\n",
- "[92]\tvalid_0's ndcg@1: 0.94395\tvalid_0's ndcg@2: 0.977058\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978711\tvalid_0's ndcg@5: 0.97876\n",
- "[93]\tvalid_0's ndcg@1: 0.944075\tvalid_0's ndcg@2: 0.977104\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978759\tvalid_0's ndcg@5: 0.978807\n",
- "[94]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977125\tvalid_0's ndcg@3: 0.978513\tvalid_0's ndcg@4: 0.978793\tvalid_0's ndcg@5: 0.978841\n",
- "[95]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977153\tvalid_0's ndcg@3: 0.97854\tvalid_0's ndcg@4: 0.97882\tvalid_0's ndcg@5: 0.978869\n",
- "[96]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977144\tvalid_0's ndcg@3: 0.978531\tvalid_0's ndcg@4: 0.978811\tvalid_0's ndcg@5: 0.97886\n",
- "[97]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977221\tvalid_0's ndcg@3: 0.978584\tvalid_0's ndcg@4: 0.978864\tvalid_0's ndcg@5: 0.978912\n",
- "[98]\tvalid_0's ndcg@1: 0.944575\tvalid_0's ndcg@2: 0.977289\tvalid_0's ndcg@3: 0.978651\tvalid_0's ndcg@4: 0.978942\tvalid_0's ndcg@5: 0.97899\n",
- "[99]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977341\tvalid_0's ndcg@3: 0.978691\tvalid_0's ndcg@4: 0.978993\tvalid_0's ndcg@5: 0.979032\n",
- "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
- "[1]\tvalid_0's ndcg@1: 0.911575\tvalid_0's ndcg@2: 0.964384\tvalid_0's ndcg@3: 0.966321\tvalid_0's ndcg@4: 0.966623\tvalid_0's ndcg@5: 0.966671\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9136\tvalid_0's ndcg@2: 0.965257\tvalid_0's ndcg@3: 0.967107\tvalid_0's ndcg@4: 0.967398\tvalid_0's ndcg@5: 0.967456\n",
- "[3]\tvalid_0's ndcg@1: 0.917425\tvalid_0's ndcg@2: 0.966732\tvalid_0's ndcg@3: 0.968545\tvalid_0's ndcg@4: 0.968814\tvalid_0's ndcg@5: 0.968882\n",
- "[4]\tvalid_0's ndcg@1: 0.9222\tvalid_0's ndcg@2: 0.968558\tvalid_0's ndcg@3: 0.970383\tvalid_0's ndcg@4: 0.970619\tvalid_0's ndcg@5: 0.970668\n",
- "[5]\tvalid_0's ndcg@1: 0.925875\tvalid_0's ndcg@2: 0.969914\tvalid_0's ndcg@3: 0.971714\tvalid_0's ndcg@4: 0.971972\tvalid_0's ndcg@5: 0.972021\n",
- "[6]\tvalid_0's ndcg@1: 0.926875\tvalid_0's ndcg@2: 0.970425\tvalid_0's ndcg@3: 0.972112\tvalid_0's ndcg@4: 0.972371\tvalid_0's ndcg@5: 0.972419\n",
- "[7]\tvalid_0's ndcg@1: 0.927475\tvalid_0's ndcg@2: 0.970631\tvalid_0's ndcg@3: 0.972306\tvalid_0's ndcg@4: 0.972586\tvalid_0's ndcg@5: 0.972634\n",
- "[8]\tvalid_0's ndcg@1: 0.93015\tvalid_0's ndcg@2: 0.971649\tvalid_0's ndcg@3: 0.973287\tvalid_0's ndcg@4: 0.973567\tvalid_0's ndcg@5: 0.973625\n",
- "[9]\tvalid_0's ndcg@1: 0.9312\tvalid_0's ndcg@2: 0.972084\tvalid_0's ndcg@3: 0.973684\tvalid_0's ndcg@4: 0.973964\tvalid_0's ndcg@5: 0.974022\n",
- "[10]\tvalid_0's ndcg@1: 0.93225\tvalid_0's ndcg@2: 0.972456\tvalid_0's ndcg@3: 0.974081\tvalid_0's ndcg@4: 0.974361\tvalid_0's ndcg@5: 0.974409\n",
- "[11]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972704\tvalid_0's ndcg@3: 0.974379\tvalid_0's ndcg@4: 0.974648\tvalid_0's ndcg@5: 0.974696\n",
- "[12]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974574\tvalid_0's ndcg@4: 0.974832\tvalid_0's ndcg@5: 0.974881\n",
- "[13]\tvalid_0's ndcg@1: 0.93415\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.97482\tvalid_0's ndcg@4: 0.975079\tvalid_0's ndcg@5: 0.975127\n",
- "[14]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973671\tvalid_0's ndcg@3: 0.975246\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975531\n",
- "[15]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.973877\tvalid_0's ndcg@3: 0.975452\tvalid_0's ndcg@4: 0.975699\tvalid_0's ndcg@5: 0.975748\n",
- "[16]\tvalid_0's ndcg@1: 0.935825\tvalid_0's ndcg@2: 0.973917\tvalid_0's ndcg@3: 0.975442\tvalid_0's ndcg@4: 0.975712\tvalid_0's ndcg@5: 0.97576\n",
- "[17]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.97411\tvalid_0's ndcg@3: 0.975697\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975995\n",
- "[18]\tvalid_0's ndcg@1: 0.936925\tvalid_0's ndcg@2: 0.974292\tvalid_0's ndcg@3: 0.975867\tvalid_0's ndcg@4: 0.976114\tvalid_0's ndcg@5: 0.976163\n",
- "[19]\tvalid_0's ndcg@1: 0.937525\tvalid_0's ndcg@2: 0.974545\tvalid_0's ndcg@3: 0.976095\tvalid_0's ndcg@4: 0.976342\tvalid_0's ndcg@5: 0.976391\n",
- "[20]\tvalid_0's ndcg@1: 0.937775\tvalid_0's ndcg@2: 0.974653\tvalid_0's ndcg@3: 0.976203\tvalid_0's ndcg@4: 0.976429\tvalid_0's ndcg@5: 0.976487\n",
- "[21]\tvalid_0's ndcg@1: 0.938825\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976597\tvalid_0's ndcg@4: 0.976823\tvalid_0's ndcg@5: 0.976881\n",
- "[22]\tvalid_0's ndcg@1: 0.93885\tvalid_0's ndcg@2: 0.975097\tvalid_0's ndcg@3: 0.976609\tvalid_0's ndcg@4: 0.976846\tvalid_0's ndcg@5: 0.976895\n",
- "[23]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976733\tvalid_0's ndcg@4: 0.976959\tvalid_0's ndcg@5: 0.977008\n",
- "[24]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976721\tvalid_0's ndcg@4: 0.976947\tvalid_0's ndcg@5: 0.977005\n",
- "[25]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975421\tvalid_0's ndcg@3: 0.976909\tvalid_0's ndcg@4: 0.977124\tvalid_0's ndcg@5: 0.977182\n",
- "[26]\tvalid_0's ndcg@1: 0.9393\tvalid_0's ndcg@2: 0.975342\tvalid_0's ndcg@3: 0.976804\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977078\n",
- "[27]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975323\tvalid_0's ndcg@3: 0.976798\tvalid_0's ndcg@4: 0.977014\tvalid_0's ndcg@5: 0.977062\n",
- "[28]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975308\tvalid_0's ndcg@3: 0.976783\tvalid_0's ndcg@4: 0.977009\tvalid_0's ndcg@5: 0.977057\n",
- "[29]\tvalid_0's ndcg@1: 0.94\tvalid_0's ndcg@2: 0.975569\tvalid_0's ndcg@3: 0.977056\tvalid_0's ndcg@4: 0.977282\tvalid_0's ndcg@5: 0.977331\n",
- "[30]\tvalid_0's ndcg@1: 0.940325\tvalid_0's ndcg@2: 0.975673\tvalid_0's ndcg@3: 0.977173\tvalid_0's ndcg@4: 0.977399\tvalid_0's ndcg@5: 0.977447\n",
- "[31]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975731\tvalid_0's ndcg@3: 0.977243\tvalid_0's ndcg@4: 0.977469\tvalid_0's ndcg@5: 0.977518\n",
- "[32]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
- "[33]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977241\tvalid_0's ndcg@4: 0.977457\tvalid_0's ndcg@5: 0.977505\n",
- "[34]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
- "[35]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975868\tvalid_0's ndcg@3: 0.977343\tvalid_0's ndcg@4: 0.977558\tvalid_0's ndcg@5: 0.977606\n",
- "[36]\tvalid_0's ndcg@1: 0.94115\tvalid_0's ndcg@2: 0.976056\tvalid_0's ndcg@3: 0.977506\tvalid_0's ndcg@4: 0.977722\tvalid_0's ndcg@5: 0.97777\n",
- "[37]\tvalid_0's ndcg@1: 0.9414\tvalid_0's ndcg@2: 0.976133\tvalid_0's ndcg@3: 0.977595\tvalid_0's ndcg@4: 0.977811\tvalid_0's ndcg@5: 0.977859\n",
- "[38]\tvalid_0's ndcg@1: 0.94175\tvalid_0's ndcg@2: 0.976278\tvalid_0's ndcg@3: 0.977715\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.97799\n",
- "[39]\tvalid_0's ndcg@1: 0.942075\tvalid_0's ndcg@2: 0.976366\tvalid_0's ndcg@3: 0.977841\tvalid_0's ndcg@4: 0.978056\tvalid_0's ndcg@5: 0.978105\n",
- "[40]\tvalid_0's ndcg@1: 0.94215\tvalid_0's ndcg@2: 0.976409\tvalid_0's ndcg@3: 0.977872\tvalid_0's ndcg@4: 0.978087\tvalid_0's ndcg@5: 0.978136\n",
- "[41]\tvalid_0's ndcg@1: 0.94245\tvalid_0's ndcg@2: 0.97652\tvalid_0's ndcg@3: 0.977983\tvalid_0's ndcg@4: 0.978198\tvalid_0's ndcg@5: 0.978246\n",
- "[42]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
- "[43]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
- "[44]\tvalid_0's ndcg@1: 0.94285\tvalid_0's ndcg@2: 0.976636\tvalid_0's ndcg@3: 0.978111\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978386\n",
- "[45]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.9768\tvalid_0's ndcg@3: 0.978262\tvalid_0's ndcg@4: 0.978488\tvalid_0's ndcg@5: 0.978537\n",
- "[46]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
- "[47]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97836\tvalid_0's ndcg@4: 0.978576\tvalid_0's ndcg@5: 0.978634\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 返回排序后的结果"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[48]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.978373\tvalid_0's ndcg@4: 0.978577\tvalid_0's ndcg@5: 0.978636\n",
- "[49]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
- "[50]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97702\tvalid_0's ndcg@3: 0.97852\tvalid_0's ndcg@4: 0.978746\tvalid_0's ndcg@5: 0.978794\n",
- "[51]\tvalid_0's ndcg@1: 0.9441\tvalid_0's ndcg@2: 0.97705\tvalid_0's ndcg@3: 0.97855\tvalid_0's ndcg@4: 0.978787\tvalid_0's ndcg@5: 0.978836\n",
- "[52]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977121\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978846\tvalid_0's ndcg@5: 0.978894\n",
- "[53]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977081\tvalid_0's ndcg@3: 0.978618\tvalid_0's ndcg@4: 0.978834\tvalid_0's ndcg@5: 0.978882\n",
- "[54]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977071\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978824\tvalid_0's ndcg@5: 0.978873\n",
- "[55]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977143\tvalid_0's ndcg@3: 0.978668\tvalid_0's ndcg@4: 0.978883\tvalid_0's ndcg@5: 0.978931\n",
- "[56]\tvalid_0's ndcg@1: 0.9444\tvalid_0's ndcg@2: 0.977177\tvalid_0's ndcg@3: 0.978702\tvalid_0's ndcg@4: 0.978906\tvalid_0's ndcg@5: 0.978955\n",
- "[57]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977263\tvalid_0's ndcg@3: 0.978788\tvalid_0's ndcg@4: 0.979003\tvalid_0's ndcg@5: 0.979051\n",
- "[58]\tvalid_0's ndcg@1: 0.9448\tvalid_0's ndcg@2: 0.977293\tvalid_0's ndcg@3: 0.978843\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979096\n",
- "[59]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977472\tvalid_0's ndcg@3: 0.978997\tvalid_0's ndcg@4: 0.979202\tvalid_0's ndcg@5: 0.97925\n",
- "[60]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.97763\tvalid_0's ndcg@3: 0.979118\tvalid_0's ndcg@4: 0.979322\tvalid_0's ndcg@5: 0.979371\n",
- "[61]\tvalid_0's ndcg@1: 0.945725\tvalid_0's ndcg@2: 0.977682\tvalid_0's ndcg@3: 0.979194\tvalid_0's ndcg@4: 0.979399\tvalid_0's ndcg@5: 0.979447\n",
- "[62]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977812\tvalid_0's ndcg@3: 0.979312\tvalid_0's ndcg@4: 0.979495\tvalid_0's ndcg@5: 0.979543\n",
- "[63]\tvalid_0's ndcg@1: 0.946\tvalid_0's ndcg@2: 0.977878\tvalid_0's ndcg@3: 0.97934\tvalid_0's ndcg@4: 0.979523\tvalid_0's ndcg@5: 0.979572\n",
- "[64]\tvalid_0's ndcg@1: 0.946525\tvalid_0's ndcg@2: 0.978056\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979714\tvalid_0's ndcg@5: 0.979762\n",
- "[65]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.978105\tvalid_0's ndcg@3: 0.979592\tvalid_0's ndcg@4: 0.979775\tvalid_0's ndcg@5: 0.979823\n",
- "[66]\tvalid_0's ndcg@1: 0.9465\tvalid_0's ndcg@2: 0.978046\tvalid_0's ndcg@3: 0.979534\tvalid_0's ndcg@4: 0.979706\tvalid_0's ndcg@5: 0.979755\n",
- "[67]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.978127\tvalid_0's ndcg@3: 0.979614\tvalid_0's ndcg@4: 0.979776\tvalid_0's ndcg@5: 0.979824\n",
- "[68]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.979608\tvalid_0's ndcg@4: 0.97978\tvalid_0's ndcg@5: 0.979828\n",
- "[69]\tvalid_0's ndcg@1: 0.946875\tvalid_0's ndcg@2: 0.978216\tvalid_0's ndcg@3: 0.979679\tvalid_0's ndcg@4: 0.979851\tvalid_0's ndcg@5: 0.9799\n",
- "[70]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.978194\tvalid_0's ndcg@3: 0.979682\tvalid_0's ndcg@4: 0.979854\tvalid_0's ndcg@5: 0.979902\n",
- "[71]\tvalid_0's ndcg@1: 0.947025\tvalid_0's ndcg@2: 0.978209\tvalid_0's ndcg@3: 0.979721\tvalid_0's ndcg@4: 0.979893\tvalid_0's ndcg@5: 0.979942\n",
- "[72]\tvalid_0's ndcg@1: 0.9472\tvalid_0's ndcg@2: 0.978273\tvalid_0's ndcg@3: 0.979773\tvalid_0's ndcg@4: 0.979956\tvalid_0's ndcg@5: 0.980005\n",
- "[73]\tvalid_0's ndcg@1: 0.947475\tvalid_0's ndcg@2: 0.978391\tvalid_0's ndcg@3: 0.979878\tvalid_0's ndcg@4: 0.980061\tvalid_0's ndcg@5: 0.980109\n",
- "[74]\tvalid_0's ndcg@1: 0.94715\tvalid_0's ndcg@2: 0.978271\tvalid_0's ndcg@3: 0.979758\tvalid_0's ndcg@4: 0.979941\tvalid_0's ndcg@5: 0.97999\n",
- "[75]\tvalid_0's ndcg@1: 0.947275\tvalid_0's ndcg@2: 0.978333\tvalid_0's ndcg@3: 0.979808\tvalid_0's ndcg@4: 0.979991\tvalid_0's ndcg@5: 0.980039\n",
- "[76]\tvalid_0's ndcg@1: 0.9474\tvalid_0's ndcg@2: 0.97841\tvalid_0's ndcg@3: 0.979873\tvalid_0's ndcg@4: 0.980045\tvalid_0's ndcg@5: 0.980093\n",
- "[77]\tvalid_0's ndcg@1: 0.94745\tvalid_0's ndcg@2: 0.97846\tvalid_0's ndcg@3: 0.979898\tvalid_0's ndcg@4: 0.98007\tvalid_0's ndcg@5: 0.980118\n",
- "[78]\tvalid_0's ndcg@1: 0.94775\tvalid_0's ndcg@2: 0.978555\tvalid_0's ndcg@3: 0.980005\tvalid_0's ndcg@4: 0.980177\tvalid_0's ndcg@5: 0.980226\n",
- "[79]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
- "[80]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
- "[81]\tvalid_0's ndcg@1: 0.948175\tvalid_0's ndcg@2: 0.978744\tvalid_0's ndcg@3: 0.980169\tvalid_0's ndcg@4: 0.980352\tvalid_0's ndcg@5: 0.98039\n",
- "[82]\tvalid_0's ndcg@1: 0.948375\tvalid_0's ndcg@2: 0.97888\tvalid_0's ndcg@3: 0.980255\tvalid_0's ndcg@4: 0.980438\tvalid_0's ndcg@5: 0.980477\n",
- "[83]\tvalid_0's ndcg@1: 0.94825\tvalid_0's ndcg@2: 0.978834\tvalid_0's ndcg@3: 0.980209\tvalid_0's ndcg@4: 0.980392\tvalid_0's ndcg@5: 0.980431\n",
- "[84]\tvalid_0's ndcg@1: 0.948275\tvalid_0's ndcg@2: 0.978844\tvalid_0's ndcg@3: 0.980219\tvalid_0's ndcg@4: 0.980402\tvalid_0's ndcg@5: 0.98044\n",
- "[85]\tvalid_0's ndcg@1: 0.948475\tvalid_0's ndcg@2: 0.978917\tvalid_0's ndcg@3: 0.980292\tvalid_0's ndcg@4: 0.980475\tvalid_0's ndcg@5: 0.980514\n",
- "[86]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979102\tvalid_0's ndcg@3: 0.980477\tvalid_0's ndcg@4: 0.98066\tvalid_0's ndcg@5: 0.980699\n",
- "[87]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979086\tvalid_0's ndcg@3: 0.980474\tvalid_0's ndcg@4: 0.980657\tvalid_0's ndcg@5: 0.980695\n",
- "[88]\tvalid_0's ndcg@1: 0.949025\tvalid_0's ndcg@2: 0.979136\tvalid_0's ndcg@3: 0.980499\tvalid_0's ndcg@4: 0.980682\tvalid_0's ndcg@5: 0.98072\n",
- "[89]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979285\tvalid_0's ndcg@3: 0.98061\tvalid_0's ndcg@4: 0.980793\tvalid_0's ndcg@5: 0.980832\n",
- "[90]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
- "[91]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
- "[92]\tvalid_0's ndcg@1: 0.9494\tvalid_0's ndcg@2: 0.97929\tvalid_0's ndcg@3: 0.98064\tvalid_0's ndcg@4: 0.980823\tvalid_0's ndcg@5: 0.980862\n",
- "[93]\tvalid_0's ndcg@1: 0.949375\tvalid_0's ndcg@2: 0.979297\tvalid_0's ndcg@3: 0.980634\tvalid_0's ndcg@4: 0.980817\tvalid_0's ndcg@5: 0.980856\n",
- "[94]\tvalid_0's ndcg@1: 0.949525\tvalid_0's ndcg@2: 0.979336\tvalid_0's ndcg@3: 0.980686\tvalid_0's ndcg@4: 0.980869\tvalid_0's ndcg@5: 0.980908\n",
- "[95]\tvalid_0's ndcg@1: 0.949825\tvalid_0's ndcg@2: 0.979416\tvalid_0's ndcg@3: 0.980791\tvalid_0's ndcg@4: 0.980974\tvalid_0's ndcg@5: 0.981012\n",
- "[96]\tvalid_0's ndcg@1: 0.94975\tvalid_0's ndcg@2: 0.979404\tvalid_0's ndcg@3: 0.980779\tvalid_0's ndcg@4: 0.980951\tvalid_0's ndcg@5: 0.98099\n",
- "[97]\tvalid_0's ndcg@1: 0.950025\tvalid_0's ndcg@2: 0.979537\tvalid_0's ndcg@3: 0.980874\tvalid_0's ndcg@4: 0.981057\tvalid_0's ndcg@5: 0.981096\n",
- "[98]\tvalid_0's ndcg@1: 0.9501\tvalid_0's ndcg@2: 0.979564\tvalid_0's ndcg@3: 0.980889\tvalid_0's ndcg@4: 0.981083\tvalid_0's ndcg@5: 0.981122\n",
- "[99]\tvalid_0's ndcg@1: 0.950275\tvalid_0's ndcg@2: 0.979629\tvalid_0's ndcg@3: 0.980967\tvalid_0's ndcg@4: 0.98115\tvalid_0's ndcg@5: 0.981188\n",
- "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
- "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.96382\tvalid_0's ndcg@3: 0.965707\tvalid_0's ndcg@4: 0.966009\tvalid_0's ndcg@5: 0.966086\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.91415\tvalid_0's ndcg@2: 0.965492\tvalid_0's ndcg@3: 0.967254\tvalid_0's ndcg@4: 0.967556\tvalid_0's ndcg@5: 0.967604\n",
- "[3]\tvalid_0's ndcg@1: 0.916025\tvalid_0's ndcg@2: 0.966389\tvalid_0's ndcg@3: 0.967976\tvalid_0's ndcg@4: 0.968278\tvalid_0's ndcg@5: 0.968355\n",
- "[4]\tvalid_0's ndcg@1: 0.919\tvalid_0's ndcg@2: 0.967392\tvalid_0's ndcg@3: 0.96903\tvalid_0's ndcg@4: 0.969364\tvalid_0's ndcg@5: 0.969431\n",
- "[5]\tvalid_0's ndcg@1: 0.921125\tvalid_0's ndcg@2: 0.968192\tvalid_0's ndcg@3: 0.969855\tvalid_0's ndcg@4: 0.970156\tvalid_0's ndcg@5: 0.970224\n",
- "[6]\tvalid_0's ndcg@1: 0.921675\tvalid_0's ndcg@2: 0.968411\tvalid_0's ndcg@3: 0.970111\tvalid_0's ndcg@4: 0.97037\tvalid_0's ndcg@5: 0.970437\n",
- "[7]\tvalid_0's ndcg@1: 0.9237\tvalid_0's ndcg@2: 0.969332\tvalid_0's ndcg@3: 0.970882\tvalid_0's ndcg@4: 0.97113\tvalid_0's ndcg@5: 0.971217\n",
- "[8]\tvalid_0's ndcg@1: 0.925775\tvalid_0's ndcg@2: 0.970129\tvalid_0's ndcg@3: 0.971642\tvalid_0's ndcg@4: 0.971922\tvalid_0's ndcg@5: 0.97199\n",
- "[9]\tvalid_0's ndcg@1: 0.926775\tvalid_0's ndcg@2: 0.970435\tvalid_0's ndcg@3: 0.971985\tvalid_0's ndcg@4: 0.972276\tvalid_0's ndcg@5: 0.972334\n"
- ]
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:01.809368Z",
+ "start_time": "2020-11-18T04:21:01.799641Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def submit(recall_df, topk=5, model_name=None):\n",
+ " recall_df = recall_df.sort_values(by=['user_id', 'pred_score'])\n",
+ " recall_df['rank'] = recall_df.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 判断是不是每个用户都有5篇文章及以上\n",
+ " tmp = recall_df.groupby('user_id').apply(lambda x: x['rank'].max())\n",
+ " assert tmp.min() >= topk\n",
+ " \n",
+ " del recall_df['pred_score']\n",
+ " submit = recall_df[recall_df['rank'] <= topk].set_index(['user_id', 'rank']).unstack(-1).reset_index()\n",
+ " \n",
+ " submit.columns = [int(col) if isinstance(col, int) else col for col in submit.columns.droplevel(0)]\n",
+ " # 按照提交格式定义列名\n",
+ " submit = submit.rename(columns={'': 'user_id', 1: 'article_1', 2: 'article_2', \n",
+ " 3: 'article_3', 4: 'article_4', 5: 'article_5'})\n",
+ " \n",
+ " save_name = save_path + model_name + '_' + datetime.today().strftime('%m-%d') + '.csv'\n",
+ " submit.to_csv(save_name, index=False, header=True)"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[10]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970761\tvalid_0's ndcg@3: 0.972311\tvalid_0's ndcg@4: 0.972612\tvalid_0's ndcg@5: 0.97267\n",
- "[11]\tvalid_0's ndcg@1: 0.928975\tvalid_0's ndcg@2: 0.97131\tvalid_0's ndcg@3: 0.972798\tvalid_0's ndcg@4: 0.973089\tvalid_0's ndcg@5: 0.973166\n",
- "[12]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971505\tvalid_0's ndcg@3: 0.972968\tvalid_0's ndcg@4: 0.973259\tvalid_0's ndcg@5: 0.973326\n",
- "[13]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971426\tvalid_0's ndcg@3: 0.972939\tvalid_0's ndcg@4: 0.97324\tvalid_0's ndcg@5: 0.973318\n",
- "[14]\tvalid_0's ndcg@1: 0.929775\tvalid_0's ndcg@2: 0.971621\tvalid_0's ndcg@3: 0.973121\tvalid_0's ndcg@4: 0.973412\tvalid_0's ndcg@5: 0.97348\n",
- "[15]\tvalid_0's ndcg@1: 0.9304\tvalid_0's ndcg@2: 0.971868\tvalid_0's ndcg@3: 0.97338\tvalid_0's ndcg@4: 0.97365\tvalid_0's ndcg@5: 0.973717\n",
- "[16]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.972096\tvalid_0's ndcg@3: 0.973558\tvalid_0's ndcg@4: 0.973849\tvalid_0's ndcg@5: 0.973926\n",
- "[17]\tvalid_0's ndcg@1: 0.93105\tvalid_0's ndcg@2: 0.972108\tvalid_0's ndcg@3: 0.973583\tvalid_0's ndcg@4: 0.973884\tvalid_0's ndcg@5: 0.973952\n",
- "[18]\tvalid_0's ndcg@1: 0.931725\tvalid_0's ndcg@2: 0.972373\tvalid_0's ndcg@3: 0.97386\tvalid_0's ndcg@4: 0.974129\tvalid_0's ndcg@5: 0.974207\n",
- "[19]\tvalid_0's ndcg@1: 0.932175\tvalid_0's ndcg@2: 0.972681\tvalid_0's ndcg@3: 0.974068\tvalid_0's ndcg@4: 0.974348\tvalid_0's ndcg@5: 0.974406\n",
- "[20]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.973019\tvalid_0's ndcg@3: 0.974382\tvalid_0's ndcg@4: 0.974673\tvalid_0's ndcg@5: 0.974731\n",
- "[21]\tvalid_0's ndcg@1: 0.933075\tvalid_0's ndcg@2: 0.97306\tvalid_0's ndcg@3: 0.974423\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.97477\n",
- "[22]\tvalid_0's ndcg@1: 0.93375\tvalid_0's ndcg@2: 0.973262\tvalid_0's ndcg@3: 0.974649\tvalid_0's ndcg@4: 0.974929\tvalid_0's ndcg@5: 0.975007\n",
- "[23]\tvalid_0's ndcg@1: 0.933675\tvalid_0's ndcg@2: 0.973219\tvalid_0's ndcg@3: 0.974606\tvalid_0's ndcg@4: 0.974886\tvalid_0's ndcg@5: 0.974973\n",
- "[24]\tvalid_0's ndcg@1: 0.934\tvalid_0's ndcg@2: 0.97337\tvalid_0's ndcg@3: 0.974745\tvalid_0's ndcg@4: 0.975014\tvalid_0's ndcg@5: 0.975101\n",
- "[25]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973674\tvalid_0's ndcg@3: 0.975062\tvalid_0's ndcg@4: 0.975342\tvalid_0's ndcg@5: 0.97541\n",
- "[26]\tvalid_0's ndcg@1: 0.93495\tvalid_0's ndcg@2: 0.973721\tvalid_0's ndcg@3: 0.975096\tvalid_0's ndcg@4: 0.975365\tvalid_0's ndcg@5: 0.975452\n",
- "[27]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.974082\tvalid_0's ndcg@3: 0.975444\tvalid_0's ndcg@4: 0.975713\tvalid_0's ndcg@5: 0.975781\n",
- "[28]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973875\tvalid_0's ndcg@3: 0.975275\tvalid_0's ndcg@4: 0.975512\tvalid_0's ndcg@5: 0.975599\n",
- "[29]\tvalid_0's ndcg@1: 0.935925\tvalid_0's ndcg@2: 0.974159\tvalid_0's ndcg@3: 0.975522\tvalid_0's ndcg@4: 0.975759\tvalid_0's ndcg@5: 0.975836\n",
- "[30]\tvalid_0's ndcg@1: 0.9362\tvalid_0's ndcg@2: 0.974214\tvalid_0's ndcg@3: 0.975589\tvalid_0's ndcg@4: 0.975847\tvalid_0's ndcg@5: 0.975924\n",
- "[31]\tvalid_0's ndcg@1: 0.93625\tvalid_0's ndcg@2: 0.974216\tvalid_0's ndcg@3: 0.975629\tvalid_0's ndcg@4: 0.975876\tvalid_0's ndcg@5: 0.975944\n",
- "[32]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974427\tvalid_0's ndcg@3: 0.975814\tvalid_0's ndcg@4: 0.97603\tvalid_0's ndcg@5: 0.976107\n",
- "[33]\tvalid_0's ndcg@1: 0.936775\tvalid_0's ndcg@2: 0.974505\tvalid_0's ndcg@3: 0.975855\tvalid_0's ndcg@4: 0.976081\tvalid_0's ndcg@5: 0.976158\n",
- "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974643\tvalid_0's ndcg@3: 0.975993\tvalid_0's ndcg@4: 0.976219\tvalid_0's ndcg@5: 0.976296\n",
- "[35]\tvalid_0's ndcg@1: 0.937675\tvalid_0's ndcg@2: 0.974805\tvalid_0's ndcg@3: 0.97618\tvalid_0's ndcg@4: 0.976406\tvalid_0's ndcg@5: 0.976484\n",
- "[36]\tvalid_0's ndcg@1: 0.9382\tvalid_0's ndcg@2: 0.974983\tvalid_0's ndcg@3: 0.976371\tvalid_0's ndcg@4: 0.976597\tvalid_0's ndcg@5: 0.976674\n",
- "[37]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.974974\tvalid_0's ndcg@3: 0.976349\tvalid_0's ndcg@4: 0.976586\tvalid_0's ndcg@5: 0.976663\n",
- "[38]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.975143\tvalid_0's ndcg@3: 0.976518\tvalid_0's ndcg@4: 0.976776\tvalid_0's ndcg@5: 0.976844\n",
- "[39]\tvalid_0's ndcg@1: 0.938575\tvalid_0's ndcg@2: 0.975106\tvalid_0's ndcg@3: 0.976481\tvalid_0's ndcg@4: 0.976739\tvalid_0's ndcg@5: 0.976807\n",
- "[40]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.97519\tvalid_0's ndcg@3: 0.976528\tvalid_0's ndcg@4: 0.976775\tvalid_0's ndcg@5: 0.976853\n",
- "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975347\tvalid_0's ndcg@3: 0.976697\tvalid_0's ndcg@4: 0.976934\tvalid_0's ndcg@5: 0.977001\n",
- "[42]\tvalid_0's ndcg@1: 0.939825\tvalid_0's ndcg@2: 0.975599\tvalid_0's ndcg@3: 0.976961\tvalid_0's ndcg@4: 0.977198\tvalid_0's ndcg@5: 0.977266\n",
- "[43]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975639\tvalid_0's ndcg@3: 0.976977\tvalid_0's ndcg@4: 0.977214\tvalid_0's ndcg@5: 0.977282\n",
- "[44]\tvalid_0's ndcg@1: 0.9398\tvalid_0's ndcg@2: 0.975605\tvalid_0's ndcg@3: 0.976955\tvalid_0's ndcg@4: 0.977192\tvalid_0's ndcg@5: 0.97726\n",
- "[45]\tvalid_0's ndcg@1: 0.9401\tvalid_0's ndcg@2: 0.9757\tvalid_0's ndcg@3: 0.977075\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
- "[46]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975845\tvalid_0's ndcg@3: 0.977183\tvalid_0's ndcg@4: 0.97742\tvalid_0's ndcg@5: 0.977497\n",
- "[47]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975854\tvalid_0's ndcg@3: 0.977204\tvalid_0's ndcg@4: 0.97743\tvalid_0's ndcg@5: 0.977508\n",
- "[48]\tvalid_0's ndcg@1: 0.940575\tvalid_0's ndcg@2: 0.975923\tvalid_0's ndcg@3: 0.977273\tvalid_0's ndcg@4: 0.977488\tvalid_0's ndcg@5: 0.977556\n",
- "[49]\tvalid_0's ndcg@1: 0.9407\tvalid_0's ndcg@2: 0.975922\tvalid_0's ndcg@3: 0.977297\tvalid_0's ndcg@4: 0.977501\tvalid_0's ndcg@5: 0.977588\n",
- "[50]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977322\tvalid_0's ndcg@4: 0.977505\tvalid_0's ndcg@5: 0.977592\n",
- "[51]\tvalid_0's ndcg@1: 0.9406\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.97725\tvalid_0's ndcg@4: 0.977422\tvalid_0's ndcg@5: 0.977509\n",
- "[52]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975997\tvalid_0's ndcg@3: 0.977422\tvalid_0's ndcg@4: 0.977594\tvalid_0's ndcg@5: 0.977691\n",
- "[53]\tvalid_0's ndcg@1: 0.940925\tvalid_0's ndcg@2: 0.975989\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977538\tvalid_0's ndcg@5: 0.977644\n",
- "[54]\tvalid_0's ndcg@1: 0.94125\tvalid_0's ndcg@2: 0.976062\tvalid_0's ndcg@3: 0.977487\tvalid_0's ndcg@4: 0.977659\tvalid_0's ndcg@5: 0.977756\n",
- "[55]\tvalid_0's ndcg@1: 0.94145\tvalid_0's ndcg@2: 0.976183\tvalid_0's ndcg@3: 0.97757\tvalid_0's ndcg@4: 0.977742\tvalid_0's ndcg@5: 0.977839\n",
- "[56]\tvalid_0's ndcg@1: 0.941475\tvalid_0's ndcg@2: 0.976176\tvalid_0's ndcg@3: 0.977576\tvalid_0's ndcg@4: 0.977748\tvalid_0's ndcg@5: 0.977845\n",
- "[57]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976139\tvalid_0's ndcg@3: 0.977539\tvalid_0's ndcg@4: 0.977712\tvalid_0's ndcg@5: 0.977808\n",
- "[58]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.97625\tvalid_0's ndcg@3: 0.97765\tvalid_0's ndcg@4: 0.977822\tvalid_0's ndcg@5: 0.977919\n",
- "[59]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.976253\tvalid_0's ndcg@3: 0.977653\tvalid_0's ndcg@4: 0.977836\tvalid_0's ndcg@5: 0.977932\n",
- "[60]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977634\tvalid_0's ndcg@4: 0.977817\tvalid_0's ndcg@5: 0.977914\n",
- "[61]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.976333\tvalid_0's ndcg@3: 0.977745\tvalid_0's ndcg@4: 0.977918\tvalid_0's ndcg@5: 0.978005\n",
- "[62]\tvalid_0's ndcg@1: 0.941975\tvalid_0's ndcg@2: 0.976345\tvalid_0's ndcg@3: 0.977757\tvalid_0's ndcg@4: 0.97794\tvalid_0's ndcg@5: 0.978027\n",
- "[63]\tvalid_0's ndcg@1: 0.9423\tvalid_0's ndcg@2: 0.976496\tvalid_0's ndcg@3: 0.977871\tvalid_0's ndcg@4: 0.978065\tvalid_0's ndcg@5: 0.978152\n",
- "[64]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976632\tvalid_0's ndcg@3: 0.977995\tvalid_0's ndcg@4: 0.978188\tvalid_0's ndcg@5: 0.978275\n",
- "[65]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976629\tvalid_0's ndcg@3: 0.977979\tvalid_0's ndcg@4: 0.978173\tvalid_0's ndcg@5: 0.97826\n",
- "[66]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976685\tvalid_0's ndcg@3: 0.978035\tvalid_0's ndcg@4: 0.978229\tvalid_0's ndcg@5: 0.978316\n",
- "[67]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976678\tvalid_0's ndcg@3: 0.978041\tvalid_0's ndcg@4: 0.978224\tvalid_0's ndcg@5: 0.97832\n",
- "[68]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976694\tvalid_0's ndcg@3: 0.978044\tvalid_0's ndcg@4: 0.978227\tvalid_0's ndcg@5: 0.978324\n",
- "[69]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976834\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978329\tvalid_0's ndcg@5: 0.978426\n",
- "[70]\tvalid_0's ndcg@1: 0.943025\tvalid_0's ndcg@2: 0.976827\tvalid_0's ndcg@3: 0.978152\tvalid_0's ndcg@4: 0.978324\tvalid_0's ndcg@5: 0.978431\n",
- "[71]\tvalid_0's ndcg@1: 0.9432\tvalid_0's ndcg@2: 0.976923\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978504\n",
- "[72]\tvalid_0's ndcg@1: 0.943225\tvalid_0's ndcg@2: 0.976917\tvalid_0's ndcg@3: 0.978254\tvalid_0's ndcg@4: 0.978405\tvalid_0's ndcg@5: 0.978511\n",
- "[73]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976936\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978409\tvalid_0's ndcg@5: 0.978496\n"
- ]
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:04.332198Z",
+ "start_time": "2020-11-18T04:21:04.325020Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序结果归一化\n",
+ "def norm_sim(sim_df, weight=0.0):\n",
+ " # print(sim_df.head())\n",
+ " min_sim = sim_df.min()\n",
+ " max_sim = sim_df.max()\n",
+ " if max_sim == min_sim:\n",
+ " sim_df = sim_df.apply(lambda sim: 1.0)\n",
+ " else:\n",
+ " sim_df = sim_df.apply(lambda sim: 1.0 * (sim - min_sim) / (max_sim - min_sim))\n",
+ "\n",
+ " sim_df = sim_df.apply(lambda sim: sim + weight) # plus one\n",
+ " return sim_df"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[74]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976957\tvalid_0's ndcg@3: 0.97827\tvalid_0's ndcg@4: 0.978431\tvalid_0's ndcg@5: 0.978528\n",
- "[75]\tvalid_0's ndcg@1: 0.943075\tvalid_0's ndcg@2: 0.976861\tvalid_0's ndcg@3: 0.978199\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978457\n",
- "[76]\tvalid_0's ndcg@1: 0.94335\tvalid_0's ndcg@2: 0.976963\tvalid_0's ndcg@3: 0.978288\tvalid_0's ndcg@4: 0.978471\tvalid_0's ndcg@5: 0.978568\n",
- "[77]\tvalid_0's ndcg@1: 0.94345\tvalid_0's ndcg@2: 0.977031\tvalid_0's ndcg@3: 0.978331\tvalid_0's ndcg@4: 0.978514\tvalid_0's ndcg@5: 0.978611\n",
- "[78]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.977088\tvalid_0's ndcg@3: 0.97835\tvalid_0's ndcg@4: 0.978533\tvalid_0's ndcg@5: 0.97863\n",
- "[79]\tvalid_0's ndcg@1: 0.943625\tvalid_0's ndcg@2: 0.977096\tvalid_0's ndcg@3: 0.978396\tvalid_0's ndcg@4: 0.978579\tvalid_0's ndcg@5: 0.978676\n",
- "[80]\tvalid_0's ndcg@1: 0.943825\tvalid_0's ndcg@2: 0.977154\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978651\tvalid_0's ndcg@5: 0.978748\n",
- "[81]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.97846\tvalid_0's ndcg@4: 0.978633\tvalid_0's ndcg@5: 0.978729\n",
- "[82]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.977361\tvalid_0's ndcg@3: 0.978673\tvalid_0's ndcg@4: 0.978845\tvalid_0's ndcg@5: 0.978933\n",
- "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977324\tvalid_0's ndcg@3: 0.978624\tvalid_0's ndcg@4: 0.978796\tvalid_0's ndcg@5: 0.978893\n",
- "[84]\tvalid_0's ndcg@1: 0.94405\tvalid_0's ndcg@2: 0.977253\tvalid_0's ndcg@3: 0.978565\tvalid_0's ndcg@4: 0.978737\tvalid_0's ndcg@5: 0.978834\n",
- "[85]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977283\tvalid_0's ndcg@3: 0.978633\tvalid_0's ndcg@4: 0.978795\tvalid_0's ndcg@5: 0.978882\n",
- "[86]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.97745\tvalid_0's ndcg@3: 0.978763\tvalid_0's ndcg@4: 0.978924\tvalid_0's ndcg@5: 0.979011\n",
- "[87]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.977419\tvalid_0's ndcg@3: 0.978756\tvalid_0's ndcg@4: 0.978918\tvalid_0's ndcg@5: 0.979005\n",
- "[88]\tvalid_0's ndcg@1: 0.944825\tvalid_0's ndcg@2: 0.977554\tvalid_0's ndcg@3: 0.978867\tvalid_0's ndcg@4: 0.979039\tvalid_0's ndcg@5: 0.979126\n",
- "[89]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977767\tvalid_0's ndcg@3: 0.979079\tvalid_0's ndcg@4: 0.979262\tvalid_0's ndcg@5: 0.97934\n",
- "[90]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977773\tvalid_0's ndcg@3: 0.979073\tvalid_0's ndcg@4: 0.979256\tvalid_0's ndcg@5: 0.979334\n",
- "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977792\tvalid_0's ndcg@3: 0.979092\tvalid_0's ndcg@4: 0.979275\tvalid_0's ndcg@5: 0.979352\n",
- "[92]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977776\tvalid_0's ndcg@3: 0.979088\tvalid_0's ndcg@4: 0.979261\tvalid_0's ndcg@5: 0.979348\n",
- "[93]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977757\tvalid_0's ndcg@3: 0.979082\tvalid_0's ndcg@4: 0.979244\tvalid_0's ndcg@5: 0.979331\n",
- "[94]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977761\tvalid_0's ndcg@3: 0.979061\tvalid_0's ndcg@4: 0.979223\tvalid_0's ndcg@5: 0.97931\n",
- "[95]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977798\tvalid_0's ndcg@3: 0.979086\tvalid_0's ndcg@4: 0.979258\tvalid_0's ndcg@5: 0.979345\n",
- "[96]\tvalid_0's ndcg@1: 0.945825\tvalid_0's ndcg@2: 0.977955\tvalid_0's ndcg@3: 0.97923\tvalid_0's ndcg@4: 0.979413\tvalid_0's ndcg@5: 0.9795\n",
- "[97]\tvalid_0's ndcg@1: 0.945925\tvalid_0's ndcg@2: 0.97796\tvalid_0's ndcg@3: 0.97926\tvalid_0's ndcg@4: 0.979443\tvalid_0's ndcg@5: 0.979531\n",
- "[98]\tvalid_0's ndcg@1: 0.9464\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.97942\tvalid_0's ndcg@4: 0.979625\tvalid_0's ndcg@5: 0.979702\n",
- "[99]\tvalid_0's ndcg@1: 0.94655\tvalid_0's ndcg@2: 0.978191\tvalid_0's ndcg@3: 0.979479\tvalid_0's ndcg@4: 0.979683\tvalid_0's ndcg@5: 0.97977\n",
- "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
- "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.963031\tvalid_0's ndcg@3: 0.965281\tvalid_0's ndcg@4: 0.965819\tvalid_0's ndcg@5: 0.965887\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9141\tvalid_0's ndcg@2: 0.964748\tvalid_0's ndcg@3: 0.96681\tvalid_0's ndcg@4: 0.967316\tvalid_0's ndcg@5: 0.967394\n",
- "[3]\tvalid_0's ndcg@1: 0.915925\tvalid_0's ndcg@2: 0.9655\tvalid_0's ndcg@3: 0.967575\tvalid_0's ndcg@4: 0.968028\tvalid_0's ndcg@5: 0.968105\n",
- "[4]\tvalid_0's ndcg@1: 0.91915\tvalid_0's ndcg@2: 0.966943\tvalid_0's ndcg@3: 0.968968\tvalid_0's ndcg@4: 0.969334\tvalid_0's ndcg@5: 0.969373\n",
- "[5]\tvalid_0's ndcg@1: 0.920625\tvalid_0's ndcg@2: 0.967598\tvalid_0's ndcg@3: 0.969498\tvalid_0's ndcg@4: 0.969896\tvalid_0's ndcg@5: 0.969944\n",
- "[6]\tvalid_0's ndcg@1: 0.922625\tvalid_0's ndcg@2: 0.968336\tvalid_0's ndcg@3: 0.970261\tvalid_0's ndcg@4: 0.970659\tvalid_0's ndcg@5: 0.970688\n",
- "[7]\tvalid_0's ndcg@1: 0.923625\tvalid_0's ndcg@2: 0.968768\tvalid_0's ndcg@3: 0.970656\tvalid_0's ndcg@4: 0.971043\tvalid_0's ndcg@5: 0.971072\n",
- "[8]\tvalid_0's ndcg@1: 0.925825\tvalid_0's ndcg@2: 0.969612\tvalid_0's ndcg@3: 0.971462\tvalid_0's ndcg@4: 0.97186\tvalid_0's ndcg@5: 0.971879\n",
- "[9]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.969899\tvalid_0's ndcg@3: 0.971711\tvalid_0's ndcg@4: 0.97211\tvalid_0's ndcg@5: 0.972129\n",
- "[10]\tvalid_0's ndcg@1: 0.927775\tvalid_0's ndcg@2: 0.97041\tvalid_0's ndcg@3: 0.972185\tvalid_0's ndcg@4: 0.972594\tvalid_0's ndcg@5: 0.972614\n",
- "[11]\tvalid_0's ndcg@1: 0.92885\tvalid_0's ndcg@2: 0.970838\tvalid_0's ndcg@3: 0.972588\tvalid_0's ndcg@4: 0.973008\tvalid_0's ndcg@5: 0.973028\n",
- "[12]\tvalid_0's ndcg@1: 0.930325\tvalid_0's ndcg@2: 0.971367\tvalid_0's ndcg@3: 0.973129\tvalid_0's ndcg@4: 0.973549\tvalid_0's ndcg@5: 0.973569\n",
- "[13]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971631\tvalid_0's ndcg@3: 0.973443\tvalid_0's ndcg@4: 0.973842\tvalid_0's ndcg@5: 0.973871\n",
- "[14]\tvalid_0's ndcg@1: 0.931525\tvalid_0's ndcg@2: 0.971778\tvalid_0's ndcg@3: 0.973616\tvalid_0's ndcg@4: 0.973993\tvalid_0's ndcg@5: 0.974022\n",
- "[15]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.9717\tvalid_0's ndcg@3: 0.973475\tvalid_0's ndcg@4: 0.973852\tvalid_0's ndcg@5: 0.973872\n",
- "[16]\tvalid_0's ndcg@1: 0.931775\tvalid_0's ndcg@2: 0.971902\tvalid_0's ndcg@3: 0.973702\tvalid_0's ndcg@4: 0.97409\tvalid_0's ndcg@5: 0.974109\n",
- "[17]\tvalid_0's ndcg@1: 0.931425\tvalid_0's ndcg@2: 0.971805\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973967\tvalid_0's ndcg@5: 0.973986\n",
- "[18]\tvalid_0's ndcg@1: 0.931575\tvalid_0's ndcg@2: 0.971876\tvalid_0's ndcg@3: 0.973651\tvalid_0's ndcg@4: 0.974027\tvalid_0's ndcg@5: 0.974047\n",
- "[19]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.97208\tvalid_0's ndcg@3: 0.973805\tvalid_0's ndcg@4: 0.974192\tvalid_0's ndcg@5: 0.974212\n",
- "[20]\tvalid_0's ndcg@1: 0.932075\tvalid_0's ndcg@2: 0.972092\tvalid_0's ndcg@3: 0.973829\tvalid_0's ndcg@4: 0.974217\tvalid_0's ndcg@5: 0.974236\n",
- "[21]\tvalid_0's ndcg@1: 0.932675\tvalid_0's ndcg@2: 0.972282\tvalid_0's ndcg@3: 0.974057\tvalid_0's ndcg@4: 0.974444\tvalid_0's ndcg@5: 0.974454\n",
- "[22]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972358\tvalid_0's ndcg@3: 0.974146\tvalid_0's ndcg@4: 0.974533\tvalid_0's ndcg@5: 0.974543\n",
- "[23]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972478\tvalid_0's ndcg@3: 0.974253\tvalid_0's ndcg@4: 0.974651\tvalid_0's ndcg@5: 0.974661\n",
- "[24]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972539\tvalid_0's ndcg@3: 0.974351\tvalid_0's ndcg@4: 0.974739\tvalid_0's ndcg@5: 0.974749\n",
- "[25]\tvalid_0's ndcg@1: 0.93475\tvalid_0's ndcg@2: 0.973\tvalid_0's ndcg@3: 0.974788\tvalid_0's ndcg@4: 0.975197\tvalid_0's ndcg@5: 0.975206\n",
- "[26]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.97312\tvalid_0's ndcg@3: 0.974895\tvalid_0's ndcg@4: 0.975315\tvalid_0's ndcg@5: 0.975325\n",
- "[27]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.974865\tvalid_0's ndcg@4: 0.975264\tvalid_0's ndcg@5: 0.975273\n",
- "[28]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974939\tvalid_0's ndcg@4: 0.975327\tvalid_0's ndcg@5: 0.975336\n",
- "[29]\tvalid_0's ndcg@1: 0.935475\tvalid_0's ndcg@2: 0.973315\tvalid_0's ndcg@3: 0.975128\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975492\n",
- "[30]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973522\tvalid_0's ndcg@3: 0.975297\tvalid_0's ndcg@4: 0.975663\tvalid_0's ndcg@5: 0.975673\n",
- "[31]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973506\tvalid_0's ndcg@3: 0.975281\tvalid_0's ndcg@4: 0.975658\tvalid_0's ndcg@5: 0.975668\n",
- "[32]\tvalid_0's ndcg@1: 0.93675\tvalid_0's ndcg@2: 0.973833\tvalid_0's ndcg@3: 0.975595\tvalid_0's ndcg@4: 0.975961\tvalid_0's ndcg@5: 0.975971\n",
- "[33]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.973763\tvalid_0's ndcg@3: 0.975488\tvalid_0's ndcg@4: 0.975865\tvalid_0's ndcg@5: 0.975874\n",
- "[34]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973893\tvalid_0's ndcg@3: 0.975568\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975966\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LGB排序模型"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[35]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974059\tvalid_0's ndcg@3: 0.975722\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.97613\n",
- "[36]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
- "[37]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
- "[38]\tvalid_0's ndcg@1: 0.938725\tvalid_0's ndcg@2: 0.974672\tvalid_0's ndcg@3: 0.97636\tvalid_0's ndcg@4: 0.976715\tvalid_0's ndcg@5: 0.976725\n",
- "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974676\tvalid_0's ndcg@3: 0.976364\tvalid_0's ndcg@4: 0.976697\tvalid_0's ndcg@5: 0.976707\n",
- "[40]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.974867\tvalid_0's ndcg@3: 0.97653\tvalid_0's ndcg@4: 0.976874\tvalid_0's ndcg@5: 0.976884\n",
- "[41]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975042\tvalid_0's ndcg@3: 0.976705\tvalid_0's ndcg@4: 0.97705\tvalid_0's ndcg@5: 0.977059\n",
- "[42]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976784\tvalid_0's ndcg@4: 0.977129\tvalid_0's ndcg@5: 0.977138\n",
- "[43]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.97517\tvalid_0's ndcg@3: 0.97687\tvalid_0's ndcg@4: 0.977215\tvalid_0's ndcg@5: 0.977225\n",
- "[44]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.97534\tvalid_0's ndcg@3: 0.977015\tvalid_0's ndcg@4: 0.97736\tvalid_0's ndcg@5: 0.97737\n",
- "[45]\tvalid_0's ndcg@1: 0.94055\tvalid_0's ndcg@2: 0.975409\tvalid_0's ndcg@3: 0.977059\tvalid_0's ndcg@4: 0.977403\tvalid_0's ndcg@5: 0.977413\n",
- "[46]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975415\tvalid_0's ndcg@3: 0.97704\tvalid_0's ndcg@4: 0.977396\tvalid_0's ndcg@5: 0.977405\n",
- "[47]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975363\tvalid_0's ndcg@3: 0.977013\tvalid_0's ndcg@4: 0.977357\tvalid_0's ndcg@5: 0.977367\n",
- "[48]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975388\tvalid_0's ndcg@3: 0.977025\tvalid_0's ndcg@4: 0.97737\tvalid_0's ndcg@5: 0.977379\n",
- "[49]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975447\tvalid_0's ndcg@3: 0.977097\tvalid_0's ndcg@4: 0.977409\tvalid_0's ndcg@5: 0.977419\n",
- "[50]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975666\tvalid_0's ndcg@3: 0.977303\tvalid_0's ndcg@4: 0.977615\tvalid_0's ndcg@5: 0.977625\n",
- "[51]\tvalid_0's ndcg@1: 0.94135\tvalid_0's ndcg@2: 0.975751\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.97771\tvalid_0's ndcg@5: 0.97772\n",
- "[52]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.975717\tvalid_0's ndcg@3: 0.977355\tvalid_0's ndcg@4: 0.977688\tvalid_0's ndcg@5: 0.977698\n",
- "[53]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.975713\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977699\tvalid_0's ndcg@5: 0.977718\n",
- "[54]\tvalid_0's ndcg@1: 0.94185\tvalid_0's ndcg@2: 0.975857\tvalid_0's ndcg@3: 0.977557\tvalid_0's ndcg@4: 0.977869\tvalid_0's ndcg@5: 0.977889\n",
- "[55]\tvalid_0's ndcg@1: 0.941925\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.9776\tvalid_0's ndcg@4: 0.977891\tvalid_0's ndcg@5: 0.97791\n",
- "[56]\tvalid_0's ndcg@1: 0.942325\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977719\tvalid_0's ndcg@4: 0.978032\tvalid_0's ndcg@5: 0.978051\n",
- "[57]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977772\tvalid_0's ndcg@4: 0.978073\tvalid_0's ndcg@5: 0.978093\n",
- "[58]\tvalid_0's ndcg@1: 0.9425\tvalid_0's ndcg@2: 0.976081\tvalid_0's ndcg@3: 0.977806\tvalid_0's ndcg@4: 0.978108\tvalid_0's ndcg@5: 0.978127\n",
- "[59]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977788\tvalid_0's ndcg@4: 0.978079\tvalid_0's ndcg@5: 0.978098\n",
- "[60]\tvalid_0's ndcg@1: 0.942375\tvalid_0's ndcg@2: 0.976067\tvalid_0's ndcg@3: 0.977779\tvalid_0's ndcg@4: 0.97807\tvalid_0's ndcg@5: 0.978089\n",
- "[61]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976043\tvalid_0's ndcg@3: 0.97773\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.97804\n",
- "[62]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976117\tvalid_0's ndcg@3: 0.977792\tvalid_0's ndcg@4: 0.978093\tvalid_0's ndcg@5: 0.978112\n",
- "[63]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977881\tvalid_0's ndcg@4: 0.978182\tvalid_0's ndcg@5: 0.978201\n",
- "[64]\tvalid_0's ndcg@1: 0.942925\tvalid_0's ndcg@2: 0.976254\tvalid_0's ndcg@3: 0.977966\tvalid_0's ndcg@4: 0.978268\tvalid_0's ndcg@5: 0.978287\n",
- "[65]\tvalid_0's ndcg@1: 0.9431\tvalid_0's ndcg@2: 0.97635\tvalid_0's ndcg@3: 0.978025\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978357\n",
- "[66]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976445\tvalid_0's ndcg@3: 0.978132\tvalid_0's ndcg@4: 0.978445\tvalid_0's ndcg@5: 0.978464\n",
- "[67]\tvalid_0's ndcg@1: 0.943275\tvalid_0's ndcg@2: 0.976399\tvalid_0's ndcg@3: 0.978074\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978416\n",
- "[68]\tvalid_0's ndcg@1: 0.943325\tvalid_0's ndcg@2: 0.976401\tvalid_0's ndcg@3: 0.978089\tvalid_0's ndcg@4: 0.978412\tvalid_0's ndcg@5: 0.978431\n",
- "[69]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976578\tvalid_0's ndcg@3: 0.97819\tvalid_0's ndcg@4: 0.978546\tvalid_0's ndcg@5: 0.978565\n",
- "[70]\tvalid_0's ndcg@1: 0.944025\tvalid_0's ndcg@2: 0.976707\tvalid_0's ndcg@3: 0.97832\tvalid_0's ndcg@4: 0.978675\tvalid_0's ndcg@5: 0.978694\n",
- "[71]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976772\tvalid_0's ndcg@3: 0.978384\tvalid_0's ndcg@4: 0.97874\tvalid_0's ndcg@5: 0.978759\n",
- "[72]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978409\tvalid_0's ndcg@4: 0.978765\tvalid_0's ndcg@5: 0.978784\n",
- "[73]\tvalid_0's ndcg@1: 0.94445\tvalid_0's ndcg@2: 0.976864\tvalid_0's ndcg@3: 0.978464\tvalid_0's ndcg@4: 0.97883\tvalid_0's ndcg@5: 0.978849\n",
- "[74]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
- "[75]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
- "[76]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.97696\tvalid_0's ndcg@3: 0.978535\tvalid_0's ndcg@4: 0.978901\tvalid_0's ndcg@5: 0.978921\n",
- "[77]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
- "[78]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
- "[79]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976907\tvalid_0's ndcg@3: 0.978507\tvalid_0's ndcg@4: 0.978863\tvalid_0's ndcg@5: 0.978882\n",
- "[80]\tvalid_0's ndcg@1: 0.94455\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97851\tvalid_0's ndcg@4: 0.978865\tvalid_0's ndcg@5: 0.978885\n",
- "[81]\tvalid_0's ndcg@1: 0.944725\tvalid_0's ndcg@2: 0.97695\tvalid_0's ndcg@3: 0.978575\tvalid_0's ndcg@4: 0.978919\tvalid_0's ndcg@5: 0.978948\n",
- "[82]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.977103\tvalid_0's ndcg@3: 0.978765\tvalid_0's ndcg@4: 0.97911\tvalid_0's ndcg@5: 0.979129\n",
- "[83]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977066\tvalid_0's ndcg@3: 0.978716\tvalid_0's ndcg@4: 0.979071\tvalid_0's ndcg@5: 0.97909\n",
- "[84]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.97715\tvalid_0's ndcg@3: 0.978775\tvalid_0's ndcg@4: 0.97912\tvalid_0's ndcg@5: 0.979139\n",
- "[85]\tvalid_0's ndcg@1: 0.945025\tvalid_0's ndcg@2: 0.977092\tvalid_0's ndcg@3: 0.978692\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979067\n",
- "[86]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977172\tvalid_0's ndcg@3: 0.97876\tvalid_0's ndcg@4: 0.979115\tvalid_0's ndcg@5: 0.979135\n",
- "[87]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.97879\tvalid_0's ndcg@4: 0.979156\tvalid_0's ndcg@5: 0.979166\n",
- "[88]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.978815\tvalid_0's ndcg@4: 0.979149\tvalid_0's ndcg@5: 0.979168\n",
- "[89]\tvalid_0's ndcg@1: 0.94555\tvalid_0's ndcg@2: 0.977333\tvalid_0's ndcg@3: 0.978933\tvalid_0's ndcg@4: 0.979267\tvalid_0's ndcg@5: 0.979277\n",
- "[90]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977462\tvalid_0's ndcg@3: 0.979062\tvalid_0's ndcg@4: 0.979396\tvalid_0's ndcg@5: 0.979406\n",
- "[91]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977481\tvalid_0's ndcg@3: 0.979081\tvalid_0's ndcg@4: 0.979414\tvalid_0's ndcg@5: 0.979424\n",
- "[92]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977437\tvalid_0's ndcg@3: 0.97905\tvalid_0's ndcg@4: 0.979384\tvalid_0's ndcg@5: 0.979393\n",
- "[93]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977421\tvalid_0's ndcg@3: 0.979046\tvalid_0's ndcg@4: 0.97938\tvalid_0's ndcg@5: 0.97939\n",
- "[94]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977431\tvalid_0's ndcg@3: 0.979068\tvalid_0's ndcg@4: 0.979391\tvalid_0's ndcg@5: 0.979401\n",
- "[95]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977449\tvalid_0's ndcg@3: 0.979074\tvalid_0's ndcg@4: 0.979408\tvalid_0's ndcg@5: 0.979418\n",
- "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979127\tvalid_0's ndcg@4: 0.979461\tvalid_0's ndcg@5: 0.97947\n"
- ]
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:07.787698Z",
+ "start_time": "2020-11-18T04:21:07.536514Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 防止中间出错之后重新读取数据\n",
+ "trn_user_item_feats_df_rank_model = trn_user_item_feats_df.copy()\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_rank_model = val_user_item_feats_df.copy()\n",
+ " \n",
+ "tst_user_item_feats_df_rank_model = tst_user_item_feats_df.copy()"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[97]\tvalid_0's ndcg@1: 0.946375\tvalid_0's ndcg@2: 0.977622\tvalid_0's ndcg@3: 0.979222\tvalid_0's ndcg@4: 0.979577\tvalid_0's ndcg@5: 0.979577\n",
- "[98]\tvalid_0's ndcg@1: 0.946625\tvalid_0's ndcg@2: 0.977714\tvalid_0's ndcg@3: 0.979339\tvalid_0's ndcg@4: 0.979673\tvalid_0's ndcg@5: 0.979673\n",
- "[99]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.977739\tvalid_0's ndcg@3: 0.979352\tvalid_0's ndcg@4: 0.979685\tvalid_0's ndcg@5: 0.979685\n",
- "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_rank_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id','label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 训练集与验证集的用户分组\n",
- " train_idx.sort_values(by=['user_id'], inplace=True)\n",
- " g_train = train_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- " \n",
- " valid_idx.sort_values(by=['user_id'], inplace=True)\n",
- " g_val = valid_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- " \n",
- " # 定义模型\n",
- " lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) \n",
- " # 训练模型\n",
- " lgb_ranker.fit(train_idx[lgb_cols], train_idx['label'], group=g_train,\n",
- " eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], eval_group= [g_val], \n",
- " eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = lgb_ranker.predict(valid_idx[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
- " \n",
- " # 对输出结果进行归一化\n",
- " valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += lgb_ranker.predict(tst_user_item_feats_df_rank_model[lgb_cols], lgb_ranker.best_iteration_)\n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_ranker_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_ranker_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:52.604397Z",
- "start_time": "2020-11-18T04:22:43.253034Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "# 单模型生成提交结果\n",
- "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_ranker')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## LGB分类模型"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:58.259730Z",
- "start_time": "2020-11-18T04:22:58.254297Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型及参数的定义\n",
- "lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=500, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:11.258774Z",
- "start_time": "2020-11-18T04:23:00.861936Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型训练\n",
- "if offline:\n",
- " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'],\n",
- " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
- " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
- "else:\n",
- " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:19.591396Z",
- "start_time": "2020-11-18T04:23:13.813850Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df['pred_score'] = lgb_Classfication.predict_proba(tst_user_item_feats_df[lgb_cols])[:,1]\n",
- "\n",
- "# 将这里的排序结果保存一份，用户后面的模型融合\n",
- "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_cls_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:32.352931Z",
- "start_time": "2020-11-18T04:23:22.346609Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_cls')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:11.241196Z",
- "start_time": "2020-11-18T04:23:41.377394Z"
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:10.839656Z",
+ "start_time": "2020-11-18T04:21:10.833109Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义特征列\n",
+ "lgb_cols = ['sim0', 'time_diff0', 'word_diff0','sim_max', 'sim_min', 'sim_sum', \n",
+ " 'sim_mean', 'score','click_size', 'time_diff_mean', 'active_level',\n",
+ " 'click_environment','click_deviceGroup', 'click_os', 'click_country', \n",
+ " 'click_region','click_referrer_type', 'user_time_hob1', 'user_time_hob2',\n",
+ " 'words_hbo', 'category_id', 'created_at_ts','words_count']"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1]\tvalid_0's auc: 0.764896\tvalid_0's binary_logloss: 0.522153\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.767857\tvalid_0's binary_logloss: 0.52057\n",
- "[3]\tvalid_0's auc: 0.783096\tvalid_0's binary_logloss: 0.519584\n",
- "[4]\tvalid_0's auc: 0.784354\tvalid_0's binary_logloss: 0.518485\n",
- "[5]\tvalid_0's auc: 0.790554\tvalid_0's binary_logloss: 0.516886\n",
- "[6]\tvalid_0's auc: 0.791954\tvalid_0's binary_logloss: 0.515334\n",
- "[7]\tvalid_0's auc: 0.794257\tvalid_0's binary_logloss: 0.514032\n",
- "[8]\tvalid_0's auc: 0.795222\tvalid_0's binary_logloss: 0.512516\n",
- "[9]\tvalid_0's auc: 0.795417\tvalid_0's binary_logloss: 0.511671\n",
- "[10]\tvalid_0's auc: 0.795913\tvalid_0's binary_logloss: 0.510226\n",
- "[11]\tvalid_0's auc: 0.798222\tvalid_0's binary_logloss: 0.508858\n",
- "[12]\tvalid_0's auc: 0.79825\tvalid_0's binary_logloss: 0.507928\n",
- "[13]\tvalid_0's auc: 0.798842\tvalid_0's binary_logloss: 0.50708\n",
- "[14]\tvalid_0's auc: 0.798935\tvalid_0's binary_logloss: 0.505752\n",
- "[15]\tvalid_0's auc: 0.799543\tvalid_0's binary_logloss: 0.504388\n",
- "[16]\tvalid_0's auc: 0.800844\tvalid_0's binary_logloss: 0.503126\n",
- "[17]\tvalid_0's auc: 0.800855\tvalid_0's binary_logloss: 0.501809\n",
- "[18]\tvalid_0's auc: 0.801653\tvalid_0's binary_logloss: 0.500676\n",
- "[19]\tvalid_0's auc: 0.801518\tvalid_0's binary_logloss: 0.49987\n",
- "[20]\tvalid_0's auc: 0.801662\tvalid_0's binary_logloss: 0.498625\n",
- "[21]\tvalid_0's auc: 0.802093\tvalid_0's binary_logloss: 0.498113\n",
- "[22]\tvalid_0's auc: 0.803071\tvalid_0's binary_logloss: 0.496933\n",
- "[23]\tvalid_0's auc: 0.803222\tvalid_0's binary_logloss: 0.495864\n",
- "[24]\tvalid_0's auc: 0.802927\tvalid_0's binary_logloss: 0.494691\n",
- "[25]\tvalid_0's auc: 0.802581\tvalid_0's binary_logloss: 0.493543\n",
- "[26]\tvalid_0's auc: 0.802965\tvalid_0's binary_logloss: 0.492444\n",
- "[27]\tvalid_0's auc: 0.80298\tvalid_0's binary_logloss: 0.491336\n",
- "[28]\tvalid_0's auc: 0.803226\tvalid_0's binary_logloss: 0.490275\n",
- "[29]\tvalid_0's auc: 0.803436\tvalid_0's binary_logloss: 0.489126\n",
- "[30]\tvalid_0's auc: 0.803796\tvalid_0's binary_logloss: 0.48802\n",
- "[31]\tvalid_0's auc: 0.803601\tvalid_0's binary_logloss: 0.486988\n",
- "[32]\tvalid_0's auc: 0.804416\tvalid_0's binary_logloss: 0.485972\n",
- "[33]\tvalid_0's auc: 0.804529\tvalid_0's binary_logloss: 0.484939\n",
- "[34]\tvalid_0's auc: 0.804534\tvalid_0's binary_logloss: 0.483927\n",
- "[35]\tvalid_0's auc: 0.804819\tvalid_0's binary_logloss: 0.483271\n",
- "[36]\tvalid_0's auc: 0.804774\tvalid_0's binary_logloss: 0.482273\n",
- "[37]\tvalid_0's auc: 0.805237\tvalid_0's binary_logloss: 0.481639\n",
- "[38]\tvalid_0's auc: 0.805546\tvalid_0's binary_logloss: 0.480959\n",
- "[39]\tvalid_0's auc: 0.805598\tvalid_0's binary_logloss: 0.479955\n",
- "[40]\tvalid_0's auc: 0.806011\tvalid_0's binary_logloss: 0.47903\n",
- "[41]\tvalid_0's auc: 0.806664\tvalid_0's binary_logloss: 0.478439\n",
- "[42]\tvalid_0's auc: 0.807021\tvalid_0's binary_logloss: 0.477798\n",
- "[43]\tvalid_0's auc: 0.80726\tvalid_0's binary_logloss: 0.476829\n",
- "[44]\tvalid_0's auc: 0.807157\tvalid_0's binary_logloss: 0.475976\n",
- "[45]\tvalid_0's auc: 0.807788\tvalid_0's binary_logloss: 0.475056\n",
- "[46]\tvalid_0's auc: 0.80805\tvalid_0's binary_logloss: 0.474446\n",
- "[47]\tvalid_0's auc: 0.808097\tvalid_0's binary_logloss: 0.473576\n",
- "[48]\tvalid_0's auc: 0.80815\tvalid_0's binary_logloss: 0.472676\n",
- "[49]\tvalid_0's auc: 0.808304\tvalid_0's binary_logloss: 0.471918\n",
- "[50]\tvalid_0's auc: 0.808749\tvalid_0's binary_logloss: 0.471481\n",
- "[51]\tvalid_0's auc: 0.808972\tvalid_0's binary_logloss: 0.471104\n",
- "[52]\tvalid_0's auc: 0.809326\tvalid_0's binary_logloss: 0.470289\n",
- "[53]\tvalid_0's auc: 0.809472\tvalid_0's binary_logloss: 0.469508\n",
- "[54]\tvalid_0's auc: 0.809505\tvalid_0's binary_logloss: 0.46869\n",
- "[55]\tvalid_0's auc: 0.809594\tvalid_0's binary_logloss: 0.467885\n",
- "[56]\tvalid_0's auc: 0.809847\tvalid_0's binary_logloss: 0.467356\n",
- "[57]\tvalid_0's auc: 0.810262\tvalid_0's binary_logloss: 0.466531\n",
- "[58]\tvalid_0's auc: 0.810407\tvalid_0's binary_logloss: 0.46573\n",
- "[59]\tvalid_0's auc: 0.810618\tvalid_0's binary_logloss: 0.465205\n",
- "[60]\tvalid_0's auc: 0.81066\tvalid_0's binary_logloss: 0.464435\n",
- "[61]\tvalid_0's auc: 0.810638\tvalid_0's binary_logloss: 0.463721\n",
- "[62]\tvalid_0's auc: 0.810658\tvalid_0's binary_logloss: 0.462982\n",
- "[63]\tvalid_0's auc: 0.811106\tvalid_0's binary_logloss: 0.462246\n",
- "[64]\tvalid_0's auc: 0.811313\tvalid_0's binary_logloss: 0.461748\n",
- "[65]\tvalid_0's auc: 0.811351\tvalid_0's binary_logloss: 0.461038\n",
- "[66]\tvalid_0's auc: 0.811433\tvalid_0's binary_logloss: 0.460323\n",
- "[67]\tvalid_0's auc: 0.81158\tvalid_0's binary_logloss: 0.459662\n",
- "[68]\tvalid_0's auc: 0.811561\tvalid_0's binary_logloss: 0.458988\n",
- "[69]\tvalid_0's auc: 0.811748\tvalid_0's binary_logloss: 0.458592\n",
- "[70]\tvalid_0's auc: 0.811919\tvalid_0's binary_logloss: 0.457934\n",
- "[71]\tvalid_0's auc: 0.812073\tvalid_0's binary_logloss: 0.457508\n",
- "[72]\tvalid_0's auc: 0.812273\tvalid_0's binary_logloss: 0.457038\n",
- "[73]\tvalid_0's auc: 0.812561\tvalid_0's binary_logloss: 0.456439\n",
- "[74]\tvalid_0's auc: 0.812633\tvalid_0's binary_logloss: 0.455789\n",
- "[75]\tvalid_0's auc: 0.812757\tvalid_0's binary_logloss: 0.455173\n",
- "[76]\tvalid_0's auc: 0.812923\tvalid_0's binary_logloss: 0.454533\n",
- "[77]\tvalid_0's auc: 0.81295\tvalid_0's binary_logloss: 0.45392\n",
- "[78]\tvalid_0's auc: 0.813073\tvalid_0's binary_logloss: 0.453517\n",
- "[79]\tvalid_0's auc: 0.813202\tvalid_0's binary_logloss: 0.452932\n",
- "[80]\tvalid_0's auc: 0.813611\tvalid_0's binary_logloss: 0.452285\n",
- "[81]\tvalid_0's auc: 0.813769\tvalid_0's binary_logloss: 0.45191\n",
- "[82]\tvalid_0's auc: 0.814468\tvalid_0's binary_logloss: 0.451455\n",
- "[83]\tvalid_0's auc: 0.814656\tvalid_0's binary_logloss: 0.450885\n",
- "[84]\tvalid_0's auc: 0.814755\tvalid_0's binary_logloss: 0.450308\n",
- "[85]\tvalid_0's auc: 0.814824\tvalid_0's binary_logloss: 0.449739\n",
- "[86]\tvalid_0's auc: 0.81499\tvalid_0's binary_logloss: 0.449348\n",
- "[87]\tvalid_0's auc: 0.815232\tvalid_0's binary_logloss: 0.448759\n",
- "[88]\tvalid_0's auc: 0.815452\tvalid_0's binary_logloss: 0.44823\n",
- "[89]\tvalid_0's auc: 0.815593\tvalid_0's binary_logloss: 0.447861\n",
- "[90]\tvalid_0's auc: 0.815591\tvalid_0's binary_logloss: 0.447323\n",
- "[91]\tvalid_0's auc: 0.815672\tvalid_0's binary_logloss: 0.446796\n",
- "[92]\tvalid_0's auc: 0.815875\tvalid_0's binary_logloss: 0.446472\n",
- "[93]\tvalid_0's auc: 0.815984\tvalid_0's binary_logloss: 0.445961\n",
- "[94]\tvalid_0's auc: 0.816026\tvalid_0's binary_logloss: 0.445439\n",
- "[95]\tvalid_0's auc: 0.816172\tvalid_0's binary_logloss: 0.444909\n",
- "[96]\tvalid_0's auc: 0.816321\tvalid_0's binary_logloss: 0.444413\n",
- "[97]\tvalid_0's auc: 0.816751\tvalid_0's binary_logloss: 0.44405\n",
- "[98]\tvalid_0's auc: 0.817226\tvalid_0's binary_logloss: 0.443626\n",
- "[99]\tvalid_0's auc: 0.817286\tvalid_0's binary_logloss: 0.443136\n",
- "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
- "[1]\tvalid_0's auc: 0.771584\tvalid_0's binary_logloss: 0.527139\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.775446\tvalid_0's binary_logloss: 0.525462\n",
- "[3]\tvalid_0's auc: 0.790092\tvalid_0's binary_logloss: 0.524461\n",
- "[4]\tvalid_0's auc: 0.791432\tvalid_0's binary_logloss: 0.523322\n",
- "[5]\tvalid_0's auc: 0.797482\tvalid_0's binary_logloss: 0.521614\n",
- "[6]\tvalid_0's auc: 0.79893\tvalid_0's binary_logloss: 0.520007\n",
- "[7]\tvalid_0's auc: 0.800753\tvalid_0's binary_logloss: 0.5187\n",
- "[8]\tvalid_0's auc: 0.802197\tvalid_0's binary_logloss: 0.517125\n",
- "[9]\tvalid_0's auc: 0.802828\tvalid_0's binary_logloss: 0.516269\n",
- "[10]\tvalid_0's auc: 0.803496\tvalid_0's binary_logloss: 0.51474\n",
- "[11]\tvalid_0's auc: 0.804972\tvalid_0's binary_logloss: 0.513321\n",
- "[12]\tvalid_0's auc: 0.804995\tvalid_0's binary_logloss: 0.512334\n",
- "[13]\tvalid_0's auc: 0.80525\tvalid_0's binary_logloss: 0.51151\n",
- "[14]\tvalid_0's auc: 0.805026\tvalid_0's binary_logloss: 0.510149\n",
- "[15]\tvalid_0's auc: 0.805622\tvalid_0's binary_logloss: 0.508708\n",
- "[16]\tvalid_0's auc: 0.806974\tvalid_0's binary_logloss: 0.507384\n",
- "[17]\tvalid_0's auc: 0.807045\tvalid_0's binary_logloss: 0.506017\n",
- "[18]\tvalid_0's auc: 0.807265\tvalid_0's binary_logloss: 0.504853\n",
- "[19]\tvalid_0's auc: 0.807126\tvalid_0's binary_logloss: 0.503972\n",
- "[20]\tvalid_0's auc: 0.806948\tvalid_0's binary_logloss: 0.502693\n",
- "[21]\tvalid_0's auc: 0.807315\tvalid_0's binary_logloss: 0.502166\n",
- "[22]\tvalid_0's auc: 0.808067\tvalid_0's binary_logloss: 0.500948\n",
- "[23]\tvalid_0's auc: 0.808226\tvalid_0's binary_logloss: 0.49987\n",
- "[24]\tvalid_0's auc: 0.808268\tvalid_0's binary_logloss: 0.498623\n",
- "[25]\tvalid_0's auc: 0.808569\tvalid_0's binary_logloss: 0.497389\n",
- "[26]\tvalid_0's auc: 0.809069\tvalid_0's binary_logloss: 0.49624\n",
- "[27]\tvalid_0's auc: 0.809312\tvalid_0's binary_logloss: 0.495095\n",
- "[28]\tvalid_0's auc: 0.809549\tvalid_0's binary_logloss: 0.494012\n",
- "[29]\tvalid_0's auc: 0.809944\tvalid_0's binary_logloss: 0.492834\n",
- "[30]\tvalid_0's auc: 0.810047\tvalid_0's binary_logloss: 0.491735\n",
- "[31]\tvalid_0's auc: 0.810086\tvalid_0's binary_logloss: 0.490633\n"
- ]
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:14.126608Z",
+ "start_time": "2020-11-18T04:21:13.493653Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型分组\n",
+ "trn_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
+ "g_train = trn_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
+ " g_val = val_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[32]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.489595\n",
- "[33]\tvalid_0's auc: 0.810539\tvalid_0's binary_logloss: 0.488536\n",
- "[34]\tvalid_0's auc: 0.810529\tvalid_0's binary_logloss: 0.487489\n",
- "[35]\tvalid_0's auc: 0.810932\tvalid_0's binary_logloss: 0.486775\n",
- "[36]\tvalid_0's auc: 0.810769\tvalid_0's binary_logloss: 0.48577\n",
- "[37]\tvalid_0's auc: 0.811363\tvalid_0's binary_logloss: 0.485123\n",
- "[38]\tvalid_0's auc: 0.811801\tvalid_0's binary_logloss: 0.484413\n",
- "[39]\tvalid_0's auc: 0.811987\tvalid_0's binary_logloss: 0.483371\n",
- "[40]\tvalid_0's auc: 0.812268\tvalid_0's binary_logloss: 0.482407\n",
- "[41]\tvalid_0's auc: 0.813297\tvalid_0's binary_logloss: 0.481742\n",
- "[42]\tvalid_0's auc: 0.813453\tvalid_0's binary_logloss: 0.481108\n",
- "[43]\tvalid_0's auc: 0.813603\tvalid_0's binary_logloss: 0.480163\n",
- "[44]\tvalid_0's auc: 0.813654\tvalid_0's binary_logloss: 0.479239\n",
- "[45]\tvalid_0's auc: 0.814267\tvalid_0's binary_logloss: 0.478299\n",
- "[46]\tvalid_0's auc: 0.81455\tvalid_0's binary_logloss: 0.477678\n",
- "[47]\tvalid_0's auc: 0.81452\tvalid_0's binary_logloss: 0.476766\n",
- "[48]\tvalid_0's auc: 0.814925\tvalid_0's binary_logloss: 0.475815\n",
- "[49]\tvalid_0's auc: 0.814907\tvalid_0's binary_logloss: 0.47503\n",
- "[50]\tvalid_0's auc: 0.815278\tvalid_0's binary_logloss: 0.474588\n",
- "[51]\tvalid_0's auc: 0.815535\tvalid_0's binary_logloss: 0.474171\n",
- "[52]\tvalid_0's auc: 0.815685\tvalid_0's binary_logloss: 0.473335\n",
- "[53]\tvalid_0's auc: 0.815787\tvalid_0's binary_logloss: 0.472509\n",
- "[54]\tvalid_0's auc: 0.815827\tvalid_0's binary_logloss: 0.471686\n",
- "[55]\tvalid_0's auc: 0.815871\tvalid_0's binary_logloss: 0.470838\n",
- "[56]\tvalid_0's auc: 0.816238\tvalid_0's binary_logloss: 0.470285\n",
- "[57]\tvalid_0's auc: 0.816269\tvalid_0's binary_logloss: 0.469495\n",
- "[58]\tvalid_0's auc: 0.816528\tvalid_0's binary_logloss: 0.468654\n",
- "[59]\tvalid_0's auc: 0.816706\tvalid_0's binary_logloss: 0.468122\n",
- "[60]\tvalid_0's auc: 0.816821\tvalid_0's binary_logloss: 0.467352\n",
- "[61]\tvalid_0's auc: 0.816759\tvalid_0's binary_logloss: 0.466622\n",
- "[62]\tvalid_0's auc: 0.81682\tvalid_0's binary_logloss: 0.465867\n",
- "[63]\tvalid_0's auc: 0.817251\tvalid_0's binary_logloss: 0.465112\n",
- "[64]\tvalid_0's auc: 0.817476\tvalid_0's binary_logloss: 0.464589\n",
- "[65]\tvalid_0's auc: 0.817613\tvalid_0's binary_logloss: 0.463831\n",
- "[66]\tvalid_0's auc: 0.817648\tvalid_0's binary_logloss: 0.463098\n",
- "[67]\tvalid_0's auc: 0.817719\tvalid_0's binary_logloss: 0.462414\n",
- "[68]\tvalid_0's auc: 0.817814\tvalid_0's binary_logloss: 0.461727\n",
- "[69]\tvalid_0's auc: 0.817973\tvalid_0's binary_logloss: 0.461329\n",
- "[70]\tvalid_0's auc: 0.818108\tvalid_0's binary_logloss: 0.460674\n",
- "[71]\tvalid_0's auc: 0.818347\tvalid_0's binary_logloss: 0.460222\n",
- "[72]\tvalid_0's auc: 0.818456\tvalid_0's binary_logloss: 0.45977\n",
- "[73]\tvalid_0's auc: 0.818727\tvalid_0's binary_logloss: 0.459157\n",
- "[74]\tvalid_0's auc: 0.818988\tvalid_0's binary_logloss: 0.458437\n",
- "[75]\tvalid_0's auc: 0.819144\tvalid_0's binary_logloss: 0.457808\n",
- "[76]\tvalid_0's auc: 0.819259\tvalid_0's binary_logloss: 0.457159\n",
- "[77]\tvalid_0's auc: 0.819343\tvalid_0's binary_logloss: 0.456512\n",
- "[78]\tvalid_0's auc: 0.81954\tvalid_0's binary_logloss: 0.456045\n",
- "[79]\tvalid_0's auc: 0.819687\tvalid_0's binary_logloss: 0.455416\n",
- "[80]\tvalid_0's auc: 0.819958\tvalid_0's binary_logloss: 0.454765\n",
- "[81]\tvalid_0's auc: 0.820115\tvalid_0's binary_logloss: 0.45436\n",
- "[82]\tvalid_0's auc: 0.820536\tvalid_0's binary_logloss: 0.453965\n",
- "[83]\tvalid_0's auc: 0.820649\tvalid_0's binary_logloss: 0.453383\n",
- "[84]\tvalid_0's auc: 0.820663\tvalid_0's binary_logloss: 0.452804\n",
- "[85]\tvalid_0's auc: 0.820809\tvalid_0's binary_logloss: 0.452167\n",
- "[86]\tvalid_0's auc: 0.821024\tvalid_0's binary_logloss: 0.451735\n",
- "[87]\tvalid_0's auc: 0.821124\tvalid_0's binary_logloss: 0.451167\n",
- "[88]\tvalid_0's auc: 0.821243\tvalid_0's binary_logloss: 0.45061\n",
- "[89]\tvalid_0's auc: 0.821404\tvalid_0's binary_logloss: 0.450215\n",
- "[90]\tvalid_0's auc: 0.821488\tvalid_0's binary_logloss: 0.449656\n",
- "[91]\tvalid_0's auc: 0.821538\tvalid_0's binary_logloss: 0.449107\n",
- "[92]\tvalid_0's auc: 0.82172\tvalid_0's binary_logloss: 0.448752\n",
- "[93]\tvalid_0's auc: 0.821809\tvalid_0's binary_logloss: 0.448188\n",
- "[94]\tvalid_0's auc: 0.82184\tvalid_0's binary_logloss: 0.447659\n",
- "[95]\tvalid_0's auc: 0.821971\tvalid_0's binary_logloss: 0.447108\n",
- "[96]\tvalid_0's auc: 0.822086\tvalid_0's binary_logloss: 0.446596\n",
- "[97]\tvalid_0's auc: 0.82247\tvalid_0's binary_logloss: 0.446244\n",
- "[98]\tvalid_0's auc: 0.822951\tvalid_0's binary_logloss: 0.445812\n",
- "[99]\tvalid_0's auc: 0.822991\tvalid_0's binary_logloss: 0.445329\n",
- "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
- "[1]\tvalid_0's auc: 0.769525\tvalid_0's binary_logloss: 0.526256\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.775857\tvalid_0's binary_logloss: 0.524594\n",
- "[3]\tvalid_0's auc: 0.785307\tvalid_0's binary_logloss: 0.523606\n",
- "[4]\tvalid_0's auc: 0.786356\tvalid_0's binary_logloss: 0.522495\n",
- "[5]\tvalid_0's auc: 0.793385\tvalid_0's binary_logloss: 0.520812\n",
- "[6]\tvalid_0's auc: 0.794014\tvalid_0's binary_logloss: 0.519253\n",
- "[7]\tvalid_0's auc: 0.795454\tvalid_0's binary_logloss: 0.517961\n",
- "[8]\tvalid_0's auc: 0.79807\tvalid_0's binary_logloss: 0.516363\n",
- "[9]\tvalid_0's auc: 0.798756\tvalid_0's binary_logloss: 0.51548\n",
- "[10]\tvalid_0's auc: 0.798314\tvalid_0's binary_logloss: 0.514021\n",
- "[11]\tvalid_0's auc: 0.799343\tvalid_0's binary_logloss: 0.512678\n",
- "[12]\tvalid_0's auc: 0.799573\tvalid_0's binary_logloss: 0.511708\n",
- "[13]\tvalid_0's auc: 0.799563\tvalid_0's binary_logloss: 0.510892\n",
- "[14]\tvalid_0's auc: 0.800333\tvalid_0's binary_logloss: 0.509532\n",
- "[15]\tvalid_0's auc: 0.800672\tvalid_0's binary_logloss: 0.508117\n",
- "[16]\tvalid_0's auc: 0.801953\tvalid_0's binary_logloss: 0.506866\n",
- "[17]\tvalid_0's auc: 0.802078\tvalid_0's binary_logloss: 0.5055\n",
- "[18]\tvalid_0's auc: 0.802449\tvalid_0's binary_logloss: 0.504358\n",
- "[19]\tvalid_0's auc: 0.802329\tvalid_0's binary_logloss: 0.503503\n",
- "[20]\tvalid_0's auc: 0.802437\tvalid_0's binary_logloss: 0.502233\n",
- "[21]\tvalid_0's auc: 0.802653\tvalid_0's binary_logloss: 0.50174\n",
- "[22]\tvalid_0's auc: 0.803753\tvalid_0's binary_logloss: 0.50056\n",
- "[23]\tvalid_0's auc: 0.803956\tvalid_0's binary_logloss: 0.499496\n",
- "[24]\tvalid_0's auc: 0.804231\tvalid_0's binary_logloss: 0.498283\n",
- "[25]\tvalid_0's auc: 0.804554\tvalid_0's binary_logloss: 0.497059\n",
- "[26]\tvalid_0's auc: 0.805133\tvalid_0's binary_logloss: 0.495963\n",
- "[27]\tvalid_0's auc: 0.805333\tvalid_0's binary_logloss: 0.494842\n",
- "[28]\tvalid_0's auc: 0.805644\tvalid_0's binary_logloss: 0.493771\n",
- "[29]\tvalid_0's auc: 0.806029\tvalid_0's binary_logloss: 0.492598\n",
- "[30]\tvalid_0's auc: 0.806321\tvalid_0's binary_logloss: 0.491474\n",
- "[31]\tvalid_0's auc: 0.806201\tvalid_0's binary_logloss: 0.490419\n",
- "[32]\tvalid_0's auc: 0.806671\tvalid_0's binary_logloss: 0.489393\n",
- "[33]\tvalid_0's auc: 0.806899\tvalid_0's binary_logloss: 0.488331\n",
- "[34]\tvalid_0's auc: 0.807105\tvalid_0's binary_logloss: 0.487277\n",
- "[35]\tvalid_0's auc: 0.807257\tvalid_0's binary_logloss: 0.486592\n",
- "[36]\tvalid_0's auc: 0.80729\tvalid_0's binary_logloss: 0.485607\n",
- "[37]\tvalid_0's auc: 0.807752\tvalid_0's binary_logloss: 0.484951\n",
- "[38]\tvalid_0's auc: 0.808191\tvalid_0's binary_logloss: 0.484269\n",
- "[39]\tvalid_0's auc: 0.808417\tvalid_0's binary_logloss: 0.483242\n",
- "[40]\tvalid_0's auc: 0.808761\tvalid_0's binary_logloss: 0.482291\n",
- "[41]\tvalid_0's auc: 0.80965\tvalid_0's binary_logloss: 0.48164\n",
- "[42]\tvalid_0's auc: 0.810065\tvalid_0's binary_logloss: 0.480962\n",
- "[43]\tvalid_0's auc: 0.810209\tvalid_0's binary_logloss: 0.479995\n",
- "[44]\tvalid_0's auc: 0.810091\tvalid_0's binary_logloss: 0.479077\n",
- "[45]\tvalid_0's auc: 0.810573\tvalid_0's binary_logloss: 0.478185\n",
- "[46]\tvalid_0's auc: 0.810924\tvalid_0's binary_logloss: 0.477558\n",
- "[47]\tvalid_0's auc: 0.810951\tvalid_0's binary_logloss: 0.476662\n",
- "[48]\tvalid_0's auc: 0.811101\tvalid_0's binary_logloss: 0.475745\n",
- "[49]\tvalid_0's auc: 0.811269\tvalid_0's binary_logloss: 0.474951\n",
- "[50]\tvalid_0's auc: 0.81173\tvalid_0's binary_logloss: 0.474514\n",
- "[51]\tvalid_0's auc: 0.811937\tvalid_0's binary_logloss: 0.474114\n",
- "[52]\tvalid_0's auc: 0.812136\tvalid_0's binary_logloss: 0.473297\n",
- "[53]\tvalid_0's auc: 0.812249\tvalid_0's binary_logloss: 0.472497\n",
- "[54]\tvalid_0's auc: 0.812121\tvalid_0's binary_logloss: 0.471696\n",
- "[55]\tvalid_0's auc: 0.812164\tvalid_0's binary_logloss: 0.470905\n",
- "[56]\tvalid_0's auc: 0.812462\tvalid_0's binary_logloss: 0.470384\n",
- "[57]\tvalid_0's auc: 0.812613\tvalid_0's binary_logloss: 0.4696\n",
- "[58]\tvalid_0's auc: 0.812615\tvalid_0's binary_logloss: 0.468778\n",
- "[59]\tvalid_0's auc: 0.812842\tvalid_0's binary_logloss: 0.468211\n",
- "[60]\tvalid_0's auc: 0.81312\tvalid_0's binary_logloss: 0.467385\n",
- "[61]\tvalid_0's auc: 0.813039\tvalid_0's binary_logloss: 0.466632\n",
- "[62]\tvalid_0's auc: 0.812942\tvalid_0's binary_logloss: 0.465933\n",
- "[63]\tvalid_0's auc: 0.813274\tvalid_0's binary_logloss: 0.465214\n",
- "[64]\tvalid_0's auc: 0.813572\tvalid_0's binary_logloss: 0.464692\n",
- "[65]\tvalid_0's auc: 0.813594\tvalid_0's binary_logloss: 0.463925\n",
- "[66]\tvalid_0's auc: 0.813719\tvalid_0's binary_logloss: 0.463177\n",
- "[67]\tvalid_0's auc: 0.814011\tvalid_0's binary_logloss: 0.462513\n",
- "[68]\tvalid_0's auc: 0.813989\tvalid_0's binary_logloss: 0.461843\n"
- ]
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:16.136151Z",
+ "start_time": "2020-11-18T04:21:16.124444Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型定义\n",
+ "lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) "
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[69]\tvalid_0's auc: 0.814218\tvalid_0's binary_logloss: 0.461443\n",
- "[70]\tvalid_0's auc: 0.814334\tvalid_0's binary_logloss: 0.460775\n",
- "[71]\tvalid_0's auc: 0.814493\tvalid_0's binary_logloss: 0.460332\n",
- "[72]\tvalid_0's auc: 0.814663\tvalid_0's binary_logloss: 0.459867\n",
- "[73]\tvalid_0's auc: 0.814856\tvalid_0's binary_logloss: 0.459266\n",
- "[74]\tvalid_0's auc: 0.815017\tvalid_0's binary_logloss: 0.458585\n",
- "[75]\tvalid_0's auc: 0.815186\tvalid_0's binary_logloss: 0.457958\n",
- "[76]\tvalid_0's auc: 0.815374\tvalid_0's binary_logloss: 0.457316\n",
- "[77]\tvalid_0's auc: 0.81554\tvalid_0's binary_logloss: 0.45665\n",
- "[78]\tvalid_0's auc: 0.81569\tvalid_0's binary_logloss: 0.456217\n",
- "[79]\tvalid_0's auc: 0.815861\tvalid_0's binary_logloss: 0.455615\n",
- "[80]\tvalid_0's auc: 0.816443\tvalid_0's binary_logloss: 0.454895\n",
- "[81]\tvalid_0's auc: 0.816659\tvalid_0's binary_logloss: 0.454503\n",
- "[82]\tvalid_0's auc: 0.817017\tvalid_0's binary_logloss: 0.454149\n",
- "[83]\tvalid_0's auc: 0.817162\tvalid_0's binary_logloss: 0.453578\n",
- "[84]\tvalid_0's auc: 0.817274\tvalid_0's binary_logloss: 0.452984\n",
- "[85]\tvalid_0's auc: 0.817283\tvalid_0's binary_logloss: 0.452416\n",
- "[86]\tvalid_0's auc: 0.817339\tvalid_0's binary_logloss: 0.452022\n",
- "[87]\tvalid_0's auc: 0.817494\tvalid_0's binary_logloss: 0.45146\n",
- "[88]\tvalid_0's auc: 0.817594\tvalid_0's binary_logloss: 0.450926\n",
- "[89]\tvalid_0's auc: 0.817771\tvalid_0's binary_logloss: 0.450553\n",
- "[90]\tvalid_0's auc: 0.81789\tvalid_0's binary_logloss: 0.449985\n",
- "[91]\tvalid_0's auc: 0.817931\tvalid_0's binary_logloss: 0.449439\n",
- "[92]\tvalid_0's auc: 0.818138\tvalid_0's binary_logloss: 0.449094\n",
- "[93]\tvalid_0's auc: 0.818334\tvalid_0's binary_logloss: 0.448527\n",
- "[94]\tvalid_0's auc: 0.818426\tvalid_0's binary_logloss: 0.447989\n",
- "[95]\tvalid_0's auc: 0.818676\tvalid_0's binary_logloss: 0.447407\n",
- "[96]\tvalid_0's auc: 0.818852\tvalid_0's binary_logloss: 0.446884\n",
- "[97]\tvalid_0's auc: 0.81945\tvalid_0's binary_logloss: 0.446455\n",
- "[98]\tvalid_0's auc: 0.819861\tvalid_0's binary_logloss: 0.446045\n",
- "[99]\tvalid_0's auc: 0.819943\tvalid_0's binary_logloss: 0.445543\n",
- "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
- "[1]\tvalid_0's auc: 0.770032\tvalid_0's binary_logloss: 0.527241\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.779881\tvalid_0's binary_logloss: 0.525545\n",
- "[3]\tvalid_0's auc: 0.791308\tvalid_0's binary_logloss: 0.524508\n",
- "[4]\tvalid_0's auc: 0.790788\tvalid_0's binary_logloss: 0.52341\n",
- "[5]\tvalid_0's auc: 0.795645\tvalid_0's binary_logloss: 0.521753\n",
- "[6]\tvalid_0's auc: 0.797745\tvalid_0's binary_logloss: 0.520131\n",
- "[7]\tvalid_0's auc: 0.79931\tvalid_0's binary_logloss: 0.518872\n",
- "[8]\tvalid_0's auc: 0.800014\tvalid_0's binary_logloss: 0.517353\n",
- "[9]\tvalid_0's auc: 0.800549\tvalid_0's binary_logloss: 0.516487\n",
- "[10]\tvalid_0's auc: 0.800261\tvalid_0's binary_logloss: 0.515039\n",
- "[11]\tvalid_0's auc: 0.801261\tvalid_0's binary_logloss: 0.513695\n",
- "[12]\tvalid_0's auc: 0.801062\tvalid_0's binary_logloss: 0.512735\n",
- "[13]\tvalid_0's auc: 0.801155\tvalid_0's binary_logloss: 0.51192\n",
- "[14]\tvalid_0's auc: 0.801315\tvalid_0's binary_logloss: 0.510559\n",
- "[15]\tvalid_0's auc: 0.80185\tvalid_0's binary_logloss: 0.509147\n",
- "[16]\tvalid_0's auc: 0.803029\tvalid_0's binary_logloss: 0.507914\n",
- "[17]\tvalid_0's auc: 0.803035\tvalid_0's binary_logloss: 0.506583\n",
- "[18]\tvalid_0's auc: 0.803433\tvalid_0's binary_logloss: 0.505441\n",
- "[19]\tvalid_0's auc: 0.803717\tvalid_0's binary_logloss: 0.504599\n",
- "[20]\tvalid_0's auc: 0.803819\tvalid_0's binary_logloss: 0.503327\n",
- "[21]\tvalid_0's auc: 0.803923\tvalid_0's binary_logloss: 0.502782\n",
- "[22]\tvalid_0's auc: 0.804939\tvalid_0's binary_logloss: 0.501596\n",
- "[23]\tvalid_0's auc: 0.804707\tvalid_0's binary_logloss: 0.500572\n",
- "[24]\tvalid_0's auc: 0.804632\tvalid_0's binary_logloss: 0.499367\n",
- "[25]\tvalid_0's auc: 0.804756\tvalid_0's binary_logloss: 0.498161\n",
- "[26]\tvalid_0's auc: 0.805067\tvalid_0's binary_logloss: 0.497061\n",
- "[27]\tvalid_0's auc: 0.805119\tvalid_0's binary_logloss: 0.495933\n",
- "[28]\tvalid_0's auc: 0.805304\tvalid_0's binary_logloss: 0.494849\n",
- "[29]\tvalid_0's auc: 0.805688\tvalid_0's binary_logloss: 0.493677\n",
- "[30]\tvalid_0's auc: 0.805822\tvalid_0's binary_logloss: 0.492594\n",
- "[31]\tvalid_0's auc: 0.805869\tvalid_0's binary_logloss: 0.49152\n",
- "[32]\tvalid_0's auc: 0.807267\tvalid_0's binary_logloss: 0.490435\n",
- "[33]\tvalid_0's auc: 0.807301\tvalid_0's binary_logloss: 0.489392\n",
- "[34]\tvalid_0's auc: 0.80736\tvalid_0's binary_logloss: 0.488325\n",
- "[35]\tvalid_0's auc: 0.807706\tvalid_0's binary_logloss: 0.487654\n",
- "[36]\tvalid_0's auc: 0.807758\tvalid_0's binary_logloss: 0.486651\n",
- "[37]\tvalid_0's auc: 0.808051\tvalid_0's binary_logloss: 0.486012\n",
- "[38]\tvalid_0's auc: 0.808429\tvalid_0's binary_logloss: 0.485355\n",
- "[39]\tvalid_0's auc: 0.808663\tvalid_0's binary_logloss: 0.484327\n",
- "[40]\tvalid_0's auc: 0.809007\tvalid_0's binary_logloss: 0.483386\n",
- "[41]\tvalid_0's auc: 0.809781\tvalid_0's binary_logloss: 0.482745\n",
- "[42]\tvalid_0's auc: 0.810071\tvalid_0's binary_logloss: 0.482124\n",
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- "[45]\tvalid_0's auc: 0.811148\tvalid_0's binary_logloss: 0.479261\n",
- "[46]\tvalid_0's auc: 0.811245\tvalid_0's binary_logloss: 0.478687\n",
- "[47]\tvalid_0's auc: 0.811214\tvalid_0's binary_logloss: 0.477812\n",
- "[48]\tvalid_0's auc: 0.811408\tvalid_0's binary_logloss: 0.47689\n",
- "[49]\tvalid_0's auc: 0.811486\tvalid_0's binary_logloss: 0.476132\n",
- "[50]\tvalid_0's auc: 0.811806\tvalid_0's binary_logloss: 0.475718\n",
- "[51]\tvalid_0's auc: 0.812017\tvalid_0's binary_logloss: 0.475342\n",
- "[52]\tvalid_0's auc: 0.812255\tvalid_0's binary_logloss: 0.474505\n",
- "[53]\tvalid_0's auc: 0.812249\tvalid_0's binary_logloss: 0.473707\n",
- "[54]\tvalid_0's auc: 0.812235\tvalid_0's binary_logloss: 0.47289\n",
- "[55]\tvalid_0's auc: 0.812233\tvalid_0's binary_logloss: 0.472091\n",
- "[56]\tvalid_0's auc: 0.812492\tvalid_0's binary_logloss: 0.471563\n",
- "[57]\tvalid_0's auc: 0.812579\tvalid_0's binary_logloss: 0.47077\n",
- "[58]\tvalid_0's auc: 0.812598\tvalid_0's binary_logloss: 0.469992\n",
- "[59]\tvalid_0's auc: 0.812885\tvalid_0's binary_logloss: 0.469458\n",
- "[60]\tvalid_0's auc: 0.812995\tvalid_0's binary_logloss: 0.468676\n",
- "[61]\tvalid_0's auc: 0.812961\tvalid_0's binary_logloss: 0.467939\n",
- "[62]\tvalid_0's auc: 0.812919\tvalid_0's binary_logloss: 0.467232\n",
- "[63]\tvalid_0's auc: 0.813291\tvalid_0's binary_logloss: 0.466491\n",
- "[64]\tvalid_0's auc: 0.813702\tvalid_0's binary_logloss: 0.465945\n",
- "[65]\tvalid_0's auc: 0.813803\tvalid_0's binary_logloss: 0.465197\n",
- "[66]\tvalid_0's auc: 0.813851\tvalid_0's binary_logloss: 0.4645\n",
- "[67]\tvalid_0's auc: 0.814011\tvalid_0's binary_logloss: 0.463814\n",
- "[68]\tvalid_0's auc: 0.814027\tvalid_0's binary_logloss: 0.463113\n",
- "[69]\tvalid_0's auc: 0.814138\tvalid_0's binary_logloss: 0.462727\n",
- "[70]\tvalid_0's auc: 0.814365\tvalid_0's binary_logloss: 0.462077\n",
- "[71]\tvalid_0's auc: 0.814432\tvalid_0's binary_logloss: 0.461655\n",
- "[72]\tvalid_0's auc: 0.8146\tvalid_0's binary_logloss: 0.461194\n",
- "[73]\tvalid_0's auc: 0.815324\tvalid_0's binary_logloss: 0.460477\n",
- "[74]\tvalid_0's auc: 0.815411\tvalid_0's binary_logloss: 0.459805\n",
- "[75]\tvalid_0's auc: 0.815548\tvalid_0's binary_logloss: 0.459189\n",
- "[76]\tvalid_0's auc: 0.815625\tvalid_0's binary_logloss: 0.458525\n",
- "[77]\tvalid_0's auc: 0.81562\tvalid_0's binary_logloss: 0.457905\n",
- "[78]\tvalid_0's auc: 0.815786\tvalid_0's binary_logloss: 0.45747\n",
- "[79]\tvalid_0's auc: 0.815834\tvalid_0's binary_logloss: 0.456884\n",
- "[80]\tvalid_0's auc: 0.816475\tvalid_0's binary_logloss: 0.45617\n",
- "[81]\tvalid_0's auc: 0.816677\tvalid_0's binary_logloss: 0.455787\n",
- "[82]\tvalid_0's auc: 0.817255\tvalid_0's binary_logloss: 0.455358\n",
- "[83]\tvalid_0's auc: 0.817383\tvalid_0's binary_logloss: 0.454775\n",
- "[84]\tvalid_0's auc: 0.817509\tvalid_0's binary_logloss: 0.454176\n",
- "[85]\tvalid_0's auc: 0.817572\tvalid_0's binary_logloss: 0.453609\n",
- "[86]\tvalid_0's auc: 0.817721\tvalid_0's binary_logloss: 0.453213\n",
- "[87]\tvalid_0's auc: 0.817992\tvalid_0's binary_logloss: 0.452586\n",
- "[88]\tvalid_0's auc: 0.81808\tvalid_0's binary_logloss: 0.45204\n",
- "[89]\tvalid_0's auc: 0.818202\tvalid_0's binary_logloss: 0.451643\n",
- "[90]\tvalid_0's auc: 0.818336\tvalid_0's binary_logloss: 0.451081\n",
- "[91]\tvalid_0's auc: 0.818347\tvalid_0's binary_logloss: 0.450531\n",
- "[92]\tvalid_0's auc: 0.818558\tvalid_0's binary_logloss: 0.450179\n",
- "[93]\tvalid_0's auc: 0.818743\tvalid_0's binary_logloss: 0.449647\n",
- "[94]\tvalid_0's auc: 0.818789\tvalid_0's binary_logloss: 0.449133\n",
- "[95]\tvalid_0's auc: 0.818849\tvalid_0's binary_logloss: 0.44862\n",
- "[96]\tvalid_0's auc: 0.81913\tvalid_0's binary_logloss: 0.448072\n",
- "[97]\tvalid_0's auc: 0.819526\tvalid_0's binary_logloss: 0.447713\n",
- "[98]\tvalid_0's auc: 0.819971\tvalid_0's binary_logloss: 0.447296\n",
- "[99]\tvalid_0's auc: 0.819972\tvalid_0's binary_logloss: 0.446814\n"
- ]
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:22.965433Z",
+ "start_time": "2020-11-18T04:21:17.799127Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型训练\n",
+ "if offline:\n",
+ " lgb_ranker.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'], group=g_train,\n",
+ " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
+ " eval_group= [g_val], eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
+ "else:\n",
+ " lgb_ranker.fit(trn_user_item_feats_df[lgb_cols], trn_user_item_feats_df['label'], group=g_train)"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
- "[1]\tvalid_0's auc: 0.768646\tvalid_0's binary_logloss: 0.527167\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.779902\tvalid_0's binary_logloss: 0.525481\n",
- "[3]\tvalid_0's auc: 0.789868\tvalid_0's binary_logloss: 0.524485\n",
- "[4]\tvalid_0's auc: 0.791895\tvalid_0's binary_logloss: 0.523382\n",
- "[5]\tvalid_0's auc: 0.795453\tvalid_0's binary_logloss: 0.521759\n",
- "[6]\tvalid_0's auc: 0.796672\tvalid_0's binary_logloss: 0.520166\n",
- "[7]\tvalid_0's auc: 0.798023\tvalid_0's binary_logloss: 0.518857\n",
- "[8]\tvalid_0's auc: 0.799331\tvalid_0's binary_logloss: 0.517297\n",
- "[9]\tvalid_0's auc: 0.800181\tvalid_0's binary_logloss: 0.516416\n",
- "[10]\tvalid_0's auc: 0.800373\tvalid_0's binary_logloss: 0.514967\n",
- "[11]\tvalid_0's auc: 0.801087\tvalid_0's binary_logloss: 0.513631\n",
- "[12]\tvalid_0's auc: 0.801122\tvalid_0's binary_logloss: 0.512658\n",
- "[13]\tvalid_0's auc: 0.801043\tvalid_0's binary_logloss: 0.511833\n",
- "[14]\tvalid_0's auc: 0.801238\tvalid_0's binary_logloss: 0.510461\n",
- "[15]\tvalid_0's auc: 0.801847\tvalid_0's binary_logloss: 0.509034\n",
- "[16]\tvalid_0's auc: 0.803139\tvalid_0's binary_logloss: 0.507759\n",
- "[17]\tvalid_0's auc: 0.803577\tvalid_0's binary_logloss: 0.506361\n",
- "[18]\tvalid_0's auc: 0.803834\tvalid_0's binary_logloss: 0.505229\n",
- "[19]\tvalid_0's auc: 0.803943\tvalid_0's binary_logloss: 0.504371\n",
- "[20]\tvalid_0's auc: 0.80415\tvalid_0's binary_logloss: 0.503102\n",
- "[21]\tvalid_0's auc: 0.804446\tvalid_0's binary_logloss: 0.502564\n",
- "[22]\tvalid_0's auc: 0.805163\tvalid_0's binary_logloss: 0.501396\n",
- "[23]\tvalid_0's auc: 0.805323\tvalid_0's binary_logloss: 0.500327\n",
- "[24]\tvalid_0's auc: 0.805314\tvalid_0's binary_logloss: 0.499123\n",
- "[25]\tvalid_0's auc: 0.80535\tvalid_0's binary_logloss: 0.497927\n",
- "[26]\tvalid_0's auc: 0.805864\tvalid_0's binary_logloss: 0.496834\n",
- "[27]\tvalid_0's auc: 0.805919\tvalid_0's binary_logloss: 0.495667\n",
- "[28]\tvalid_0's auc: 0.806272\tvalid_0's binary_logloss: 0.494606\n",
- "[29]\tvalid_0's auc: 0.806599\tvalid_0's binary_logloss: 0.49343\n",
- "[30]\tvalid_0's auc: 0.806932\tvalid_0's binary_logloss: 0.492303\n",
- "[31]\tvalid_0's auc: 0.806656\tvalid_0's binary_logloss: 0.491249\n",
- "[32]\tvalid_0's auc: 0.807436\tvalid_0's binary_logloss: 0.490188\n",
- "[33]\tvalid_0's auc: 0.807629\tvalid_0's binary_logloss: 0.489117\n",
- "[34]\tvalid_0's auc: 0.807501\tvalid_0's binary_logloss: 0.48808\n",
- "[35]\tvalid_0's auc: 0.807885\tvalid_0's binary_logloss: 0.487383\n",
- "[36]\tvalid_0's auc: 0.807921\tvalid_0's binary_logloss: 0.48636\n",
- "[37]\tvalid_0's auc: 0.808267\tvalid_0's binary_logloss: 0.485724\n",
- "[38]\tvalid_0's auc: 0.808563\tvalid_0's binary_logloss: 0.485076\n",
- "[39]\tvalid_0's auc: 0.808813\tvalid_0's binary_logloss: 0.484039\n",
- "[40]\tvalid_0's auc: 0.809023\tvalid_0's binary_logloss: 0.483091\n",
- "[41]\tvalid_0's auc: 0.809782\tvalid_0's binary_logloss: 0.482441\n",
- "[42]\tvalid_0's auc: 0.810135\tvalid_0's binary_logloss: 0.48179\n",
- "[43]\tvalid_0's auc: 0.810219\tvalid_0's binary_logloss: 0.48082\n",
- "[44]\tvalid_0's auc: 0.81031\tvalid_0's binary_logloss: 0.479906\n",
- "[45]\tvalid_0's auc: 0.810514\tvalid_0's binary_logloss: 0.479024\n",
- "[46]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.478437\n",
- "[47]\tvalid_0's auc: 0.810611\tvalid_0's binary_logloss: 0.477529\n",
- "[48]\tvalid_0's auc: 0.810781\tvalid_0's binary_logloss: 0.476637\n",
- "[49]\tvalid_0's auc: 0.81089\tvalid_0's binary_logloss: 0.475883\n",
- "[50]\tvalid_0's auc: 0.811266\tvalid_0's binary_logloss: 0.475459\n",
- "[51]\tvalid_0's auc: 0.811402\tvalid_0's binary_logloss: 0.475078\n",
- "[52]\tvalid_0's auc: 0.811765\tvalid_0's binary_logloss: 0.474246\n",
- "[53]\tvalid_0's auc: 0.811891\tvalid_0's binary_logloss: 0.473452\n",
- "[54]\tvalid_0's auc: 0.811868\tvalid_0's binary_logloss: 0.47263\n",
- "[55]\tvalid_0's auc: 0.81192\tvalid_0's binary_logloss: 0.471804\n",
- "[56]\tvalid_0's auc: 0.812272\tvalid_0's binary_logloss: 0.471275\n",
- "[57]\tvalid_0's auc: 0.812639\tvalid_0's binary_logloss: 0.470396\n",
- "[58]\tvalid_0's auc: 0.812764\tvalid_0's binary_logloss: 0.469597\n",
- "[59]\tvalid_0's auc: 0.813084\tvalid_0's binary_logloss: 0.469049\n",
- "[60]\tvalid_0's auc: 0.813342\tvalid_0's binary_logloss: 0.468244\n",
- "[61]\tvalid_0's auc: 0.813302\tvalid_0's binary_logloss: 0.467499\n",
- "[62]\tvalid_0's auc: 0.813221\tvalid_0's binary_logloss: 0.466758\n",
- "[63]\tvalid_0's auc: 0.813697\tvalid_0's binary_logloss: 0.466017\n",
- "[64]\tvalid_0's auc: 0.813985\tvalid_0's binary_logloss: 0.465501\n",
- "[65]\tvalid_0's auc: 0.81416\tvalid_0's binary_logloss: 0.464725\n",
- "[66]\tvalid_0's auc: 0.814227\tvalid_0's binary_logloss: 0.46398\n",
- "[67]\tvalid_0's auc: 0.814397\tvalid_0's binary_logloss: 0.463309\n",
- "[68]\tvalid_0's auc: 0.814426\tvalid_0's binary_logloss: 0.462627\n",
- "[69]\tvalid_0's auc: 0.814593\tvalid_0's binary_logloss: 0.462244\n",
- "[70]\tvalid_0's auc: 0.814789\tvalid_0's binary_logloss: 0.461571\n",
- "[71]\tvalid_0's auc: 0.814889\tvalid_0's binary_logloss: 0.461144\n",
- "[72]\tvalid_0's auc: 0.815078\tvalid_0's binary_logloss: 0.460684\n",
- "[73]\tvalid_0's auc: 0.815439\tvalid_0's binary_logloss: 0.460063\n",
- "[74]\tvalid_0's auc: 0.815511\tvalid_0's binary_logloss: 0.459386\n",
- "[75]\tvalid_0's auc: 0.815574\tvalid_0's binary_logloss: 0.45877\n",
- "[76]\tvalid_0's auc: 0.815634\tvalid_0's binary_logloss: 0.458128\n",
- "[77]\tvalid_0's auc: 0.815618\tvalid_0's binary_logloss: 0.457495\n",
- "[78]\tvalid_0's auc: 0.81582\tvalid_0's binary_logloss: 0.457057\n",
- "[79]\tvalid_0's auc: 0.81594\tvalid_0's binary_logloss: 0.456475\n",
- "[80]\tvalid_0's auc: 0.815961\tvalid_0's binary_logloss: 0.455885\n",
- "[81]\tvalid_0's auc: 0.816153\tvalid_0's binary_logloss: 0.455511\n",
- "[82]\tvalid_0's auc: 0.816433\tvalid_0's binary_logloss: 0.455186\n",
- "[83]\tvalid_0's auc: 0.816546\tvalid_0's binary_logloss: 0.454625\n",
- "[84]\tvalid_0's auc: 0.816586\tvalid_0's binary_logloss: 0.454039\n",
- "[85]\tvalid_0's auc: 0.816584\tvalid_0's binary_logloss: 0.453482\n",
- "[86]\tvalid_0's auc: 0.816881\tvalid_0's binary_logloss: 0.453048\n",
- "[87]\tvalid_0's auc: 0.817029\tvalid_0's binary_logloss: 0.452485\n",
- "[88]\tvalid_0's auc: 0.81707\tvalid_0's binary_logloss: 0.451941\n",
- "[89]\tvalid_0's auc: 0.817298\tvalid_0's binary_logloss: 0.451544\n",
- "[90]\tvalid_0's auc: 0.817343\tvalid_0's binary_logloss: 0.450975\n",
- "[91]\tvalid_0's auc: 0.817357\tvalid_0's binary_logloss: 0.450422\n",
- "[92]\tvalid_0's auc: 0.817592\tvalid_0's binary_logloss: 0.450109\n",
- "[93]\tvalid_0's auc: 0.817729\tvalid_0's binary_logloss: 0.449542\n",
- "[94]\tvalid_0's auc: 0.817834\tvalid_0's binary_logloss: 0.448982\n",
- "[95]\tvalid_0's auc: 0.81809\tvalid_0's binary_logloss: 0.448398\n",
- "[96]\tvalid_0's auc: 0.818269\tvalid_0's binary_logloss: 0.447908\n",
- "[97]\tvalid_0's auc: 0.818682\tvalid_0's binary_logloss: 0.447547\n",
- "[98]\tvalid_0's auc: 0.819015\tvalid_0's binary_logloss: 0.447165\n",
- "[99]\tvalid_0's auc: 0.819016\tvalid_0's binary_logloss: 0.446669\n",
- "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_rank_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 模型及参数的定义\n",
- " lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) \n",
- " # 训练模型\n",
- " lgb_Classfication.fit(train_idx[lgb_cols], train_idx['label'],eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], \n",
- " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = lgb_Classfication.predict_proba(valid_idx[lgb_cols], \n",
- " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
- " \n",
- " # 对输出结果进行归一化 分类模型输出的值本身就是一个概率值不需要进行归一化\n",
- " # valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += lgb_Classfication.predict_proba(tst_user_item_feats_df_rank_model[lgb_cols], \n",
- " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_cls_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_cls_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:23.074237Z",
- "start_time": "2020-11-18T04:24:13.812284Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_cls')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## DIN模型"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户的历史点击行为列表\n",
- "这个是为后面的DIN模型服务的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:30.508213Z",
- "start_time": "2020-11-18T04:24:27.426372Z"
- }
- },
- "outputs": [],
- "source": [
- "if offline:\n",
- " all_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
- "else:\n",
- " trn_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
- " tst_data = pd.read_csv('./data_raw/testA_click_log.csv')\n",
- " all_data = trn_data.append(tst_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:25:28.082071Z",
- "start_time": "2020-11-18T04:24:33.649524Z"
- }
- },
- "outputs": [],
- "source": [
- "hist_click =all_data[['user_id', 'click_article_id']].groupby('user_id').agg({list}).reset_index()\n",
- "his_behavior_df = pd.DataFrame()\n",
- "his_behavior_df['user_id'] = hist_click['user_id']\n",
- "his_behavior_df['hist_click_article_id'] = hist_click['click_article_id']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:25:52.925866Z",
- "start_time": "2020-11-18T04:25:52.863922Z"
- }
- },
- "outputs": [],
- "source": [
- "trn_user_item_feats_df_din_model = trn_user_item_feats_df.copy()\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_din_model = val_user_item_feats_df.copy()\n",
- "else: \n",
- " val_user_item_feats_df_din_model = None\n",
- " \n",
- "tst_user_item_feats_df_din_model = tst_user_item_feats_df.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:00.070681Z",
- "start_time": "2020-11-18T04:25:56.417197Z"
- }
- },
- "outputs": [],
- "source": [
- "trn_user_item_feats_df_din_model = trn_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_din_model = val_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
- "else:\n",
- " val_user_item_feats_df_din_model = None\n",
- "\n",
- "tst_user_item_feats_df_din_model = tst_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### DIN模型简介\n",
- "我们下面尝试使用DIN模型， DIN的全称是Deep Interest Network， 这是阿里2018年基于前面的深度学习模型无法表达用户多样化的兴趣而提出的一个模型， 它可以通过考虑【给定的候选广告】和【用户的历史行为】的相关性，来计算用户兴趣的表示向量。具体来说就是通过引入局部激活单元，通过软搜索历史行为的相关部分来关注相关的用户兴趣，并采用加权和来获得有关候选广告的用户兴趣的表示。与候选广告相关性较高的行为会获得较高的激活权重，并支配着用户兴趣。该表示向量在不同广告上有所不同，大大提高了模型的表达能力。所以该模型对于此次新闻推荐的任务也比较适合， 我们在这里通过当前的候选文章与用户历史点击文章的相关性来计算用户对于文章的兴趣。 该模型的结构如下：\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "我们这里直接调包来使用这个模型， 关于这个模型的详细细节部分我们会在下一期的推荐系统组队学习中给出。下面说一下该模型如何具体使用：deepctr的函数原型如下：\n",
- "> def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,\n",
- "> dnn_hidden_units=(200, 80), dnn_activation='relu', att_hidden_size=(80, 40), att_activation=\"dice\",\n",
- "> att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024,\n",
- "> task='binary'):\n",
- "> \n",
- "> * dnn_feature_columns: 特征列， 包含数据所有特征的列表\n",
- "> * history_feature_list: 用户历史行为列， 反应用户历史行为的特征的列表\n",
- "> * dnn_use_bn: 是否使用BatchNormalization\n",
- "> * dnn_hidden_units: 全连接层网络的层数和每一层神经元的个数， 一个列表或者元组\n",
- "> * dnn_activation_relu: 全连接网络的激活单元类型\n",
- "> * att_hidden_size: 注意力层的全连接网络的层数和每一层神经元的个数\n",
- "> * att_activation: 注意力层的激活单元类型\n",
- "> * att_weight_normalization: 是否归一化注意力得分\n",
- "> * l2_reg_dnn: 全连接网络的正则化系数\n",
- "> * l2_reg_embedding: embedding向量的正则化稀疏\n",
- "> * dnn_dropout: 全连接网络的神经元的失活概率\n",
- "> * task: 任务， 可以是分类， 也可是是回归\n",
- "\n",
- "在具体使用的时候， 我们必须要传入特征列和历史行为列， 但是再传入之前， 我们需要进行一下特征列的预处理。具体如下：\n",
- "\n",
- "1. 首先，我们要处理数据集， 得到数据， 由于我们是基于用户过去的行为去预测用户是否点击当前文章， 所以我们需要把数据的特征列划分成数值型特征， 离散型特征和历史行为特征列三部分， 对于每一部分， DIN模型的处理会有不同\n",
- " 1. 对于离散型特征， 在我们的数据集中就是那些类别型的特征， 比如user_id这种， 这种类别型特征， 我们首先要经过embedding处理得到每个特征的低维稠密型表示， 既然要经过embedding， 那么我们就需要为每一列的类别特征的取值建立一个字典，并指明embedding维度， 所以在使用deepctr的DIN模型准备数据的时候， 我们需要通过SparseFeat函数指明这些类别型特征, 这个函数的传入参数就是列名， 列的唯一取值(建立字典用)和embedding维度。\n",
- " 2. 对于用户历史行为特征列， 比如文章id， 文章的类别等这种， 同样的我们需要先经过embedding处理， 只不过和上面不一样的地方是，对于这种特征， 我们在得到每个特征的embedding表示之后， 还需要通过一个Attention_layer计算用户的历史行为和当前候选文章的相关性以此得到当前用户的embedding向量， 这个向量就可以基于当前的候选文章与用户过去点击过得历史文章的相似性的程度来反应用户的兴趣， 并且随着用户的不同的历史点击来变化，去动态的模拟用户兴趣的变化过程。这类特征对于每个用户都是一个历史行为序列， 对于每个用户， 历史行为序列长度会不一样， 可能有的用户点击的历史文章多，有的点击的历史文章少， 所以我们还需要把这个长度统一起来， 在为DIN模型准备数据的时候， 我们首先要通过SparseFeat函数指明这些类别型特征， 然后还需要通过VarLenSparseFeat函数再进行序列填充， 使得每个用户的历史序列一样长， 所以这个函数参数中会有个maxlen，来指明序列的最大长度是多少。\n",
- " 3. 对于连续型特征列， 我们只需要用DenseFeat函数来指明列名和维度即可。\n",
- "2. 处理完特征列之后， 我们把相应的数据与列进行对应，就得到了最后的数据。\n",
- "\n",
- "下面根据具体的代码感受一下， 逻辑是这样， 首先我们需要写一个数据准备函数， 在这里面就是根据上面的具体步骤准备数据， 得到数据和特征列， 然后就是建立DIN模型并训练， 最后基于模型进行测试。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:08.405211Z",
- "start_time": "2020-11-18T04:26:04.887013Z"
- }
- },
- "outputs": [],
- "source": [
- "# 导入deepctr\n",
- "from deepctr.models import DIN\n",
- "from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, get_feature_names\n",
- "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
- "\n",
- "from tensorflow.keras import backend as K\n",
- "from tensorflow.keras.layers import *\n",
- "from tensorflow.keras.models import *\n",
- "from tensorflow.keras.callbacks import * \n",
- "import tensorflow as tf\n",
- "\n",
- "import os\n",
- "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
- "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:13.485712Z",
- "start_time": "2020-11-18T04:26:13.476042Z"
- }
- },
- "outputs": [],
- "source": [
- "# 数据准备函数\n",
- "def get_din_feats_columns(df, dense_fea, sparse_fea, behavior_fea, his_behavior_fea, emb_dim=32, max_len=100):\n",
- " \"\"\"\n",
- " 数据准备函数:\n",
- " df: 数据集\n",
- " dense_fea: 数值型特征列\n",
- " sparse_fea: 离散型特征列\n",
- " behavior_fea: 用户的候选行为特征列\n",
- " his_behavior_fea: 用户的历史行为特征列\n",
- " embedding_dim: embedding的维度， 这里为了简单， 统一把离散型特征列采用一样的隐向量维度\n",
- " max_len: 用户序列的最大长度\n",
- " \"\"\"\n",
- " \n",
- " sparse_feature_columns = [SparseFeat(feat, vocabulary_size=df[feat].nunique() + 1, embedding_dim=emb_dim) for feat in sparse_fea]\n",
- " \n",
- " dense_feature_columns = [DenseFeat(feat, 1, ) for feat in dense_fea]\n",
- " \n",
- " var_feature_columns = [VarLenSparseFeat(SparseFeat(feat, vocabulary_size=df['click_article_id'].nunique() + 1,\n",
- " embedding_dim=emb_dim, embedding_name='click_article_id'), maxlen=max_len) for feat in hist_behavior_fea]\n",
- " \n",
- " dnn_feature_columns = sparse_feature_columns + dense_feature_columns + var_feature_columns\n",
- " \n",
- " # 建立x, x是一个字典的形式\n",
- " x = {}\n",
- " for name in get_feature_names(dnn_feature_columns):\n",
- " if name in his_behavior_fea:\n",
- " # 这是历史行为序列\n",
- " his_list = [l for l in df[name]]\n",
- " x[name] = pad_sequences(his_list, maxlen=max_len, padding='post') # 二维数组\n",
- " else:\n",
- " x[name] = df[name].values\n",
- " \n",
- " return x, dnn_feature_columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:18.783217Z",
- "start_time": "2020-11-18T04:26:18.776795Z"
- }
- },
- "outputs": [],
- "source": [
- "# 把特征分开\n",
- "sparse_fea = ['user_id', 'click_article_id', 'category_id', 'click_environment', 'click_deviceGroup', \n",
- " 'click_os', 'click_country', 'click_region', 'click_referrer_type', 'is_cat_hab']\n",
- "\n",
- "behavior_fea = ['click_article_id']\n",
- "\n",
- "hist_behavior_fea = ['hist_click_article_id']\n",
- "\n",
- "dense_fea = ['sim0', 'time_diff0', 'word_diff0', 'sim_max', 'sim_min', 'sim_sum', 'sim_mean', 'score',\n",
- " 'rank','click_size','time_diff_mean','active_level','user_time_hob1','user_time_hob2',\n",
- " 'words_hbo','words_count']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:25.469810Z",
- "start_time": "2020-11-18T04:26:24.779347Z"
- }
- },
- "outputs": [],
- "source": [
- "# dense特征进行归一化, 神经网络训练都需要将数值进行归一化处理\n",
- "mm = MinMaxScaler()\n",
- "\n",
- "# 下面是做一些特殊处理，当在其他的地方出现无效值的时候，不处理无法进行归一化，刚开始可以先把他注释掉，在运行了下面的代码\n",
- "# 之后如果发现报错，应该先去想办法处理如何不出现inf之类的值\n",
- "# trn_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
- "# tst_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
- "\n",
- "for feat in dense_fea:\n",
- " trn_user_item_feats_df_din_model[feat] = mm.fit_transform(trn_user_item_feats_df_din_model[[feat]])\n",
- " \n",
- " if val_user_item_feats_df_din_model is not None:\n",
- " val_user_item_feats_df_din_model[feat] = mm.fit_transform(val_user_item_feats_df_din_model[[feat]])\n",
- " \n",
- " tst_user_item_feats_df_din_model[feat] = mm.fit_transform(tst_user_item_feats_df_din_model[[feat]])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:36.727753Z",
- "start_time": "2020-11-18T04:26:28.854705Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:28.616665Z",
+ "start_time": "2020-11-18T04:21:24.672280Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df['pred_score'] = lgb_ranker.predict(tst_user_item_feats_df[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
+ "\n",
+ "# 将这里的排序结果保存一份，用户后面的模型融合\n",
+ "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_ranker_score.csv', index=False)"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
- ]
- }
- ],
- "source": [
- "# 准备训练数据\n",
- "x_trn, dnn_feature_columns = get_din_feats_columns(trn_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- "y_trn = trn_user_item_feats_df_din_model['label'].values\n",
- "\n",
- "if offline:\n",
- " # 准备验证数据\n",
- " x_val, dnn_feature_columns = get_din_feats_columns(val_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_val = val_user_item_feats_df_din_model['label'].values\n",
- " \n",
- "dense_fea = [x for x in dense_fea if x != 'label']\n",
- "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:45.146318Z",
- "start_time": "2020-11-18T04:26:40.423914Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:40.253692Z",
+ "start_time": "2020-11-18T04:21:30.546587Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_ranker')"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:255: add_dispatch_support.
.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
- "Model: \"model\"\n",
- "__________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- "user_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_article_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "category_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_environment (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_deviceGroup (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_os (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_country (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_region (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_referrer_type (InputLayer [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "is_cat_hab (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_user_id (Embedding) (None, 1, 32) 1600032 user_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_seq_emb_hist_click_artic multiple 525664 click_article_id[0][0] \n",
- " hist_click_article_id[0][0] \n",
- " click_article_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_category_id (Embeddi (None, 1, 32) 7776 category_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_environment (E (None, 1, 32) 128 click_environment[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_deviceGroup (E (None, 1, 32) 160 click_deviceGroup[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_os (Embedding) (None, 1, 32) 288 click_os[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_country (Embed (None, 1, 32) 384 click_country[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_region (Embedd (None, 1, 32) 928 click_region[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_referrer_type (None, 1, 32) 256 click_referrer_type[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_is_cat_hab (Embeddin (None, 1, 32) 64 is_cat_hab[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask (NoMask) (None, 1, 32) 0 sparse_emb_user_id[0][0] \n",
- " sparse_seq_emb_hist_click_article\n",
- " sparse_emb_category_id[0][0] \n",
- " sparse_emb_click_environment[0][0\n",
- " sparse_emb_click_deviceGroup[0][0\n",
- " sparse_emb_click_os[0][0] \n",
- " sparse_emb_click_country[0][0] \n",
- " sparse_emb_click_region[0][0] \n",
- " sparse_emb_click_referrer_type[0]\n",
- " sparse_emb_is_cat_hab[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "hist_click_article_id (InputLay [(None, 50)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "concatenate (Concatenate) (None, 1, 320) 0 no_mask[0][0] \n",
- " no_mask[1][0] \n",
- " no_mask[2][0] \n",
- " no_mask[3][0] \n",
- " no_mask[4][0] \n",
- " no_mask[5][0] \n",
- " no_mask[6][0] \n",
- " no_mask[7][0] \n",
- " no_mask[8][0] \n",
- " no_mask[9][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_1 (NoMask) (None, 1, 320) 0 concatenate[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "attention_sequence_pooling_laye (None, 1, 32) 13961 sparse_seq_emb_hist_click_article\n",
- " sparse_seq_emb_hist_click_article\n",
- "__________________________________________________________________________________________________\n",
- "concatenate_1 (Concatenate) (None, 1, 352) 0 no_mask_1[0][0] \n",
- " attention_sequence_pooling_layer[\n",
- "__________________________________________________________________________________________________\n",
- "sim0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "time_diff0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "word_diff0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_max (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_min (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_sum (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_mean (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "score (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "rank (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_size (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "time_diff_mean (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "active_level (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "user_time_hob1 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "user_time_hob2 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "words_hbo (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "words_count (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "flatten (Flatten) (None, 352) 0 concatenate_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_3 (NoMask) (None, 1) 0 sim0[0][0] \n",
- " time_diff0[0][0] \n",
- " word_diff0[0][0] \n",
- " sim_max[0][0] \n",
- " sim_min[0][0] \n",
- " sim_sum[0][0] \n",
- " sim_mean[0][0] \n",
- " score[0][0] \n",
- " rank[0][0] \n",
- " click_size[0][0] \n",
- " time_diff_mean[0][0] \n",
- " active_level[0][0] \n",
- " user_time_hob1[0][0] \n",
- " user_time_hob2[0][0] \n",
- " words_hbo[0][0] \n",
- " words_count[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_2 (NoMask) (None, 352) 0 flatten[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "concatenate_2 (Concatenate) (None, 16) 0 no_mask_3[0][0] \n",
- " no_mask_3[1][0] \n",
- " no_mask_3[2][0] \n",
- " no_mask_3[3][0] \n",
- " no_mask_3[4][0] \n",
- " no_mask_3[5][0] \n",
- " no_mask_3[6][0] \n",
- " no_mask_3[7][0] \n",
- " no_mask_3[8][0] \n",
- " no_mask_3[9][0] \n",
- " no_mask_3[10][0] \n",
- " no_mask_3[11][0] \n",
- " no_mask_3[12][0] \n",
- " no_mask_3[13][0] \n",
- " no_mask_3[14][0] \n",
- " no_mask_3[15][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 352) 0 no_mask_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten_2 (Flatten) (None, 16) 0 concatenate_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_4 (NoMask) multiple 0 flatten_1[0][0] \n",
- " flatten_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "concatenate_3 (Concatenate) (None, 368) 0 no_mask_4[0][0] \n",
- " no_mask_4[1][0] \n",
- "__________________________________________________________________________________________________\n",
- "dnn_1 (DNN) (None, 80) 89880 concatenate_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense (Dense) (None, 1) 80 dnn_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "prediction_layer (PredictionLay (None, 1) 1 dense[0][0] \n",
- "==================================================================================================\n",
- "Total params: 2,239,602\n",
- "Trainable params: 2,239,362\n",
- "Non-trainable params: 240\n",
- "__________________________________________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "# 建立模型\n",
- "model = DIN(dnn_feature_columns, behavior_fea)\n",
- "\n",
- "# 查看模型结构\n",
- "model.summary()\n",
- "\n",
- "# 模型编译\n",
- "model.compile('adam', 'binary_crossentropy',metrics=['binary_crossentropy', tf.keras.metrics.AUC()])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:28:43.885773Z",
- "start_time": "2020-11-18T04:26:48.746787Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:26.195838Z",
+ "start_time": "2020-11-18T04:21:46.115002Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1]\tvalid_0's ndcg@1: 0.909975\tvalid_0's ndcg@2: 0.963068\tvalid_0's ndcg@3: 0.96533\tvalid_0's ndcg@4: 0.965729\tvalid_0's ndcg@5: 0.965864\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9143\tvalid_0's ndcg@2: 0.964711\tvalid_0's ndcg@3: 0.966961\tvalid_0's ndcg@4: 0.967338\tvalid_0's ndcg@5: 0.967483\n",
+ "[3]\tvalid_0's ndcg@1: 0.9181\tvalid_0's ndcg@2: 0.966114\tvalid_0's ndcg@3: 0.968289\tvalid_0's ndcg@4: 0.968773\tvalid_0's ndcg@5: 0.96887\n",
+ "[4]\tvalid_0's ndcg@1: 0.925575\tvalid_0's ndcg@2: 0.969093\tvalid_0's ndcg@3: 0.971193\tvalid_0's ndcg@4: 0.971603\tvalid_0's ndcg@5: 0.97169\n",
+ "[5]\tvalid_0's ndcg@1: 0.9267\tvalid_0's ndcg@2: 0.969635\tvalid_0's ndcg@3: 0.97166\tvalid_0's ndcg@4: 0.972037\tvalid_0's ndcg@5: 0.972133\n",
+ "[6]\tvalid_0's ndcg@1: 0.927\tvalid_0's ndcg@2: 0.969682\tvalid_0's ndcg@3: 0.971757\tvalid_0's ndcg@4: 0.972134\tvalid_0's ndcg@5: 0.972231\n",
+ "[7]\tvalid_0's ndcg@1: 0.928825\tvalid_0's ndcg@2: 0.970451\tvalid_0's ndcg@3: 0.972476\tvalid_0's ndcg@4: 0.97282\tvalid_0's ndcg@5: 0.972927\n",
+ "[8]\tvalid_0's ndcg@1: 0.930025\tvalid_0's ndcg@2: 0.970988\tvalid_0's ndcg@3: 0.972951\tvalid_0's ndcg@4: 0.973295\tvalid_0's ndcg@5: 0.973402\n",
+ "[9]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971347\tvalid_0's ndcg@3: 0.973384\tvalid_0's ndcg@4: 0.973707\tvalid_0's ndcg@5: 0.973794\n",
+ "[10]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.971385\tvalid_0's ndcg@3: 0.973372\tvalid_0's ndcg@4: 0.973717\tvalid_0's ndcg@5: 0.973794\n",
+ "[11]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.971433\tvalid_0's ndcg@3: 0.973333\tvalid_0's ndcg@4: 0.973699\tvalid_0's ndcg@5: 0.973767\n",
+ "[12]\tvalid_0's ndcg@1: 0.93145\tvalid_0's ndcg@2: 0.971656\tvalid_0's ndcg@3: 0.973493\tvalid_0's ndcg@4: 0.973881\tvalid_0's ndcg@5: 0.973949\n",
+ "[13]\tvalid_0's ndcg@1: 0.932525\tvalid_0's ndcg@2: 0.971927\tvalid_0's ndcg@3: 0.973839\tvalid_0's ndcg@4: 0.974227\tvalid_0's ndcg@5: 0.974304\n",
+ "[14]\tvalid_0's ndcg@1: 0.932575\tvalid_0's ndcg@2: 0.971898\tvalid_0's ndcg@3: 0.973823\tvalid_0's ndcg@4: 0.974243\tvalid_0's ndcg@5: 0.97432\n",
+ "[15]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972239\tvalid_0's ndcg@3: 0.974189\tvalid_0's ndcg@4: 0.974587\tvalid_0's ndcg@5: 0.974665\n",
+ "[16]\tvalid_0's ndcg@1: 0.933475\tvalid_0's ndcg@2: 0.972309\tvalid_0's ndcg@3: 0.974209\tvalid_0's ndcg@4: 0.974596\tvalid_0's ndcg@5: 0.974674\n",
+ "[17]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972369\tvalid_0's ndcg@3: 0.974307\tvalid_0's ndcg@4: 0.974684\tvalid_0's ndcg@5: 0.974761\n",
+ "[18]\tvalid_0's ndcg@1: 0.9339\tvalid_0's ndcg@2: 0.972497\tvalid_0's ndcg@3: 0.974372\tvalid_0's ndcg@4: 0.974749\tvalid_0's ndcg@5: 0.974836\n",
+ "[19]\tvalid_0's ndcg@1: 0.9345\tvalid_0's ndcg@2: 0.972845\tvalid_0's ndcg@3: 0.974645\tvalid_0's ndcg@4: 0.974979\tvalid_0's ndcg@5: 0.975085\n",
+ "[20]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.97484\tvalid_0's ndcg@4: 0.975174\tvalid_0's ndcg@5: 0.975271\n",
+ "[21]\tvalid_0's ndcg@1: 0.935\tvalid_0's ndcg@2: 0.973092\tvalid_0's ndcg@3: 0.97488\tvalid_0's ndcg@4: 0.975192\tvalid_0's ndcg@5: 0.975289\n",
+ "[22]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.974988\tvalid_0's ndcg@4: 0.975289\tvalid_0's ndcg@5: 0.975386\n",
+ "[23]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974824\tvalid_0's ndcg@4: 0.975136\tvalid_0's ndcg@5: 0.975223\n",
+ "[24]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973274\tvalid_0's ndcg@3: 0.975087\tvalid_0's ndcg@4: 0.975388\tvalid_0's ndcg@5: 0.975475\n",
+ "[25]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973345\tvalid_0's ndcg@3: 0.97512\tvalid_0's ndcg@4: 0.975443\tvalid_0's ndcg@5: 0.97553\n",
+ "[26]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.975\tvalid_0's ndcg@4: 0.975313\tvalid_0's ndcg@5: 0.9754\n",
+ "[27]\tvalid_0's ndcg@1: 0.935175\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.974983\tvalid_0's ndcg@4: 0.975295\tvalid_0's ndcg@5: 0.975382\n",
+ "[28]\tvalid_0's ndcg@1: 0.935425\tvalid_0's ndcg@2: 0.973328\tvalid_0's ndcg@3: 0.975041\tvalid_0's ndcg@4: 0.975374\tvalid_0's ndcg@5: 0.975471\n",
+ "[29]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973225\tvalid_0's ndcg@3: 0.974963\tvalid_0's ndcg@4: 0.975297\tvalid_0's ndcg@5: 0.975403\n",
+ "[30]\tvalid_0's ndcg@1: 0.9353\tvalid_0's ndcg@2: 0.973235\tvalid_0's ndcg@3: 0.97501\tvalid_0's ndcg@4: 0.975311\tvalid_0's ndcg@5: 0.975418\n",
+ "[31]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973361\tvalid_0's ndcg@3: 0.975099\tvalid_0's ndcg@4: 0.975422\tvalid_0's ndcg@5: 0.975528\n",
+ "[32]\tvalid_0's ndcg@1: 0.9364\tvalid_0's ndcg@2: 0.973641\tvalid_0's ndcg@3: 0.975391\tvalid_0's ndcg@4: 0.975714\tvalid_0's ndcg@5: 0.97582\n",
+ "[33]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973751\tvalid_0's ndcg@3: 0.975501\tvalid_0's ndcg@4: 0.975824\tvalid_0's ndcg@5: 0.975931\n",
+ "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.973902\tvalid_0's ndcg@3: 0.975677\tvalid_0's ndcg@4: 0.975989\tvalid_0's ndcg@5: 0.976095\n",
+ "[35]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974105\tvalid_0's ndcg@3: 0.975892\tvalid_0's ndcg@4: 0.976194\tvalid_0's ndcg@5: 0.9763\n",
+ "[36]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974184\tvalid_0's ndcg@3: 0.975984\tvalid_0's ndcg@4: 0.976296\tvalid_0's ndcg@5: 0.976402\n",
+ "[37]\tvalid_0's ndcg@1: 0.93845\tvalid_0's ndcg@2: 0.974366\tvalid_0's ndcg@3: 0.976166\tvalid_0's ndcg@4: 0.976467\tvalid_0's ndcg@5: 0.976574\n",
+ "[38]\tvalid_0's ndcg@1: 0.938925\tvalid_0's ndcg@2: 0.974557\tvalid_0's ndcg@3: 0.976332\tvalid_0's ndcg@4: 0.976655\tvalid_0's ndcg@5: 0.976751\n",
+ "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974471\tvalid_0's ndcg@3: 0.976234\tvalid_0's ndcg@4: 0.976557\tvalid_0's ndcg@5: 0.976653\n",
+ "[40]\tvalid_0's ndcg@1: 0.938325\tvalid_0's ndcg@2: 0.974335\tvalid_0's ndcg@3: 0.97611\tvalid_0's ndcg@4: 0.976433\tvalid_0's ndcg@5: 0.97653\n",
+ "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.974669\tvalid_0's ndcg@3: 0.976431\tvalid_0's ndcg@4: 0.976743\tvalid_0's ndcg@5: 0.97683\n",
+ "[42]\tvalid_0's ndcg@1: 0.939375\tvalid_0's ndcg@2: 0.974833\tvalid_0's ndcg@3: 0.976546\tvalid_0's ndcg@4: 0.976858\tvalid_0's ndcg@5: 0.976945\n",
+ "[43]\tvalid_0's ndcg@1: 0.939625\tvalid_0's ndcg@2: 0.974878\tvalid_0's ndcg@3: 0.976628\tvalid_0's ndcg@4: 0.97694\tvalid_0's ndcg@5: 0.977027\n",
+ "[44]\tvalid_0's ndcg@1: 0.9395\tvalid_0's ndcg@2: 0.974832\tvalid_0's ndcg@3: 0.97657\tvalid_0's ndcg@4: 0.976893\tvalid_0's ndcg@5: 0.97698\n",
+ "[45]\tvalid_0's ndcg@1: 0.939775\tvalid_0's ndcg@2: 0.974949\tvalid_0's ndcg@3: 0.976674\tvalid_0's ndcg@4: 0.976997\tvalid_0's ndcg@5: 0.977084\n",
+ "[46]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.974945\tvalid_0's ndcg@3: 0.976708\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977107\n",
+ "[47]\tvalid_0's ndcg@1: 0.94005\tvalid_0's ndcg@2: 0.975004\tvalid_0's ndcg@3: 0.976766\tvalid_0's ndcg@4: 0.977078\tvalid_0's ndcg@5: 0.977175\n",
+ "[48]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
+ "[49]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
+ "[50]\tvalid_0's ndcg@1: 0.9405\tvalid_0's ndcg@2: 0.975264\tvalid_0's ndcg@3: 0.976989\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
+ "[51]\tvalid_0's ndcg@1: 0.941125\tvalid_0's ndcg@2: 0.975526\tvalid_0's ndcg@3: 0.977226\tvalid_0's ndcg@4: 0.977528\tvalid_0's ndcg@5: 0.977605\n",
+ "[52]\tvalid_0's ndcg@1: 0.941\tvalid_0's ndcg@2: 0.97548\tvalid_0's ndcg@3: 0.977193\tvalid_0's ndcg@4: 0.977484\tvalid_0's ndcg@5: 0.977561\n",
+ "[53]\tvalid_0's ndcg@1: 0.9411\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.977259\tvalid_0's ndcg@4: 0.977539\tvalid_0's ndcg@5: 0.977616\n",
+ "[54]\tvalid_0's ndcg@1: 0.9412\tvalid_0's ndcg@2: 0.975712\tvalid_0's ndcg@3: 0.977299\tvalid_0's ndcg@4: 0.97759\tvalid_0's ndcg@5: 0.977667\n",
+ "[55]\tvalid_0's ndcg@1: 0.94155\tvalid_0's ndcg@2: 0.975841\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977719\tvalid_0's ndcg@5: 0.977797\n",
+ "[56]\tvalid_0's ndcg@1: 0.941825\tvalid_0's ndcg@2: 0.975943\tvalid_0's ndcg@3: 0.97753\tvalid_0's ndcg@4: 0.977821\tvalid_0's ndcg@5: 0.977898\n",
+ "[57]\tvalid_0's ndcg@1: 0.9416\tvalid_0's ndcg@2: 0.975891\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977741\tvalid_0's ndcg@5: 0.977818\n",
+ "[58]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977494\tvalid_0's ndcg@4: 0.977795\tvalid_0's ndcg@5: 0.977873\n",
+ "[59]\tvalid_0's ndcg@1: 0.942025\tvalid_0's ndcg@2: 0.975985\tvalid_0's ndcg@3: 0.977547\tvalid_0's ndcg@4: 0.977881\tvalid_0's ndcg@5: 0.977958\n",
+ "[60]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975994\tvalid_0's ndcg@3: 0.977569\tvalid_0's ndcg@4: 0.977892\tvalid_0's ndcg@5: 0.977969\n",
+ "[61]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977559\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.97796\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[62]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976027\tvalid_0's ndcg@3: 0.97764\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.978028\n",
+ "[63]\tvalid_0's ndcg@1: 0.942125\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977622\tvalid_0's ndcg@4: 0.977912\tvalid_0's ndcg@5: 0.977999\n",
+ "[64]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977793\tvalid_0's ndcg@4: 0.978105\tvalid_0's ndcg@5: 0.978192\n",
+ "[65]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976227\tvalid_0's ndcg@3: 0.977802\tvalid_0's ndcg@4: 0.978125\tvalid_0's ndcg@5: 0.978212\n",
+ "[66]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976132\tvalid_0's ndcg@3: 0.977695\tvalid_0's ndcg@4: 0.978018\tvalid_0's ndcg@5: 0.978105\n",
+ "[67]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976092\tvalid_0's ndcg@3: 0.977679\tvalid_0's ndcg@4: 0.978002\tvalid_0's ndcg@5: 0.978089\n",
+ "[68]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976148\tvalid_0's ndcg@3: 0.977698\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.978108\n",
+ "[69]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976123\tvalid_0's ndcg@3: 0.977686\tvalid_0's ndcg@4: 0.978009\tvalid_0's ndcg@5: 0.978096\n",
+ "[70]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976222\tvalid_0's ndcg@3: 0.977785\tvalid_0's ndcg@4: 0.978097\tvalid_0's ndcg@5: 0.978184\n",
+ "[71]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976188\tvalid_0's ndcg@3: 0.977763\tvalid_0's ndcg@4: 0.978075\tvalid_0's ndcg@5: 0.978162\n",
+ "[72]\tvalid_0's ndcg@1: 0.9427\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977809\tvalid_0's ndcg@4: 0.978121\tvalid_0's ndcg@5: 0.978208\n",
+ "[73]\tvalid_0's ndcg@1: 0.9428\tvalid_0's ndcg@2: 0.976255\tvalid_0's ndcg@3: 0.977843\tvalid_0's ndcg@4: 0.978155\tvalid_0's ndcg@5: 0.978242\n",
+ "[74]\tvalid_0's ndcg@1: 0.94295\tvalid_0's ndcg@2: 0.97631\tvalid_0's ndcg@3: 0.977898\tvalid_0's ndcg@4: 0.97821\tvalid_0's ndcg@5: 0.978297\n",
+ "[75]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976329\tvalid_0's ndcg@3: 0.977941\tvalid_0's ndcg@4: 0.978232\tvalid_0's ndcg@5: 0.978319\n",
+ "[76]\tvalid_0's ndcg@1: 0.9433\tvalid_0's ndcg@2: 0.976471\tvalid_0's ndcg@3: 0.978059\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978437\n",
+ "[77]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976416\tvalid_0's ndcg@3: 0.977991\tvalid_0's ndcg@4: 0.978314\tvalid_0's ndcg@5: 0.978381\n",
+ "[78]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976657\tvalid_0's ndcg@3: 0.978194\tvalid_0's ndcg@4: 0.978517\tvalid_0's ndcg@5: 0.978585\n",
+ "[79]\tvalid_0's ndcg@1: 0.94365\tvalid_0's ndcg@2: 0.976663\tvalid_0's ndcg@3: 0.978188\tvalid_0's ndcg@4: 0.978501\tvalid_0's ndcg@5: 0.978578\n",
+ "[80]\tvalid_0's ndcg@1: 0.943725\tvalid_0's ndcg@2: 0.976628\tvalid_0's ndcg@3: 0.978203\tvalid_0's ndcg@4: 0.978515\tvalid_0's ndcg@5: 0.978593\n",
+ "[81]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97672\tvalid_0's ndcg@3: 0.978295\tvalid_0's ndcg@4: 0.978607\tvalid_0's ndcg@5: 0.978685\n",
+ "[82]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978397\tvalid_0's ndcg@4: 0.97872\tvalid_0's ndcg@5: 0.978787\n",
+ "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976788\tvalid_0's ndcg@3: 0.978375\tvalid_0's ndcg@4: 0.978698\tvalid_0's ndcg@5: 0.978766\n",
+ "[84]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.97679\tvalid_0's ndcg@3: 0.97839\tvalid_0's ndcg@4: 0.978702\tvalid_0's ndcg@5: 0.97878\n",
+ "[85]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.976809\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978723\tvalid_0's ndcg@5: 0.9788\n",
+ "[86]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976939\tvalid_0's ndcg@3: 0.978502\tvalid_0's ndcg@4: 0.978814\tvalid_0's ndcg@5: 0.978891\n",
+ "[87]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.976976\tvalid_0's ndcg@3: 0.978551\tvalid_0's ndcg@4: 0.978852\tvalid_0's ndcg@5: 0.97893\n",
+ "[88]\tvalid_0's ndcg@1: 0.944925\tvalid_0's ndcg@2: 0.977102\tvalid_0's ndcg@3: 0.978677\tvalid_0's ndcg@4: 0.978968\tvalid_0's ndcg@5: 0.979045\n",
+ "[89]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978758\tvalid_0's ndcg@4: 0.979048\tvalid_0's ndcg@5: 0.979126\n",
+ "[90]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.978735\tvalid_0's ndcg@4: 0.979026\tvalid_0's ndcg@5: 0.979104\n",
+ "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978858\tvalid_0's ndcg@4: 0.979138\tvalid_0's ndcg@5: 0.979215\n",
+ "[92]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.977267\tvalid_0's ndcg@3: 0.978905\tvalid_0's ndcg@4: 0.979174\tvalid_0's ndcg@5: 0.979251\n",
+ "[93]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977193\tvalid_0's ndcg@3: 0.978818\tvalid_0's ndcg@4: 0.979098\tvalid_0's ndcg@5: 0.979176\n",
+ "[94]\tvalid_0's ndcg@1: 0.94545\tvalid_0's ndcg@2: 0.97728\tvalid_0's ndcg@3: 0.97888\tvalid_0's ndcg@4: 0.97916\tvalid_0's ndcg@5: 0.979238\n",
+ "[95]\tvalid_0's ndcg@1: 0.9458\tvalid_0's ndcg@2: 0.977394\tvalid_0's ndcg@3: 0.979006\tvalid_0's ndcg@4: 0.979286\tvalid_0's ndcg@5: 0.979364\n",
+ "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979114\tvalid_0's ndcg@4: 0.979394\tvalid_0's ndcg@5: 0.979472\n",
+ "[97]\tvalid_0's ndcg@1: 0.946475\tvalid_0's ndcg@2: 0.977659\tvalid_0's ndcg@3: 0.979259\tvalid_0's ndcg@4: 0.979539\tvalid_0's ndcg@5: 0.979616\n",
+ "[98]\tvalid_0's ndcg@1: 0.94675\tvalid_0's ndcg@2: 0.97776\tvalid_0's ndcg@3: 0.97936\tvalid_0's ndcg@4: 0.979651\tvalid_0's ndcg@5: 0.979719\n",
+ "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
+ "[100]\tvalid_0's ndcg@1: 0.9468\tvalid_0's ndcg@2: 0.977794\tvalid_0's ndcg@3: 0.979369\tvalid_0's ndcg@4: 0.979671\tvalid_0's ndcg@5: 0.979739\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
+ "[1]\tvalid_0's ndcg@1: 0.909075\tvalid_0's ndcg@2: 0.963019\tvalid_0's ndcg@3: 0.965069\tvalid_0's ndcg@4: 0.965543\tvalid_0's ndcg@5: 0.965601\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9123\tvalid_0's ndcg@2: 0.964273\tvalid_0's ndcg@3: 0.966248\tvalid_0's ndcg@4: 0.966722\tvalid_0's ndcg@5: 0.966789\n",
+ "[3]\tvalid_0's ndcg@1: 0.915075\tvalid_0's ndcg@2: 0.965691\tvalid_0's ndcg@3: 0.967466\tvalid_0's ndcg@4: 0.967854\tvalid_0's ndcg@5: 0.967922\n",
+ "[4]\tvalid_0's ndcg@1: 0.91845\tvalid_0's ndcg@2: 0.967047\tvalid_0's ndcg@3: 0.968735\tvalid_0's ndcg@4: 0.969133\tvalid_0's ndcg@5: 0.969201\n",
+ "[5]\tvalid_0's ndcg@1: 0.92355\tvalid_0's ndcg@2: 0.968961\tvalid_0's ndcg@3: 0.970674\tvalid_0's ndcg@4: 0.97104\tvalid_0's ndcg@5: 0.971098\n",
+ "[6]\tvalid_0's ndcg@1: 0.9253\tvalid_0's ndcg@2: 0.969607\tvalid_0's ndcg@3: 0.971345\tvalid_0's ndcg@4: 0.971689\tvalid_0's ndcg@5: 0.971747\n",
+ "[7]\tvalid_0's ndcg@1: 0.926225\tvalid_0's ndcg@2: 0.969933\tvalid_0's ndcg@3: 0.971708\tvalid_0's ndcg@4: 0.972031\tvalid_0's ndcg@5: 0.972079\n",
+ "[8]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.970104\tvalid_0's ndcg@3: 0.971804\tvalid_0's ndcg@4: 0.972116\tvalid_0's ndcg@5: 0.972184\n",
+ "[9]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970682\tvalid_0's ndcg@3: 0.972307\tvalid_0's ndcg@4: 0.972598\tvalid_0's ndcg@5: 0.972675\n",
+ "[10]\tvalid_0's ndcg@1: 0.92775\tvalid_0's ndcg@2: 0.970653\tvalid_0's ndcg@3: 0.972316\tvalid_0's ndcg@4: 0.972617\tvalid_0's ndcg@5: 0.972685\n",
+ "[11]\tvalid_0's ndcg@1: 0.9283\tvalid_0's ndcg@2: 0.97084\tvalid_0's ndcg@3: 0.97254\tvalid_0's ndcg@4: 0.97281\tvalid_0's ndcg@5: 0.972887\n",
+ "[12]\tvalid_0's ndcg@1: 0.9287\tvalid_0's ndcg@2: 0.971051\tvalid_0's ndcg@3: 0.972701\tvalid_0's ndcg@4: 0.97297\tvalid_0's ndcg@5: 0.973048\n",
+ "[13]\tvalid_0's ndcg@1: 0.9297\tvalid_0's ndcg@2: 0.971389\tvalid_0's ndcg@3: 0.973001\tvalid_0's ndcg@4: 0.973313\tvalid_0's ndcg@5: 0.9734\n",
+ "[14]\tvalid_0's ndcg@1: 0.92955\tvalid_0's ndcg@2: 0.971444\tvalid_0's ndcg@3: 0.972994\tvalid_0's ndcg@4: 0.973284\tvalid_0's ndcg@5: 0.973371\n",
+ "[15]\tvalid_0's ndcg@1: 0.930225\tvalid_0's ndcg@2: 0.97174\tvalid_0's ndcg@3: 0.973253\tvalid_0's ndcg@4: 0.973543\tvalid_0's ndcg@5: 0.97363\n",
+ "[16]\tvalid_0's ndcg@1: 0.930425\tvalid_0's ndcg@2: 0.971798\tvalid_0's ndcg@3: 0.973298\tvalid_0's ndcg@4: 0.97361\tvalid_0's ndcg@5: 0.973698\n",
+ "[17]\tvalid_0's ndcg@1: 0.93125\tvalid_0's ndcg@2: 0.971992\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973903\tvalid_0's ndcg@5: 0.97398\n",
+ "[18]\tvalid_0's ndcg@1: 0.931925\tvalid_0's ndcg@2: 0.972257\tvalid_0's ndcg@3: 0.973845\tvalid_0's ndcg@4: 0.974146\tvalid_0's ndcg@5: 0.974224\n",
+ "[19]\tvalid_0's ndcg@1: 0.932375\tvalid_0's ndcg@2: 0.972376\tvalid_0's ndcg@3: 0.974038\tvalid_0's ndcg@4: 0.974318\tvalid_0's ndcg@5: 0.974376\n",
+ "[20]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.972269\tvalid_0's ndcg@3: 0.973907\tvalid_0's ndcg@4: 0.974187\tvalid_0's ndcg@5: 0.974245\n",
+ "[21]\tvalid_0's ndcg@1: 0.932725\tvalid_0's ndcg@2: 0.972568\tvalid_0's ndcg@3: 0.974181\tvalid_0's ndcg@4: 0.974471\tvalid_0's ndcg@5: 0.974529\n",
+ "[22]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972735\tvalid_0's ndcg@3: 0.974298\tvalid_0's ndcg@4: 0.974599\tvalid_0's ndcg@5: 0.974657\n",
+ "[23]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972642\tvalid_0's ndcg@3: 0.974255\tvalid_0's ndcg@4: 0.974545\tvalid_0's ndcg@5: 0.974594\n",
+ "[24]\tvalid_0's ndcg@1: 0.933175\tvalid_0's ndcg@2: 0.972734\tvalid_0's ndcg@3: 0.974347\tvalid_0's ndcg@4: 0.974638\tvalid_0's ndcg@5: 0.974686\n",
+ "[25]\tvalid_0's ndcg@1: 0.9331\tvalid_0's ndcg@2: 0.972754\tvalid_0's ndcg@3: 0.974366\tvalid_0's ndcg@4: 0.974636\tvalid_0's ndcg@5: 0.974674\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[26]\tvalid_0's ndcg@1: 0.933275\tvalid_0's ndcg@2: 0.972787\tvalid_0's ndcg@3: 0.974424\tvalid_0's ndcg@4: 0.974694\tvalid_0's ndcg@5: 0.974732\n",
+ "[27]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972809\tvalid_0's ndcg@3: 0.974434\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.974732\n",
+ "[28]\tvalid_0's ndcg@1: 0.933625\tvalid_0's ndcg@2: 0.972932\tvalid_0's ndcg@3: 0.974557\tvalid_0's ndcg@4: 0.974826\tvalid_0's ndcg@5: 0.974855\n",
+ "[29]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972937\tvalid_0's ndcg@3: 0.974587\tvalid_0's ndcg@4: 0.974856\tvalid_0's ndcg@5: 0.974885\n",
+ "[30]\tvalid_0's ndcg@1: 0.93355\tvalid_0's ndcg@2: 0.972873\tvalid_0's ndcg@3: 0.974523\tvalid_0's ndcg@4: 0.974792\tvalid_0's ndcg@5: 0.974821\n",
+ "[31]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973065\tvalid_0's ndcg@3: 0.974753\tvalid_0's ndcg@4: 0.975022\tvalid_0's ndcg@5: 0.975051\n",
+ "[32]\tvalid_0's ndcg@1: 0.93435\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974815\tvalid_0's ndcg@4: 0.975084\tvalid_0's ndcg@5: 0.975113\n",
+ "[33]\tvalid_0's ndcg@1: 0.934475\tvalid_0's ndcg@2: 0.97323\tvalid_0's ndcg@3: 0.974855\tvalid_0's ndcg@4: 0.975135\tvalid_0's ndcg@5: 0.975164\n",
+ "[34]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973113\tvalid_0's ndcg@3: 0.974738\tvalid_0's ndcg@4: 0.975028\tvalid_0's ndcg@5: 0.975057\n",
+ "[35]\tvalid_0's ndcg@1: 0.93455\tvalid_0's ndcg@2: 0.973258\tvalid_0's ndcg@3: 0.97487\tvalid_0's ndcg@4: 0.975172\tvalid_0's ndcg@5: 0.975201\n",
+ "[36]\tvalid_0's ndcg@1: 0.9344\tvalid_0's ndcg@2: 0.973265\tvalid_0's ndcg@3: 0.974828\tvalid_0's ndcg@4: 0.975129\tvalid_0's ndcg@5: 0.975158\n",
+ "[37]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973438\tvalid_0's ndcg@3: 0.975013\tvalid_0's ndcg@4: 0.975304\tvalid_0's ndcg@5: 0.975323\n",
+ "[38]\tvalid_0's ndcg@1: 0.934975\tvalid_0's ndcg@2: 0.973541\tvalid_0's ndcg@3: 0.975066\tvalid_0's ndcg@4: 0.975367\tvalid_0's ndcg@5: 0.975386\n",
+ "[39]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973667\tvalid_0's ndcg@3: 0.975192\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975502\n",
+ "[40]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973624\tvalid_0's ndcg@3: 0.975174\tvalid_0's ndcg@4: 0.975454\tvalid_0's ndcg@5: 0.975473\n",
+ "[41]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973686\tvalid_0's ndcg@3: 0.975223\tvalid_0's ndcg@4: 0.975503\tvalid_0's ndcg@5: 0.975522\n",
+ "[42]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973716\tvalid_0's ndcg@3: 0.975266\tvalid_0's ndcg@4: 0.975546\tvalid_0's ndcg@5: 0.975565\n",
+ "[43]\tvalid_0's ndcg@1: 0.93615\tvalid_0's ndcg@2: 0.974022\tvalid_0's ndcg@3: 0.975534\tvalid_0's ndcg@4: 0.975814\tvalid_0's ndcg@5: 0.975843\n",
+ "[44]\tvalid_0's ndcg@1: 0.936225\tvalid_0's ndcg@2: 0.974112\tvalid_0's ndcg@3: 0.975562\tvalid_0's ndcg@4: 0.975853\tvalid_0's ndcg@5: 0.975882\n",
+ "[45]\tvalid_0's ndcg@1: 0.9365\tvalid_0's ndcg@2: 0.974167\tvalid_0's ndcg@3: 0.975654\tvalid_0's ndcg@4: 0.975945\tvalid_0's ndcg@5: 0.975974\n",
+ "[46]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974206\tvalid_0's ndcg@3: 0.975694\tvalid_0's ndcg@4: 0.975995\tvalid_0's ndcg@5: 0.976024\n",
+ "[47]\tvalid_0's ndcg@1: 0.93685\tvalid_0's ndcg@2: 0.974311\tvalid_0's ndcg@3: 0.975786\tvalid_0's ndcg@4: 0.976077\tvalid_0's ndcg@5: 0.976106\n",
+ "[48]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974408\tvalid_0's ndcg@3: 0.975845\tvalid_0's ndcg@4: 0.976147\tvalid_0's ndcg@5: 0.976185\n",
+ "[49]\tvalid_0's ndcg@1: 0.936975\tvalid_0's ndcg@2: 0.974342\tvalid_0's ndcg@3: 0.975829\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.976159\n",
+ "[50]\tvalid_0's ndcg@1: 0.9371\tvalid_0's ndcg@2: 0.974388\tvalid_0's ndcg@3: 0.97585\tvalid_0's ndcg@4: 0.976152\tvalid_0's ndcg@5: 0.976191\n",
+ "[51]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974329\tvalid_0's ndcg@3: 0.975841\tvalid_0's ndcg@4: 0.976121\tvalid_0's ndcg@5: 0.97616\n",
+ "[52]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974578\tvalid_0's ndcg@3: 0.976078\tvalid_0's ndcg@4: 0.976369\tvalid_0's ndcg@5: 0.976407\n",
+ "[53]\tvalid_0's ndcg@1: 0.9378\tvalid_0's ndcg@2: 0.974615\tvalid_0's ndcg@3: 0.976115\tvalid_0's ndcg@4: 0.976405\tvalid_0's ndcg@5: 0.976444\n",
+ "[54]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974689\tvalid_0's ndcg@3: 0.976214\tvalid_0's ndcg@4: 0.976483\tvalid_0's ndcg@5: 0.976521\n",
+ "[55]\tvalid_0's ndcg@1: 0.938225\tvalid_0's ndcg@2: 0.974803\tvalid_0's ndcg@3: 0.976303\tvalid_0's ndcg@4: 0.976572\tvalid_0's ndcg@5: 0.976611\n",
+ "[56]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.9748\tvalid_0's ndcg@3: 0.976275\tvalid_0's ndcg@4: 0.976555\tvalid_0's ndcg@5: 0.976594\n",
+ "[57]\tvalid_0's ndcg@1: 0.938525\tvalid_0's ndcg@2: 0.974914\tvalid_0's ndcg@3: 0.976414\tvalid_0's ndcg@4: 0.976683\tvalid_0's ndcg@5: 0.976722\n",
+ "[58]\tvalid_0's ndcg@1: 0.93875\tvalid_0's ndcg@2: 0.975028\tvalid_0's ndcg@3: 0.976503\tvalid_0's ndcg@4: 0.976773\tvalid_0's ndcg@5: 0.976811\n",
+ "[59]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975198\tvalid_0's ndcg@3: 0.976648\tvalid_0's ndcg@4: 0.976918\tvalid_0's ndcg@5: 0.976956\n",
+ "[60]\tvalid_0's ndcg@1: 0.939025\tvalid_0's ndcg@2: 0.975177\tvalid_0's ndcg@3: 0.976615\tvalid_0's ndcg@4: 0.976884\tvalid_0's ndcg@5: 0.976923\n",
+ "[61]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975205\tvalid_0's ndcg@3: 0.976642\tvalid_0's ndcg@4: 0.976912\tvalid_0's ndcg@5: 0.97695\n",
+ "[62]\tvalid_0's ndcg@1: 0.93965\tvalid_0's ndcg@2: 0.975424\tvalid_0's ndcg@3: 0.976836\tvalid_0's ndcg@4: 0.977116\tvalid_0's ndcg@5: 0.977155\n",
+ "[63]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.976996\tvalid_0's ndcg@4: 0.977276\tvalid_0's ndcg@5: 0.977315\n",
+ "[64]\tvalid_0's ndcg@1: 0.940375\tvalid_0's ndcg@2: 0.975723\tvalid_0's ndcg@3: 0.977123\tvalid_0's ndcg@4: 0.977392\tvalid_0's ndcg@5: 0.977431\n",
+ "[65]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977154\tvalid_0's ndcg@4: 0.977423\tvalid_0's ndcg@5: 0.977462\n",
+ "[66]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975744\tvalid_0's ndcg@3: 0.977156\tvalid_0's ndcg@4: 0.977426\tvalid_0's ndcg@5: 0.977464\n",
+ "[67]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.97576\tvalid_0's ndcg@3: 0.977172\tvalid_0's ndcg@4: 0.977431\tvalid_0's ndcg@5: 0.977469\n",
+ "[68]\tvalid_0's ndcg@1: 0.940675\tvalid_0's ndcg@2: 0.975849\tvalid_0's ndcg@3: 0.977249\tvalid_0's ndcg@4: 0.977508\tvalid_0's ndcg@5: 0.977546\n",
+ "[69]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.976017\tvalid_0's ndcg@3: 0.977454\tvalid_0's ndcg@4: 0.977724\tvalid_0's ndcg@5: 0.977762\n",
+ "[70]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.977362\tvalid_0's ndcg@4: 0.977631\tvalid_0's ndcg@5: 0.97767\n",
+ "[71]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.97735\tvalid_0's ndcg@4: 0.97763\tvalid_0's ndcg@5: 0.977668\n",
+ "[72]\tvalid_0's ndcg@1: 0.941325\tvalid_0's ndcg@2: 0.976058\tvalid_0's ndcg@3: 0.97747\tvalid_0's ndcg@4: 0.977739\tvalid_0's ndcg@5: 0.977778\n",
+ "[73]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977476\tvalid_0's ndcg@4: 0.977756\tvalid_0's ndcg@5: 0.977795\n",
+ "[74]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.97759\tvalid_0's ndcg@4: 0.97788\tvalid_0's ndcg@5: 0.977919\n",
+ "[75]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.977602\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.977921\n",
+ "[76]\tvalid_0's ndcg@1: 0.94195\tvalid_0's ndcg@2: 0.976273\tvalid_0's ndcg@3: 0.977685\tvalid_0's ndcg@4: 0.977965\tvalid_0's ndcg@5: 0.978004\n",
+ "[77]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.97627\tvalid_0's ndcg@3: 0.97767\tvalid_0's ndcg@4: 0.97795\tvalid_0's ndcg@5: 0.977989\n",
+ "[78]\tvalid_0's ndcg@1: 0.94235\tvalid_0's ndcg@2: 0.976452\tvalid_0's ndcg@3: 0.977839\tvalid_0's ndcg@4: 0.978119\tvalid_0's ndcg@5: 0.978158\n",
+ "[79]\tvalid_0's ndcg@1: 0.94265\tvalid_0's ndcg@2: 0.976562\tvalid_0's ndcg@3: 0.977937\tvalid_0's ndcg@4: 0.978228\tvalid_0's ndcg@5: 0.978267\n",
+ "[80]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976667\tvalid_0's ndcg@3: 0.978067\tvalid_0's ndcg@4: 0.978347\tvalid_0's ndcg@5: 0.978385\n",
+ "[81]\tvalid_0's ndcg@1: 0.94305\tvalid_0's ndcg@2: 0.97671\tvalid_0's ndcg@3: 0.978098\tvalid_0's ndcg@4: 0.978378\tvalid_0's ndcg@5: 0.978416\n",
+ "[82]\tvalid_0's ndcg@1: 0.943175\tvalid_0's ndcg@2: 0.97674\tvalid_0's ndcg@3: 0.978115\tvalid_0's ndcg@4: 0.978417\tvalid_0's ndcg@5: 0.978456\n",
+ "[83]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976752\tvalid_0's ndcg@3: 0.97814\tvalid_0's ndcg@4: 0.978441\tvalid_0's ndcg@5: 0.97848\n",
+ "[84]\tvalid_0's ndcg@1: 0.943375\tvalid_0's ndcg@2: 0.976767\tvalid_0's ndcg@3: 0.978179\tvalid_0's ndcg@4: 0.978481\tvalid_0's ndcg@5: 0.97852\n",
+ "[85]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976721\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978437\tvalid_0's ndcg@5: 0.978475\n",
+ "[86]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976792\tvalid_0's ndcg@3: 0.978204\tvalid_0's ndcg@4: 0.978506\tvalid_0's ndcg@5: 0.978535\n",
+ "[87]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.976851\tvalid_0's ndcg@3: 0.978239\tvalid_0's ndcg@4: 0.97854\tvalid_0's ndcg@5: 0.978569\n",
+ "[88]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976882\tvalid_0's ndcg@3: 0.978282\tvalid_0's ndcg@4: 0.978572\tvalid_0's ndcg@5: 0.978611\n",
+ "[89]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.976915\tvalid_0's ndcg@3: 0.97834\tvalid_0's ndcg@4: 0.97863\tvalid_0's ndcg@5: 0.978669\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[90]\tvalid_0's ndcg@1: 0.943925\tvalid_0's ndcg@2: 0.976986\tvalid_0's ndcg@3: 0.978398\tvalid_0's ndcg@4: 0.978689\tvalid_0's ndcg@5: 0.978728\n",
+ "[91]\tvalid_0's ndcg@1: 0.943875\tvalid_0's ndcg@2: 0.976999\tvalid_0's ndcg@3: 0.978399\tvalid_0's ndcg@4: 0.978679\tvalid_0's ndcg@5: 0.978717\n",
+ "[92]\tvalid_0's ndcg@1: 0.94395\tvalid_0's ndcg@2: 0.977058\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978711\tvalid_0's ndcg@5: 0.97876\n",
+ "[93]\tvalid_0's ndcg@1: 0.944075\tvalid_0's ndcg@2: 0.977104\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978759\tvalid_0's ndcg@5: 0.978807\n",
+ "[94]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977125\tvalid_0's ndcg@3: 0.978513\tvalid_0's ndcg@4: 0.978793\tvalid_0's ndcg@5: 0.978841\n",
+ "[95]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977153\tvalid_0's ndcg@3: 0.97854\tvalid_0's ndcg@4: 0.97882\tvalid_0's ndcg@5: 0.978869\n",
+ "[96]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977144\tvalid_0's ndcg@3: 0.978531\tvalid_0's ndcg@4: 0.978811\tvalid_0's ndcg@5: 0.97886\n",
+ "[97]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977221\tvalid_0's ndcg@3: 0.978584\tvalid_0's ndcg@4: 0.978864\tvalid_0's ndcg@5: 0.978912\n",
+ "[98]\tvalid_0's ndcg@1: 0.944575\tvalid_0's ndcg@2: 0.977289\tvalid_0's ndcg@3: 0.978651\tvalid_0's ndcg@4: 0.978942\tvalid_0's ndcg@5: 0.97899\n",
+ "[99]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977341\tvalid_0's ndcg@3: 0.978691\tvalid_0's ndcg@4: 0.978993\tvalid_0's ndcg@5: 0.979032\n",
+ "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
+ "[1]\tvalid_0's ndcg@1: 0.911575\tvalid_0's ndcg@2: 0.964384\tvalid_0's ndcg@3: 0.966321\tvalid_0's ndcg@4: 0.966623\tvalid_0's ndcg@5: 0.966671\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9136\tvalid_0's ndcg@2: 0.965257\tvalid_0's ndcg@3: 0.967107\tvalid_0's ndcg@4: 0.967398\tvalid_0's ndcg@5: 0.967456\n",
+ "[3]\tvalid_0's ndcg@1: 0.917425\tvalid_0's ndcg@2: 0.966732\tvalid_0's ndcg@3: 0.968545\tvalid_0's ndcg@4: 0.968814\tvalid_0's ndcg@5: 0.968882\n",
+ "[4]\tvalid_0's ndcg@1: 0.9222\tvalid_0's ndcg@2: 0.968558\tvalid_0's ndcg@3: 0.970383\tvalid_0's ndcg@4: 0.970619\tvalid_0's ndcg@5: 0.970668\n",
+ "[5]\tvalid_0's ndcg@1: 0.925875\tvalid_0's ndcg@2: 0.969914\tvalid_0's ndcg@3: 0.971714\tvalid_0's ndcg@4: 0.971972\tvalid_0's ndcg@5: 0.972021\n",
+ "[6]\tvalid_0's ndcg@1: 0.926875\tvalid_0's ndcg@2: 0.970425\tvalid_0's ndcg@3: 0.972112\tvalid_0's ndcg@4: 0.972371\tvalid_0's ndcg@5: 0.972419\n",
+ "[7]\tvalid_0's ndcg@1: 0.927475\tvalid_0's ndcg@2: 0.970631\tvalid_0's ndcg@3: 0.972306\tvalid_0's ndcg@4: 0.972586\tvalid_0's ndcg@5: 0.972634\n",
+ "[8]\tvalid_0's ndcg@1: 0.93015\tvalid_0's ndcg@2: 0.971649\tvalid_0's ndcg@3: 0.973287\tvalid_0's ndcg@4: 0.973567\tvalid_0's ndcg@5: 0.973625\n",
+ "[9]\tvalid_0's ndcg@1: 0.9312\tvalid_0's ndcg@2: 0.972084\tvalid_0's ndcg@3: 0.973684\tvalid_0's ndcg@4: 0.973964\tvalid_0's ndcg@5: 0.974022\n",
+ "[10]\tvalid_0's ndcg@1: 0.93225\tvalid_0's ndcg@2: 0.972456\tvalid_0's ndcg@3: 0.974081\tvalid_0's ndcg@4: 0.974361\tvalid_0's ndcg@5: 0.974409\n",
+ "[11]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972704\tvalid_0's ndcg@3: 0.974379\tvalid_0's ndcg@4: 0.974648\tvalid_0's ndcg@5: 0.974696\n",
+ "[12]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974574\tvalid_0's ndcg@4: 0.974832\tvalid_0's ndcg@5: 0.974881\n",
+ "[13]\tvalid_0's ndcg@1: 0.93415\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.97482\tvalid_0's ndcg@4: 0.975079\tvalid_0's ndcg@5: 0.975127\n",
+ "[14]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973671\tvalid_0's ndcg@3: 0.975246\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975531\n",
+ "[15]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.973877\tvalid_0's ndcg@3: 0.975452\tvalid_0's ndcg@4: 0.975699\tvalid_0's ndcg@5: 0.975748\n",
+ "[16]\tvalid_0's ndcg@1: 0.935825\tvalid_0's ndcg@2: 0.973917\tvalid_0's ndcg@3: 0.975442\tvalid_0's ndcg@4: 0.975712\tvalid_0's ndcg@5: 0.97576\n",
+ "[17]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.97411\tvalid_0's ndcg@3: 0.975697\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975995\n",
+ "[18]\tvalid_0's ndcg@1: 0.936925\tvalid_0's ndcg@2: 0.974292\tvalid_0's ndcg@3: 0.975867\tvalid_0's ndcg@4: 0.976114\tvalid_0's ndcg@5: 0.976163\n",
+ "[19]\tvalid_0's ndcg@1: 0.937525\tvalid_0's ndcg@2: 0.974545\tvalid_0's ndcg@3: 0.976095\tvalid_0's ndcg@4: 0.976342\tvalid_0's ndcg@5: 0.976391\n",
+ "[20]\tvalid_0's ndcg@1: 0.937775\tvalid_0's ndcg@2: 0.974653\tvalid_0's ndcg@3: 0.976203\tvalid_0's ndcg@4: 0.976429\tvalid_0's ndcg@5: 0.976487\n",
+ "[21]\tvalid_0's ndcg@1: 0.938825\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976597\tvalid_0's ndcg@4: 0.976823\tvalid_0's ndcg@5: 0.976881\n",
+ "[22]\tvalid_0's ndcg@1: 0.93885\tvalid_0's ndcg@2: 0.975097\tvalid_0's ndcg@3: 0.976609\tvalid_0's ndcg@4: 0.976846\tvalid_0's ndcg@5: 0.976895\n",
+ "[23]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976733\tvalid_0's ndcg@4: 0.976959\tvalid_0's ndcg@5: 0.977008\n",
+ "[24]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976721\tvalid_0's ndcg@4: 0.976947\tvalid_0's ndcg@5: 0.977005\n",
+ "[25]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975421\tvalid_0's ndcg@3: 0.976909\tvalid_0's ndcg@4: 0.977124\tvalid_0's ndcg@5: 0.977182\n",
+ "[26]\tvalid_0's ndcg@1: 0.9393\tvalid_0's ndcg@2: 0.975342\tvalid_0's ndcg@3: 0.976804\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977078\n",
+ "[27]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975323\tvalid_0's ndcg@3: 0.976798\tvalid_0's ndcg@4: 0.977014\tvalid_0's ndcg@5: 0.977062\n",
+ "[28]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975308\tvalid_0's ndcg@3: 0.976783\tvalid_0's ndcg@4: 0.977009\tvalid_0's ndcg@5: 0.977057\n",
+ "[29]\tvalid_0's ndcg@1: 0.94\tvalid_0's ndcg@2: 0.975569\tvalid_0's ndcg@3: 0.977056\tvalid_0's ndcg@4: 0.977282\tvalid_0's ndcg@5: 0.977331\n",
+ "[30]\tvalid_0's ndcg@1: 0.940325\tvalid_0's ndcg@2: 0.975673\tvalid_0's ndcg@3: 0.977173\tvalid_0's ndcg@4: 0.977399\tvalid_0's ndcg@5: 0.977447\n",
+ "[31]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975731\tvalid_0's ndcg@3: 0.977243\tvalid_0's ndcg@4: 0.977469\tvalid_0's ndcg@5: 0.977518\n",
+ "[32]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
+ "[33]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977241\tvalid_0's ndcg@4: 0.977457\tvalid_0's ndcg@5: 0.977505\n",
+ "[34]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
+ "[35]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975868\tvalid_0's ndcg@3: 0.977343\tvalid_0's ndcg@4: 0.977558\tvalid_0's ndcg@5: 0.977606\n",
+ "[36]\tvalid_0's ndcg@1: 0.94115\tvalid_0's ndcg@2: 0.976056\tvalid_0's ndcg@3: 0.977506\tvalid_0's ndcg@4: 0.977722\tvalid_0's ndcg@5: 0.97777\n",
+ "[37]\tvalid_0's ndcg@1: 0.9414\tvalid_0's ndcg@2: 0.976133\tvalid_0's ndcg@3: 0.977595\tvalid_0's ndcg@4: 0.977811\tvalid_0's ndcg@5: 0.977859\n",
+ "[38]\tvalid_0's ndcg@1: 0.94175\tvalid_0's ndcg@2: 0.976278\tvalid_0's ndcg@3: 0.977715\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.97799\n",
+ "[39]\tvalid_0's ndcg@1: 0.942075\tvalid_0's ndcg@2: 0.976366\tvalid_0's ndcg@3: 0.977841\tvalid_0's ndcg@4: 0.978056\tvalid_0's ndcg@5: 0.978105\n",
+ "[40]\tvalid_0's ndcg@1: 0.94215\tvalid_0's ndcg@2: 0.976409\tvalid_0's ndcg@3: 0.977872\tvalid_0's ndcg@4: 0.978087\tvalid_0's ndcg@5: 0.978136\n",
+ "[41]\tvalid_0's ndcg@1: 0.94245\tvalid_0's ndcg@2: 0.97652\tvalid_0's ndcg@3: 0.977983\tvalid_0's ndcg@4: 0.978198\tvalid_0's ndcg@5: 0.978246\n",
+ "[42]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
+ "[43]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
+ "[44]\tvalid_0's ndcg@1: 0.94285\tvalid_0's ndcg@2: 0.976636\tvalid_0's ndcg@3: 0.978111\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978386\n",
+ "[45]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.9768\tvalid_0's ndcg@3: 0.978262\tvalid_0's ndcg@4: 0.978488\tvalid_0's ndcg@5: 0.978537\n",
+ "[46]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
+ "[47]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97836\tvalid_0's ndcg@4: 0.978576\tvalid_0's ndcg@5: 0.978634\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[48]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.978373\tvalid_0's ndcg@4: 0.978577\tvalid_0's ndcg@5: 0.978636\n",
+ "[49]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
+ "[50]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97702\tvalid_0's ndcg@3: 0.97852\tvalid_0's ndcg@4: 0.978746\tvalid_0's ndcg@5: 0.978794\n",
+ "[51]\tvalid_0's ndcg@1: 0.9441\tvalid_0's ndcg@2: 0.97705\tvalid_0's ndcg@3: 0.97855\tvalid_0's ndcg@4: 0.978787\tvalid_0's ndcg@5: 0.978836\n",
+ "[52]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977121\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978846\tvalid_0's ndcg@5: 0.978894\n",
+ "[53]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977081\tvalid_0's ndcg@3: 0.978618\tvalid_0's ndcg@4: 0.978834\tvalid_0's ndcg@5: 0.978882\n",
+ "[54]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977071\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978824\tvalid_0's ndcg@5: 0.978873\n",
+ "[55]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977143\tvalid_0's ndcg@3: 0.978668\tvalid_0's ndcg@4: 0.978883\tvalid_0's ndcg@5: 0.978931\n",
+ "[56]\tvalid_0's ndcg@1: 0.9444\tvalid_0's ndcg@2: 0.977177\tvalid_0's ndcg@3: 0.978702\tvalid_0's ndcg@4: 0.978906\tvalid_0's ndcg@5: 0.978955\n",
+ "[57]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977263\tvalid_0's ndcg@3: 0.978788\tvalid_0's ndcg@4: 0.979003\tvalid_0's ndcg@5: 0.979051\n",
+ "[58]\tvalid_0's ndcg@1: 0.9448\tvalid_0's ndcg@2: 0.977293\tvalid_0's ndcg@3: 0.978843\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979096\n",
+ "[59]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977472\tvalid_0's ndcg@3: 0.978997\tvalid_0's ndcg@4: 0.979202\tvalid_0's ndcg@5: 0.97925\n",
+ "[60]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.97763\tvalid_0's ndcg@3: 0.979118\tvalid_0's ndcg@4: 0.979322\tvalid_0's ndcg@5: 0.979371\n",
+ "[61]\tvalid_0's ndcg@1: 0.945725\tvalid_0's ndcg@2: 0.977682\tvalid_0's ndcg@3: 0.979194\tvalid_0's ndcg@4: 0.979399\tvalid_0's ndcg@5: 0.979447\n",
+ "[62]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977812\tvalid_0's ndcg@3: 0.979312\tvalid_0's ndcg@4: 0.979495\tvalid_0's ndcg@5: 0.979543\n",
+ "[63]\tvalid_0's ndcg@1: 0.946\tvalid_0's ndcg@2: 0.977878\tvalid_0's ndcg@3: 0.97934\tvalid_0's ndcg@4: 0.979523\tvalid_0's ndcg@5: 0.979572\n",
+ "[64]\tvalid_0's ndcg@1: 0.946525\tvalid_0's ndcg@2: 0.978056\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979714\tvalid_0's ndcg@5: 0.979762\n",
+ "[65]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.978105\tvalid_0's ndcg@3: 0.979592\tvalid_0's ndcg@4: 0.979775\tvalid_0's ndcg@5: 0.979823\n",
+ "[66]\tvalid_0's ndcg@1: 0.9465\tvalid_0's ndcg@2: 0.978046\tvalid_0's ndcg@3: 0.979534\tvalid_0's ndcg@4: 0.979706\tvalid_0's ndcg@5: 0.979755\n",
+ "[67]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.978127\tvalid_0's ndcg@3: 0.979614\tvalid_0's ndcg@4: 0.979776\tvalid_0's ndcg@5: 0.979824\n",
+ "[68]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.979608\tvalid_0's ndcg@4: 0.97978\tvalid_0's ndcg@5: 0.979828\n",
+ "[69]\tvalid_0's ndcg@1: 0.946875\tvalid_0's ndcg@2: 0.978216\tvalid_0's ndcg@3: 0.979679\tvalid_0's ndcg@4: 0.979851\tvalid_0's ndcg@5: 0.9799\n",
+ "[70]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.978194\tvalid_0's ndcg@3: 0.979682\tvalid_0's ndcg@4: 0.979854\tvalid_0's ndcg@5: 0.979902\n",
+ "[71]\tvalid_0's ndcg@1: 0.947025\tvalid_0's ndcg@2: 0.978209\tvalid_0's ndcg@3: 0.979721\tvalid_0's ndcg@4: 0.979893\tvalid_0's ndcg@5: 0.979942\n",
+ "[72]\tvalid_0's ndcg@1: 0.9472\tvalid_0's ndcg@2: 0.978273\tvalid_0's ndcg@3: 0.979773\tvalid_0's ndcg@4: 0.979956\tvalid_0's ndcg@5: 0.980005\n",
+ "[73]\tvalid_0's ndcg@1: 0.947475\tvalid_0's ndcg@2: 0.978391\tvalid_0's ndcg@3: 0.979878\tvalid_0's ndcg@4: 0.980061\tvalid_0's ndcg@5: 0.980109\n",
+ "[74]\tvalid_0's ndcg@1: 0.94715\tvalid_0's ndcg@2: 0.978271\tvalid_0's ndcg@3: 0.979758\tvalid_0's ndcg@4: 0.979941\tvalid_0's ndcg@5: 0.97999\n",
+ "[75]\tvalid_0's ndcg@1: 0.947275\tvalid_0's ndcg@2: 0.978333\tvalid_0's ndcg@3: 0.979808\tvalid_0's ndcg@4: 0.979991\tvalid_0's ndcg@5: 0.980039\n",
+ "[76]\tvalid_0's ndcg@1: 0.9474\tvalid_0's ndcg@2: 0.97841\tvalid_0's ndcg@3: 0.979873\tvalid_0's ndcg@4: 0.980045\tvalid_0's ndcg@5: 0.980093\n",
+ "[77]\tvalid_0's ndcg@1: 0.94745\tvalid_0's ndcg@2: 0.97846\tvalid_0's ndcg@3: 0.979898\tvalid_0's ndcg@4: 0.98007\tvalid_0's ndcg@5: 0.980118\n",
+ "[78]\tvalid_0's ndcg@1: 0.94775\tvalid_0's ndcg@2: 0.978555\tvalid_0's ndcg@3: 0.980005\tvalid_0's ndcg@4: 0.980177\tvalid_0's ndcg@5: 0.980226\n",
+ "[79]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
+ "[80]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
+ "[81]\tvalid_0's ndcg@1: 0.948175\tvalid_0's ndcg@2: 0.978744\tvalid_0's ndcg@3: 0.980169\tvalid_0's ndcg@4: 0.980352\tvalid_0's ndcg@5: 0.98039\n",
+ "[82]\tvalid_0's ndcg@1: 0.948375\tvalid_0's ndcg@2: 0.97888\tvalid_0's ndcg@3: 0.980255\tvalid_0's ndcg@4: 0.980438\tvalid_0's ndcg@5: 0.980477\n",
+ "[83]\tvalid_0's ndcg@1: 0.94825\tvalid_0's ndcg@2: 0.978834\tvalid_0's ndcg@3: 0.980209\tvalid_0's ndcg@4: 0.980392\tvalid_0's ndcg@5: 0.980431\n",
+ "[84]\tvalid_0's ndcg@1: 0.948275\tvalid_0's ndcg@2: 0.978844\tvalid_0's ndcg@3: 0.980219\tvalid_0's ndcg@4: 0.980402\tvalid_0's ndcg@5: 0.98044\n",
+ "[85]\tvalid_0's ndcg@1: 0.948475\tvalid_0's ndcg@2: 0.978917\tvalid_0's ndcg@3: 0.980292\tvalid_0's ndcg@4: 0.980475\tvalid_0's ndcg@5: 0.980514\n",
+ "[86]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979102\tvalid_0's ndcg@3: 0.980477\tvalid_0's ndcg@4: 0.98066\tvalid_0's ndcg@5: 0.980699\n",
+ "[87]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979086\tvalid_0's ndcg@3: 0.980474\tvalid_0's ndcg@4: 0.980657\tvalid_0's ndcg@5: 0.980695\n",
+ "[88]\tvalid_0's ndcg@1: 0.949025\tvalid_0's ndcg@2: 0.979136\tvalid_0's ndcg@3: 0.980499\tvalid_0's ndcg@4: 0.980682\tvalid_0's ndcg@5: 0.98072\n",
+ "[89]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979285\tvalid_0's ndcg@3: 0.98061\tvalid_0's ndcg@4: 0.980793\tvalid_0's ndcg@5: 0.980832\n",
+ "[90]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
+ "[91]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
+ "[92]\tvalid_0's ndcg@1: 0.9494\tvalid_0's ndcg@2: 0.97929\tvalid_0's ndcg@3: 0.98064\tvalid_0's ndcg@4: 0.980823\tvalid_0's ndcg@5: 0.980862\n",
+ "[93]\tvalid_0's ndcg@1: 0.949375\tvalid_0's ndcg@2: 0.979297\tvalid_0's ndcg@3: 0.980634\tvalid_0's ndcg@4: 0.980817\tvalid_0's ndcg@5: 0.980856\n",
+ "[94]\tvalid_0's ndcg@1: 0.949525\tvalid_0's ndcg@2: 0.979336\tvalid_0's ndcg@3: 0.980686\tvalid_0's ndcg@4: 0.980869\tvalid_0's ndcg@5: 0.980908\n",
+ "[95]\tvalid_0's ndcg@1: 0.949825\tvalid_0's ndcg@2: 0.979416\tvalid_0's ndcg@3: 0.980791\tvalid_0's ndcg@4: 0.980974\tvalid_0's ndcg@5: 0.981012\n",
+ "[96]\tvalid_0's ndcg@1: 0.94975\tvalid_0's ndcg@2: 0.979404\tvalid_0's ndcg@3: 0.980779\tvalid_0's ndcg@4: 0.980951\tvalid_0's ndcg@5: 0.98099\n",
+ "[97]\tvalid_0's ndcg@1: 0.950025\tvalid_0's ndcg@2: 0.979537\tvalid_0's ndcg@3: 0.980874\tvalid_0's ndcg@4: 0.981057\tvalid_0's ndcg@5: 0.981096\n",
+ "[98]\tvalid_0's ndcg@1: 0.9501\tvalid_0's ndcg@2: 0.979564\tvalid_0's ndcg@3: 0.980889\tvalid_0's ndcg@4: 0.981083\tvalid_0's ndcg@5: 0.981122\n",
+ "[99]\tvalid_0's ndcg@1: 0.950275\tvalid_0's ndcg@2: 0.979629\tvalid_0's ndcg@3: 0.980967\tvalid_0's ndcg@4: 0.98115\tvalid_0's ndcg@5: 0.981188\n",
+ "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
+ "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.96382\tvalid_0's ndcg@3: 0.965707\tvalid_0's ndcg@4: 0.966009\tvalid_0's ndcg@5: 0.966086\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.91415\tvalid_0's ndcg@2: 0.965492\tvalid_0's ndcg@3: 0.967254\tvalid_0's ndcg@4: 0.967556\tvalid_0's ndcg@5: 0.967604\n",
+ "[3]\tvalid_0's ndcg@1: 0.916025\tvalid_0's ndcg@2: 0.966389\tvalid_0's ndcg@3: 0.967976\tvalid_0's ndcg@4: 0.968278\tvalid_0's ndcg@5: 0.968355\n",
+ "[4]\tvalid_0's ndcg@1: 0.919\tvalid_0's ndcg@2: 0.967392\tvalid_0's ndcg@3: 0.96903\tvalid_0's ndcg@4: 0.969364\tvalid_0's ndcg@5: 0.969431\n",
+ "[5]\tvalid_0's ndcg@1: 0.921125\tvalid_0's ndcg@2: 0.968192\tvalid_0's ndcg@3: 0.969855\tvalid_0's ndcg@4: 0.970156\tvalid_0's ndcg@5: 0.970224\n",
+ "[6]\tvalid_0's ndcg@1: 0.921675\tvalid_0's ndcg@2: 0.968411\tvalid_0's ndcg@3: 0.970111\tvalid_0's ndcg@4: 0.97037\tvalid_0's ndcg@5: 0.970437\n",
+ "[7]\tvalid_0's ndcg@1: 0.9237\tvalid_0's ndcg@2: 0.969332\tvalid_0's ndcg@3: 0.970882\tvalid_0's ndcg@4: 0.97113\tvalid_0's ndcg@5: 0.971217\n",
+ "[8]\tvalid_0's ndcg@1: 0.925775\tvalid_0's ndcg@2: 0.970129\tvalid_0's ndcg@3: 0.971642\tvalid_0's ndcg@4: 0.971922\tvalid_0's ndcg@5: 0.97199\n",
+ "[9]\tvalid_0's ndcg@1: 0.926775\tvalid_0's ndcg@2: 0.970435\tvalid_0's ndcg@3: 0.971985\tvalid_0's ndcg@4: 0.972276\tvalid_0's ndcg@5: 0.972334\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[10]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970761\tvalid_0's ndcg@3: 0.972311\tvalid_0's ndcg@4: 0.972612\tvalid_0's ndcg@5: 0.97267\n",
+ "[11]\tvalid_0's ndcg@1: 0.928975\tvalid_0's ndcg@2: 0.97131\tvalid_0's ndcg@3: 0.972798\tvalid_0's ndcg@4: 0.973089\tvalid_0's ndcg@5: 0.973166\n",
+ "[12]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971505\tvalid_0's ndcg@3: 0.972968\tvalid_0's ndcg@4: 0.973259\tvalid_0's ndcg@5: 0.973326\n",
+ "[13]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971426\tvalid_0's ndcg@3: 0.972939\tvalid_0's ndcg@4: 0.97324\tvalid_0's ndcg@5: 0.973318\n",
+ "[14]\tvalid_0's ndcg@1: 0.929775\tvalid_0's ndcg@2: 0.971621\tvalid_0's ndcg@3: 0.973121\tvalid_0's ndcg@4: 0.973412\tvalid_0's ndcg@5: 0.97348\n",
+ "[15]\tvalid_0's ndcg@1: 0.9304\tvalid_0's ndcg@2: 0.971868\tvalid_0's ndcg@3: 0.97338\tvalid_0's ndcg@4: 0.97365\tvalid_0's ndcg@5: 0.973717\n",
+ "[16]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.972096\tvalid_0's ndcg@3: 0.973558\tvalid_0's ndcg@4: 0.973849\tvalid_0's ndcg@5: 0.973926\n",
+ "[17]\tvalid_0's ndcg@1: 0.93105\tvalid_0's ndcg@2: 0.972108\tvalid_0's ndcg@3: 0.973583\tvalid_0's ndcg@4: 0.973884\tvalid_0's ndcg@5: 0.973952\n",
+ "[18]\tvalid_0's ndcg@1: 0.931725\tvalid_0's ndcg@2: 0.972373\tvalid_0's ndcg@3: 0.97386\tvalid_0's ndcg@4: 0.974129\tvalid_0's ndcg@5: 0.974207\n",
+ "[19]\tvalid_0's ndcg@1: 0.932175\tvalid_0's ndcg@2: 0.972681\tvalid_0's ndcg@3: 0.974068\tvalid_0's ndcg@4: 0.974348\tvalid_0's ndcg@5: 0.974406\n",
+ "[20]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.973019\tvalid_0's ndcg@3: 0.974382\tvalid_0's ndcg@4: 0.974673\tvalid_0's ndcg@5: 0.974731\n",
+ "[21]\tvalid_0's ndcg@1: 0.933075\tvalid_0's ndcg@2: 0.97306\tvalid_0's ndcg@3: 0.974423\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.97477\n",
+ "[22]\tvalid_0's ndcg@1: 0.93375\tvalid_0's ndcg@2: 0.973262\tvalid_0's ndcg@3: 0.974649\tvalid_0's ndcg@4: 0.974929\tvalid_0's ndcg@5: 0.975007\n",
+ "[23]\tvalid_0's ndcg@1: 0.933675\tvalid_0's ndcg@2: 0.973219\tvalid_0's ndcg@3: 0.974606\tvalid_0's ndcg@4: 0.974886\tvalid_0's ndcg@5: 0.974973\n",
+ "[24]\tvalid_0's ndcg@1: 0.934\tvalid_0's ndcg@2: 0.97337\tvalid_0's ndcg@3: 0.974745\tvalid_0's ndcg@4: 0.975014\tvalid_0's ndcg@5: 0.975101\n",
+ "[25]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973674\tvalid_0's ndcg@3: 0.975062\tvalid_0's ndcg@4: 0.975342\tvalid_0's ndcg@5: 0.97541\n",
+ "[26]\tvalid_0's ndcg@1: 0.93495\tvalid_0's ndcg@2: 0.973721\tvalid_0's ndcg@3: 0.975096\tvalid_0's ndcg@4: 0.975365\tvalid_0's ndcg@5: 0.975452\n",
+ "[27]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.974082\tvalid_0's ndcg@3: 0.975444\tvalid_0's ndcg@4: 0.975713\tvalid_0's ndcg@5: 0.975781\n",
+ "[28]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973875\tvalid_0's ndcg@3: 0.975275\tvalid_0's ndcg@4: 0.975512\tvalid_0's ndcg@5: 0.975599\n",
+ "[29]\tvalid_0's ndcg@1: 0.935925\tvalid_0's ndcg@2: 0.974159\tvalid_0's ndcg@3: 0.975522\tvalid_0's ndcg@4: 0.975759\tvalid_0's ndcg@5: 0.975836\n",
+ "[30]\tvalid_0's ndcg@1: 0.9362\tvalid_0's ndcg@2: 0.974214\tvalid_0's ndcg@3: 0.975589\tvalid_0's ndcg@4: 0.975847\tvalid_0's ndcg@5: 0.975924\n",
+ "[31]\tvalid_0's ndcg@1: 0.93625\tvalid_0's ndcg@2: 0.974216\tvalid_0's ndcg@3: 0.975629\tvalid_0's ndcg@4: 0.975876\tvalid_0's ndcg@5: 0.975944\n",
+ "[32]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974427\tvalid_0's ndcg@3: 0.975814\tvalid_0's ndcg@4: 0.97603\tvalid_0's ndcg@5: 0.976107\n",
+ "[33]\tvalid_0's ndcg@1: 0.936775\tvalid_0's ndcg@2: 0.974505\tvalid_0's ndcg@3: 0.975855\tvalid_0's ndcg@4: 0.976081\tvalid_0's ndcg@5: 0.976158\n",
+ "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974643\tvalid_0's ndcg@3: 0.975993\tvalid_0's ndcg@4: 0.976219\tvalid_0's ndcg@5: 0.976296\n",
+ "[35]\tvalid_0's ndcg@1: 0.937675\tvalid_0's ndcg@2: 0.974805\tvalid_0's ndcg@3: 0.97618\tvalid_0's ndcg@4: 0.976406\tvalid_0's ndcg@5: 0.976484\n",
+ "[36]\tvalid_0's ndcg@1: 0.9382\tvalid_0's ndcg@2: 0.974983\tvalid_0's ndcg@3: 0.976371\tvalid_0's ndcg@4: 0.976597\tvalid_0's ndcg@5: 0.976674\n",
+ "[37]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.974974\tvalid_0's ndcg@3: 0.976349\tvalid_0's ndcg@4: 0.976586\tvalid_0's ndcg@5: 0.976663\n",
+ "[38]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.975143\tvalid_0's ndcg@3: 0.976518\tvalid_0's ndcg@4: 0.976776\tvalid_0's ndcg@5: 0.976844\n",
+ "[39]\tvalid_0's ndcg@1: 0.938575\tvalid_0's ndcg@2: 0.975106\tvalid_0's ndcg@3: 0.976481\tvalid_0's ndcg@4: 0.976739\tvalid_0's ndcg@5: 0.976807\n",
+ "[40]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.97519\tvalid_0's ndcg@3: 0.976528\tvalid_0's ndcg@4: 0.976775\tvalid_0's ndcg@5: 0.976853\n",
+ "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975347\tvalid_0's ndcg@3: 0.976697\tvalid_0's ndcg@4: 0.976934\tvalid_0's ndcg@5: 0.977001\n",
+ "[42]\tvalid_0's ndcg@1: 0.939825\tvalid_0's ndcg@2: 0.975599\tvalid_0's ndcg@3: 0.976961\tvalid_0's ndcg@4: 0.977198\tvalid_0's ndcg@5: 0.977266\n",
+ "[43]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975639\tvalid_0's ndcg@3: 0.976977\tvalid_0's ndcg@4: 0.977214\tvalid_0's ndcg@5: 0.977282\n",
+ "[44]\tvalid_0's ndcg@1: 0.9398\tvalid_0's ndcg@2: 0.975605\tvalid_0's ndcg@3: 0.976955\tvalid_0's ndcg@4: 0.977192\tvalid_0's ndcg@5: 0.97726\n",
+ "[45]\tvalid_0's ndcg@1: 0.9401\tvalid_0's ndcg@2: 0.9757\tvalid_0's ndcg@3: 0.977075\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
+ "[46]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975845\tvalid_0's ndcg@3: 0.977183\tvalid_0's ndcg@4: 0.97742\tvalid_0's ndcg@5: 0.977497\n",
+ "[47]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975854\tvalid_0's ndcg@3: 0.977204\tvalid_0's ndcg@4: 0.97743\tvalid_0's ndcg@5: 0.977508\n",
+ "[48]\tvalid_0's ndcg@1: 0.940575\tvalid_0's ndcg@2: 0.975923\tvalid_0's ndcg@3: 0.977273\tvalid_0's ndcg@4: 0.977488\tvalid_0's ndcg@5: 0.977556\n",
+ "[49]\tvalid_0's ndcg@1: 0.9407\tvalid_0's ndcg@2: 0.975922\tvalid_0's ndcg@3: 0.977297\tvalid_0's ndcg@4: 0.977501\tvalid_0's ndcg@5: 0.977588\n",
+ "[50]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977322\tvalid_0's ndcg@4: 0.977505\tvalid_0's ndcg@5: 0.977592\n",
+ "[51]\tvalid_0's ndcg@1: 0.9406\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.97725\tvalid_0's ndcg@4: 0.977422\tvalid_0's ndcg@5: 0.977509\n",
+ "[52]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975997\tvalid_0's ndcg@3: 0.977422\tvalid_0's ndcg@4: 0.977594\tvalid_0's ndcg@5: 0.977691\n",
+ "[53]\tvalid_0's ndcg@1: 0.940925\tvalid_0's ndcg@2: 0.975989\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977538\tvalid_0's ndcg@5: 0.977644\n",
+ "[54]\tvalid_0's ndcg@1: 0.94125\tvalid_0's ndcg@2: 0.976062\tvalid_0's ndcg@3: 0.977487\tvalid_0's ndcg@4: 0.977659\tvalid_0's ndcg@5: 0.977756\n",
+ "[55]\tvalid_0's ndcg@1: 0.94145\tvalid_0's ndcg@2: 0.976183\tvalid_0's ndcg@3: 0.97757\tvalid_0's ndcg@4: 0.977742\tvalid_0's ndcg@5: 0.977839\n",
+ "[56]\tvalid_0's ndcg@1: 0.941475\tvalid_0's ndcg@2: 0.976176\tvalid_0's ndcg@3: 0.977576\tvalid_0's ndcg@4: 0.977748\tvalid_0's ndcg@5: 0.977845\n",
+ "[57]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976139\tvalid_0's ndcg@3: 0.977539\tvalid_0's ndcg@4: 0.977712\tvalid_0's ndcg@5: 0.977808\n",
+ "[58]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.97625\tvalid_0's ndcg@3: 0.97765\tvalid_0's ndcg@4: 0.977822\tvalid_0's ndcg@5: 0.977919\n",
+ "[59]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.976253\tvalid_0's ndcg@3: 0.977653\tvalid_0's ndcg@4: 0.977836\tvalid_0's ndcg@5: 0.977932\n",
+ "[60]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977634\tvalid_0's ndcg@4: 0.977817\tvalid_0's ndcg@5: 0.977914\n",
+ "[61]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.976333\tvalid_0's ndcg@3: 0.977745\tvalid_0's ndcg@4: 0.977918\tvalid_0's ndcg@5: 0.978005\n",
+ "[62]\tvalid_0's ndcg@1: 0.941975\tvalid_0's ndcg@2: 0.976345\tvalid_0's ndcg@3: 0.977757\tvalid_0's ndcg@4: 0.97794\tvalid_0's ndcg@5: 0.978027\n",
+ "[63]\tvalid_0's ndcg@1: 0.9423\tvalid_0's ndcg@2: 0.976496\tvalid_0's ndcg@3: 0.977871\tvalid_0's ndcg@4: 0.978065\tvalid_0's ndcg@5: 0.978152\n",
+ "[64]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976632\tvalid_0's ndcg@3: 0.977995\tvalid_0's ndcg@4: 0.978188\tvalid_0's ndcg@5: 0.978275\n",
+ "[65]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976629\tvalid_0's ndcg@3: 0.977979\tvalid_0's ndcg@4: 0.978173\tvalid_0's ndcg@5: 0.97826\n",
+ "[66]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976685\tvalid_0's ndcg@3: 0.978035\tvalid_0's ndcg@4: 0.978229\tvalid_0's ndcg@5: 0.978316\n",
+ "[67]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976678\tvalid_0's ndcg@3: 0.978041\tvalid_0's ndcg@4: 0.978224\tvalid_0's ndcg@5: 0.97832\n",
+ "[68]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976694\tvalid_0's ndcg@3: 0.978044\tvalid_0's ndcg@4: 0.978227\tvalid_0's ndcg@5: 0.978324\n",
+ "[69]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976834\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978329\tvalid_0's ndcg@5: 0.978426\n",
+ "[70]\tvalid_0's ndcg@1: 0.943025\tvalid_0's ndcg@2: 0.976827\tvalid_0's ndcg@3: 0.978152\tvalid_0's ndcg@4: 0.978324\tvalid_0's ndcg@5: 0.978431\n",
+ "[71]\tvalid_0's ndcg@1: 0.9432\tvalid_0's ndcg@2: 0.976923\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978504\n",
+ "[72]\tvalid_0's ndcg@1: 0.943225\tvalid_0's ndcg@2: 0.976917\tvalid_0's ndcg@3: 0.978254\tvalid_0's ndcg@4: 0.978405\tvalid_0's ndcg@5: 0.978511\n",
+ "[73]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976936\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978409\tvalid_0's ndcg@5: 0.978496\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[74]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976957\tvalid_0's ndcg@3: 0.97827\tvalid_0's ndcg@4: 0.978431\tvalid_0's ndcg@5: 0.978528\n",
+ "[75]\tvalid_0's ndcg@1: 0.943075\tvalid_0's ndcg@2: 0.976861\tvalid_0's ndcg@3: 0.978199\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978457\n",
+ "[76]\tvalid_0's ndcg@1: 0.94335\tvalid_0's ndcg@2: 0.976963\tvalid_0's ndcg@3: 0.978288\tvalid_0's ndcg@4: 0.978471\tvalid_0's ndcg@5: 0.978568\n",
+ "[77]\tvalid_0's ndcg@1: 0.94345\tvalid_0's ndcg@2: 0.977031\tvalid_0's ndcg@3: 0.978331\tvalid_0's ndcg@4: 0.978514\tvalid_0's ndcg@5: 0.978611\n",
+ "[78]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.977088\tvalid_0's ndcg@3: 0.97835\tvalid_0's ndcg@4: 0.978533\tvalid_0's ndcg@5: 0.97863\n",
+ "[79]\tvalid_0's ndcg@1: 0.943625\tvalid_0's ndcg@2: 0.977096\tvalid_0's ndcg@3: 0.978396\tvalid_0's ndcg@4: 0.978579\tvalid_0's ndcg@5: 0.978676\n",
+ "[80]\tvalid_0's ndcg@1: 0.943825\tvalid_0's ndcg@2: 0.977154\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978651\tvalid_0's ndcg@5: 0.978748\n",
+ "[81]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.97846\tvalid_0's ndcg@4: 0.978633\tvalid_0's ndcg@5: 0.978729\n",
+ "[82]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.977361\tvalid_0's ndcg@3: 0.978673\tvalid_0's ndcg@4: 0.978845\tvalid_0's ndcg@5: 0.978933\n",
+ "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977324\tvalid_0's ndcg@3: 0.978624\tvalid_0's ndcg@4: 0.978796\tvalid_0's ndcg@5: 0.978893\n",
+ "[84]\tvalid_0's ndcg@1: 0.94405\tvalid_0's ndcg@2: 0.977253\tvalid_0's ndcg@3: 0.978565\tvalid_0's ndcg@4: 0.978737\tvalid_0's ndcg@5: 0.978834\n",
+ "[85]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977283\tvalid_0's ndcg@3: 0.978633\tvalid_0's ndcg@4: 0.978795\tvalid_0's ndcg@5: 0.978882\n",
+ "[86]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.97745\tvalid_0's ndcg@3: 0.978763\tvalid_0's ndcg@4: 0.978924\tvalid_0's ndcg@5: 0.979011\n",
+ "[87]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.977419\tvalid_0's ndcg@3: 0.978756\tvalid_0's ndcg@4: 0.978918\tvalid_0's ndcg@5: 0.979005\n",
+ "[88]\tvalid_0's ndcg@1: 0.944825\tvalid_0's ndcg@2: 0.977554\tvalid_0's ndcg@3: 0.978867\tvalid_0's ndcg@4: 0.979039\tvalid_0's ndcg@5: 0.979126\n",
+ "[89]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977767\tvalid_0's ndcg@3: 0.979079\tvalid_0's ndcg@4: 0.979262\tvalid_0's ndcg@5: 0.97934\n",
+ "[90]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977773\tvalid_0's ndcg@3: 0.979073\tvalid_0's ndcg@4: 0.979256\tvalid_0's ndcg@5: 0.979334\n",
+ "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977792\tvalid_0's ndcg@3: 0.979092\tvalid_0's ndcg@4: 0.979275\tvalid_0's ndcg@5: 0.979352\n",
+ "[92]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977776\tvalid_0's ndcg@3: 0.979088\tvalid_0's ndcg@4: 0.979261\tvalid_0's ndcg@5: 0.979348\n",
+ "[93]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977757\tvalid_0's ndcg@3: 0.979082\tvalid_0's ndcg@4: 0.979244\tvalid_0's ndcg@5: 0.979331\n",
+ "[94]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977761\tvalid_0's ndcg@3: 0.979061\tvalid_0's ndcg@4: 0.979223\tvalid_0's ndcg@5: 0.97931\n",
+ "[95]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977798\tvalid_0's ndcg@3: 0.979086\tvalid_0's ndcg@4: 0.979258\tvalid_0's ndcg@5: 0.979345\n",
+ "[96]\tvalid_0's ndcg@1: 0.945825\tvalid_0's ndcg@2: 0.977955\tvalid_0's ndcg@3: 0.97923\tvalid_0's ndcg@4: 0.979413\tvalid_0's ndcg@5: 0.9795\n",
+ "[97]\tvalid_0's ndcg@1: 0.945925\tvalid_0's ndcg@2: 0.97796\tvalid_0's ndcg@3: 0.97926\tvalid_0's ndcg@4: 0.979443\tvalid_0's ndcg@5: 0.979531\n",
+ "[98]\tvalid_0's ndcg@1: 0.9464\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.97942\tvalid_0's ndcg@4: 0.979625\tvalid_0's ndcg@5: 0.979702\n",
+ "[99]\tvalid_0's ndcg@1: 0.94655\tvalid_0's ndcg@2: 0.978191\tvalid_0's ndcg@3: 0.979479\tvalid_0's ndcg@4: 0.979683\tvalid_0's ndcg@5: 0.97977\n",
+ "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
+ "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.963031\tvalid_0's ndcg@3: 0.965281\tvalid_0's ndcg@4: 0.965819\tvalid_0's ndcg@5: 0.965887\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9141\tvalid_0's ndcg@2: 0.964748\tvalid_0's ndcg@3: 0.96681\tvalid_0's ndcg@4: 0.967316\tvalid_0's ndcg@5: 0.967394\n",
+ "[3]\tvalid_0's ndcg@1: 0.915925\tvalid_0's ndcg@2: 0.9655\tvalid_0's ndcg@3: 0.967575\tvalid_0's ndcg@4: 0.968028\tvalid_0's ndcg@5: 0.968105\n",
+ "[4]\tvalid_0's ndcg@1: 0.91915\tvalid_0's ndcg@2: 0.966943\tvalid_0's ndcg@3: 0.968968\tvalid_0's ndcg@4: 0.969334\tvalid_0's ndcg@5: 0.969373\n",
+ "[5]\tvalid_0's ndcg@1: 0.920625\tvalid_0's ndcg@2: 0.967598\tvalid_0's ndcg@3: 0.969498\tvalid_0's ndcg@4: 0.969896\tvalid_0's ndcg@5: 0.969944\n",
+ "[6]\tvalid_0's ndcg@1: 0.922625\tvalid_0's ndcg@2: 0.968336\tvalid_0's ndcg@3: 0.970261\tvalid_0's ndcg@4: 0.970659\tvalid_0's ndcg@5: 0.970688\n",
+ "[7]\tvalid_0's ndcg@1: 0.923625\tvalid_0's ndcg@2: 0.968768\tvalid_0's ndcg@3: 0.970656\tvalid_0's ndcg@4: 0.971043\tvalid_0's ndcg@5: 0.971072\n",
+ "[8]\tvalid_0's ndcg@1: 0.925825\tvalid_0's ndcg@2: 0.969612\tvalid_0's ndcg@3: 0.971462\tvalid_0's ndcg@4: 0.97186\tvalid_0's ndcg@5: 0.971879\n",
+ "[9]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.969899\tvalid_0's ndcg@3: 0.971711\tvalid_0's ndcg@4: 0.97211\tvalid_0's ndcg@5: 0.972129\n",
+ "[10]\tvalid_0's ndcg@1: 0.927775\tvalid_0's ndcg@2: 0.97041\tvalid_0's ndcg@3: 0.972185\tvalid_0's ndcg@4: 0.972594\tvalid_0's ndcg@5: 0.972614\n",
+ "[11]\tvalid_0's ndcg@1: 0.92885\tvalid_0's ndcg@2: 0.970838\tvalid_0's ndcg@3: 0.972588\tvalid_0's ndcg@4: 0.973008\tvalid_0's ndcg@5: 0.973028\n",
+ "[12]\tvalid_0's ndcg@1: 0.930325\tvalid_0's ndcg@2: 0.971367\tvalid_0's ndcg@3: 0.973129\tvalid_0's ndcg@4: 0.973549\tvalid_0's ndcg@5: 0.973569\n",
+ "[13]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971631\tvalid_0's ndcg@3: 0.973443\tvalid_0's ndcg@4: 0.973842\tvalid_0's ndcg@5: 0.973871\n",
+ "[14]\tvalid_0's ndcg@1: 0.931525\tvalid_0's ndcg@2: 0.971778\tvalid_0's ndcg@3: 0.973616\tvalid_0's ndcg@4: 0.973993\tvalid_0's ndcg@5: 0.974022\n",
+ "[15]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.9717\tvalid_0's ndcg@3: 0.973475\tvalid_0's ndcg@4: 0.973852\tvalid_0's ndcg@5: 0.973872\n",
+ "[16]\tvalid_0's ndcg@1: 0.931775\tvalid_0's ndcg@2: 0.971902\tvalid_0's ndcg@3: 0.973702\tvalid_0's ndcg@4: 0.97409\tvalid_0's ndcg@5: 0.974109\n",
+ "[17]\tvalid_0's ndcg@1: 0.931425\tvalid_0's ndcg@2: 0.971805\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973967\tvalid_0's ndcg@5: 0.973986\n",
+ "[18]\tvalid_0's ndcg@1: 0.931575\tvalid_0's ndcg@2: 0.971876\tvalid_0's ndcg@3: 0.973651\tvalid_0's ndcg@4: 0.974027\tvalid_0's ndcg@5: 0.974047\n",
+ "[19]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.97208\tvalid_0's ndcg@3: 0.973805\tvalid_0's ndcg@4: 0.974192\tvalid_0's ndcg@5: 0.974212\n",
+ "[20]\tvalid_0's ndcg@1: 0.932075\tvalid_0's ndcg@2: 0.972092\tvalid_0's ndcg@3: 0.973829\tvalid_0's ndcg@4: 0.974217\tvalid_0's ndcg@5: 0.974236\n",
+ "[21]\tvalid_0's ndcg@1: 0.932675\tvalid_0's ndcg@2: 0.972282\tvalid_0's ndcg@3: 0.974057\tvalid_0's ndcg@4: 0.974444\tvalid_0's ndcg@5: 0.974454\n",
+ "[22]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972358\tvalid_0's ndcg@3: 0.974146\tvalid_0's ndcg@4: 0.974533\tvalid_0's ndcg@5: 0.974543\n",
+ "[23]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972478\tvalid_0's ndcg@3: 0.974253\tvalid_0's ndcg@4: 0.974651\tvalid_0's ndcg@5: 0.974661\n",
+ "[24]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972539\tvalid_0's ndcg@3: 0.974351\tvalid_0's ndcg@4: 0.974739\tvalid_0's ndcg@5: 0.974749\n",
+ "[25]\tvalid_0's ndcg@1: 0.93475\tvalid_0's ndcg@2: 0.973\tvalid_0's ndcg@3: 0.974788\tvalid_0's ndcg@4: 0.975197\tvalid_0's ndcg@5: 0.975206\n",
+ "[26]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.97312\tvalid_0's ndcg@3: 0.974895\tvalid_0's ndcg@4: 0.975315\tvalid_0's ndcg@5: 0.975325\n",
+ "[27]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.974865\tvalid_0's ndcg@4: 0.975264\tvalid_0's ndcg@5: 0.975273\n",
+ "[28]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974939\tvalid_0's ndcg@4: 0.975327\tvalid_0's ndcg@5: 0.975336\n",
+ "[29]\tvalid_0's ndcg@1: 0.935475\tvalid_0's ndcg@2: 0.973315\tvalid_0's ndcg@3: 0.975128\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975492\n",
+ "[30]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973522\tvalid_0's ndcg@3: 0.975297\tvalid_0's ndcg@4: 0.975663\tvalid_0's ndcg@5: 0.975673\n",
+ "[31]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973506\tvalid_0's ndcg@3: 0.975281\tvalid_0's ndcg@4: 0.975658\tvalid_0's ndcg@5: 0.975668\n",
+ "[32]\tvalid_0's ndcg@1: 0.93675\tvalid_0's ndcg@2: 0.973833\tvalid_0's ndcg@3: 0.975595\tvalid_0's ndcg@4: 0.975961\tvalid_0's ndcg@5: 0.975971\n",
+ "[33]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.973763\tvalid_0's ndcg@3: 0.975488\tvalid_0's ndcg@4: 0.975865\tvalid_0's ndcg@5: 0.975874\n",
+ "[34]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973893\tvalid_0's ndcg@3: 0.975568\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975966\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[35]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974059\tvalid_0's ndcg@3: 0.975722\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.97613\n",
+ "[36]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
+ "[37]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
+ "[38]\tvalid_0's ndcg@1: 0.938725\tvalid_0's ndcg@2: 0.974672\tvalid_0's ndcg@3: 0.97636\tvalid_0's ndcg@4: 0.976715\tvalid_0's ndcg@5: 0.976725\n",
+ "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974676\tvalid_0's ndcg@3: 0.976364\tvalid_0's ndcg@4: 0.976697\tvalid_0's ndcg@5: 0.976707\n",
+ "[40]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.974867\tvalid_0's ndcg@3: 0.97653\tvalid_0's ndcg@4: 0.976874\tvalid_0's ndcg@5: 0.976884\n",
+ "[41]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975042\tvalid_0's ndcg@3: 0.976705\tvalid_0's ndcg@4: 0.97705\tvalid_0's ndcg@5: 0.977059\n",
+ "[42]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976784\tvalid_0's ndcg@4: 0.977129\tvalid_0's ndcg@5: 0.977138\n",
+ "[43]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.97517\tvalid_0's ndcg@3: 0.97687\tvalid_0's ndcg@4: 0.977215\tvalid_0's ndcg@5: 0.977225\n",
+ "[44]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.97534\tvalid_0's ndcg@3: 0.977015\tvalid_0's ndcg@4: 0.97736\tvalid_0's ndcg@5: 0.97737\n",
+ "[45]\tvalid_0's ndcg@1: 0.94055\tvalid_0's ndcg@2: 0.975409\tvalid_0's ndcg@3: 0.977059\tvalid_0's ndcg@4: 0.977403\tvalid_0's ndcg@5: 0.977413\n",
+ "[46]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975415\tvalid_0's ndcg@3: 0.97704\tvalid_0's ndcg@4: 0.977396\tvalid_0's ndcg@5: 0.977405\n",
+ "[47]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975363\tvalid_0's ndcg@3: 0.977013\tvalid_0's ndcg@4: 0.977357\tvalid_0's ndcg@5: 0.977367\n",
+ "[48]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975388\tvalid_0's ndcg@3: 0.977025\tvalid_0's ndcg@4: 0.97737\tvalid_0's ndcg@5: 0.977379\n",
+ "[49]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975447\tvalid_0's ndcg@3: 0.977097\tvalid_0's ndcg@4: 0.977409\tvalid_0's ndcg@5: 0.977419\n",
+ "[50]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975666\tvalid_0's ndcg@3: 0.977303\tvalid_0's ndcg@4: 0.977615\tvalid_0's ndcg@5: 0.977625\n",
+ "[51]\tvalid_0's ndcg@1: 0.94135\tvalid_0's ndcg@2: 0.975751\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.97771\tvalid_0's ndcg@5: 0.97772\n",
+ "[52]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.975717\tvalid_0's ndcg@3: 0.977355\tvalid_0's ndcg@4: 0.977688\tvalid_0's ndcg@5: 0.977698\n",
+ "[53]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.975713\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977699\tvalid_0's ndcg@5: 0.977718\n",
+ "[54]\tvalid_0's ndcg@1: 0.94185\tvalid_0's ndcg@2: 0.975857\tvalid_0's ndcg@3: 0.977557\tvalid_0's ndcg@4: 0.977869\tvalid_0's ndcg@5: 0.977889\n",
+ "[55]\tvalid_0's ndcg@1: 0.941925\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.9776\tvalid_0's ndcg@4: 0.977891\tvalid_0's ndcg@5: 0.97791\n",
+ "[56]\tvalid_0's ndcg@1: 0.942325\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977719\tvalid_0's ndcg@4: 0.978032\tvalid_0's ndcg@5: 0.978051\n",
+ "[57]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977772\tvalid_0's ndcg@4: 0.978073\tvalid_0's ndcg@5: 0.978093\n",
+ "[58]\tvalid_0's ndcg@1: 0.9425\tvalid_0's ndcg@2: 0.976081\tvalid_0's ndcg@3: 0.977806\tvalid_0's ndcg@4: 0.978108\tvalid_0's ndcg@5: 0.978127\n",
+ "[59]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977788\tvalid_0's ndcg@4: 0.978079\tvalid_0's ndcg@5: 0.978098\n",
+ "[60]\tvalid_0's ndcg@1: 0.942375\tvalid_0's ndcg@2: 0.976067\tvalid_0's ndcg@3: 0.977779\tvalid_0's ndcg@4: 0.97807\tvalid_0's ndcg@5: 0.978089\n",
+ "[61]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976043\tvalid_0's ndcg@3: 0.97773\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.97804\n",
+ "[62]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976117\tvalid_0's ndcg@3: 0.977792\tvalid_0's ndcg@4: 0.978093\tvalid_0's ndcg@5: 0.978112\n",
+ "[63]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977881\tvalid_0's ndcg@4: 0.978182\tvalid_0's ndcg@5: 0.978201\n",
+ "[64]\tvalid_0's ndcg@1: 0.942925\tvalid_0's ndcg@2: 0.976254\tvalid_0's ndcg@3: 0.977966\tvalid_0's ndcg@4: 0.978268\tvalid_0's ndcg@5: 0.978287\n",
+ "[65]\tvalid_0's ndcg@1: 0.9431\tvalid_0's ndcg@2: 0.97635\tvalid_0's ndcg@3: 0.978025\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978357\n",
+ "[66]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976445\tvalid_0's ndcg@3: 0.978132\tvalid_0's ndcg@4: 0.978445\tvalid_0's ndcg@5: 0.978464\n",
+ "[67]\tvalid_0's ndcg@1: 0.943275\tvalid_0's ndcg@2: 0.976399\tvalid_0's ndcg@3: 0.978074\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978416\n",
+ "[68]\tvalid_0's ndcg@1: 0.943325\tvalid_0's ndcg@2: 0.976401\tvalid_0's ndcg@3: 0.978089\tvalid_0's ndcg@4: 0.978412\tvalid_0's ndcg@5: 0.978431\n",
+ "[69]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976578\tvalid_0's ndcg@3: 0.97819\tvalid_0's ndcg@4: 0.978546\tvalid_0's ndcg@5: 0.978565\n",
+ "[70]\tvalid_0's ndcg@1: 0.944025\tvalid_0's ndcg@2: 0.976707\tvalid_0's ndcg@3: 0.97832\tvalid_0's ndcg@4: 0.978675\tvalid_0's ndcg@5: 0.978694\n",
+ "[71]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976772\tvalid_0's ndcg@3: 0.978384\tvalid_0's ndcg@4: 0.97874\tvalid_0's ndcg@5: 0.978759\n",
+ "[72]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978409\tvalid_0's ndcg@4: 0.978765\tvalid_0's ndcg@5: 0.978784\n",
+ "[73]\tvalid_0's ndcg@1: 0.94445\tvalid_0's ndcg@2: 0.976864\tvalid_0's ndcg@3: 0.978464\tvalid_0's ndcg@4: 0.97883\tvalid_0's ndcg@5: 0.978849\n",
+ "[74]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
+ "[75]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
+ "[76]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.97696\tvalid_0's ndcg@3: 0.978535\tvalid_0's ndcg@4: 0.978901\tvalid_0's ndcg@5: 0.978921\n",
+ "[77]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
+ "[78]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
+ "[79]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976907\tvalid_0's ndcg@3: 0.978507\tvalid_0's ndcg@4: 0.978863\tvalid_0's ndcg@5: 0.978882\n",
+ "[80]\tvalid_0's ndcg@1: 0.94455\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97851\tvalid_0's ndcg@4: 0.978865\tvalid_0's ndcg@5: 0.978885\n",
+ "[81]\tvalid_0's ndcg@1: 0.944725\tvalid_0's ndcg@2: 0.97695\tvalid_0's ndcg@3: 0.978575\tvalid_0's ndcg@4: 0.978919\tvalid_0's ndcg@5: 0.978948\n",
+ "[82]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.977103\tvalid_0's ndcg@3: 0.978765\tvalid_0's ndcg@4: 0.97911\tvalid_0's ndcg@5: 0.979129\n",
+ "[83]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977066\tvalid_0's ndcg@3: 0.978716\tvalid_0's ndcg@4: 0.979071\tvalid_0's ndcg@5: 0.97909\n",
+ "[84]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.97715\tvalid_0's ndcg@3: 0.978775\tvalid_0's ndcg@4: 0.97912\tvalid_0's ndcg@5: 0.979139\n",
+ "[85]\tvalid_0's ndcg@1: 0.945025\tvalid_0's ndcg@2: 0.977092\tvalid_0's ndcg@3: 0.978692\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979067\n",
+ "[86]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977172\tvalid_0's ndcg@3: 0.97876\tvalid_0's ndcg@4: 0.979115\tvalid_0's ndcg@5: 0.979135\n",
+ "[87]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.97879\tvalid_0's ndcg@4: 0.979156\tvalid_0's ndcg@5: 0.979166\n",
+ "[88]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.978815\tvalid_0's ndcg@4: 0.979149\tvalid_0's ndcg@5: 0.979168\n",
+ "[89]\tvalid_0's ndcg@1: 0.94555\tvalid_0's ndcg@2: 0.977333\tvalid_0's ndcg@3: 0.978933\tvalid_0's ndcg@4: 0.979267\tvalid_0's ndcg@5: 0.979277\n",
+ "[90]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977462\tvalid_0's ndcg@3: 0.979062\tvalid_0's ndcg@4: 0.979396\tvalid_0's ndcg@5: 0.979406\n",
+ "[91]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977481\tvalid_0's ndcg@3: 0.979081\tvalid_0's ndcg@4: 0.979414\tvalid_0's ndcg@5: 0.979424\n",
+ "[92]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977437\tvalid_0's ndcg@3: 0.97905\tvalid_0's ndcg@4: 0.979384\tvalid_0's ndcg@5: 0.979393\n",
+ "[93]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977421\tvalid_0's ndcg@3: 0.979046\tvalid_0's ndcg@4: 0.97938\tvalid_0's ndcg@5: 0.97939\n",
+ "[94]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977431\tvalid_0's ndcg@3: 0.979068\tvalid_0's ndcg@4: 0.979391\tvalid_0's ndcg@5: 0.979401\n",
+ "[95]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977449\tvalid_0's ndcg@3: 0.979074\tvalid_0's ndcg@4: 0.979408\tvalid_0's ndcg@5: 0.979418\n",
+ "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979127\tvalid_0's ndcg@4: 0.979461\tvalid_0's ndcg@5: 0.97947\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[97]\tvalid_0's ndcg@1: 0.946375\tvalid_0's ndcg@2: 0.977622\tvalid_0's ndcg@3: 0.979222\tvalid_0's ndcg@4: 0.979577\tvalid_0's ndcg@5: 0.979577\n",
+ "[98]\tvalid_0's ndcg@1: 0.946625\tvalid_0's ndcg@2: 0.977714\tvalid_0's ndcg@3: 0.979339\tvalid_0's ndcg@4: 0.979673\tvalid_0's ndcg@5: 0.979673\n",
+ "[99]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.977739\tvalid_0's ndcg@3: 0.979352\tvalid_0's ndcg@4: 0.979685\tvalid_0's ndcg@5: 0.979685\n",
+ "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_rank_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id','label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 训练集与验证集的用户分组\n",
+ " train_idx.sort_values(by=['user_id'], inplace=True)\n",
+ " g_train = train_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id'], inplace=True)\n",
+ " g_val = valid_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ " \n",
+ " # 定义模型\n",
+ " lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) \n",
+ " # 训练模型\n",
+ " lgb_ranker.fit(train_idx[lgb_cols], train_idx['label'], group=g_train,\n",
+ " eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], eval_group= [g_val], \n",
+ " eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = lgb_ranker.predict(valid_idx[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
+ " \n",
+ " # 对输出结果进行归一化\n",
+ " valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += lgb_ranker.predict(tst_user_item_feats_df_rank_model[lgb_cols], lgb_ranker.best_iteration_)\n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_ranker_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_ranker_feats.csv', index=False)"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/2\n",
- "290964/290964 [==============================] - 55s 189us/sample - loss: 0.4209 - binary_crossentropy: 0.4206 - auc: 0.7842\n",
- "Epoch 2/2\n",
- "290964/290964 [==============================] - 52s 178us/sample - loss: 0.3630 - binary_crossentropy: 0.3618 - auc: 0.8478\n"
- ]
- }
- ],
- "source": [
- "# 模型训练\n",
- "if offline:\n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=10, validation_data=(x_val, y_val) , batch_size=256)\n",
- "else:\n",
- " # 也可以使用上面的语句用自己采样出来的验证集\n",
- " # history = model.fit(x_trn, y_trn, verbose=1, epochs=3, validation_split=0.3, batch_size=256)\n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, batch_size=256)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:29:20.436591Z",
- "start_time": "2020-11-18T04:28:58.102057Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:52.604397Z",
+ "start_time": "2020-11-18T04:22:43.253034Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "# 单模型生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_ranker')"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "500000/500000 [==============================] - 20s 39us/sample\n"
- ]
- }
- ],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df_din_model['pred_score'] = model.predict(x_tst, verbose=1, batch_size=256)\n",
- "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'din_rank_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:29:34.985535Z",
- "start_time": "2020-11-18T04:29:26.264531Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "submit(rank_results, topk=5, model_name='din')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-15T06:15:49.490705Z",
- "start_time": "2020-11-15T06:15:49.473794Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:38:53.760383Z",
- "start_time": "2020-11-18T04:29:51.737721Z"
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LGB分类模型"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 232681 samples, validate on 58283 samples\n",
- "Epoch 1/2\n",
- "232681/232681 [==============================] - 44s 189us/sample - loss: 0.2864 - binary_crossentropy: 0.2846 - auc: 0.9008 - val_loss: 0.2830 - val_binary_crossentropy: 0.2813 - val_auc: 0.9072\n",
- "Epoch 2/2\n",
- "232681/232681 [==============================] - 44s 187us/sample - loss: 0.2832 - binary_crossentropy: 0.2816 - auc: 0.9034 - val_loss: 0.2846 - val_binary_crossentropy: 0.2830 - val_auc: 0.9053\n",
- "58283/58283 [==============================] - 2s 36us/sample\n",
- "500000/500000 [==============================] - 19s 37us/sample\n",
- "Train on 232798 samples, validate on 58166 samples\n",
- "Epoch 1/2\n",
- "232798/232798 [==============================] - 43s 184us/sample - loss: 0.2818 - binary_crossentropy: 0.2802 - auc: 0.9051 - val_loss: 0.2968 - val_binary_crossentropy: 0.2953 - val_auc: 0.9062\n",
- "Epoch 2/2\n",
- "232798/232798 [==============================] - 44s 187us/sample - loss: 0.2796 - binary_crossentropy: 0.2782 - auc: 0.9069 - val_loss: 0.2820 - val_binary_crossentropy: 0.2806 - val_auc: 0.9071\n",
- "58166/58166 [==============================] - 2s 38us/sample\n",
- "500000/500000 [==============================] - 18s 37us/sample\n",
- "Train on 232847 samples, validate on 58117 samples\n",
- "Epoch 1/2\n",
- "232847/232847 [==============================] - 43s 185us/sample - loss: 0.2786 - binary_crossentropy: 0.2773 - auc: 0.9080 - val_loss: 0.2761 - val_binary_crossentropy: 0.2749 - val_auc: 0.9113\n",
- "Epoch 2/2\n",
- "232847/232847 [==============================] - 39s 166us/sample - loss: 0.2766 - binary_crossentropy: 0.2754 - auc: 0.9097 - val_loss: 0.2872 - val_binary_crossentropy: 0.2862 - val_auc: 0.9090\n",
- "58117/58117 [==============================] - 2s 34us/sample\n",
- "500000/500000 [==============================] - 17s 33us/sample\n",
- "Train on 232716 samples, validate on 58248 samples\n",
- "Epoch 1/2\n",
- "232716/232716 [==============================] - 39s 169us/sample - loss: 0.2763 - binary_crossentropy: 0.2753 - auc: 0.9100 - val_loss: 0.2739 - val_binary_crossentropy: 0.2730 - val_auc: 0.9116\n",
- "Epoch 2/2\n",
- "232716/232716 [==============================] - 39s 168us/sample - loss: 0.2743 - binary_crossentropy: 0.2735 - auc: 0.9119 - val_loss: 0.2859 - val_binary_crossentropy: 0.2851 - val_auc: 0.9090\n",
- "58248/58248 [==============================] - 2s 35us/sample\n",
- "500000/500000 [==============================] - 17s 34us/sample\n",
- "Train on 232814 samples, validate on 58150 samples\n",
- "Epoch 1/2\n",
- "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2747 - binary_crossentropy: 0.2739 - auc: 0.9115 - val_loss: 0.2702 - val_binary_crossentropy: 0.2695 - val_auc: 0.9163\n",
- "Epoch 2/2\n",
- "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2725 - binary_crossentropy: 0.2719 - auc: 0.9132 - val_loss: 0.2751 - val_binary_crossentropy: 0.2745 - val_auc: 0.9151\n",
- "58150/58150 [==============================] - 2s 34us/sample\n",
- "500000/500000 [==============================] - 17s 34us/sample\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_din_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "dense_fea = [x for x in dense_fea if x != 'label']\n",
- "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 准备训练数据\n",
- " x_trn, dnn_feature_columns = get_din_feats_columns(train_idx, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_trn = train_idx['label'].values\n",
- "\n",
- " # 准备验证数据\n",
- " x_val, dnn_feature_columns = get_din_feats_columns(valid_idx, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_val = valid_idx['label'].values\n",
- " \n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, validation_data=(x_val, y_val) , batch_size=256)\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = model.predict(x_val, verbose=1, batch_size=256) \n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += model.predict(x_tst, verbose=1, batch_size=256)[:, 0] \n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_din_cls_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_din_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_din_model['pred_score'] = tst_user_item_feats_df_din_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_din_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_din_model['pred_rank'] = tst_user_item_feats_df_din_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_din_cls_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 模型融合"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 加权融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:27.351996Z",
- "start_time": "2020-11-18T04:44:26.561275Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取多个模型的排序结果文件\n",
- "lgb_ranker = pd.read_csv(save_path + 'lgb_ranker_score.csv')\n",
- "lgb_cls = pd.read_csv(save_path + 'lgb_cls_score.csv')\n",
- "din_ranker = pd.read_csv(save_path + 'din_rank_score.csv')\n",
- "\n",
- "# 这里也可以换成交叉验证输出的测试结果进行加权融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:31.593981Z",
- "start_time": "2020-11-18T04:44:31.589439Z"
- }
- },
- "outputs": [],
- "source": [
- "rank_model = {'lgb_ranker': lgb_ranker, \n",
- " 'lgb_cls': lgb_cls, \n",
- " 'din_ranker': din_ranker}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:36.135860Z",
- "start_time": "2020-11-18T04:44:36.130577Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_ensumble_predict_topk(rank_model, topk=5):\n",
- " final_recall = rank_model['lgb_cls'].append(rank_model['din_ranker'])\n",
- " rank_model['lgb_ranker']['pred_score'] = rank_model['lgb_ranker']['pred_score'].transform(lambda x: norm_sim(x))\n",
- " \n",
- " final_recall = final_recall.append(rank_model['lgb_ranker'])\n",
- " final_recall = final_recall.groupby(['user_id', 'click_article_id'])['pred_score'].sum().reset_index()\n",
- " \n",
- " submit(final_recall, topk=topk, model_name='ensemble_fuse')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:51.659270Z",
- "start_time": "2020-11-18T04:44:40.445659Z"
- }
- },
- "outputs": [],
- "source": [
- "get_ensumble_predict_topk(rank_model)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Staking"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:58.025992Z",
- "start_time": "2020-11-18T04:44:56.146962Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取多个模型的交叉验证生成的结果文件\n",
- "# 训练集\n",
- "trn_lgb_ranker_feats = pd.read_csv(save_path + 'trn_lgb_ranker_feats.csv')\n",
- "trn_lgb_cls_feats = pd.read_csv(save_path + 'trn_lgb_cls_feats.csv')\n",
- "trn_din_cls_feats = pd.read_csv(save_path + 'trn_din_cls_feats.csv')\n",
- "\n",
- "# 测试集\n",
- "tst_lgb_ranker_feats = pd.read_csv(save_path + 'tst_lgb_ranker_feats.csv')\n",
- "tst_lgb_cls_feats = pd.read_csv(save_path + 'tst_lgb_cls_feats.csv')\n",
- "tst_din_cls_feats = pd.read_csv(save_path + 'tst_din_cls_feats.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:07.701862Z",
- "start_time": "2020-11-18T04:45:07.644335Z"
- }
- },
- "outputs": [],
- "source": [
- "# 将多个模型输出的特征进行拼接\n",
- "\n",
- "finall_trn_ranker_feats = trn_lgb_ranker_feats[['user_id', 'click_article_id', 'label']]\n",
- "finall_tst_ranker_feats = tst_lgb_ranker_feats[['user_id', 'click_article_id']]\n",
- "\n",
- "for idx, trn_model in enumerate([trn_lgb_ranker_feats, trn_lgb_cls_feats, trn_din_cls_feats]):\n",
- " for feat in [ 'pred_score', 'pred_rank']:\n",
- " col_name = feat + '_' + str(idx)\n",
- " finall_trn_ranker_feats[col_name] = trn_model[feat]\n",
- "\n",
- "for idx, tst_model in enumerate([tst_lgb_ranker_feats, tst_lgb_cls_feats, tst_din_cls_feats]):\n",
- " for feat in [ 'pred_score', 'pred_rank']:\n",
- " col_name = feat + '_' + str(idx)\n",
- " finall_tst_ranker_feats[col_name] = tst_model[feat]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:15.044242Z",
- "start_time": "2020-11-18T04:45:13.138252Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义一个逻辑回归模型再次拟合交叉验证产生的特征对测试集进行预测\n",
- "# 这里需要注意的是，在做交叉验证的时候可以构造多一些与输出预测值相关的特征，来丰富这里简单模型的特征\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "\n",
- "feat_cols = ['pred_score_0', 'pred_rank_0', 'pred_score_1', 'pred_rank_1', 'pred_score_2', 'pred_rank_2']\n",
- "\n",
- "trn_x = finall_trn_ranker_feats[feat_cols]\n",
- "trn_y = finall_trn_ranker_feats['label']\n",
- "\n",
- "tst_x = finall_tst_ranker_feats[feat_cols]\n",
- "\n",
- "# 定义模型\n",
- "lr = LogisticRegression()\n",
- "\n",
- "# 模型训练\n",
- "lr.fit(trn_x, trn_y)\n",
- "\n",
- "# 模型预测\n",
- "finall_tst_ranker_feats['pred_score'] = lr.predict_proba(tst_x)[:, 1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:29.018764Z",
- "start_time": "2020-11-18T04:45:19.423130Z"
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:58.259730Z",
+ "start_time": "2020-11-18T04:22:58.254297Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型及参数的定义\n",
+ "lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=500, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:11.258774Z",
+ "start_time": "2020-11-18T04:23:00.861936Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型训练\n",
+ "if offline:\n",
+ " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'],\n",
+ " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
+ " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
+ "else:\n",
+ " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:19.591396Z",
+ "start_time": "2020-11-18T04:23:13.813850Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df['pred_score'] = lgb_Classfication.predict_proba(tst_user_item_feats_df[lgb_cols])[:,1]\n",
+ "\n",
+ "# 将这里的排序结果保存一份，用户后面的模型融合\n",
+ "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_cls_score.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:32.352931Z",
+ "start_time": "2020-11-18T04:23:22.346609Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_cls')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:11.241196Z",
+ "start_time": "2020-11-18T04:23:41.377394Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1]\tvalid_0's auc: 0.764896\tvalid_0's binary_logloss: 0.522153\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.767857\tvalid_0's binary_logloss: 0.52057\n",
+ "[3]\tvalid_0's auc: 0.783096\tvalid_0's binary_logloss: 0.519584\n",
+ "[4]\tvalid_0's auc: 0.784354\tvalid_0's binary_logloss: 0.518485\n",
+ "[5]\tvalid_0's auc: 0.790554\tvalid_0's binary_logloss: 0.516886\n",
+ "[6]\tvalid_0's auc: 0.791954\tvalid_0's binary_logloss: 0.515334\n",
+ "[7]\tvalid_0's auc: 0.794257\tvalid_0's binary_logloss: 0.514032\n",
+ "[8]\tvalid_0's auc: 0.795222\tvalid_0's binary_logloss: 0.512516\n",
+ "[9]\tvalid_0's auc: 0.795417\tvalid_0's binary_logloss: 0.511671\n",
+ "[10]\tvalid_0's auc: 0.795913\tvalid_0's binary_logloss: 0.510226\n",
+ "[11]\tvalid_0's auc: 0.798222\tvalid_0's binary_logloss: 0.508858\n",
+ "[12]\tvalid_0's auc: 0.79825\tvalid_0's binary_logloss: 0.507928\n",
+ "[13]\tvalid_0's auc: 0.798842\tvalid_0's binary_logloss: 0.50708\n",
+ "[14]\tvalid_0's auc: 0.798935\tvalid_0's binary_logloss: 0.505752\n",
+ "[15]\tvalid_0's auc: 0.799543\tvalid_0's binary_logloss: 0.504388\n",
+ "[16]\tvalid_0's auc: 0.800844\tvalid_0's binary_logloss: 0.503126\n",
+ "[17]\tvalid_0's auc: 0.800855\tvalid_0's binary_logloss: 0.501809\n",
+ "[18]\tvalid_0's auc: 0.801653\tvalid_0's binary_logloss: 0.500676\n",
+ "[19]\tvalid_0's auc: 0.801518\tvalid_0's binary_logloss: 0.49987\n",
+ "[20]\tvalid_0's auc: 0.801662\tvalid_0's binary_logloss: 0.498625\n",
+ "[21]\tvalid_0's auc: 0.802093\tvalid_0's binary_logloss: 0.498113\n",
+ "[22]\tvalid_0's auc: 0.803071\tvalid_0's binary_logloss: 0.496933\n",
+ "[23]\tvalid_0's auc: 0.803222\tvalid_0's binary_logloss: 0.495864\n",
+ "[24]\tvalid_0's auc: 0.802927\tvalid_0's binary_logloss: 0.494691\n",
+ "[25]\tvalid_0's auc: 0.802581\tvalid_0's binary_logloss: 0.493543\n",
+ "[26]\tvalid_0's auc: 0.802965\tvalid_0's binary_logloss: 0.492444\n",
+ "[27]\tvalid_0's auc: 0.80298\tvalid_0's binary_logloss: 0.491336\n",
+ "[28]\tvalid_0's auc: 0.803226\tvalid_0's binary_logloss: 0.490275\n",
+ "[29]\tvalid_0's auc: 0.803436\tvalid_0's binary_logloss: 0.489126\n",
+ "[30]\tvalid_0's auc: 0.803796\tvalid_0's binary_logloss: 0.48802\n",
+ "[31]\tvalid_0's auc: 0.803601\tvalid_0's binary_logloss: 0.486988\n",
+ "[32]\tvalid_0's auc: 0.804416\tvalid_0's binary_logloss: 0.485972\n",
+ "[33]\tvalid_0's auc: 0.804529\tvalid_0's binary_logloss: 0.484939\n",
+ "[34]\tvalid_0's auc: 0.804534\tvalid_0's binary_logloss: 0.483927\n",
+ "[35]\tvalid_0's auc: 0.804819\tvalid_0's binary_logloss: 0.483271\n",
+ "[36]\tvalid_0's auc: 0.804774\tvalid_0's binary_logloss: 0.482273\n",
+ "[37]\tvalid_0's auc: 0.805237\tvalid_0's binary_logloss: 0.481639\n",
+ "[38]\tvalid_0's auc: 0.805546\tvalid_0's binary_logloss: 0.480959\n",
+ "[39]\tvalid_0's auc: 0.805598\tvalid_0's binary_logloss: 0.479955\n",
+ "[40]\tvalid_0's auc: 0.806011\tvalid_0's binary_logloss: 0.47903\n",
+ "[41]\tvalid_0's auc: 0.806664\tvalid_0's binary_logloss: 0.478439\n",
+ "[42]\tvalid_0's auc: 0.807021\tvalid_0's binary_logloss: 0.477798\n",
+ "[43]\tvalid_0's auc: 0.80726\tvalid_0's binary_logloss: 0.476829\n",
+ "[44]\tvalid_0's auc: 0.807157\tvalid_0's binary_logloss: 0.475976\n",
+ "[45]\tvalid_0's auc: 0.807788\tvalid_0's binary_logloss: 0.475056\n",
+ "[46]\tvalid_0's auc: 0.80805\tvalid_0's binary_logloss: 0.474446\n",
+ "[47]\tvalid_0's auc: 0.808097\tvalid_0's binary_logloss: 0.473576\n",
+ "[48]\tvalid_0's auc: 0.80815\tvalid_0's binary_logloss: 0.472676\n",
+ "[49]\tvalid_0's auc: 0.808304\tvalid_0's binary_logloss: 0.471918\n",
+ "[50]\tvalid_0's auc: 0.808749\tvalid_0's binary_logloss: 0.471481\n",
+ "[51]\tvalid_0's auc: 0.808972\tvalid_0's binary_logloss: 0.471104\n",
+ "[52]\tvalid_0's auc: 0.809326\tvalid_0's binary_logloss: 0.470289\n",
+ "[53]\tvalid_0's auc: 0.809472\tvalid_0's binary_logloss: 0.469508\n",
+ "[54]\tvalid_0's auc: 0.809505\tvalid_0's binary_logloss: 0.46869\n",
+ "[55]\tvalid_0's auc: 0.809594\tvalid_0's binary_logloss: 0.467885\n",
+ "[56]\tvalid_0's auc: 0.809847\tvalid_0's binary_logloss: 0.467356\n",
+ "[57]\tvalid_0's auc: 0.810262\tvalid_0's binary_logloss: 0.466531\n",
+ "[58]\tvalid_0's auc: 0.810407\tvalid_0's binary_logloss: 0.46573\n",
+ "[59]\tvalid_0's auc: 0.810618\tvalid_0's binary_logloss: 0.465205\n",
+ "[60]\tvalid_0's auc: 0.81066\tvalid_0's binary_logloss: 0.464435\n",
+ "[61]\tvalid_0's auc: 0.810638\tvalid_0's binary_logloss: 0.463721\n",
+ "[62]\tvalid_0's auc: 0.810658\tvalid_0's binary_logloss: 0.462982\n",
+ "[63]\tvalid_0's auc: 0.811106\tvalid_0's binary_logloss: 0.462246\n",
+ "[64]\tvalid_0's auc: 0.811313\tvalid_0's binary_logloss: 0.461748\n",
+ "[65]\tvalid_0's auc: 0.811351\tvalid_0's binary_logloss: 0.461038\n",
+ "[66]\tvalid_0's auc: 0.811433\tvalid_0's binary_logloss: 0.460323\n",
+ "[67]\tvalid_0's auc: 0.81158\tvalid_0's binary_logloss: 0.459662\n",
+ "[68]\tvalid_0's auc: 0.811561\tvalid_0's binary_logloss: 0.458988\n",
+ "[69]\tvalid_0's auc: 0.811748\tvalid_0's binary_logloss: 0.458592\n",
+ "[70]\tvalid_0's auc: 0.811919\tvalid_0's binary_logloss: 0.457934\n",
+ "[71]\tvalid_0's auc: 0.812073\tvalid_0's binary_logloss: 0.457508\n",
+ "[72]\tvalid_0's auc: 0.812273\tvalid_0's binary_logloss: 0.457038\n",
+ "[73]\tvalid_0's auc: 0.812561\tvalid_0's binary_logloss: 0.456439\n",
+ "[74]\tvalid_0's auc: 0.812633\tvalid_0's binary_logloss: 0.455789\n",
+ "[75]\tvalid_0's auc: 0.812757\tvalid_0's binary_logloss: 0.455173\n",
+ "[76]\tvalid_0's auc: 0.812923\tvalid_0's binary_logloss: 0.454533\n",
+ "[77]\tvalid_0's auc: 0.81295\tvalid_0's binary_logloss: 0.45392\n",
+ "[78]\tvalid_0's auc: 0.813073\tvalid_0's binary_logloss: 0.453517\n",
+ "[79]\tvalid_0's auc: 0.813202\tvalid_0's binary_logloss: 0.452932\n",
+ "[80]\tvalid_0's auc: 0.813611\tvalid_0's binary_logloss: 0.452285\n",
+ "[81]\tvalid_0's auc: 0.813769\tvalid_0's binary_logloss: 0.45191\n",
+ "[82]\tvalid_0's auc: 0.814468\tvalid_0's binary_logloss: 0.451455\n",
+ "[83]\tvalid_0's auc: 0.814656\tvalid_0's binary_logloss: 0.450885\n",
+ "[84]\tvalid_0's auc: 0.814755\tvalid_0's binary_logloss: 0.450308\n",
+ "[85]\tvalid_0's auc: 0.814824\tvalid_0's binary_logloss: 0.449739\n",
+ "[86]\tvalid_0's auc: 0.81499\tvalid_0's binary_logloss: 0.449348\n",
+ "[87]\tvalid_0's auc: 0.815232\tvalid_0's binary_logloss: 0.448759\n",
+ "[88]\tvalid_0's auc: 0.815452\tvalid_0's binary_logloss: 0.44823\n",
+ "[89]\tvalid_0's auc: 0.815593\tvalid_0's binary_logloss: 0.447861\n",
+ "[90]\tvalid_0's auc: 0.815591\tvalid_0's binary_logloss: 0.447323\n",
+ "[91]\tvalid_0's auc: 0.815672\tvalid_0's binary_logloss: 0.446796\n",
+ "[92]\tvalid_0's auc: 0.815875\tvalid_0's binary_logloss: 0.446472\n",
+ "[93]\tvalid_0's auc: 0.815984\tvalid_0's binary_logloss: 0.445961\n",
+ "[94]\tvalid_0's auc: 0.816026\tvalid_0's binary_logloss: 0.445439\n",
+ "[95]\tvalid_0's auc: 0.816172\tvalid_0's binary_logloss: 0.444909\n",
+ "[96]\tvalid_0's auc: 0.816321\tvalid_0's binary_logloss: 0.444413\n",
+ "[97]\tvalid_0's auc: 0.816751\tvalid_0's binary_logloss: 0.44405\n",
+ "[98]\tvalid_0's auc: 0.817226\tvalid_0's binary_logloss: 0.443626\n",
+ "[99]\tvalid_0's auc: 0.817286\tvalid_0's binary_logloss: 0.443136\n",
+ "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
+ "[1]\tvalid_0's auc: 0.771584\tvalid_0's binary_logloss: 0.527139\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.775446\tvalid_0's binary_logloss: 0.525462\n",
+ "[3]\tvalid_0's auc: 0.790092\tvalid_0's binary_logloss: 0.524461\n",
+ "[4]\tvalid_0's auc: 0.791432\tvalid_0's binary_logloss: 0.523322\n",
+ "[5]\tvalid_0's auc: 0.797482\tvalid_0's binary_logloss: 0.521614\n",
+ "[6]\tvalid_0's auc: 0.79893\tvalid_0's binary_logloss: 0.520007\n",
+ "[7]\tvalid_0's auc: 0.800753\tvalid_0's binary_logloss: 0.5187\n",
+ "[8]\tvalid_0's auc: 0.802197\tvalid_0's binary_logloss: 0.517125\n",
+ "[9]\tvalid_0's auc: 0.802828\tvalid_0's binary_logloss: 0.516269\n",
+ "[10]\tvalid_0's auc: 0.803496\tvalid_0's binary_logloss: 0.51474\n",
+ "[11]\tvalid_0's auc: 0.804972\tvalid_0's binary_logloss: 0.513321\n",
+ "[12]\tvalid_0's auc: 0.804995\tvalid_0's binary_logloss: 0.512334\n",
+ "[13]\tvalid_0's auc: 0.80525\tvalid_0's binary_logloss: 0.51151\n",
+ "[14]\tvalid_0's auc: 0.805026\tvalid_0's binary_logloss: 0.510149\n",
+ "[15]\tvalid_0's auc: 0.805622\tvalid_0's binary_logloss: 0.508708\n",
+ "[16]\tvalid_0's auc: 0.806974\tvalid_0's binary_logloss: 0.507384\n",
+ "[17]\tvalid_0's auc: 0.807045\tvalid_0's binary_logloss: 0.506017\n",
+ "[18]\tvalid_0's auc: 0.807265\tvalid_0's binary_logloss: 0.504853\n",
+ "[19]\tvalid_0's auc: 0.807126\tvalid_0's binary_logloss: 0.503972\n",
+ "[20]\tvalid_0's auc: 0.806948\tvalid_0's binary_logloss: 0.502693\n",
+ "[21]\tvalid_0's auc: 0.807315\tvalid_0's binary_logloss: 0.502166\n",
+ "[22]\tvalid_0's auc: 0.808067\tvalid_0's binary_logloss: 0.500948\n",
+ "[23]\tvalid_0's auc: 0.808226\tvalid_0's binary_logloss: 0.49987\n",
+ "[24]\tvalid_0's auc: 0.808268\tvalid_0's binary_logloss: 0.498623\n",
+ "[25]\tvalid_0's auc: 0.808569\tvalid_0's binary_logloss: 0.497389\n",
+ "[26]\tvalid_0's auc: 0.809069\tvalid_0's binary_logloss: 0.49624\n",
+ "[27]\tvalid_0's auc: 0.809312\tvalid_0's binary_logloss: 0.495095\n",
+ "[28]\tvalid_0's auc: 0.809549\tvalid_0's binary_logloss: 0.494012\n",
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+ "[30]\tvalid_0's auc: 0.810047\tvalid_0's binary_logloss: 0.491735\n",
+ "[31]\tvalid_0's auc: 0.810086\tvalid_0's binary_logloss: 0.490633\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[32]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.489595\n",
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+ "[88]\tvalid_0's auc: 0.821243\tvalid_0's binary_logloss: 0.45061\n",
+ "[89]\tvalid_0's auc: 0.821404\tvalid_0's binary_logloss: 0.450215\n",
+ "[90]\tvalid_0's auc: 0.821488\tvalid_0's binary_logloss: 0.449656\n",
+ "[91]\tvalid_0's auc: 0.821538\tvalid_0's binary_logloss: 0.449107\n",
+ "[92]\tvalid_0's auc: 0.82172\tvalid_0's binary_logloss: 0.448752\n",
+ "[93]\tvalid_0's auc: 0.821809\tvalid_0's binary_logloss: 0.448188\n",
+ "[94]\tvalid_0's auc: 0.82184\tvalid_0's binary_logloss: 0.447659\n",
+ "[95]\tvalid_0's auc: 0.821971\tvalid_0's binary_logloss: 0.447108\n",
+ "[96]\tvalid_0's auc: 0.822086\tvalid_0's binary_logloss: 0.446596\n",
+ "[97]\tvalid_0's auc: 0.82247\tvalid_0's binary_logloss: 0.446244\n",
+ "[98]\tvalid_0's auc: 0.822951\tvalid_0's binary_logloss: 0.445812\n",
+ "[99]\tvalid_0's auc: 0.822991\tvalid_0's binary_logloss: 0.445329\n",
+ "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
+ "[1]\tvalid_0's auc: 0.769525\tvalid_0's binary_logloss: 0.526256\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.775857\tvalid_0's binary_logloss: 0.524594\n",
+ "[3]\tvalid_0's auc: 0.785307\tvalid_0's binary_logloss: 0.523606\n",
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+ "[68]\tvalid_0's auc: 0.813989\tvalid_0's binary_logloss: 0.461843\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[69]\tvalid_0's auc: 0.814218\tvalid_0's binary_logloss: 0.461443\n",
+ "[70]\tvalid_0's auc: 0.814334\tvalid_0's binary_logloss: 0.460775\n",
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+ "[83]\tvalid_0's auc: 0.817162\tvalid_0's binary_logloss: 0.453578\n",
+ "[84]\tvalid_0's auc: 0.817274\tvalid_0's binary_logloss: 0.452984\n",
+ "[85]\tvalid_0's auc: 0.817283\tvalid_0's binary_logloss: 0.452416\n",
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+ "[88]\tvalid_0's auc: 0.817594\tvalid_0's binary_logloss: 0.450926\n",
+ "[89]\tvalid_0's auc: 0.817771\tvalid_0's binary_logloss: 0.450553\n",
+ "[90]\tvalid_0's auc: 0.81789\tvalid_0's binary_logloss: 0.449985\n",
+ "[91]\tvalid_0's auc: 0.817931\tvalid_0's binary_logloss: 0.449439\n",
+ "[92]\tvalid_0's auc: 0.818138\tvalid_0's binary_logloss: 0.449094\n",
+ "[93]\tvalid_0's auc: 0.818334\tvalid_0's binary_logloss: 0.448527\n",
+ "[94]\tvalid_0's auc: 0.818426\tvalid_0's binary_logloss: 0.447989\n",
+ "[95]\tvalid_0's auc: 0.818676\tvalid_0's binary_logloss: 0.447407\n",
+ "[96]\tvalid_0's auc: 0.818852\tvalid_0's binary_logloss: 0.446884\n",
+ "[97]\tvalid_0's auc: 0.81945\tvalid_0's binary_logloss: 0.446455\n",
+ "[98]\tvalid_0's auc: 0.819861\tvalid_0's binary_logloss: 0.446045\n",
+ "[99]\tvalid_0's auc: 0.819943\tvalid_0's binary_logloss: 0.445543\n",
+ "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
+ "[1]\tvalid_0's auc: 0.770032\tvalid_0's binary_logloss: 0.527241\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.779881\tvalid_0's binary_logloss: 0.525545\n",
+ "[3]\tvalid_0's auc: 0.791308\tvalid_0's binary_logloss: 0.524508\n",
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+ "[7]\tvalid_0's auc: 0.79931\tvalid_0's binary_logloss: 0.518872\n",
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+ "[9]\tvalid_0's auc: 0.800549\tvalid_0's binary_logloss: 0.516487\n",
+ "[10]\tvalid_0's auc: 0.800261\tvalid_0's binary_logloss: 0.515039\n",
+ "[11]\tvalid_0's auc: 0.801261\tvalid_0's binary_logloss: 0.513695\n",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
+ "[1]\tvalid_0's auc: 0.768646\tvalid_0's binary_logloss: 0.527167\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.779902\tvalid_0's binary_logloss: 0.525481\n",
+ "[3]\tvalid_0's auc: 0.789868\tvalid_0's binary_logloss: 0.524485\n",
+ "[4]\tvalid_0's auc: 0.791895\tvalid_0's binary_logloss: 0.523382\n",
+ "[5]\tvalid_0's auc: 0.795453\tvalid_0's binary_logloss: 0.521759\n",
+ "[6]\tvalid_0's auc: 0.796672\tvalid_0's binary_logloss: 0.520166\n",
+ "[7]\tvalid_0's auc: 0.798023\tvalid_0's binary_logloss: 0.518857\n",
+ "[8]\tvalid_0's auc: 0.799331\tvalid_0's binary_logloss: 0.517297\n",
+ "[9]\tvalid_0's auc: 0.800181\tvalid_0's binary_logloss: 0.516416\n",
+ "[10]\tvalid_0's auc: 0.800373\tvalid_0's binary_logloss: 0.514967\n",
+ "[11]\tvalid_0's auc: 0.801087\tvalid_0's binary_logloss: 0.513631\n",
+ "[12]\tvalid_0's auc: 0.801122\tvalid_0's binary_logloss: 0.512658\n",
+ "[13]\tvalid_0's auc: 0.801043\tvalid_0's binary_logloss: 0.511833\n",
+ "[14]\tvalid_0's auc: 0.801238\tvalid_0's binary_logloss: 0.510461\n",
+ "[15]\tvalid_0's auc: 0.801847\tvalid_0's binary_logloss: 0.509034\n",
+ "[16]\tvalid_0's auc: 0.803139\tvalid_0's binary_logloss: 0.507759\n",
+ "[17]\tvalid_0's auc: 0.803577\tvalid_0's binary_logloss: 0.506361\n",
+ "[18]\tvalid_0's auc: 0.803834\tvalid_0's binary_logloss: 0.505229\n",
+ "[19]\tvalid_0's auc: 0.803943\tvalid_0's binary_logloss: 0.504371\n",
+ "[20]\tvalid_0's auc: 0.80415\tvalid_0's binary_logloss: 0.503102\n",
+ "[21]\tvalid_0's auc: 0.804446\tvalid_0's binary_logloss: 0.502564\n",
+ "[22]\tvalid_0's auc: 0.805163\tvalid_0's binary_logloss: 0.501396\n",
+ "[23]\tvalid_0's auc: 0.805323\tvalid_0's binary_logloss: 0.500327\n",
+ "[24]\tvalid_0's auc: 0.805314\tvalid_0's binary_logloss: 0.499123\n",
+ "[25]\tvalid_0's auc: 0.80535\tvalid_0's binary_logloss: 0.497927\n",
+ "[26]\tvalid_0's auc: 0.805864\tvalid_0's binary_logloss: 0.496834\n",
+ "[27]\tvalid_0's auc: 0.805919\tvalid_0's binary_logloss: 0.495667\n",
+ "[28]\tvalid_0's auc: 0.806272\tvalid_0's binary_logloss: 0.494606\n",
+ "[29]\tvalid_0's auc: 0.806599\tvalid_0's binary_logloss: 0.49343\n",
+ "[30]\tvalid_0's auc: 0.806932\tvalid_0's binary_logloss: 0.492303\n",
+ "[31]\tvalid_0's auc: 0.806656\tvalid_0's binary_logloss: 0.491249\n",
+ "[32]\tvalid_0's auc: 0.807436\tvalid_0's binary_logloss: 0.490188\n",
+ "[33]\tvalid_0's auc: 0.807629\tvalid_0's binary_logloss: 0.489117\n",
+ "[34]\tvalid_0's auc: 0.807501\tvalid_0's binary_logloss: 0.48808\n",
+ "[35]\tvalid_0's auc: 0.807885\tvalid_0's binary_logloss: 0.487383\n",
+ "[36]\tvalid_0's auc: 0.807921\tvalid_0's binary_logloss: 0.48636\n",
+ "[37]\tvalid_0's auc: 0.808267\tvalid_0's binary_logloss: 0.485724\n",
+ "[38]\tvalid_0's auc: 0.808563\tvalid_0's binary_logloss: 0.485076\n",
+ "[39]\tvalid_0's auc: 0.808813\tvalid_0's binary_logloss: 0.484039\n",
+ "[40]\tvalid_0's auc: 0.809023\tvalid_0's binary_logloss: 0.483091\n",
+ "[41]\tvalid_0's auc: 0.809782\tvalid_0's binary_logloss: 0.482441\n",
+ "[42]\tvalid_0's auc: 0.810135\tvalid_0's binary_logloss: 0.48179\n",
+ "[43]\tvalid_0's auc: 0.810219\tvalid_0's binary_logloss: 0.48082\n",
+ "[44]\tvalid_0's auc: 0.81031\tvalid_0's binary_logloss: 0.479906\n",
+ "[45]\tvalid_0's auc: 0.810514\tvalid_0's binary_logloss: 0.479024\n",
+ "[46]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.478437\n",
+ "[47]\tvalid_0's auc: 0.810611\tvalid_0's binary_logloss: 0.477529\n",
+ "[48]\tvalid_0's auc: 0.810781\tvalid_0's binary_logloss: 0.476637\n",
+ "[49]\tvalid_0's auc: 0.81089\tvalid_0's binary_logloss: 0.475883\n",
+ "[50]\tvalid_0's auc: 0.811266\tvalid_0's binary_logloss: 0.475459\n",
+ "[51]\tvalid_0's auc: 0.811402\tvalid_0's binary_logloss: 0.475078\n",
+ "[52]\tvalid_0's auc: 0.811765\tvalid_0's binary_logloss: 0.474246\n",
+ "[53]\tvalid_0's auc: 0.811891\tvalid_0's binary_logloss: 0.473452\n",
+ "[54]\tvalid_0's auc: 0.811868\tvalid_0's binary_logloss: 0.47263\n",
+ "[55]\tvalid_0's auc: 0.81192\tvalid_0's binary_logloss: 0.471804\n",
+ "[56]\tvalid_0's auc: 0.812272\tvalid_0's binary_logloss: 0.471275\n",
+ "[57]\tvalid_0's auc: 0.812639\tvalid_0's binary_logloss: 0.470396\n",
+ "[58]\tvalid_0's auc: 0.812764\tvalid_0's binary_logloss: 0.469597\n",
+ "[59]\tvalid_0's auc: 0.813084\tvalid_0's binary_logloss: 0.469049\n",
+ "[60]\tvalid_0's auc: 0.813342\tvalid_0's binary_logloss: 0.468244\n",
+ "[61]\tvalid_0's auc: 0.813302\tvalid_0's binary_logloss: 0.467499\n",
+ "[62]\tvalid_0's auc: 0.813221\tvalid_0's binary_logloss: 0.466758\n",
+ "[63]\tvalid_0's auc: 0.813697\tvalid_0's binary_logloss: 0.466017\n",
+ "[64]\tvalid_0's auc: 0.813985\tvalid_0's binary_logloss: 0.465501\n",
+ "[65]\tvalid_0's auc: 0.81416\tvalid_0's binary_logloss: 0.464725\n",
+ "[66]\tvalid_0's auc: 0.814227\tvalid_0's binary_logloss: 0.46398\n",
+ "[67]\tvalid_0's auc: 0.814397\tvalid_0's binary_logloss: 0.463309\n",
+ "[68]\tvalid_0's auc: 0.814426\tvalid_0's binary_logloss: 0.462627\n",
+ "[69]\tvalid_0's auc: 0.814593\tvalid_0's binary_logloss: 0.462244\n",
+ "[70]\tvalid_0's auc: 0.814789\tvalid_0's binary_logloss: 0.461571\n",
+ "[71]\tvalid_0's auc: 0.814889\tvalid_0's binary_logloss: 0.461144\n",
+ "[72]\tvalid_0's auc: 0.815078\tvalid_0's binary_logloss: 0.460684\n",
+ "[73]\tvalid_0's auc: 0.815439\tvalid_0's binary_logloss: 0.460063\n",
+ "[74]\tvalid_0's auc: 0.815511\tvalid_0's binary_logloss: 0.459386\n",
+ "[75]\tvalid_0's auc: 0.815574\tvalid_0's binary_logloss: 0.45877\n",
+ "[76]\tvalid_0's auc: 0.815634\tvalid_0's binary_logloss: 0.458128\n",
+ "[77]\tvalid_0's auc: 0.815618\tvalid_0's binary_logloss: 0.457495\n",
+ "[78]\tvalid_0's auc: 0.81582\tvalid_0's binary_logloss: 0.457057\n",
+ "[79]\tvalid_0's auc: 0.81594\tvalid_0's binary_logloss: 0.456475\n",
+ "[80]\tvalid_0's auc: 0.815961\tvalid_0's binary_logloss: 0.455885\n",
+ "[81]\tvalid_0's auc: 0.816153\tvalid_0's binary_logloss: 0.455511\n",
+ "[82]\tvalid_0's auc: 0.816433\tvalid_0's binary_logloss: 0.455186\n",
+ "[83]\tvalid_0's auc: 0.816546\tvalid_0's binary_logloss: 0.454625\n",
+ "[84]\tvalid_0's auc: 0.816586\tvalid_0's binary_logloss: 0.454039\n",
+ "[85]\tvalid_0's auc: 0.816584\tvalid_0's binary_logloss: 0.453482\n",
+ "[86]\tvalid_0's auc: 0.816881\tvalid_0's binary_logloss: 0.453048\n",
+ "[87]\tvalid_0's auc: 0.817029\tvalid_0's binary_logloss: 0.452485\n",
+ "[88]\tvalid_0's auc: 0.81707\tvalid_0's binary_logloss: 0.451941\n",
+ "[89]\tvalid_0's auc: 0.817298\tvalid_0's binary_logloss: 0.451544\n",
+ "[90]\tvalid_0's auc: 0.817343\tvalid_0's binary_logloss: 0.450975\n",
+ "[91]\tvalid_0's auc: 0.817357\tvalid_0's binary_logloss: 0.450422\n",
+ "[92]\tvalid_0's auc: 0.817592\tvalid_0's binary_logloss: 0.450109\n",
+ "[93]\tvalid_0's auc: 0.817729\tvalid_0's binary_logloss: 0.449542\n",
+ "[94]\tvalid_0's auc: 0.817834\tvalid_0's binary_logloss: 0.448982\n",
+ "[95]\tvalid_0's auc: 0.81809\tvalid_0's binary_logloss: 0.448398\n",
+ "[96]\tvalid_0's auc: 0.818269\tvalid_0's binary_logloss: 0.447908\n",
+ "[97]\tvalid_0's auc: 0.818682\tvalid_0's binary_logloss: 0.447547\n",
+ "[98]\tvalid_0's auc: 0.819015\tvalid_0's binary_logloss: 0.447165\n",
+ "[99]\tvalid_0's auc: 0.819016\tvalid_0's binary_logloss: 0.446669\n",
+ "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_rank_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 模型及参数的定义\n",
+ " lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) \n",
+ " # 训练模型\n",
+ " lgb_Classfication.fit(train_idx[lgb_cols], train_idx['label'],eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], \n",
+ " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = lgb_Classfication.predict_proba(valid_idx[lgb_cols], \n",
+ " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
+ " \n",
+ " # 对输出结果进行归一化 分类模型输出的值本身就是一个概率值不需要进行归一化\n",
+ " # valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += lgb_Classfication.predict_proba(tst_user_item_feats_df_rank_model[lgb_cols], \n",
+ " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_cls_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_cls_feats.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:23.074237Z",
+ "start_time": "2020-11-18T04:24:13.812284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_cls')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DIN模型"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户的历史点击行为列表\n",
+ "这个是为后面的DIN模型服务的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:30.508213Z",
+ "start_time": "2020-11-18T04:24:27.426372Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "if offline:\n",
+ " all_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
+ "else:\n",
+ " trn_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
+ " tst_data = pd.read_csv('./data_raw/testA_click_log.csv')\n",
+ " all_data = trn_data.append(tst_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:25:28.082071Z",
+ "start_time": "2020-11-18T04:24:33.649524Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "hist_click =all_data[['user_id', 'click_article_id']].groupby('user_id').agg({list}).reset_index()\n",
+ "his_behavior_df = pd.DataFrame()\n",
+ "his_behavior_df['user_id'] = hist_click['user_id']\n",
+ "his_behavior_df['hist_click_article_id'] = hist_click['click_article_id']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:25:52.925866Z",
+ "start_time": "2020-11-18T04:25:52.863922Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "trn_user_item_feats_df_din_model = trn_user_item_feats_df.copy()\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_din_model = val_user_item_feats_df.copy()\n",
+ "else: \n",
+ " val_user_item_feats_df_din_model = None\n",
+ " \n",
+ "tst_user_item_feats_df_din_model = tst_user_item_feats_df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:00.070681Z",
+ "start_time": "2020-11-18T04:25:56.417197Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "trn_user_item_feats_df_din_model = trn_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_din_model = val_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
+ "else:\n",
+ " val_user_item_feats_df_din_model = None\n",
+ "\n",
+ "tst_user_item_feats_df_din_model = tst_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### DIN模型简介\n",
+ "我们下面尝试使用DIN模型， DIN的全称是Deep Interest Network， 这是阿里2018年基于前面的深度学习模型无法表达用户多样化的兴趣而提出的一个模型， 它可以通过考虑【给定的候选广告】和【用户的历史行为】的相关性，来计算用户兴趣的表示向量。具体来说就是通过引入局部激活单元，通过软搜索历史行为的相关部分来关注相关的用户兴趣，并采用加权和来获得有关候选广告的用户兴趣的表示。与候选广告相关性较高的行为会获得较高的激活权重，并支配着用户兴趣。该表示向量在不同广告上有所不同，大大提高了模型的表达能力。所以该模型对于此次新闻推荐的任务也比较适合， 我们在这里通过当前的候选文章与用户历史点击文章的相关性来计算用户对于文章的兴趣。 该模型的结构如下：\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "我们这里直接调包来使用这个模型， 关于这个模型的详细细节部分我们会在下一期的推荐系统组队学习中给出。下面说一下该模型如何具体使用：deepctr的函数原型如下：\n",
+ "> def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,\n",
+ "> dnn_hidden_units=(200, 80), dnn_activation='relu', att_hidden_size=(80, 40), att_activation=\"dice\",\n",
+ "> att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024,\n",
+ "> task='binary'):\n",
+ "> \n",
+ "> * dnn_feature_columns: 特征列， 包含数据所有特征的列表\n",
+ "> * history_feature_list: 用户历史行为列， 反应用户历史行为的特征的列表\n",
+ "> * dnn_use_bn: 是否使用BatchNormalization\n",
+ "> * dnn_hidden_units: 全连接层网络的层数和每一层神经元的个数， 一个列表或者元组\n",
+ "> * dnn_activation_relu: 全连接网络的激活单元类型\n",
+ "> * att_hidden_size: 注意力层的全连接网络的层数和每一层神经元的个数\n",
+ "> * att_activation: 注意力层的激活单元类型\n",
+ "> * att_weight_normalization: 是否归一化注意力得分\n",
+ "> * l2_reg_dnn: 全连接网络的正则化系数\n",
+ "> * l2_reg_embedding: embedding向量的正则化稀疏\n",
+ "> * dnn_dropout: 全连接网络的神经元的失活概率\n",
+ "> * task: 任务， 可以是分类， 也可是是回归\n",
+ "\n",
+ "在具体使用的时候， 我们必须要传入特征列和历史行为列， 但是再传入之前， 我们需要进行一下特征列的预处理。具体如下：\n",
+ "\n",
+ "1. 首先，我们要处理数据集， 得到数据， 由于我们是基于用户过去的行为去预测用户是否点击当前文章， 所以我们需要把数据的特征列划分成数值型特征， 离散型特征和历史行为特征列三部分， 对于每一部分， DIN模型的处理会有不同\n",
+ " 1. 对于离散型特征， 在我们的数据集中就是那些类别型的特征， 比如user_id这种， 这种类别型特征， 我们首先要经过embedding处理得到每个特征的低维稠密型表示， 既然要经过embedding， 那么我们就需要为每一列的类别特征的取值建立一个字典，并指明embedding维度， 所以在使用deepctr的DIN模型准备数据的时候， 我们需要通过SparseFeat函数指明这些类别型特征, 这个函数的传入参数就是列名， 列的唯一取值(建立字典用)和embedding维度。\n",
+ " 2. 对于用户历史行为特征列， 比如文章id， 文章的类别等这种， 同样的我们需要先经过embedding处理， 只不过和上面不一样的地方是，对于这种特征， 我们在得到每个特征的embedding表示之后， 还需要通过一个Attention_layer计算用户的历史行为和当前候选文章的相关性以此得到当前用户的embedding向量， 这个向量就可以基于当前的候选文章与用户过去点击过得历史文章的相似性的程度来反应用户的兴趣， 并且随着用户的不同的历史点击来变化，去动态的模拟用户兴趣的变化过程。这类特征对于每个用户都是一个历史行为序列， 对于每个用户， 历史行为序列长度会不一样， 可能有的用户点击的历史文章多，有的点击的历史文章少， 所以我们还需要把这个长度统一起来， 在为DIN模型准备数据的时候， 我们首先要通过SparseFeat函数指明这些类别型特征， 然后还需要通过VarLenSparseFeat函数再进行序列填充， 使得每个用户的历史序列一样长， 所以这个函数参数中会有个maxlen，来指明序列的最大长度是多少。\n",
+ " 3. 对于连续型特征列， 我们只需要用DenseFeat函数来指明列名和维度即可。\n",
+ "2. 处理完特征列之后， 我们把相应的数据与列进行对应，就得到了最后的数据。\n",
+ "\n",
+ "下面根据具体的代码感受一下， 逻辑是这样， 首先我们需要写一个数据准备函数， 在这里面就是根据上面的具体步骤准备数据， 得到数据和特征列， 然后就是建立DIN模型并训练， 最后基于模型进行测试。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:08.405211Z",
+ "start_time": "2020-11-18T04:26:04.887013Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 导入deepctr\n",
+ "from deepctr.models import DIN\n",
+ "from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, get_feature_names\n",
+ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "from tensorflow.keras import backend as K\n",
+ "from tensorflow.keras.layers import *\n",
+ "from tensorflow.keras.models import *\n",
+ "from tensorflow.keras.callbacks import * \n",
+ "import tensorflow as tf\n",
+ "\n",
+ "import os\n",
+ "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
+ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:13.485712Z",
+ "start_time": "2020-11-18T04:26:13.476042Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 数据准备函数\n",
+ "def get_din_feats_columns(df, dense_fea, sparse_fea, behavior_fea, his_behavior_fea, emb_dim=32, max_len=100):\n",
+ " \"\"\"\n",
+ " 数据准备函数:\n",
+ " df: 数据集\n",
+ " dense_fea: 数值型特征列\n",
+ " sparse_fea: 离散型特征列\n",
+ " behavior_fea: 用户的候选行为特征列\n",
+ " his_behavior_fea: 用户的历史行为特征列\n",
+ " embedding_dim: embedding的维度， 这里为了简单， 统一把离散型特征列采用一样的隐向量维度\n",
+ " max_len: 用户序列的最大长度\n",
+ " \"\"\"\n",
+ " \n",
+ " sparse_feature_columns = [SparseFeat(feat, vocabulary_size=df[feat].nunique() + 1, embedding_dim=emb_dim) for feat in sparse_fea]\n",
+ " \n",
+ " dense_feature_columns = [DenseFeat(feat, 1, ) for feat in dense_fea]\n",
+ " \n",
+ " var_feature_columns = [VarLenSparseFeat(SparseFeat(feat, vocabulary_size=df['click_article_id'].nunique() + 1,\n",
+ " embedding_dim=emb_dim, embedding_name='click_article_id'), maxlen=max_len) for feat in hist_behavior_fea]\n",
+ " \n",
+ " dnn_feature_columns = sparse_feature_columns + dense_feature_columns + var_feature_columns\n",
+ " \n",
+ " # 建立x, x是一个字典的形式\n",
+ " x = {}\n",
+ " for name in get_feature_names(dnn_feature_columns):\n",
+ " if name in his_behavior_fea:\n",
+ " # 这是历史行为序列\n",
+ " his_list = [l for l in df[name]]\n",
+ " x[name] = pad_sequences(his_list, maxlen=max_len, padding='post') # 二维数组\n",
+ " else:\n",
+ " x[name] = df[name].values\n",
+ " \n",
+ " return x, dnn_feature_columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:18.783217Z",
+ "start_time": "2020-11-18T04:26:18.776795Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 把特征分开\n",
+ "sparse_fea = ['user_id', 'click_article_id', 'category_id', 'click_environment', 'click_deviceGroup', \n",
+ " 'click_os', 'click_country', 'click_region', 'click_referrer_type', 'is_cat_hab']\n",
+ "\n",
+ "behavior_fea = ['click_article_id']\n",
+ "\n",
+ "hist_behavior_fea = ['hist_click_article_id']\n",
+ "\n",
+ "dense_fea = ['sim0', 'time_diff0', 'word_diff0', 'sim_max', 'sim_min', 'sim_sum', 'sim_mean', 'score',\n",
+ " 'rank','click_size','time_diff_mean','active_level','user_time_hob1','user_time_hob2',\n",
+ " 'words_hbo','words_count']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:25.469810Z",
+ "start_time": "2020-11-18T04:26:24.779347Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# dense特征进行归一化, 神经网络训练都需要将数值进行归一化处理\n",
+ "mm = MinMaxScaler()\n",
+ "\n",
+ "# 下面是做一些特殊处理，当在其他的地方出现无效值的时候，不处理无法进行归一化，刚开始可以先把他注释掉，在运行了下面的代码\n",
+ "# 之后如果发现报错，应该先去想办法处理如何不出现inf之类的值\n",
+ "# trn_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
+ "# tst_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
+ "\n",
+ "for feat in dense_fea:\n",
+ " trn_user_item_feats_df_din_model[feat] = mm.fit_transform(trn_user_item_feats_df_din_model[[feat]])\n",
+ " \n",
+ " if val_user_item_feats_df_din_model is not None:\n",
+ " val_user_item_feats_df_din_model[feat] = mm.fit_transform(val_user_item_feats_df_din_model[[feat]])\n",
+ " \n",
+ " tst_user_item_feats_df_din_model[feat] = mm.fit_transform(tst_user_item_feats_df_din_model[[feat]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:36.727753Z",
+ "start_time": "2020-11-18T04:26:28.854705Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 准备训练数据\n",
+ "x_trn, dnn_feature_columns = get_din_feats_columns(trn_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ "y_trn = trn_user_item_feats_df_din_model['label'].values\n",
+ "\n",
+ "if offline:\n",
+ " # 准备验证数据\n",
+ " x_val, dnn_feature_columns = get_din_feats_columns(val_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_val = val_user_item_feats_df_din_model['label'].values\n",
+ " \n",
+ "dense_fea = [x for x in dense_fea if x != 'label']\n",
+ "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:45.146318Z",
+ "start_time": "2020-11-18T04:26:40.423914Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:255: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
+ "Model: \"model\"\n",
+ "__________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "==================================================================================================\n",
+ "user_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_article_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "category_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_environment (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_deviceGroup (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_os (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_country (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_region (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_referrer_type (InputLayer [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "is_cat_hab (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_user_id (Embedding) (None, 1, 32) 1600032 user_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_seq_emb_hist_click_artic multiple 525664 click_article_id[0][0] \n",
+ " hist_click_article_id[0][0] \n",
+ " click_article_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_category_id (Embeddi (None, 1, 32) 7776 category_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_environment (E (None, 1, 32) 128 click_environment[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_deviceGroup (E (None, 1, 32) 160 click_deviceGroup[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_os (Embedding) (None, 1, 32) 288 click_os[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_country (Embed (None, 1, 32) 384 click_country[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_region (Embedd (None, 1, 32) 928 click_region[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_referrer_type (None, 1, 32) 256 click_referrer_type[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_is_cat_hab (Embeddin (None, 1, 32) 64 is_cat_hab[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask (NoMask) (None, 1, 32) 0 sparse_emb_user_id[0][0] \n",
+ " sparse_seq_emb_hist_click_article\n",
+ " sparse_emb_category_id[0][0] \n",
+ " sparse_emb_click_environment[0][0\n",
+ " sparse_emb_click_deviceGroup[0][0\n",
+ " sparse_emb_click_os[0][0] \n",
+ " sparse_emb_click_country[0][0] \n",
+ " sparse_emb_click_region[0][0] \n",
+ " sparse_emb_click_referrer_type[0]\n",
+ " sparse_emb_is_cat_hab[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "hist_click_article_id (InputLay [(None, 50)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate (Concatenate) (None, 1, 320) 0 no_mask[0][0] \n",
+ " no_mask[1][0] \n",
+ " no_mask[2][0] \n",
+ " no_mask[3][0] \n",
+ " no_mask[4][0] \n",
+ " no_mask[5][0] \n",
+ " no_mask[6][0] \n",
+ " no_mask[7][0] \n",
+ " no_mask[8][0] \n",
+ " no_mask[9][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_1 (NoMask) (None, 1, 320) 0 concatenate[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "attention_sequence_pooling_laye (None, 1, 32) 13961 sparse_seq_emb_hist_click_article\n",
+ " sparse_seq_emb_hist_click_article\n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_1 (Concatenate) (None, 1, 352) 0 no_mask_1[0][0] \n",
+ " attention_sequence_pooling_layer[\n",
+ "__________________________________________________________________________________________________\n",
+ "sim0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "time_diff0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "word_diff0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_max (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_min (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_sum (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_mean (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "score (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "rank (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_size (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "time_diff_mean (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "active_level (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "user_time_hob1 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "user_time_hob2 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "words_hbo (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "words_count (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten (Flatten) (None, 352) 0 concatenate_1[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_3 (NoMask) (None, 1) 0 sim0[0][0] \n",
+ " time_diff0[0][0] \n",
+ " word_diff0[0][0] \n",
+ " sim_max[0][0] \n",
+ " sim_min[0][0] \n",
+ " sim_sum[0][0] \n",
+ " sim_mean[0][0] \n",
+ " score[0][0] \n",
+ " rank[0][0] \n",
+ " click_size[0][0] \n",
+ " time_diff_mean[0][0] \n",
+ " active_level[0][0] \n",
+ " user_time_hob1[0][0] \n",
+ " user_time_hob2[0][0] \n",
+ " words_hbo[0][0] \n",
+ " words_count[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_2 (NoMask) (None, 352) 0 flatten[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_2 (Concatenate) (None, 16) 0 no_mask_3[0][0] \n",
+ " no_mask_3[1][0] \n",
+ " no_mask_3[2][0] \n",
+ " no_mask_3[3][0] \n",
+ " no_mask_3[4][0] \n",
+ " no_mask_3[5][0] \n",
+ " no_mask_3[6][0] \n",
+ " no_mask_3[7][0] \n",
+ " no_mask_3[8][0] \n",
+ " no_mask_3[9][0] \n",
+ " no_mask_3[10][0] \n",
+ " no_mask_3[11][0] \n",
+ " no_mask_3[12][0] \n",
+ " no_mask_3[13][0] \n",
+ " no_mask_3[14][0] \n",
+ " no_mask_3[15][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 352) 0 no_mask_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 16) 0 concatenate_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_4 (NoMask) multiple 0 flatten_1[0][0] \n",
+ " flatten_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_3 (Concatenate) (None, 368) 0 no_mask_4[0][0] \n",
+ " no_mask_4[1][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "dnn_1 (DNN) (None, 80) 89880 concatenate_3[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "dense (Dense) (None, 1) 80 dnn_1[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "prediction_layer (PredictionLay (None, 1) 1 dense[0][0] \n",
+ "==================================================================================================\n",
+ "Total params: 2,239,602\n",
+ "Trainable params: 2,239,362\n",
+ "Non-trainable params: 240\n",
+ "__________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 建立模型\n",
+ "model = DIN(dnn_feature_columns, behavior_fea)\n",
+ "\n",
+ "# 查看模型结构\n",
+ "model.summary()\n",
+ "\n",
+ "# 模型编译\n",
+ "model.compile('adam', 'binary_crossentropy',metrics=['binary_crossentropy', tf.keras.metrics.AUC()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:28:43.885773Z",
+ "start_time": "2020-11-18T04:26:48.746787Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/2\n",
+ "290964/290964 [==============================] - 55s 189us/sample - loss: 0.4209 - binary_crossentropy: 0.4206 - auc: 0.7842\n",
+ "Epoch 2/2\n",
+ "290964/290964 [==============================] - 52s 178us/sample - loss: 0.3630 - binary_crossentropy: 0.3618 - auc: 0.8478\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 模型训练\n",
+ "if offline:\n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=10, validation_data=(x_val, y_val) , batch_size=256)\n",
+ "else:\n",
+ " # 也可以使用上面的语句用自己采样出来的验证集\n",
+ " # history = model.fit(x_trn, y_trn, verbose=1, epochs=3, validation_split=0.3, batch_size=256)\n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, batch_size=256)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:29:20.436591Z",
+ "start_time": "2020-11-18T04:28:58.102057Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "500000/500000 [==============================] - 20s 39us/sample\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = model.predict(x_tst, verbose=1, batch_size=256)\n",
+ "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'din_rank_score.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:29:34.985535Z",
+ "start_time": "2020-11-18T04:29:26.264531Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "submit(rank_results, topk=5, model_name='din')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-15T06:15:49.490705Z",
+ "start_time": "2020-11-15T06:15:49.473794Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:38:53.760383Z",
+ "start_time": "2020-11-18T04:29:51.737721Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 232681 samples, validate on 58283 samples\n",
+ "Epoch 1/2\n",
+ "232681/232681 [==============================] - 44s 189us/sample - loss: 0.2864 - binary_crossentropy: 0.2846 - auc: 0.9008 - val_loss: 0.2830 - val_binary_crossentropy: 0.2813 - val_auc: 0.9072\n",
+ "Epoch 2/2\n",
+ "232681/232681 [==============================] - 44s 187us/sample - loss: 0.2832 - binary_crossentropy: 0.2816 - auc: 0.9034 - val_loss: 0.2846 - val_binary_crossentropy: 0.2830 - val_auc: 0.9053\n",
+ "58283/58283 [==============================] - 2s 36us/sample\n",
+ "500000/500000 [==============================] - 19s 37us/sample\n",
+ "Train on 232798 samples, validate on 58166 samples\n",
+ "Epoch 1/2\n",
+ "232798/232798 [==============================] - 43s 184us/sample - loss: 0.2818 - binary_crossentropy: 0.2802 - auc: 0.9051 - val_loss: 0.2968 - val_binary_crossentropy: 0.2953 - val_auc: 0.9062\n",
+ "Epoch 2/2\n",
+ "232798/232798 [==============================] - 44s 187us/sample - loss: 0.2796 - binary_crossentropy: 0.2782 - auc: 0.9069 - val_loss: 0.2820 - val_binary_crossentropy: 0.2806 - val_auc: 0.9071\n",
+ "58166/58166 [==============================] - 2s 38us/sample\n",
+ "500000/500000 [==============================] - 18s 37us/sample\n",
+ "Train on 232847 samples, validate on 58117 samples\n",
+ "Epoch 1/2\n",
+ "232847/232847 [==============================] - 43s 185us/sample - loss: 0.2786 - binary_crossentropy: 0.2773 - auc: 0.9080 - val_loss: 0.2761 - val_binary_crossentropy: 0.2749 - val_auc: 0.9113\n",
+ "Epoch 2/2\n",
+ "232847/232847 [==============================] - 39s 166us/sample - loss: 0.2766 - binary_crossentropy: 0.2754 - auc: 0.9097 - val_loss: 0.2872 - val_binary_crossentropy: 0.2862 - val_auc: 0.9090\n",
+ "58117/58117 [==============================] - 2s 34us/sample\n",
+ "500000/500000 [==============================] - 17s 33us/sample\n",
+ "Train on 232716 samples, validate on 58248 samples\n",
+ "Epoch 1/2\n",
+ "232716/232716 [==============================] - 39s 169us/sample - loss: 0.2763 - binary_crossentropy: 0.2753 - auc: 0.9100 - val_loss: 0.2739 - val_binary_crossentropy: 0.2730 - val_auc: 0.9116\n",
+ "Epoch 2/2\n",
+ "232716/232716 [==============================] - 39s 168us/sample - loss: 0.2743 - binary_crossentropy: 0.2735 - auc: 0.9119 - val_loss: 0.2859 - val_binary_crossentropy: 0.2851 - val_auc: 0.9090\n",
+ "58248/58248 [==============================] - 2s 35us/sample\n",
+ "500000/500000 [==============================] - 17s 34us/sample\n",
+ "Train on 232814 samples, validate on 58150 samples\n",
+ "Epoch 1/2\n",
+ "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2747 - binary_crossentropy: 0.2739 - auc: 0.9115 - val_loss: 0.2702 - val_binary_crossentropy: 0.2695 - val_auc: 0.9163\n",
+ "Epoch 2/2\n",
+ "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2725 - binary_crossentropy: 0.2719 - auc: 0.9132 - val_loss: 0.2751 - val_binary_crossentropy: 0.2745 - val_auc: 0.9151\n",
+ "58150/58150 [==============================] - 2s 34us/sample\n",
+ "500000/500000 [==============================] - 17s 34us/sample\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_din_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "dense_fea = [x for x in dense_fea if x != 'label']\n",
+ "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 准备训练数据\n",
+ " x_trn, dnn_feature_columns = get_din_feats_columns(train_idx, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_trn = train_idx['label'].values\n",
+ "\n",
+ " # 准备验证数据\n",
+ " x_val, dnn_feature_columns = get_din_feats_columns(valid_idx, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_val = valid_idx['label'].values\n",
+ " \n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, validation_data=(x_val, y_val) , batch_size=256)\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = model.predict(x_val, verbose=1, batch_size=256) \n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += model.predict(x_tst, verbose=1, batch_size=256)[:, 0] \n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_din_cls_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = tst_user_item_feats_df_din_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_din_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_din_model['pred_rank'] = tst_user_item_feats_df_din_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_din_cls_feats.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 模型融合"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 加权融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:27.351996Z",
+ "start_time": "2020-11-18T04:44:26.561275Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取多个模型的排序结果文件\n",
+ "lgb_ranker = pd.read_csv(save_path + 'lgb_ranker_score.csv')\n",
+ "lgb_cls = pd.read_csv(save_path + 'lgb_cls_score.csv')\n",
+ "din_ranker = pd.read_csv(save_path + 'din_rank_score.csv')\n",
+ "\n",
+ "# 这里也可以换成交叉验证输出的测试结果进行加权融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:31.593981Z",
+ "start_time": "2020-11-18T04:44:31.589439Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "rank_model = {'lgb_ranker': lgb_ranker, \n",
+ " 'lgb_cls': lgb_cls, \n",
+ " 'din_ranker': din_ranker}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:36.135860Z",
+ "start_time": "2020-11-18T04:44:36.130577Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_ensumble_predict_topk(rank_model, topk=5):\n",
+ " final_recall = rank_model['lgb_cls'].append(rank_model['din_ranker'])\n",
+ " rank_model['lgb_ranker']['pred_score'] = rank_model['lgb_ranker']['pred_score'].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " final_recall = final_recall.append(rank_model['lgb_ranker'])\n",
+ " final_recall = final_recall.groupby(['user_id', 'click_article_id'])['pred_score'].sum().reset_index()\n",
+ " \n",
+ " submit(final_recall, topk=topk, model_name='ensemble_fuse')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:51.659270Z",
+ "start_time": "2020-11-18T04:44:40.445659Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "get_ensumble_predict_topk(rank_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Staking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:58.025992Z",
+ "start_time": "2020-11-18T04:44:56.146962Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取多个模型的交叉验证生成的结果文件\n",
+ "# 训练集\n",
+ "trn_lgb_ranker_feats = pd.read_csv(save_path + 'trn_lgb_ranker_feats.csv')\n",
+ "trn_lgb_cls_feats = pd.read_csv(save_path + 'trn_lgb_cls_feats.csv')\n",
+ "trn_din_cls_feats = pd.read_csv(save_path + 'trn_din_cls_feats.csv')\n",
+ "\n",
+ "# 测试集\n",
+ "tst_lgb_ranker_feats = pd.read_csv(save_path + 'tst_lgb_ranker_feats.csv')\n",
+ "tst_lgb_cls_feats = pd.read_csv(save_path + 'tst_lgb_cls_feats.csv')\n",
+ "tst_din_cls_feats = pd.read_csv(save_path + 'tst_din_cls_feats.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:07.701862Z",
+ "start_time": "2020-11-18T04:45:07.644335Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 将多个模型输出的特征进行拼接\n",
+ "\n",
+ "finall_trn_ranker_feats = trn_lgb_ranker_feats[['user_id', 'click_article_id', 'label']]\n",
+ "finall_tst_ranker_feats = tst_lgb_ranker_feats[['user_id', 'click_article_id']]\n",
+ "\n",
+ "for idx, trn_model in enumerate([trn_lgb_ranker_feats, trn_lgb_cls_feats, trn_din_cls_feats]):\n",
+ " for feat in [ 'pred_score', 'pred_rank']:\n",
+ " col_name = feat + '_' + str(idx)\n",
+ " finall_trn_ranker_feats[col_name] = trn_model[feat]\n",
+ "\n",
+ "for idx, tst_model in enumerate([tst_lgb_ranker_feats, tst_lgb_cls_feats, tst_din_cls_feats]):\n",
+ " for feat in [ 'pred_score', 'pred_rank']:\n",
+ " col_name = feat + '_' + str(idx)\n",
+ " finall_tst_ranker_feats[col_name] = tst_model[feat]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:15.044242Z",
+ "start_time": "2020-11-18T04:45:13.138252Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义一个逻辑回归模型再次拟合交叉验证产生的特征对测试集进行预测\n",
+ "# 这里需要注意的是，在做交叉验证的时候可以构造多一些与输出预测值相关的特征，来丰富这里简单模型的特征\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "\n",
+ "feat_cols = ['pred_score_0', 'pred_rank_0', 'pred_score_1', 'pred_rank_1', 'pred_score_2', 'pred_rank_2']\n",
+ "\n",
+ "trn_x = finall_trn_ranker_feats[feat_cols]\n",
+ "trn_y = finall_trn_ranker_feats['label']\n",
+ "\n",
+ "tst_x = finall_tst_ranker_feats[feat_cols]\n",
+ "\n",
+ "# 定义模型\n",
+ "lr = LogisticRegression()\n",
+ "\n",
+ "# 模型训练\n",
+ "lr.fit(trn_x, trn_y)\n",
+ "\n",
+ "# 模型预测\n",
+ "finall_tst_ranker_feats['pred_score'] = lr.predict_proba(tst_x)[:, 1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:29.018764Z",
+ "start_time": "2020-11-18T04:45:19.423130Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = finall_tst_ranker_feats[['user_id', 'click_article_id', 'pred_score']]\n",
+ "submit(rank_results, topk=5, model_name='ensumble_staking')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 总结\n",
+ "本章主要学习了三个排序模型，包括LGB的Rank， LGB的Classifier还有深度学习的DIN模型， 当然，对于这三个模型的原理部分，我们并没有给出详细的介绍， 请大家课下自己探索原理，也欢迎大家把自己的探索与所学分享出来，我们一块学习和进步。最后，我们进行了简单的模型融合策略，包括简单的加权和Stacking。\n",
+ "\n",
+ "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
+ "\n",
+ ""
+ ]
}
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = finall_tst_ranker_feats[['user_id', 'click_article_id', 'pred_score']]\n",
- "submit(rank_results, topk=5, model_name='ensumble_staking')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 总结\n",
- "本章主要学习了三个排序模型，包括LGB的Rank， LGB的Classifier还有深度学习的DIN模型， 当然，对于这三个模型的原理部分，我们并没有给出详细的介绍， 请大家课下自己探索原理，也欢迎大家把自己的探索与所学分享出来，我们一块学习和进步。最后，我们进行了简单的模型融合策略，包括简单的加权和Stacking。\n",
- "\n",
- "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
- ]
- }
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+ "language": "python",
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+ "codemirror_mode": {
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+ "version": 3
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+ "mimetype": "text/x-python",
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+ "latex_envs": {
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diff --git "a/docs/ch03/ch3.1/jupyter/\346\225\260\346\215\256\345\210\206\346\236\220.ipynb" "b/docs/ch03/ch3.1/jupyter/\346\225\260\346\215\256\345\210\206\346\236\220.ipynb"
index c9cbc0c37..6bc2d7d2b 100644
--- "a/docs/ch03/ch3.1/jupyter/\346\225\260\346\215\256\345\210\206\346\236\220.ipynb"
+++ "b/docs/ch03/ch3.1/jupyter/\346\225\260\346\215\256\345\210\206\346\236\220.ipynb"
@@ -1,3980 +1,3980 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 数据分析\n",
- "\n",
- "数据分析的价值主要在于熟悉了解整个数据集的基本情况包括每个文件里有哪些数据，具体的文件中的每个字段表示什么实际含义，以及数据集中特征之间的相关性，在推荐场景下主要就是分析用户本身的基本属性，文章基本属性，以及用户和文章交互的一些分布，这些都有利于后面的召回策略的选择，以及特征工程。\n",
- "\n",
- "**建议：当特征工程和模型调参已经很难继续上分了，可以回来在重新从新的角度去分析这些数据，或许可以找到上分的灵感**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 导包"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:13:59.322486Z",
- "start_time": "2020-11-13T15:13:55.601445Z"
- }
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "plt.rc('font', family='SimHei', size=13)\n",
- "\n",
- "import os,gc,re,warnings,sys\n",
- "warnings.filterwarnings(\"ignore\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 读取数据"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:14:18.918041Z",
- "start_time": "2020-11-13T15:14:02.568798Z"
- }
- },
- "outputs": [],
- "source": [
- "# path = './data/' # 自定义的路径\n",
- "path = './' # 天池平台路径\n",
- "\n",
- "#####train\n",
- "trn_click = pd.read_csv(path+'train_click_log.csv')\n",
- "#trn_click = pd.read_csv(path+'train_click_log.csv', names=['user_id','item_id','click_time','click_environment','click_deviceGroup','click_os','click_country','click_region','click_referrer_type'])\n",
- "item_df = pd.read_csv(path+'articles.csv')\n",
- "item_df = item_df.rename(columns={'article_id': 'click_article_id'}) #重命名，方便后续match\n",
- "item_emb_df = pd.read_csv(path+'articles_emb.csv')\n",
- "\n",
- "#####test\n",
- "tst_click = pd.read_csv(path+'testA_click_log.csv')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 数据预处理\n",
- "计算用户点击rank和点击次数"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:14:31.746748Z",
- "start_time": "2020-11-13T15:14:31.409643Z"
- }
- },
- "outputs": [],
- "source": [
- "# 对每个用户的点击时间戳进行排序\n",
- "trn_click['rank'] = trn_click.groupby(['user_id'])['click_timestamp'].rank(ascending=False).astype(int)\n",
- "tst_click['rank'] = tst_click.groupby(['user_id'])['click_timestamp'].rank(ascending=False).astype(int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:15:04.503079Z",
- "start_time": "2020-11-13T15:15:04.394329Z"
- }
- },
- "outputs": [],
- "source": [
- "#计算用户点击文章的次数，并添加新的一列count\n",
- "trn_click['click_cnts'] = trn_click.groupby(['user_id'])['click_timestamp'].transform('count')\n",
- "tst_click['click_cnts'] = tst_click.groupby(['user_id'])['click_timestamp'].transform('count')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 数据浏览"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户点击日志文件_训练集"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:16:07.764776Z",
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- "mean 1.221198e+05 1.951541e+05 1.507588e+12 3.947786e+00 \n",
- "std 5.540349e+04 9.292286e+04 3.363466e+08 3.276715e-01 \n",
- "min 0.000000e+00 3.000000e+00 1.507030e+12 1.000000e+00 \n",
- "25% 7.934700e+04 1.239090e+05 1.507297e+12 4.000000e+00 \n",
- "50% 1.309670e+05 2.038900e+05 1.507596e+12 4.000000e+00 \n",
- "75% 1.704010e+05 2.777120e+05 1.507841e+12 4.000000e+00 \n",
- "max 1.999990e+05 3.640460e+05 1.510603e+12 4.000000e+00 \n",
- "\n",
- " click_deviceGroup click_os click_country click_region \\\n",
- "count 1.112623e+06 1.112623e+06 1.112623e+06 1.112623e+06 \n",
- "mean 1.815981e+00 1.301976e+01 1.310776e+00 1.813587e+01 \n",
- "std 1.035170e+00 6.967844e+00 1.618264e+00 7.105832e+00 \n",
- "min 1.000000e+00 2.000000e+00 1.000000e+00 1.000000e+00 \n",
- "25% 1.000000e+00 2.000000e+00 1.000000e+00 1.300000e+01 \n",
- "50% 1.000000e+00 1.700000e+01 1.000000e+00 2.100000e+01 \n",
- "75% 3.000000e+00 1.700000e+01 1.000000e+00 2.500000e+01 \n",
- "max 5.000000e+00 2.000000e+01 1.100000e+01 2.800000e+01 \n",
- "\n",
- " click_referrer_type rank click_cnts category_id \\\n",
- "count 1.112623e+06 1.112623e+06 1.112623e+06 1.112623e+06 \n",
- "mean 1.910063e+00 7.118518e+00 1.323704e+01 3.056176e+02 \n",
- "std 1.220012e+00 1.016095e+01 1.631503e+01 1.155791e+02 \n",
- "min 1.000000e+00 1.000000e+00 2.000000e+00 1.000000e+00 \n",
- "25% 1.000000e+00 2.000000e+00 4.000000e+00 2.500000e+02 \n",
- "50% 2.000000e+00 4.000000e+00 8.000000e+00 3.280000e+02 \n",
- "75% 2.000000e+00 8.000000e+00 1.600000e+01 4.100000e+02 \n",
- "max 7.000000e+00 2.410000e+02 2.410000e+02 4.600000e+02 \n",
- "\n",
- " created_at_ts words_count \n",
- "count 1.112623e+06 1.112623e+06 \n",
- "mean 1.506598e+12 2.011981e+02 \n",
- "std 8.343066e+09 5.223881e+01 \n",
- "min 1.166573e+12 0.000000e+00 \n",
- "25% 1.507220e+12 1.700000e+02 \n",
- "50% 1.507553e+12 1.970000e+02 \n",
- "75% 1.507756e+12 2.280000e+02 \n",
- "max 1.510666e+12 6.690000e+03 "
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click.describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "200000"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#训练集中的用户数量为20w\n",
- "trn_click.user_id.nunique()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T16:03:01.378461Z",
- "start_time": "2020-11-13T16:03:01.300712Z"
- }
- },
- "outputs": [
+ "source": [
+ "#用户点击日志信息\n",
+ "trn_click.info()"
+ ]
+ },
{
- "data": {
- "text/plain": [
- "2"
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " 1.112623e+06 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 1.221198e+05 \n",
+ " 1.951541e+05 \n",
+ " 1.507588e+12 \n",
+ " 3.947786e+00 \n",
+ " 1.815981e+00 \n",
+ " 1.301976e+01 \n",
+ " 1.310776e+00 \n",
+ " 1.813587e+01 \n",
+ " 1.910063e+00 \n",
+ " 7.118518e+00 \n",
+ " 1.323704e+01 \n",
+ " 3.056176e+02 \n",
+ " 1.506598e+12 \n",
+ " 2.011981e+02 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 5.540349e+04 \n",
+ " 9.292286e+04 \n",
+ " 3.363466e+08 \n",
+ " 3.276715e-01 \n",
+ " 1.035170e+00 \n",
+ " 6.967844e+00 \n",
+ " 1.618264e+00 \n",
+ " 7.105832e+00 \n",
+ " 1.220012e+00 \n",
+ " 1.016095e+01 \n",
+ " 1.631503e+01 \n",
+ " 1.155791e+02 \n",
+ " 8.343066e+09 \n",
+ " 5.223881e+01 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000e+00 \n",
+ " 3.000000e+00 \n",
+ " 1.507030e+12 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.166573e+12 \n",
+ " 0.000000e+00 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 7.934700e+04 \n",
+ " 1.239090e+05 \n",
+ " 1.507297e+12 \n",
+ " 4.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.300000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 4.000000e+00 \n",
+ " 2.500000e+02 \n",
+ " 1.507220e+12 \n",
+ " 1.700000e+02 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.309670e+05 \n",
+ " 2.038900e+05 \n",
+ " 1.507596e+12 \n",
+ " 4.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.700000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.100000e+01 \n",
+ " 2.000000e+00 \n",
+ " 4.000000e+00 \n",
+ " 8.000000e+00 \n",
+ " 3.280000e+02 \n",
+ " 1.507553e+12 \n",
+ " 1.970000e+02 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 1.704010e+05 \n",
+ " 2.777120e+05 \n",
+ " 1.507841e+12 \n",
+ " 4.000000e+00 \n",
+ " 3.000000e+00 \n",
+ " 1.700000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.500000e+01 \n",
+ " 2.000000e+00 \n",
+ " 8.000000e+00 \n",
+ " 1.600000e+01 \n",
+ " 4.100000e+02 \n",
+ " 1.507756e+12 \n",
+ " 2.280000e+02 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 1.999990e+05 \n",
+ " 3.640460e+05 \n",
+ " 1.510603e+12 \n",
+ " 4.000000e+00 \n",
+ " 5.000000e+00 \n",
+ " 2.000000e+01 \n",
+ " 1.100000e+01 \n",
+ " 2.800000e+01 \n",
+ " 7.000000e+00 \n",
+ " 2.410000e+02 \n",
+ " 2.410000e+02 \n",
+ " 4.600000e+02 \n",
+ " 1.510666e+12 \n",
+ " 6.690000e+03 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure()\n",
- "plt.figure(figsize=(15, 20))\n",
- "i = 1\n",
- "for col in ['click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', \n",
- " 'click_region', 'click_referrer_type', 'rank', 'click_cnts']:\n",
- " plot_envs = plt.subplot(5, 2, i)\n",
- " i += 1\n",
- " v = trn_click[col].value_counts().reset_index()[:10]\n",
- " fig = sns.barplot(x=v['index'], y=v[col])\n",
- " for item in fig.get_xticklabels():\n",
- " item.set_rotation(90)\n",
- " plt.title(col)\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "注：此处click_cnts直方图表示的是每篇文章对应用户的点击次数累计图\n",
- "\n",
- "也可以以用户角度分析，画出每个用户点击文章次数的直方图"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4 1084627\n",
- "2 25894\n",
- "1 2102\n",
- "Name: click_environment, dtype: int64"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click['click_environment'].value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从点击环境click_environment来看，仅有2102次（占0.19%）点击环境为1；仅有25894次（占2.3%）点击环境为2；剩余（占97.6%）点击环境为4。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1 678187\n",
- "3 395558\n",
- "4 38731\n",
- "5 141\n",
- "2 6\n",
- "Name: click_deviceGroup, dtype: int64"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click['click_deviceGroup'].value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从点击设备组click_deviceGroup来看，设备1占大部分（61%），设备3占36%。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 测试集用户点击日志"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " 518010.000000 \n",
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- " 518010.000000 \n",
- " 518010.000000 \n",
- " 5.180100e+05 \n",
- " 518010.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 227342.428169 \n",
- " 193803.792550 \n",
- " 1.507387e+12 \n",
- " 3.947300 \n",
- " 1.738285 \n",
- " 13.628467 \n",
- " 1.348209 \n",
- " 18.250250 \n",
- " 1.819614 \n",
- " 15.521785 \n",
- " 30.043586 \n",
- " 305.324961 \n",
- " 1.506883e+12 \n",
- " 210.966331 \n",
- " \n",
- " \n",
- " std \n",
- " 14613.907188 \n",
- " 88279.388177 \n",
- " 3.706127e+08 \n",
- " 0.323916 \n",
- " 1.020858 \n",
- " 6.625564 \n",
- " 1.703524 \n",
- " 7.060798 \n",
- " 1.082657 \n",
- " 33.957702 \n",
- " 56.868021 \n",
- " 110.411513 \n",
- " 5.816668e+09 \n",
- " 83.040065 \n",
- " \n",
- " \n",
- " min \n",
- " 200000.000000 \n",
- " 137.000000 \n",
- " 1.506959e+12 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 2.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.265812e+12 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 214926.000000 \n",
- " 128551.000000 \n",
- " 1.507026e+12 \n",
- " 4.000000 \n",
- " 1.000000 \n",
- " 12.000000 \n",
- " 1.000000 \n",
- " 13.000000 \n",
- " 1.000000 \n",
- " 4.000000 \n",
- " 10.000000 \n",
- " 252.000000 \n",
- " 1.506970e+12 \n",
- " 176.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 229109.000000 \n",
- " 199197.000000 \n",
- " 1.507308e+12 \n",
- " 4.000000 \n",
- " 1.000000 \n",
- " 17.000000 \n",
- " 1.000000 \n",
- " 21.000000 \n",
- " 2.000000 \n",
- " 8.000000 \n",
- " 19.000000 \n",
- " 323.000000 \n",
- " 1.507249e+12 \n",
- " 199.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 240182.000000 \n",
- " 272143.000000 \n",
- " 1.507666e+12 \n",
- " 4.000000 \n",
- " 3.000000 \n",
- " 17.000000 \n",
- " 1.000000 \n",
- " 25.000000 \n",
- " 2.000000 \n",
- " 18.000000 \n",
- " 35.000000 \n",
- " 399.000000 \n",
- " 1.507630e+12 \n",
- " 232.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 364043.000000 \n",
- " 1.508832e+12 \n",
- " 4.000000 \n",
- " 5.000000 \n",
- " 20.000000 \n",
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- " 28.000000 \n",
- " 7.000000 \n",
- " 938.000000 \n",
- " 938.000000 \n",
- " 460.000000 \n",
- " 1.509949e+12 \n",
- " 3082.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " click_article_id \n",
- " category_id \n",
- " created_at_ts \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 0 \n",
- " 0 \n",
- " 1513144419000 \n",
- " 168 \n",
- " \n",
- " \n",
- " 1 \n",
- " 1 \n",
- " 1 \n",
- " 1405341936000 \n",
- " 189 \n",
- " \n",
- " \n",
- " 2 \n",
- " 2 \n",
- " 1 \n",
- " 1408667706000 \n",
- " 250 \n",
- " \n",
- " \n",
- " 3 \n",
- " 3 \n",
- " 1 \n",
- " 1408468313000 \n",
- " 230 \n",
- " \n",
- " \n",
- " 4 \n",
- " 4 \n",
- " 1 \n",
- " 1407071171000 \n",
- " 162 \n",
- " \n",
- " \n",
- " 364042 \n",
- " 364042 \n",
- " 460 \n",
- " 1434034118000 \n",
- " 144 \n",
- " \n",
- " \n",
- " 364043 \n",
- " 364043 \n",
- " 460 \n",
- " 1434148472000 \n",
- " 463 \n",
- " \n",
- " \n",
- " 364044 \n",
- " 364044 \n",
- " 460 \n",
- " 1457974279000 \n",
- " 177 \n",
- " \n",
- " \n",
- " 364045 \n",
- " 364045 \n",
- " 460 \n",
- " 1515964737000 \n",
- " 126 \n",
- " \n",
- " \n",
- " 364046 \n",
- " 364046 \n",
- " 460 \n",
- " 1505811330000 \n",
- " 479 \n",
- " \n",
- " \n",
- "
\n",
- "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " click_article_id category_id created_at_ts words_count\n",
- "0 0 0 1513144419000 168\n",
- "1 1 1 1405341936000 189\n",
- "2 2 1 1408667706000 250\n",
- "3 3 1 1408468313000 230\n",
- "4 4 1 1407071171000 162\n",
- "364042 364042 460 1434034118000 144\n",
- "364043 364043 460 1434148472000 463\n",
- "364044 364044 460 1457974279000 177\n",
- "364045 364045 460 1515964737000 126\n",
- "364046 364046 460 1505811330000 479"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#新闻文章数据集浏览\n",
- "item_df.head().append(item_df.tail())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:28:13.084501Z",
- "start_time": "2020-11-13T15:28:13.062561Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "176 3485\n",
- "182 3480\n",
- "179 3463\n",
- "178 3458\n",
- "174 3456\n",
- "183 3432\n",
- "184 3427\n",
- "173 3414\n",
- "180 3403\n",
- "177 3391\n",
- "170 3387\n",
- "187 3355\n",
- "169 3352\n",
- "185 3348\n",
- "175 3346\n",
- "181 3330\n",
- "186 3328\n",
- "189 3327\n",
- "171 3327\n",
- "172 3322\n",
- "165 3308\n",
- "188 3288\n",
- "167 3269\n",
- "190 3261\n",
- "192 3257\n",
- "168 3248\n",
- "193 3225\n",
- "166 3199\n",
- "191 3182\n",
- "194 3164\n",
- " ... \n",
- "601 1\n",
- "857 1\n",
- "1977 1\n",
- "1626 1\n",
- "697 1\n",
- "1720 1\n",
- "696 1\n",
- "706 1\n",
- "592 1\n",
- "1605 1\n",
- "586 1\n",
- "582 1\n",
- "1606 1\n",
- "972 1\n",
- "716 1\n",
- "584 1\n",
- "1608 1\n",
- "715 1\n",
- "841 1\n",
- "968 1\n",
- "964 1\n",
- "587 1\n",
- "1099 1\n",
- "1355 1\n",
- "711 1\n",
- "845 1\n",
- "710 1\n",
- "965 1\n",
- "847 1\n",
- "1535 1\n",
- "Name: words_count, Length: 866, dtype: int64"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "item_df['words_count'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:28:59.029535Z",
- "start_time": "2020-11-13T15:28:58.816106Z"
- }
- },
- "outputs": [
+ "source": [
+ "plt.figure()\n",
+ "plt.figure(figsize=(15, 20))\n",
+ "i = 1\n",
+ "for col in ['click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', \n",
+ " 'click_region', 'click_referrer_type', 'rank', 'click_cnts']:\n",
+ " plot_envs = plt.subplot(5, 2, i)\n",
+ " i += 1\n",
+ " v = trn_click[col].value_counts().reset_index()[:10]\n",
+ " fig = sns.barplot(x=v['index'], y=v[col])\n",
+ " for item in fig.get_xticklabels():\n",
+ " item.set_rotation(90)\n",
+ " plt.title(col)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "461\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "注：此处click_cnts直方图表示的是每篇文章对应用户的点击次数累计图\n",
+ "\n",
+ "也可以以用户角度分析，画出每个用户点击文章次数的直方图"
+ ]
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 1084627\n",
+ "2 25894\n",
+ "1 2102\n",
+ "Name: click_environment, dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trn_click['click_environment'].value_counts()"
]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从点击环境click_environment来看，仅有2102次（占0.19%）点击环境为1；仅有25894次（占2.3%）点击环境为2；剩余（占97.6%）点击环境为4。"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "print(item_df['category_id'].nunique()) # 461个文章主题\n",
- "item_df['category_id'].hist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(364047, 4)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "item_df.shape # 364047篇文章"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻文章embedding向量表示"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
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- " \n",
- " \n",
- " article_id \n",
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- " emb_2 \n",
- " emb_3 \n",
- " emb_4 \n",
- " emb_5 \n",
- " emb_6 \n",
- " emb_7 \n",
- " emb_8 \n",
- " ... \n",
- " emb_240 \n",
- " emb_241 \n",
- " emb_242 \n",
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- " emb_244 \n",
- " emb_245 \n",
- " emb_246 \n",
- " emb_247 \n",
- " emb_248 \n",
- " emb_249 \n",
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- " 0 \n",
- " 0 \n",
- " -0.161183 \n",
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- " 0.830055 \n",
- " 0.901365 \n",
- " -0.335148 \n",
- " -0.559561 \n",
- " -0.500603 \n",
- " ... \n",
- " 0.321248 \n",
- " 0.313999 \n",
- " 0.636412 \n",
- " 0.169179 \n",
- " 0.540524 \n",
- " -0.813182 \n",
- " 0.286870 \n",
- " -0.231686 \n",
- " 0.597416 \n",
- " 0.409623 \n",
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- " 1 \n",
- " 1 \n",
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- " -0.974058 \n",
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- " -0.594137 \n",
- " 0.182828 \n",
- " 0.397090 \n",
- " -0.834364 \n",
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- " 2 \n",
- " 2 \n",
- " -0.619619 \n",
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- " -0.207360 \n",
- " -0.128861 \n",
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- " \n",
- " 3 \n",
- " 3 \n",
- " -0.740843 \n",
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- " -0.537838 \n",
- " 0.243541 \n",
- " -0.885329 \n",
- " \n",
- " \n",
- " 4 \n",
- " 4 \n",
- " -0.279052 \n",
- " -0.972315 \n",
- " 0.685374 \n",
- " 0.113056 \n",
- " 0.238315 \n",
- " 0.271913 \n",
- " -0.568816 \n",
- " 0.341194 \n",
- " -0.600554 \n",
- " ... \n",
- " 0.238286 \n",
- " 0.809268 \n",
- " 0.427521 \n",
- " -0.615932 \n",
- " -0.503697 \n",
- " 0.614450 \n",
- " -0.917760 \n",
- " -0.424061 \n",
- " 0.185484 \n",
- " -0.580292 \n",
- " \n",
- " \n",
- "
\n",
- "
5 rows × 251 columns
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " count \n",
- " \n",
- " \n",
- " \n",
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- " 289197 \n",
- " 1 \n",
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- " 36162 \n",
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- " \n",
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- " 2 \n",
- " 168401 \n",
- " 1 \n",
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- " \n",
- " 6 \n",
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- " 42567 \n",
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- " \n",
- " \n",
- "
\n",
- "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 249999 \n",
+ " 160974 \n",
+ " 1506959142820 \n",
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+ " 19 \n",
+ " 19 \n",
+ " 281 \n",
+ " 1506912747000 \n",
+ " 259 \n",
+ " \n",
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+ " 249999 \n",
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+ " 18 \n",
+ " 19 \n",
+ " 281 \n",
+ " 1506942089000 \n",
+ " 173 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 249998 \n",
+ " 160974 \n",
+ " 1506959056066 \n",
+ " 4 \n",
+ " 1 \n",
+ " 12 \n",
+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 1506912747000 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 249998 \n",
+ " 202557 \n",
+ " 1506959086066 \n",
+ " 4 \n",
+ " 1 \n",
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+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 4 \n",
+ " 5 \n",
+ " 327 \n",
+ " 1506938401000 \n",
+ " 219 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 249997 \n",
+ " 183665 \n",
+ " 1506959088613 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 15 \n",
+ " 5 \n",
+ " 7 \n",
+ " 7 \n",
+ " 301 \n",
+ " 1500895686000 \n",
+ " 256 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 311242 \n",
- " 86295 \n",
- " 74254 \n",
- " 10 \n",
- " \n",
- " \n",
- " 311243 \n",
- " 86295 \n",
- " 76268 \n",
- " 10 \n",
- " \n",
- " \n",
- " 393761 \n",
- " 103237 \n",
- " 205948 \n",
- " 10 \n",
- " \n",
- " \n",
- " 393763 \n",
- " 103237 \n",
- " 235689 \n",
- " 10 \n",
- " \n",
- " \n",
- " 576902 \n",
- " 134850 \n",
- " 69463 \n",
- " 13 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 5.180100e+05 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 5.180100e+05 \n",
+ " 518010.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 227342.428169 \n",
+ " 193803.792550 \n",
+ " 1.507387e+12 \n",
+ " 3.947300 \n",
+ " 1.738285 \n",
+ " 13.628467 \n",
+ " 1.348209 \n",
+ " 18.250250 \n",
+ " 1.819614 \n",
+ " 15.521785 \n",
+ " 30.043586 \n",
+ " 305.324961 \n",
+ " 1.506883e+12 \n",
+ " 210.966331 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 14613.907188 \n",
+ " 88279.388177 \n",
+ " 3.706127e+08 \n",
+ " 0.323916 \n",
+ " 1.020858 \n",
+ " 6.625564 \n",
+ " 1.703524 \n",
+ " 7.060798 \n",
+ " 1.082657 \n",
+ " 33.957702 \n",
+ " 56.868021 \n",
+ " 110.411513 \n",
+ " 5.816668e+09 \n",
+ " 83.040065 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 200000.000000 \n",
+ " 137.000000 \n",
+ " 1.506959e+12 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 2.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.265812e+12 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 214926.000000 \n",
+ " 128551.000000 \n",
+ " 1.507026e+12 \n",
+ " 4.000000 \n",
+ " 1.000000 \n",
+ " 12.000000 \n",
+ " 1.000000 \n",
+ " 13.000000 \n",
+ " 1.000000 \n",
+ " 4.000000 \n",
+ " 10.000000 \n",
+ " 252.000000 \n",
+ " 1.506970e+12 \n",
+ " 176.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 229109.000000 \n",
+ " 199197.000000 \n",
+ " 1.507308e+12 \n",
+ " 4.000000 \n",
+ " 1.000000 \n",
+ " 17.000000 \n",
+ " 1.000000 \n",
+ " 21.000000 \n",
+ " 2.000000 \n",
+ " 8.000000 \n",
+ " 19.000000 \n",
+ " 323.000000 \n",
+ " 1.507249e+12 \n",
+ " 199.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 240182.000000 \n",
+ " 272143.000000 \n",
+ " 1.507666e+12 \n",
+ " 4.000000 \n",
+ " 3.000000 \n",
+ " 17.000000 \n",
+ " 1.000000 \n",
+ " 25.000000 \n",
+ " 2.000000 \n",
+ " 18.000000 \n",
+ " 35.000000 \n",
+ " 399.000000 \n",
+ " 1.507630e+12 \n",
+ " 232.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 364043.000000 \n",
+ " 1.508832e+12 \n",
+ " 4.000000 \n",
+ " 5.000000 \n",
+ " 20.000000 \n",
+ " 11.000000 \n",
+ " 28.000000 \n",
+ " 7.000000 \n",
+ " 938.000000 \n",
+ " 938.000000 \n",
+ " 460.000000 \n",
+ " 1.509949e+12 \n",
+ " 3082.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50000"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#测试集中的用户数量为5w\n",
+ "tst_click.user_id.nunique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:56:07.717463Z",
+ "start_time": "2020-11-13T15:56:07.693494Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tst_click.groupby('user_id')['click_article_id'].count().min() # 注意测试集里面有只点击过一次文章的用户"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章信息数据表"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:20:34.183761Z",
+ "start_time": "2020-11-13T15:20:34.164770Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " click_article_id \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
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+ " \n",
+ " 0 \n",
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+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1408667706000 \n",
+ " 250 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 1408468313000 \n",
+ " 230 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 1 \n",
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+ " 162 \n",
+ " \n",
+ " \n",
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+ " 364043 \n",
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+ " 364046 \n",
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+ " 1505811330000 \n",
+ " 479 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:28:13.084501Z",
+ "start_time": "2020-11-13T15:28:13.062561Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "176 3485\n",
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+ "191 3182\n",
+ "194 3164\n",
+ " ... \n",
+ "601 1\n",
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+ "1626 1\n",
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+ "Name: words_count, Length: 866, dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "item_df['words_count'].value_counts()"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:28:59.029535Z",
+ "start_time": "2020-11-13T15:28:58.816106Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "461\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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1AENDQ4yOjvYz/TcYWgBrl41P3nCaTXW+M+XgwYNzbk6zYbrzMBv/t6bDbP1czDVzPQ8z9TPbVzGoql8A5yVZCHwbeN+MzGbyeawH1gMMDw/XyMjIlMb507vu5eadg//6593XjAz8NY9mdHSUqebwRDLdeZiN79eeDmuXjc/Kz8VcM9fzMFO/R47pbqKqehV4EPgQsDDJoYwtBva15X3AWQBt+zuBl3vjh/U5UlySNCD93E307nZEQJIFdBd6n6ErCle1ZquAe9vy5rZO2/5AVVWLX93uNjobWAo8DOwAlra7k06hu8i8eRr2TZLUp36Ohc4ENrbrBr8G3F1V30nyNLApyZeBx4DbW/vbga8nGQMO0P1yp6qeSnI38DQwDlzXTj+R5HPAFuAkYENVPTVteyhJmtSkxaCqngA+OEH8Obo7gQ6P/xT4+BHGuhG4cYL4fcB9fcxXUzTRrX9rl43P+Pnt3Td9dEbHlzQ9fAeyJMliIEmyGEiSsBhIkrAYSJKwGEiSsBhIkrAYSJKwGEiSsBhIkrAYSJLo8/sMpDejfr+KcRCf0STNdR4ZSJIsBpIki4EkCYuBJAmLgSSJ/r4D+awkDyZ5OslTST7f4qcn2ZpkV3s+rcWT5NYkY0meSHJ+z1irWvtdSVb1xC9IsrP1uTVJZmJnJUkT6+fIYBxYW1XnAMuB65KcA6wDtlXVUmBbWwe4nO7L7pcCa4DboCsewA3ARXRfl3nDoQLS2nymp9+K4981SVK/Ji0GVfVCVf2gLf8f4BlgEbAS2NiabQSubMsrgTursx1YmORM4DJga1UdqKpXgK3Airbt1KraXlUF3NkzliRpAI7pTWdJlgAfBB4ChqrqhbbpRWCoLS8C9vR029tiR4vvnSA+0euvoTvaYGhoiNHR0WOZ/i8NLejeaDRoU53vdJhofweRh7m2zxOZrf8Pc4156Mz1PMzUz1TfxSDJrwN/CXyhql7vPa1fVZWkZmB+v6Kq1gPrAYaHh2tkZGRK4/zpXfdy887Bv/l69zUjA3/NQyZ6h+3aZeMznoe5ts8TGUQe3gzMQ2eu52Gmfqb6upsoyVvoCsFdVfWtFn6pneKhPe9v8X3AWT3dF7fY0eKLJ4hLkgakn7uJAtwOPFNVf9yzaTNw6I6gVcC9PfFr211Fy4HX2umkLcClSU5rF44vBba0ba8nWd5e69qesSRJA9DPsdCHgd8HdiZ5vMX+E3ATcHeS1cDzwCfatvuAK4Ax4CfApwGq6kCSLwE7WrsvVtWBtvxZ4A5gAXB/e0iSBmTSYlBV3weOdN//JRO0L+C6I4y1AdgwQfwR4NzJ5iJJmhm+A1mSZDGQJFkMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJEkcw3cgS1OxpM/vIZY0uzwykCRZDCRJfRSDJBuS7E/yZE/s9CRbk+xqz6e1eJLcmmQsyRNJzu/ps6q135VkVU/8giQ7W59bkxzpKzYlSTOknyODO4AVh8XWAduqaimwra0DXA4sbY81wG3QFQ/gBuAi4ELghkMFpLX5TE+/w19LkjTDJi0GVfW/gAOHhVcCG9vyRuDKnvid1dkOLExyJnAZsLWqDlTVK8BWYEXbdmpVba+qAu7sGUuSNCBTvZtoqKpeaMsvAkNteRGwp6fd3hY7WnzvBPEJJVlDd8TB0NAQo6OjU5v8Ali7bHxKfY/HVOc7HSba39nKw1xjHjrmoTPX8zBTv0eO+9bSqqokNR2T6eO11gPrAYaHh2tkZGRK4/zpXfdy887B31W7+5qRgb/mIZ+a4BbPtcvGZyUPc4156JiHzlzPw0z9Hpnq3UQvtVM8tOf9Lb4POKun3eIWO1p88QRxSdIATbUYbAYO3RG0Cri3J35tu6toOfBaO520Bbg0yWntwvGlwJa27fUky9tdRNf2jCVJGpBJj4WSfAMYAd6VZC/dXUE3AXcnWQ08D3yiNb8PuAIYA34CfBqgqg4k+RKwo7X7YlUduij9Wbo7lhYA97eHJGmAJi0GVfXJI2y6ZIK2BVx3hHE2ABsmiD8CnDvZPCRJM2fuXiU5Afk5PZLmKj+OQpJkMZAkWQwkSVgMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJElYDCRJWAwkSVgMJElYDCRJzKFikGRFkmeTjCVZN9vzkaT5ZE4UgyQnAV8DLgfOAT6Z5JzZnZUkzR9zohgAFwJjVfVcVf0c2ASsnOU5SdK8kaqa7TmQ5CpgRVX9m7b++8BFVfW5w9qtAda01fcCz07xJd8F/MMU+55IzEPHPHTMQ+dEzsM/r6p3T7Th5EHP5HhU1Xpg/fGOk+SRqhqehim9qZmHjnnomIfOfM3DXDlNtA84q2d9cYtJkgZgrhSDHcDSJGcnOQW4Gtg8y3OSpHljTpwmqqrxJJ8DtgAnARuq6qkZfMnjPtV0gjAPHfPQMQ+deZmHOXEBWZI0u+bKaSJJ0iyyGEiS5lcxmG8feZFkQ5L9SZ7siZ2eZGuSXe35tBZPkltbbp5Icv7szXz6JDkryYNJnk7yVJLPt/i8ygNAkrcleTjJ37Rc/FGLn53kobbP32w3cZDkrW19rG1fMqs7MM2SnJTksSTfaevzMg+HzJtiME8/8uIOYMVhsXXAtqpaCmxr69DlZWl7rAFuG9AcZ9o4sLaqzgGWA9e1f/f5lgeAnwEXV9UHgPOAFUmWA18Bbqmq9wCvAKtb+9XAKy1+S2t3Ivk88EzP+nzNQ6eq5sUD+BCwpWf9euD62Z7XAPZ7CfBkz/qzwJlt+Uzg2bb858AnJ2p3Ij2Ae4GPmAfeDvwAuIju3bYnt/gvf07o7u77UFs+ubXLbM99mvZ/Md0fARcD3wEyH/PQ+5g3RwbAImBPz/reFptvhqrqhbb8IjDUlk/4/LTD+w8CDzFP89BOjTwO7Ae2Aj8EXq2q8dakd39/mYu2/TXgjIFOeOb8CfCHwP9r62cwP/PwS/OpGOgw1f2pMy/uLU7y68BfAl+oqtd7t82nPFTVL6rqPLq/jC8E3je7Mxq8JL8L7K+qR2d7LnPJfCoGfuRF56UkZwK05/0tfsLmJ8lb6ArBXVX1rRaed3noVVWvAg/SnQ5ZmOTQG1B79/eXuWjb3wm8PNiZzogPAx9LspvuE5IvBr7K/MvDr5hPxcCPvOhsBla15VV059APxa9td9MsB17rOY3yppUkwO3AM1X1xz2b5lUeAJK8O8nCtryA7trJM3RF4arW7PBcHMrRVcAD7SjqTa2qrq+qxVW1hO73wANVdQ3zLA9vMNsXLQb5AK4A/jfdedL/PNvzGcD+fgN4AfhHunOgq+nOdW4DdgHfA05vbUN3t9UPgZ3A8GzPf5py8Dt0p4CeAB5vjyvmWx7avv0L4LGWiyeB/9LivwU8DIwBfwG8tcXf1tbH2vbfmu19mIGcjADfme95qCo/jkKSNL9OE0mSjsBiIEmyGEiSLAaSJCwGkiQsBpIkLAaSJOD/A1s6ptvCJXLcAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print(item_df['category_id'].nunique()) # 461个文章主题\n",
+ "item_df['category_id'].hist()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(364047, 4)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "item_df.shape # 364047篇文章"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章embedding向量表示"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
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5 rows × 251 columns
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+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
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+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "item_emb_df.shape"
]
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- "metadata": {},
- "output_type": "display_data"
},
{
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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 数据分析"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户重复点击"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:30:20.899771Z",
+ "start_time": "2020-11-13T15:30:20.750817Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "#####merge\n",
+ "user_click_merge = trn_click.append(tst_click)"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:30:26.290038Z",
+ "start_time": "2020-11-13T15:30:25.339579Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 30760 \n",
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+ " 36162 \n",
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+ " \n",
+ " 7 \n",
+ " 3 \n",
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+ " 8 \n",
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+ " 39894 \n",
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+ " \n",
+ " \n",
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+ " 4 \n",
+ " 42567 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:34:27.418638Z",
+ "start_time": "2020-11-13T15:34:27.372761Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 311242 \n",
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+ " 10 \n",
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+ " 69463 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:32:53.298575Z",
+ "start_time": "2020-11-13T15:32:53.285611Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 2, 4, 3, 6, 5, 10, 7, 13])"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "user_click_count['count'].unique()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1 1605541\n",
+ "2 11621\n",
+ "3 422\n",
+ "4 77\n",
+ "5 26\n",
+ "6 12\n",
+ "10 4\n",
+ "7 3\n",
+ "13 1\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#用户点击新闻次数\n",
+ "user_click_count.loc[:,'count'].value_counts() "
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### 可以看出：有1605541（约占99.2%）的用户未重复阅读过文章，仅有极少数用户重复点击过某篇文章。 这个也可以单独制作成特征"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击环境变化分析"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:39:41.961797Z",
+ "start_time": "2020-11-13T15:39:41.949829Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_envs(df, cols, r, c):\n",
+ " plt.figure()\n",
+ " plt.figure(figsize=(10, 5))\n",
+ " i = 1\n",
+ " for col in cols:\n",
+ " plt.subplot(r, c, i)\n",
+ " i += 1\n",
+ " v = df[col].value_counts().reset_index()\n",
+ " fig = sns.barplot(x=v['index'], y=v[col])\n",
+ " for item in fig.get_xticklabels():\n",
+ " item.set_rotation(90)\n",
+ " plt.title(col)\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:39:55.476626Z",
+ "start_time": "2020-11-13T15:39:48.764592Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "output_type": "display_data"
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\n",
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+ "output_type": "display_data"
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\n",
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\n",
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+ "output_type": "display_data"
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\n",
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\n",
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+ "output_type": "display_data"
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kqcA6wIW15uUkzZJ0maQ3D9Nvv7LPrDvvvLOT3yNiiSZpKeAyYF/gnrLsa/uro3Tteb6WvsnZiLGRc2xEA7qZqu5O270cE7UHcJbt+lfNU23PK1PiXSjpj7ZvqHeyfTxwPMD06dPdw/giBoLtJyUda3tT4Koevc0i5WuJLzkbMf5yjo0YI91ceT5M0ncl7Slp16FllD7zgMm19bVLWzt7UN2I+BTb88rPG6nml9504W4R0cYFkt4qacRxzi2SrxGDJTkb0YBuiud9gU2A7Xl6yMaOI3UArgDWk7SOpGWoknehq9eSNgBWBX5ba1tV0rLl9erAaxh+HFdELOjfgTOBxyQ9IOlBSQ+M0if5GjFYkrMRDehm2MYrbK/fzcFtPyHpAOA8YBIww/a1ko4AZtWGgewBnGa7/pXQi4HjJD1JVeR/rn4HcUQMz/bKi9An+RoxQJKzEc3opni+VNKG3SaX7XOBc1vaDm1ZP7xNv0uBl3bzXhETXZm6annbD5X1VwLLlM2/t/3gSP2TrxGDJTkbMf66KZ5fCVwt6SaqB6UI8ChT1UXE+Po8cAfwhbL+Q+BPwHJUNw9+rKG4IiIilgjdFM/b9yyKiBgrrwdeUVu/z/ZO5cbB3zQUU0RExBKj4xsGbd9CNYfk44BrS0T0j6VsP1Fb/xhUXxEBKzUTUkR0qtzIl290I/pYN08Y/ADVk4n+DjxZmg0kySP6xzKSVh4a22z7FwCSVqEauhERfUbSRcDOVOfkK4E7JF1i+0ONBhYRbXUzVd2BwPq2N7L90rKkcI7oL98BTpc0ZaihPFnsh8B3G4sqIkayiu0HgF2BU2xvAWzTcEwRMYxuxjzPBe7vVSARsfhsf1nSI8DFklakurH3QappqL7VbHQRMYxnSHo+8Hbgk00HExEj66Z4vhG4SNI5VLNtANXJesyjiohFZvvbwLclrVzWR5yeLiIadwTVXM2X2L6iPC77+oZjiohhdFM8/7Usy/D0vLER0YckPRc4ClgT2EHShsCrbJ/QbGQR0cr2mVRPBB1avxF4a3MRRcRIOi6ebX8aQNJKZf2hXgUVEYvtJOBEnv4K+C/A6UCK54g+I2lt4OtUj8iGalrJA23f2lxUETGcjm8YlPQSSb8HrgWulXSlpI16F1pELIbVbZ9BmRmnTF83v9mQImIYJwIzqb4pWhP4SWmLiD7UzWwbxwMfsj3V9lTgw1R39kdE/3lY0rMpc7GXx3Tnht+I/rSG7RNtP1GWk4A1mg4qItrrZszzirZ/NbRi+6JyN39E9J8PUV3JWlfSJVQn4rc1G1JEDONuSe+kmlISYE/g7gbjiYgRdDXbhqT/D/heWX8n1QwcEdFnbF8laUtgfarp6mbbfrzhsCKivXdTjXn+CtW3RZcC+zYaUUQMq5thG++munp1NvAjYPXSFhF9RtL+wEq2r7X9J2AlSe9vOq6IWJjtW2zvbHsN28+x/Wbbfx3aLumQJuOLiAV1VDxLmgScbfuDtl9uezPbB9m+t8fxRcSieZ/t+4ZWSq6+r7lwImIx7NZ0ABHxtI6KZ9vzgSclrdLjeCJibEySpKGV8gE487NHDCaNvktEjJduxjw/BPxR0vnAw0ONtj845lFFxOL6OXC6pOPK+r+XtogYPG46gIh4WjfF89lliYj+9zGqgvk/y/r5wHebCyciFkOuPEf0kY5vGLR9crtltH6Stpc0W9IcSR9vs30fSXdKuros761t21vS9WXZu/NfK2Jis/2k7W/ZfltZjivDr0aUfI0Yf5JWa9O2Tm31zNbttf2SsxHjbNQrz5LOsP12SX+kzVdHtjceoe8k4FjgDcCtwBWSZtq+rmXX020f0NJ3NeAwYHp53ytL39ykGDGM5GvEQPqJpB1sPwAgaUPgDOAlALaPatcpORvRjE6GbRxYfu64CMffHJhj+0YASacBuwCtid3OdsD5tu8pfc8HtufpSeQjYmHJ14jBcxRVAf0mqrnZTwH26qBfcjaiAaMO27B9e3n5VuDxMh/lU8so3dcC5tbWby1trd4q6RpJZ0ma3E1fSftJmiVp1p133jnarxOxROv3fIXkbEQr2+dQPSDlF8BJwFtsX91B15xjIxrQzUNSVgbOl/QbSQdIeu4YxfATYFr5Ovl8YNRx1HW2j7c93fb0NdZYY4xCihh4fZmvkJyNGCLp65KOkXQMsDWwCnATcEBpGws5x0aMsW5uGPy07Y2A/YHnA/8r6ZejdJsHTK6tr13a6se92/ZjZfW7wGad9o2I9pKvEQNhFnBlbfkC1RN8h9ZHk5yNaEA3U9UNuQP4G3A38JxR9r0CWK/cNTwP2AN4R30HSc+vfdW8M/Dn8vo84ChJq5b1bYE8ojSiO8nXiD41NGOVpBWBfwzNiFNuBFy2g0MkZyMa0HHxLOn9wNuBNaimzXlfmzt6F2D7CUkHUCXpJGCG7WslHQHMsj0T+KCknYEngHuAfUrfeyQdSfWPA8ARQzc2RMTIkq8RA+UCYBuqh5EBLE81/vnVI3VKzkY0o5srz5OBgzq8ieEpts8Fzm1pO7T2+hCG+bRrewYwo5v3iwgg+RoxSJazPVQ4Y/shSSt00jE5GzH+uhnzfAjV47nXlDRlaOlhbBGxiEq+riRpXwBJa7Q8dCEi+sfDkl4+tCJpM+DRBuOJiBF0M2zjAOBw4O/Ak6XZwLAPXYiIZkgaevjB+sCJwNLA94HXNBlXRLR1EHCmpNuoHsX9PGD3RiOKiGF1M2zjIGB923f3KJaIGDtvATYFrgKwfZuklZsNKSLasX2FpA2oPuwCzLb9eJMxRcTwuime5wL39yqQiBhT/7RtSYan7uaPiD4iaWvbF0ratWXTiyRh++xGAouIEXVTPN8IXCTpHGBozkhsf3nMo4qIxXWGpOOAZ0l6H/Bu4DsNxxQRC9oSuBDYqc02AymeI/pQN8XzX8uyTFkiok/Z/pKkNwAPUH0VfKjt8xsOKyJqbB9Wfu7bdCwR0bmOi2fbnwaQtILtR3oXUkSMhVIsp2CO6FOSPjTS9nyzG9Gfuplt41XACcBKwBRJLwP+3fb7exVcRHRH0oNUX/e2ZfuZ4xhORIxspJt4h83jiGhWN8M2vgpsB8wEsP0HSf/ai6AiYtHYXhmgPDnsduB7VFNf7QU8v8HQIqJF7Rvdk4EDbd9X1lcFjm4wtIgYQccPSQGwPbelaf4YxhIRY2dn29+0/aDtB2x/C9il6aAioq2NhwpnANv3Uk01GRF9qJviea6kVwOWtLSkg4E/9yiuiFg8D0vaS9IkSUtJ2gt4uOmgIqKtpcrVZgAkrUZ33wxHxDjqJjn/A/gasBYwD/gFsH8vgoqIxfYOqnz9GtXYyUtKW0T0n6OB30o6s6zvBny2wXgiYgTdzLZxF9W4ybYkHWL7v8ckqohYLLZvZoRhGsnXiP5h+xRJs4CtS9Outq9rMqaIGF5XY55HsdsYHisieiv5GtFHbF9n+xtlSeEc0cfGsnjWGB4rInor+RoREbEIxrJ4zpyUEYMj+RoREbEIcuU5YmJKvkZERCyCjovnMnVOa9s6tdUzW7dHRDOSrxEREb3RzZXnn0h66tG+kjYEfjK0bvuodp0kbS9ptqQ5kj7eZvuHJF0n6RpJF0iaWts2X9LVZZnZRawRE13yNWICSM5GjL9uiuejqE7IK0najOrK1TtH6iBpEnAssAOwIbBnOYnX/R6Ybntj4CzgC7Vtj9repCw7dxFrxESXfI1YwiVnI5rRzTzP50hamurhKCsDb7H9l1G6bQ7MsX0jgKTTqOaefWoaHtu/qu1/GaOc4CNidMnXiAkhORvRgFGLZ0lfZ8E781cBbgAOkITtD47QfS1gbm39VmCLEfZ/D/Cz2vpyZeL4J4DP2f5/beLbD9gPYMqUKSMcOmLJ1+/5WmJMzkaMjZxjIxrQyZXnWS3rV/YiEEnvBKYDW9aap9qeJ+kFwIWS/mj7hno/28cDxwNMnz4902/FRNfX+QrJ2Ygm5BwbMXZGLZ5tnwwgaUXgH7bnl/VJwLKjdJ8HTK6tr13aFiBpG+CTwJa2H6u997zy80ZJFwGbUl1Fi4g2kq8RE0pyNqIB3dwweAGwfG19eeCXo/S5AlhP0jqSlgH2ABa4o1fSpsBxwM6276i1rypp2fJ6deA11MZxRcSIkq8RS77kbEQDOr5hEFjO9kNDK7YfkrTCSB1sPyHpAOA8YBIww/a1ko4AZtmeCXwRWAk4UxLAX8tdvy8GjpP0JFWR/znbSeyIziRfI5ZwydmIZnRTPD8s6eW2rwIo0189Olon2+cC57a0HVp7vc0w/S4FXtpFfBHxtORrxASQnI0Yf90UzwdRfXK9jerRvs8Ddu9FUBGx2A4i+RoRETHmupnn+QpJGwDrl6bZth/vTVgRsTiSrxEREb3RyTzPW9u+UNKuLZteVOaNPbtHsUVEl5KvERERvdXJlectgQuBndpsM5CTcUT/SL5GRET0UCfzPB9Wfu7b+3AiYnEkXyMiInqrk2EbHxppu+0vj104EbE4kq8RERG91cmwjZVH2JZHdUb0l+RrRERED3UybOPTAJJOBg60fV9ZXxU4uqfRRURXkq8RERG91c3juTceOhED2L4X2HTMI4qIsZB8jYiI6IFuiuelytUrACStRncPWYmI8ZN8jYiI6IFuTqZHA7+VdGZZ3w347NiHFBFjIPkaERHRA908YfAUSbOArUvTrrav601YEbE4kq8RERG90dXXuOXkmxNwxABIvkZERIy9bsY8R0RERERMaCmeIyIiIiI6lOI5IiIiIqJDKZ4jIiIiIjqU4jkiIiIiokM9L54lbS9ptqQ5kj7eZvuykk4v238naVpt2yGlfbak7Xoda8REl3yNGCzJ2Yjx19PiWdIk4FhgB2BDYE9JG7bs9h7gXtsvBL4CfL703RDYA9gI2B74ZjleRPRA8jVisCRnI5rR6yvPmwNzbN9o+5/AacAuLfvsApxcXp8FvF6SSvtpth+zfRMwpxwvInoj+RoxWJKzEQ3o6iEpi2AtYG5t/VZgi+H2sf2EpPuBZ5f2y1r6rtX6BpL2A/Yrqw9Jmj02oUcXVgfuajqIQaAv7T2Wh5s6lgdjHPIVkrN9IjnboYmes8nXvpB87dB45Wuvi+ees308cHzTcUxkkmbZnt50HDEYkrPNS85Gp5KvzUu+9p9eD9uYB0yura9d2truI+kZwCrA3R32jYixk3yNGCzJ2YgG9Lp4vgJYT9I6kpahujlhZss+M4Gh6+xvAy607dK+R7lTeB1gPeDyHscbMZElXyMGS3I2ogE9HbZRxlcdAJwHTAJm2L5W0hHALNszgROA70maA9xDlfyU/c4ArgOeAPa3Pb+X8cYiy1d6S4Dk64SSnF0CJGcnjORrn1H1ATQiIiIiIkaTJwxGRERERHQoxXNERERERIdSPEdEREREdCjFcyw2Sac0HUNERETEeBj4h6TE+JLUOg2SgNdJehaA7Z3HPaiIiIiIcZLiObq1NtXURt8FTFU8TweObjKoiOiepH1tn9h0HBERgyRT1UVXJC0FHAi8EfiI7asl3Wj7BQ2HFhFdkvRX21OajiMiKpKuAs4Gfmj7hqbjifZy5Tm6YvtJ4CuSziw//07+P4roW5KuGW4T8NzxjCUiRrUq8CzgV5L+BvwQON32bY1GFQvIledYLJLeBLzG9ieajiUiFlY+4G4H3Nu6CbjU9prjH1VEtCPpKtsvL6//BdgT2BX4M9XV6DxtsA+keI6IWIJJOgE40fbFbbb9wPY7GggrItqoF8+1tknAG4Ddbe/bTGRRl+I5IiIiog9IOs32Hk3HESPLPM8RERERfWCkwllSrjr3iRTPsRBJl3a5/1aSftqreCIiIoJPNx1AVDJLQizE9qubjiEiOiPp0m5yVtJWwMG2d+xZUBGxSDI7zmBI8RwLkfSQ7ZXKSfZw4C7gJcCVwDttW9L2wFeBR4CLa31XBL5e9l8aONz2/0j6GnC37SMkbQd8EtiqTH0XEYsoH3YjlijPZYTZccY/nGgnwzZiNJsCBwEbAi8AXiNpOeA7wE7AZsDzavt/ErjQ9ubA64AvloL6EGB3Sa8DjgH2TeEcsfgkPVR+biXpIklnSfo/SadKUtm2fWm7imraq6G+K0qaIelySb+XtEtp/5qkQ8vr7ST9ujwgKSJ666fASrZvaVluBi5qNrQYkivPMZrLbd8KIOlqYBrwEHCT7etL+/eB/cr+2wI7Szq4rC8HTLH9Z0nvA34N/FeenBTRE5sCGwG3AZdQfdidRfVhd2tgDnB6bf+hD7vvlvQs4HJJv6T6sHuFpN9Qfdh9Yz7sRvSe7feMsC3TSvaJFM8xmsdqr+cz+v8zAt5qe3abbS8F7gbyUIaI3siH3YiIHsvXcLEo/g+YJmndsr5nbdt5wAdqXxdvWn5OBT5MdWVsB0lbjGO8ERPFon7Y3aQsU2z/uWzLh92IiDZSPEfXbP+D6srVOWUM5R21zUdS3Sh4jaRrgSNLIX0C1R3+twHvAb5bxk5HRG/lw25En8hUsEuGDNuIhdheqfy8iNoNCrYPqL3+ObBBm76PAv/e5rDb1Pa5kuqqVkT0mO1/SBr6sPsI8Btg5bL5SKpZc64pNwTeJGknah92Jb0HOEnSK8oH54hYRJkdZ8mQx3NHREREjINFnAr2BbZ3zFSw/SNXniMiIiLGX2bHGVAZ8xwREREx/i63fWspdK+mmh1nA8rsOK6GBny/tv+2wMfLTDoX8fTsOI8A7wPOB76R2XF6L1eeIyIiIsZfpoIdULnyHBEREdEfMjvOAEjxHBEREdEHMhXsYMhsGxERERERHcqV54iIiIiIDqV4joiIiIjoUIrniIiIiIgOpXiOiIiIiOhQiueIiIiIiA6leI6IiIiI6FCK54iIiIiIDqV4joiIiIjoUIrniIiIiIgOpXiOiIiIiOhQiueIiIiIiA6leI6IiIiI6FCK5yWApH0kXVxbf0jSC0bpM02SJT2j9xFGxHAGNX8l7SXpF029f0Qn+jm/JL1G0vUlpjf38r1ibKVwWgLZXqnpGMaKpGnATcDStp9oOJyInhuU/LV9KnBq03FEdKPP8usI4Bu2v9Z0IK0kHQ680PY7m46lH+XKcwy8XD2P6F7yJqJ3OsyvqcC1Y3V8SZMW9xjRmRTPA0bSZElnS7pT0t2SvtFmH0t6YXm9vKSjJd0i6X5JF0tavk2ft0q6WdJLRnn/10q6VNJ9kuZK2qe0ryLplBLXLZI+JWmpsu1wSd+vHWOBr8QkXSTpSEmXSHpQ0i8krV52/3X5eV/5autV5Wu4SyR9RdLdwBGS7pH00tp7PEfSI5LW6ObvG9FLTeZvLe/eI+mvwIWl/d2S/izpXknnSZpa67OtpNnlvb8p6X8lvbdsa/06/NWSrij7XiHp1bVtI+V4xJgYpPySdAPwAuAn5dy2bDmPniDpdknzJH1GpSBuc947XNJJkr4l6VxJDwOvk7SmpB+Vv8FNkj5Yi/FwSWdJ+r6kB4B9hvldtgc+AexeYvuDpN0kXdmy34ck/U95fZKkb0s6v+T4/7b8W7JB2XZP+Tfl7cP9LQdBiucBUpLop8AtwDRgLeC0Ubp9CdgMeDWwGvBR4MmW4+4LfB7YxvafRnj/qcDPgK8DawCbAFeXzV8HVqH6x2BL4F3Avh3+agDvKPs/B1gGOLi0/2v5+SzbK9n+bVnfArgReC5wJNXfof710p7ABbbv7CKGiJ5pOn9rtgReDGwnaReqk+SuVDn9G+CH5birA2cBhwDPBmaXONr9bqsB5wDHlH2/DJwj6dm13YbL8YjFNmj5ZXtd4K/ATuXc9hhwEvAE8EJgU2Bb4L21Y9fPe58tbe8or1cGLgV+Avyh/P6vBw6StF3tGLtQ5fWzGGbYle2fA0cBp5fYXgbMBNaR9OLarv8GnFJb34vqfLw6VW1wKoCkFYHzgR9Q5f8ewDclbdj2LzgIbGcZkAV4FXAn8IyW9n2Ai2vrpkq+pYBHgZe1Oda0st/BwHXA2h28/yHAj9u0TwL+CWxYa/t34KLy+nDg+23e+xll/SLgU7Xt7wd+3m7f2u/715YYtqD6h0hlfRbw9qb/m2XJMrT0Qf4O9XlBre1nwHtq60sBj1B9nfwu4Le1bQLmAu9tjZvqJHp5y/v9FtinvB42x7NkGYtl0PKrrN9MVZRDVRA/Bixf239P4Fe136P1vHcScEptfYs2+xwCnFheHw78usO/5+HUztul7VvAZ8vrjYB7gWVrsZxW23clYD4wGdgd+E3LsY4DDmv6/5tFXXLlebBMBm5x5zfOrQ4sB9wwwj4fAY61fWuH79/uWKsDS1N94h9yC9Un3079rfb6EarEG8nc+ort35V+W0nagOofx5ldvH9ErzWdv0PquTMV+JqqYVj3AfdQFclrAWvW93V1xhvufdZkwfyHhf8N6DbHI7oxaPnVairVefT22v7HUV2pbXfs4d5vzaH+5RifoCrMRzpGp04G3iFJVB+Yz3B1xXyhY9t+iOr3XbPEtUVLXHsBz1uMWBqVweKDZS4wRdIzOvwH4i7gH8C6VF/jtLMt8HNJf7P9ow7ef/Nh3udxqgS5rrRNAeaV1w8DK9T27yZh3EX7yVRDN/4GnGX7H128T0SvNZ2/Q+q5M5fqStJCX99KWg9Yu7au+nqL26jyv24K8PMOY4pYXAOVX23MpbryvPoI8bc777W+30221+swvpEstJ/tyyT9E/gXquEi72jZZfLQC0krUQ2Fua3E9b+239Dhe/e9XHkeLJcDtwOfk7SipOUkvWa4nW0/CcwAvlxuIpik6oa7ZWu7XQtsDxwraedR3v9UYBtJb5f0DEnPlrSJ7fnAGcBnJa1cxkZ/CBi6SfBq4F8lTZG0CtXXSJ26k2oM2ojzchbfB95CVUCfMsq+EeOt6fxt59vAIZI2gqdu/N2tbDsHeKmkN6u6uXd/hv/gey7wIknvKP827A5sSDUGNWI8DFp+tcZzO/AL4GhJz5S0lKR1JW3ZxftdDjwo6WOqboacJOklkl6xCLH/HZimcuN/zSnAN4DHbV/csu2NqiYVWIZq7PNltudS/TvwIkn/JmnpsryiZfz0QEnxPEBKkboT1ZCEv1J9hbr7KN0OBv4IXEH1FcrnafnvbvsPwI7AdyTtMML7/xV4I/DhcqyrgZeVzR+gusJ8I3Ax1Y0BM0q/84HTgWuAK+nihGr7EaqbIS4pX/e8coR95wJXUX1i/k2n7xExHprO32Fi+nE55mmq7r7/E7BD2XYXsBvwBeBuqmJ4FtXVsdbj3F1i+HDZ96PAjuUYET03aPk1jHdR3Ux7HdV44rOA53fxfvNLrJtQPR/hLuC7VDfzd+vM8vNuSVfV2r8HvISnL47V/QA4jOpvuRnlJn7bD1Jdxd+D6kr036j+Lsu2OcZAGLq5KmKJIGkGcJvtTzUdS8SSpFyBuhXYy/avmo4nIsafqqn87gBebvv6WvtJwK0T5dybMc+xxFD1NMJdqab4iYjFVKa4+h3VrAQfobrZ6bJGg4qIJv0ncEW9cJ6IMmwjFiBpL1WTorcui/QUpPEi6Uiqr8S+aPumpuOJaEIP8vdVVLMR3EX1lfibbT86ZgFHDJBBPT8OR9LPhvl9PjHM/jcDB1INz5rQMmwjIiIiIqJDufIcEREREdGhJWrM8+qrr+5p06Y1HUbEuLjyyivvsr1G03EsjuRsTCSDnrPJ15hIRsrXJap4njZtGrNmzWo6jIhxIan1iW4DJzkbE8mg52zyNSaSkfK1p8M2JM2QdIekPw2zXZKOkTRH0jWSXl7btrek68uydy/jjIhKcjZicCRfI5rR6zHPJ1E9nWc4OwDrlWU/4FsAklajmmh7C6rHQR8madWeRhoRkJyNGCQnkXyNGHc9LZ5t/5rqSTPD2QU4xZXLgGdJej6wHXC+7Xts3wucz8j/QETEGEjORgyO5GtEM5oe87wWMLe2fmtpG659IZL2o/pEzZQpU8YssM0+csqYHStiyJVffFfTISyu5GxMKAOes8nXmFDGK18Hfqo628fbnm57+hprDOxNzBETRnI2YnAkXyMW1nTxPA+YXFtfu7QN1x4RzUrORgyO5GtEDzRdPM8E3lXuCH4lcL/t24HzgG0lrVpuYti2tEVEs5KzEYMj+RrRAz0d8yzph8BWwOqSbqW6u3dpANvfBs4F3gjMAR4B9i3b7pF0JHBFOdQRtke6KSIixkByNmJwJF8jmtHT4tn2nqNsN7D/MNtmADN6EVdEtJecjRgcydeIZjQ9bCMiIiIiYmCkeI6IiIiI6FCK54iIiIiIDqV4joiIaJCkF0m6QNKfyvrGkj7VdFwR0V6K54iIiGZ9BzgEeBzA9jXAHo1GFBHDSvEcERHRrBVsX97S9kQjkUTEqFI8R0RENOsuSesCBpD0NuD2ZkOKiOH0dJ7niIiIGNX+wPHABpLmATcBezUbUkQMJ8VzREREg2zfCGwjaUVgKdsPNh1TRAwvwzYiIiIaJOnZko4BfgNcJOlrkp7ddFwR0V6K54iIiGadBtwJvBV4W3l9eqMRRcSwMmwjIiKiWc+3fWRt/TOSdm8smogYUa48R0RENOsXkvaQtFRZ3g6c13RQEdFeiueIiIhmvQ/4AfBYWU4D/l3Sg5IeaDSyiFhIhm1EREQ0yPbKTccQEZ3LleeIiIgGSfqRpDdKyjk5YgB0nKiSXtrLQCIiIiaob1E9FOV6SZ+TtH7TAUXE8Lr5lPtNSZdLer+kVXoWUURExARi+5e29wJeDtwM/FLSpZL2lbR0s9FFRKuOi2fb/0L1yXgycKWkH0h6w2j9JG0vabakOZI+3mb7VyRdXZa/SLqvtm1+bdvMTmONiEWTfI1oRnkoyj7Ae4HfA1+jKqbPH6VfcjZinHV1w6Dt6yV9CpgFHANsKknAJ2yf3bq/pEnAscAbgFuBKyTNtH1d7Zj/Vdv/A8CmtUM8anuTbmKMiIqktYCp1PLc9q9H2D/5GtEAST8G1ge+B+xk+/ay6XRJs0bol5yNaEDHxbOkjYF9gTdRfRLeyfZVktYEfgssVDwDmwNzbN9YjnEasAtwXZt9AfYEDus8/IhoR9Lngd2pcm1+aTYwbPFM8jWiKd+xfW69QdKyth+zPX2EfsnZiAZ0M+b568BVwMts72/7KgDbtwGfGqbPWsDc2vqtpW0hkqYC6wAX1pqXkzRL0mWS3txFrBET3ZuB9W2/0fZOZdl5lD7J14hmfKZN22876JecjWhAR1eey1dD82x/r9324dq7tAdwlu35tbaptudJegFwoaQ/2r6hJbb9gP0ApkyZMgZhRCwRbgSWpnrgQi8sUr5CcjZiiKTnURW7y0vaFFDZ9ExghTF+u5xjI8ZIR8Wz7fmSJktaxvY/uzj+PKobDIesXdra2QPYv+V955WfN0q6iGqs1g0t+xwPHA8wffp0dxFbxJLsEeBqSRdQK6Btf3CEPj3P17I9ORtR2Y7qJsG1gaN5unh+APhEB/1zjo1oQDc3DN4EXFLuyH14qNH2l0focwWwnqR1qBJ6D+AdrTtJ2gBYldrXVJJWBR6x/Zik1YHXAF/oIt6IiWxmWbqRfI0YR7ZPBk6W9FbbPxpuP0l7l31bJWcjGtBN8XxDWZYChh4lOuKnUNtPSDoAOA+YBMywfa2kI4BZtodO7nsAp9muH+/FwHGSnizv+bn6HcQRMTzbJ0taBnhRaZpt+/FR+iRfIxowUuFcHAgsVDwnZyOa0U3xfJ3tM+sNknYbrVO5g/jclrZDW9YPb9PvUiBPNYxYBJK2ojrZ3kz1VfDkcvVqpNk2kq8R/UnDbUjORoy/bmbbOKTDtoho3tHAtra3tP2vVGMrv9JwTBGxaDLWOKKPjHrlWdIOwBuBtSQdU9v0TOCJXgUWEYtladuzh1Zs/yWP+Y0YWMNeeY6I8dfJsI3bqJ4ouDNwZa39QeC/2vaIiKbNkvRd4PtlfS+qPI6IPiJpKeBtts8YYbdLxiueiBjdqMWz7T8Af5D0g9FuOIqIvvGfVNNSDU1N9xvgm82FExHt2H5S0keBYYtn2weMY0gRMYpubhjcXNLhwNTST4Btv6AXgUXEorP9GPDlskREf/ulpIOB01lwKth7mgspIobTTfF8AtUwjSuB+aPsGxENkHSG7bdL+iNtbjKyvXEDYUXEyHYvP+sPMTGQi1MRfaib4vl+2z/rWSQRMRYOLD93bDSKiOiY7XWajiEiOtdN8fwrSV8EzmbBx/1eNeZRRcQisX17+XlL07FERGckrQB8CJhiez9J6wHr2/5pw6FFRBvdFM9blJ/Ta20Gth67cCJiLEh6kIWHbdxPNePGh23fOP5RRcQwTqQaEvnqsj4POBNI8RzRhzounm2/rpeBRMSY+ipwK/ADqpt79wDWBa4CZgBbNRVYRCxkXdu7S9oTwPYjkjK3c0Sf6rh4lnRou3bbR4xdOBExRna2/bLa+vGSrrb9MUmfaCyqiGjnn5KWp3xbJGldasMjI6K/dPN47odry3xgB2BaD2KKiMX3iKS3S1qqLG8H/lG25VG/Ef3lMODnwGRJpwIXAB9tNqSIGE43wzaOrq9L+hJw3phHFBFjYS/ga1QPRjFwGfDOcnUrD1yI6BPlCYOrArsCr6QaZnWg7bsaDSwihtXNDYOtVgDWHqtAImLslBsCdxpm88XjGUtEDG/oCYPl8dznNB1PRIyu42Ebkv4o6ZqyXAvMpropKSL6jKQXSbpA0p/K+saSPtV0XBHR1i8lHSxpsqTVhpamg4qI9rq58lx/6MITwN9tPzHG8UTE2PgO8BHgOADb10j6AfCZRqOKiHbyhMGIAdLNmOdbJL0M+JfS9Gvgmp5EFRGLawXbl7fMdpUPuxF9pox5/rjt05uOJSI6082wjQOBU4HnlOVUSR/oVWARsVjuKtNdDU199Tbg9mZDiohWtp+k+pYoIgZEN8M23gNsYfthAEmfB34LfL0XgUXEYtkfOB7YQNI84CaqGTgiov/8UtLBwOlU08ECYPue5kKKiOF0M8+zqOZ3HjK/tI3cSdpe0mxJcyR9vM32fSTdKenqsry3tm1vSdeXZe8uYo2YsCRNAt5vextgDWAD26+1fUsHfZOvEeNvd6oPvL+mekz3lcCsTjomZyPGXzdXnk8Efifpx2X9zcAJI3UoJ/FjgTdQPSr4CkkzbV/Xsuvptg9o6bsa1cTx06m+er6y9L23i5gjJhzb8yW9trx+eLT9hyRfI5phe51F6ZecjWhGx1eebX8Z2Be4pyz72v7qKN02B+bYvtH2P4HTgF06fMvtgPNt31OS+Xxg+07jjZjgfi9ppqR/k7Tr0DJKn+RrRAMkrSDpU5KOL+vrSdpxtH4kZyMa0c0Ng68Errd9jO1jgBskbTFKt7WAubX1W0tbq7eW+aPPkjS5m76S9pM0S9KsO++8s9NfJ2JJtxxwN7A11cNSdmLB6Sbb6Xm+QnI2oo0TgX8Cry7r8+hsWsmcYyMa0M2Y528BD9XWHypti+snwDTbG1N98j25m862j7c93fb0NdZYYwzCiRh8tvdts7x7aLukQxbx0IuVryW25GzEgta1/QXgcQDbj9DBPUUdyjk2Yox1dcOgbQ+tlOl1RhszPQ+YXFtfu7Q9xfbdth8rq98FNuu0b0Qsst3atCVfI5rxT0nL8/TUkusCj43cBUjORjSim+L5RkkflLR0WQ4EbhylzxXAepLWkbQMsAcws76DpOfXVncG/lxenwdsK2lVSasC25a2iFh87a5qJV8jmnEY8HNgsqRTgQuAj3bQLzkb0YBuZtv4D+AY4FNUn44vAPYbqYPtJyQdQJWQk4AZtq+VdAQwy/ZM4IOSdqZ6+tk9wD6l7z2SjqT6xwHgiMx5GTFmvFBD8jViXEl6je1LqKao2xV4JdUH2wNt3zVa/+RsRDO6eTz3HVSfatuSdIjt/27T71zg3Ja2Q2uvDwHajr+0PQOY0WmMEdGxtuMpk68R4+oYqmEUv7X9cuCcbg+QnI0Yf91ceR7NbsBCxXNEjD9Jq7VeRZK0ju2byuqZDYQVEQt6vExPt7akY1o32v5gAzFFxCjGsngeqzuDI2Lx/UTSDrYfAJC0IXAG8BIA20c1GVxEANX0kdtQzbl8ZcOxRESHxrJ4XmgMZUQ05iiqAvpNwPrAKcBezYYUEXVlXPNpkv5s+w9NxxMRncmV54glkO1zJC0N/AJYGXiL7b80HFZEtPeopAuA59p+iaSNgZ1td/KglIgYZx0XzxlDGdH/JH2dBb8FWgW4AThAUsZQRvSn7wAfAY4DsH2NpB/Q2VMGI2KcdXPlOWMoI/rfrJb1jKOM6H8r2L5cWuAL3CeaCiYiRtZN8ZwxlBF9zvbJAJJWBP5he35ZnwQs22RsETGsu8pTBYeeMPg24PZmQ4qI4XQzz3PGUEYMjguo7uJ/qKwvT5W7r24soogYzv7A8cAGkuYBN5GLUxF9a9TiOWMoIwbScraHCmdsPyRphSYDioiFlW+F3m97m/KN0VK2H2w6rogYXidXnjOGMmLwPCzp5bavApC0GfBowzFFRAvb8yW9trx+uOl4ImJ0oxbPGUMZMZAOAs6UdBvVNJLPA3ZvNKKIGM7vJc2kmrXqqQLa9tnNhRQRw+nmhsGMoYwYELavkLQB1c29ALNtP95kTBExrOWAu4Gta20GUjxH9KFuiueMoYzoc5K2tn2hpF1bNr2o3KOQk3FEn7G970jbJR1i+7/HK56IGFk3xXPGUEb0vy2BC4Gd2mzLlayIwbQbkOI5ok90UzwfRMZQRvQ124eVnyNeyYqIgaLRd4mI8dLNPM8ZQxnR5yR9aKTttr88XrFExJjx6LtExHjpZJ7njKGMGBwrj7AtJ+CIwZQrzxF9pJMrzxlDGTEgbH8aQNLJwIG27yvrqwJHNxhaRAxD0mq272lpW8f2TWX1zAbCiohhLDXaDvUxlG2Wd4/WX9L2kmZLmiPp4222f0jSdZKukXSBpKm1bfMlXV2Wmd3+chET2MZDhTOA7XuBTUfrlHyNaMRPJD1zaEXShsBPhtZtHzVcx+RsxPjrZNjGIo+hLA9SORZ4A3ArcIWkmbavq+32e2C67Uck/SfwBZ6+EfFR25uMFmNELGQpSauWohlJqzFKvidfIxpzFFUB/Saq+4pOAfYarVNyNqIZnQzbWJwxlJsDc2zfCCDpNGAX4KnEtv2r2v6XAe/sIKaIGNnRwG8lDX3duxvw2VH6JF8jGmD7HElLUz14bGXgLbb/0kHX5GxEAzp5PPfijKFcC5hbW78V2GKE/d8D/Ky2vpykWcATwOds/7/WDpL2A/YDmDJlyijhREwMtk8puTP0xLJdW65GtdPzfIXkbMQQSV9nwYtQqwA3AAeUG/I/OMohco6NaEA38zwvNIZS0qhjKDsl6Z3AdKobFIdMtT1P0guACyX90fYN9X62jweOB5g+fXpmE4goSrE8WsG8SBY1X0tcydmIyqyW9St79UY5x0aMnW6K567HUALzgMm19bVL2wIkbQN8EtjS9mND7bbnlZ83SrqI6oanhU7GETEmkq8R48j2yQCSVgT+YXt+WZ8ELNvBIZKzEQ0YdbaNmqExlEdKOhK4lOrGg5FcAawnaR1JywB7AAvc0VuuXh8H7Gz7jlr7qpKWLa9XB15Dj66iRQSQfI1oygXA8rX15YFfdtAvORvRgG6eMNj1GErbT0g6ADgPmATMsH2tpCOAWbZnAl8EVqJ69DfAX23vDLwYOE7Sk1RF/uc6GLMZEYso+RrRmOVsPzS0YvshSSuM1ik5G9GMboZtLNIYStvnAue2tB1ae73NMP0uBV7azXtFxOJJvkY04mFJL7d9FYCkzYBHO+mYnI0Yf10VzxERETHmDqK6Mnwb1aO4n8fTczFHRJ9J8RwREdEg21dI2oDqASkAs20/3mRMETG8FM8RERENkLS17Qsl7dqy6UVlnuezGwksIkaU4jkiIqIZWwIXAju12WYgxXNEH0rxHBER0QDbh5Wf+zYdS0R0LsVzREREAyR9aKTttr88XrFEROdSPEdERDRj5RG25VHYEX0qxXNEREQDbH8aQNLJwIG27yvrq1I91Tci+lA3j+eOiIiIsbfxUOEMYPteYNPmwomIkaR4joiIaNZS5WozAJJWI98MR/StJGdERESzjgZ+K+nMsr4b8NkG44mIEaR4joiIaJDtUyTNArYuTbvavq7JmCJieCmeIyIiGlaK5RTMEQMgY54jIiIiIjqU4jkiIiIiokMpniMiIiIiOpTiOSIiIiKiQymeIyIiIiI61PPiWdL2kmZLmiPp4222Lyvp9LL9d5Km1bYdUtpnS9qu17FGTHTJ14jBkpyNGH89LZ4lTQKOBXYANgT2lLRhy27vAe61/ULgK8DnS98NgT2AjYDtgW+W40VEDyRfIwZLcjaiGb2+8rw5MMf2jbb/CZwG7NKyzy7AyeX1WcDrJam0n2b7Mds3AXPK8SKiN5KvEYMlORvRgF4/JGUtYG5t/VZgi+H2sf2EpPuBZ5f2y1r6rtX6BpL2A/Yrqw9Jmj02oUcXVgfuajqIQaAv7T2Wh5s6lgdjHPIVkrN9IjnboYmes8nXvpB87dB45evAP2HQ9vHA8U3HMZFJmmV7etNxxGBIzjYvORudSr42L/naf3o9bGMeMLm2vnZpa7uPpGcAqwB3d9g3IsZO8jVisCRnIxrQ6+L5CmA9SetIWobq5oSZLfvMBIaus78NuNC2S/se5U7hdYD1gMt7HG/ERJZ8jRgsydmIBvR02EYZX3UAcB4wCZhh+1pJRwCzbM8ETgC+J2kOcA9V8lP2OwO4DngC2N/2/F7GG4ssX+ktAZKvE0pydgmQnJ0wkq99RtUH0IiIiIiIGE2eMBgRERER0aEUzxERERERHUrxHBERERHRoRTPEREREX1C0gaSXi9ppZb27ZuKKRaU4jnGjKR9m44hIiJiUEn6IPA/wAeAP0mqP279qGaiilYpnmMsfbrpACLiafUrVZJWkXSCpGsk/UDSc5uMLSLaeh+wme03A1sB/5+kA8s2NRVULGjgH88d40vSNcNtAnIyjugvRwE/L6+PBm4HdgJ2BY4D3txMWBExjKVsPwRg+2ZJWwFnSZpKiue+keI5uvVcYDvg3pZ2AZeOfzgR0aHptjcpr78iae+Rdo6IRvxd0ia2rwaw/ZCkHYEZwEsbjSyekuI5uvVTYKWhxK6TdNG4RxMRI3mOpA9Rfbh9piT56SdjZdheRP95F9UTH59i+wngXZKOayakaJUnDEZELKEkHdbS9E3bd0p6HvAF2+9qIq6IiEGW4jkiYgkmaQNgLeB3Q2MpS/v2tn8+fM+IiGgnX9tFRCyhJH2ATHsVETGmUjzHQiR1deOfpK0k/bRX8UTEItuPTHsV0Tdyfl0y5IbBWIjtVzcdQ0SMiUx7FdFHcn5dMuTKcyxE0kPl51aSLpJ0lqT/k3SqJJVt25e2q6jmjB3qu6KkGZIul/T7oa+JJX1N0qHl9XaSfi0p//9F9NbfJW0ytFIK6R2B1cm0VxHjLufXJUOuPMdoNgU2Am4DLgFeI2kW8B1ga2AOcHpt/08CF9p+t6RnAZdL+iVwCHCFpN8AxwBvtP3k+P0aERNSpr2K6F85vw6ofDKJ0Vxu+9aSiFcD04ANgJtsX1/mjP1+bf9tgY9Luhq4CFgOmGL7EarHjp4PfMP2DeP2G0RMUCV3/zbMtkvGO56IWEDOrwMqV55jNI/VXs9n9P9nBLzV9uw2214K3A2sOUaxRUREDKqcXwdUrjzHovg/YJqkdcv6nrVt5wEfqI3d2rT8nAp8mOprqh0kbTGO8UZERAyCnF8HQIrn6Jrtf1BNgXVOuaHhjtrmI4GlgWskXQscWRL9BOBg27cB7wG+K2m5cQ49YomTqa8ilhw5vw6GPGEwImICKdPVHWx7x4ZDiYgYSLnyHBExwDL1VUTE+MoNgxERS45MfRUR0WO5khARseTI1FcRET2WK88REUuOTH0VEdFjufIcEbFky9RXERFjKMVzRMQSLFNfRUSMrUxVFxERERHRoVx5joiIiIjoUIrniIiIiIgOpXiOiIiIiOhQiueIiIiIiA6leI6IiIiI6FCK54iIiIiIDqV4joiIiIjo0P8PTSiMo7XMJNQAAAAASUVORK5CYII=\n",
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\n",
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\n",
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+ "output_type": "display_data"
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 分析用户点击环境变化是否明显，这里随机采样10个用户分析这些用户的点击环境分布\n",
+ "sample_user_ids = np.random.choice(tst_click['user_id'].unique(), size=10, replace=False)\n",
+ "sample_users = user_click_merge[user_click_merge['user_id'].isin(sample_user_ids)]\n",
+ "cols = ['click_environment','click_deviceGroup', 'click_os', 'click_country', 'click_region','click_referrer_type']\n",
+ "for _, user_df in sample_users.groupby('user_id'):\n",
+ " plot_envs(user_df, cols, 2, 3)"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 分析用户点击环境变化是否明显，这里随机采样10个用户分析这些用户的点击环境分布\n",
- "sample_user_ids = np.random.choice(tst_click['user_id'].unique(), size=10, replace=False)\n",
- "sample_users = user_click_merge[user_click_merge['user_id'].isin(sample_user_ids)]\n",
- "cols = ['click_environment','click_deviceGroup', 'click_os', 'click_country', 'click_region','click_referrer_type']\n",
- "for _, user_df in sample_users.groupby('user_id'):\n",
- " plot_envs(user_df, cols, 2, 3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户点击新闻数量的分布"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:40:04.296033Z",
- "start_time": "2020-11-13T15:40:03.980868Z"
- }
- },
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "[]"
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性"
]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": "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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击新闻数量的分布"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "user_click_item_count = sorted(user_click_merge.groupby('user_id')['click_article_id'].count(), reverse=True)\n",
- "plt.plot(user_click_item_count)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以根据用户的点击文章次数看出用户的活跃度"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#点击次数在前50的用户\n",
- "plt.plot(user_click_item_count[:50])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "点击次数排前50的用户的点击次数都在100次以上。思路：我们可以定义点击次数大于等于100次的用户为活跃用户，这是一种简单的处理思路， 判断用户活跃度，更加全面的是再结合上点击时间，后面我们会基于点击次数和点击时间两个方面来判断用户活跃度。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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ZfV2a3V5addta2nOxpPMlTfSjjZIezB7r7Gc/EGmbb5P0R7M8dnX2Pl4kaWX2/l4413td0j9Kuiq7/WVJfxBBm1dIOj+7fZKaF4VZPciv9Rxtruy1TqEn/l5Jj4cQngwhvCbp65LWVVxT2dZJuj27fbuk3245/tXQtF3SKbZXSLpC0tYQwoshhB9L2ippzTzXnCuEsE3SizMOl9LG7L63hBC2h+a7/Kstv6syOW3Os07S10MIr4YQ/lfS42q+z2d9r2e9z9+U9M3s51v/fpUJIRwKIfwgu/1TSQ9LOksD/FrP0eY8fX+tUwjxsyT9sOX7/Zr7jxa7IOke2zvdvIi0JC0PIRzKbv9I0vLsdl7bU/yblNXGs7LbM4/H6oZs6GDj5LCCOm/zaZKOhBCOzjgeDdujkn5R0gOqyWs9o81SRa91CiE+aC4KIZwv6QOSrrd9ceudWY9joNd91qGNmb+TdI6k90g6JOmvK62mT2yfKOlbkm4MIbzUet+gvtaztLmy1zqFED8g6eyW79+WHUtSCOFA9vWwpLvU/Fj1bPbRUdnXw9nD89qe4t+krDYeyG7PPB6dEMKzIYQ3QgjHJH1Fzdda6rzNL6g59DA043jlbA+rGWZ3hBD+OTs80K/1bG2u8rVOIcS/L+ncbMb2OElXSbq74pq6YvsE2ydN3pZ0uaQJNdszOSN/raQt2e27JV2TzepfKOkn2cfU70q63PbS7GPb5dmxmJXSxuy+l2xfmI0fXtPyu6IyGWSZD6n5WkvNNl9le5HtlZLOVXMCb9b3etab/Z6kD2c/3/r3q0z2998g6eEQwt+03DWwr3Vemyt9rauc6S36T81Z7X1qzubeWnU9PbTj7WrOQu+W9NBkW9QcB7tX0mOS/l3SqdlxS/pS1u69khotv+s6NSdJHpf0+1W3bUY7N6v5kfJ1Ncf01pfZRkmN7D/JE5K+qOzM4wjb/LWsTXuy/8wrWh5/a1b/o2pZcZH3Xs/eOw9mf4t/krQogjZfpOZQyR5Ju7J/awf5tZ6jzZW91px2DwAJS2E4BQCQgxAHgIQR4gCQMEIcABJGiANAwghxAEgYIQ4ACft/AbwTsfQSxAYAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#点击次数排名在[25000:50000]之间\n",
- "plt.plot(user_click_item_count[25000:50000])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出点击次数小于等于两次的用户非常的多，这些用户可以认为是非活跃用户"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻点击次数分析"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:42:14.526476Z",
- "start_time": "2020-11-13T15:42:14.463642Z"
- }
- },
- "outputs": [],
- "source": [
- "item_click_count = sorted(user_click_merge.groupby('click_article_id')['user_id'].count(), reverse=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:42:16.198000Z",
- "start_time": "2020-11-13T15:42:16.044455Z"
- }
- },
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "[]"
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:40:04.296033Z",
+ "start_time": "2020-11-13T15:40:03.980868Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "user_click_item_count = sorted(user_click_merge.groupby('user_id')['click_article_id'].count(), reverse=True)\n",
+ "plt.plot(user_click_item_count)"
]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以根据用户的点击文章次数看出用户的活跃度"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[:100])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出点击次数最多的前100篇新闻，点击次数大于1000次"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[:20])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "点击次数最多的前20篇新闻，点击次数大于2500。思路：可以定义这些新闻为热门新闻， 这个也是简单的处理方式，后面我们也是根据点击次数和时间进行文章热度的一个划分。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[3500:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以发现很多新闻只被点击过一两次。思路：可以定义这些新闻是冷门新闻"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻共现频次：两篇新闻连续出现的次数"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 433597.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 3.184139 \n",
- " \n",
- " \n",
- " std \n",
- " 18.851753 \n",
- " \n",
- " \n",
- " min \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 2.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 2202.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " count\n",
- "count 433597.000000\n",
- "mean 3.184139\n",
- "std 18.851753\n",
- "min 1.000000\n",
- "25% 1.000000\n",
- "50% 1.000000\n",
- "75% 2.000000\n",
- "max 2202.000000"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "tmp = user_click_merge.sort_values('click_timestamp')\n",
- "tmp['next_item'] = tmp.groupby(['user_id'])['click_article_id'].transform(lambda x:x.shift(-1))\n",
- "union_item = tmp.groupby(['click_article_id','next_item'])['click_timestamp'].agg({'count'}).reset_index().sort_values('count', ascending=False)\n",
- "union_item[['count']].describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "由统计数据可以看出，平均共现次数3.18，最高为2202。\n",
- "\n",
- "说明用户看的新闻，相关性是比较强的。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#画个图直观地看一看\n",
- "x = union_item['click_article_id']\n",
- "y = union_item['count']\n",
- "plt.scatter(x, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(union_item['count'].values[40000:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "大概有75000个pair至少共现一次"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻文章信息"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#不同类型的新闻出现的次数\n",
- "plt.plot(user_click_merge['category_id'].value_counts().values)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#出现次数比较少的新闻类型, 有些新闻类型，基本上就出现过几次\n",
- "plt.plot(user_click_merge['category_id'].value_counts().values[150:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "count 1.630633e+06\n",
- "mean 2.043012e+02\n",
- "std 6.382198e+01\n",
- "min 0.000000e+00\n",
- "25% 1.720000e+02\n",
- "50% 1.970000e+02\n",
- "75% 2.290000e+02\n",
- "max 6.690000e+03\n",
- "Name: words_count, dtype: float64"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#新闻字数的描述性统计\n",
- "user_click_merge['words_count'].describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(user_click_merge['words_count'].values)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户点击的新闻类型的偏好\n",
- "\n",
- "此特征可以用于度量用户的兴趣是否广泛。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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lF4BlN69oVkmH/fdfWcJvX7yo6c8raXQM9Ip7/1tP5bSZ09h34GBTn3fZzStYu+2VwxcPaYepk7o5ZcbUtm1f6jQGesX1dHfRv3BW05931omT+daK5/nWiueb/tzH486Pv4Pzz2h+/6QcGegalZuWvZ2123a3bfvbdu3lf923hme2v8qSeTPaVsfRpk7q8sNntY2nz1WWtu1+jQu+9GC7yzjGh85bwFc/srTdZahivEi0Km1O71Ru+PWlbN01+hlAzXbHyo08tbV971okA13Z+uC5C9pdwhEef2EX//joC5zzxfvbXcqE9JnLz+ZjF03sWVcGutQk11y8iJOn1z/bpsbHXQ9vYuC5lw30sTw4It4LfB3oBr6ZUvpyU6qSMrT0tJNYetpJ7S5jQlr53Mv8YM02Lv/aD9tdyohuWvZ2Tj95fK+XMOpAj4hu4M+By4GNwE8i4p6U0uPNKk6SGnHNxYu4f/WWdpdRVyu+VT2WI/QLgHUppfUAEfH3wFWAgS6ppa5623yvM8DYLhI9H9gw5PbGou0IEXFtRAxExMDg4OAYNidJqmfc3wOklG5MKfWnlPr7+vrGe3OSNGGNJdA3AacNub2gaJMktcFYAv0nwFkRsSgiJgNXA/c0pyxJ0vEa9YeiKaX9EfE7wP3Upi3enFJa3bTKJEnHZUzz0FNK9wL3NqkWSdIYeLkZSaoIA12SKqKlp8+NiEHguVE+fDawvYnl5MA+Twz2ufrG2t8zUkql875bGuhjEREDjZwPuErs88Rgn6uvVf11yEWSKsJAl6SKyCnQb2x3AW1gnycG+1x9LelvNmPokqT6cjpClyTVYaBLUkVkEegR8d6IeDIi1kXEde2u53hFxLMR8VhErIqIgaJtVkQ8EBFri98zi/aIiP9d9PXRiDhvyPMsK9ZfGxHLhrSfXzz/uuKx0YY+3hwR2yLiZ0Paxr2PI22jjX3+YkRsKvb1qoi4csh91xf1PxkR7xnSPuzruzjx3fKi/bbiJHhExJTi9rri/oUt6u9pEfFQRDweEasj4lNFe2X3c50+d+Z+Til19A+1E389DbwRmAw8Aixpd13H2YdngdlHtf0xcF2xfB3wlWL5SuA+IIALgeVF+yxgffF7ZrE8s7hvRbFuFI+9og19vAQ4D/hZK/s40jba2OcvAn84zLpLitfuFGBR8Zrurvf6Bm4Hri6W/xL4eLH8CeAvi+Wrgdta1N95wHnFci/wVNGvyu7nOn3uyP3c0n/0o/yDvgO4f8jt64Hr213XcfbhWY4N9CeBeUNeNE8Wy98AfuPo9YDfAL4xpP0bRds8YM2Q9iPWa3E/F3JkuI17H0faRhv7PNI/9CNet9TOUvqOkV7fRaBtB3qK9sPrHXpssdxTrBdt2N93U7umcOX38zB97sj9nMOQS0OXuutwCfhuRKyMiGuLtrkppc3F8hZgbrE8Un/rtW8cpr0TtKKPI22jnX6nGGK4ecjQwPH2+WRgR0pp/1HtRzxXcf/OYv2WKd7+nwssZ4Ls56P6DB24n3MI9Cq4OKV0HnAF8MmIuGTonan2X3Cl54+2oo8d8nf8C+BNwNuAzcBX21rNOIiI6cCdwKdTSruG3lfV/TxMnztyP+cQ6Nlf6i6ltKn4vQ24C7gA2BoR8wCK39uK1Ufqb732BcO0d4JW9HGkbbRFSmlrSulASukg8FfU9jUcf59fBE6KiJ6j2o94ruL+GcX64y4iJlELtltTSt8umiu9n4frc6fu5xwCPetL3UXEiRHRe2gZeDfwM2p9OPTp/jJqY3MU7R8tZghcCOws3mreD7w7ImYWb+/eTW2sbTOwKyIuLGYEfHTIc7VbK/o40jba4lDoFD5IbV9Drc6ri5kLi4CzqH0AOOzruzgKfQj4cPH4o/9+h/r8YeD7xfrjqvjb3wQ8kVL62pC7KrufR+pzx+7ndnywMIoPIq6k9uny08Dn2l3Pcdb+RmqfaD8CrD5UP7WxsAeBtcD3gFlFewB/XvT1MaB/yHP9NrCu+PnYkPb+4gX1NPBntOcDsm9Re+v5OrVxwGta0ceRttHGPv9t0adHi3+Q84as/7mi/icZMhNppNd38dpZUfwt/gGYUrRPLW6vK+5/Y4v6ezG1oY5HgVXFz5VV3s91+tyR+9mv/ktSReQw5CJJaoCBLkkVYaBLUkUY6JJUEQa6JFWEgS5JFWGgS1JF/H85cMkmMcaqfgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图中可以看出有一小部分用户阅读类型是极其广泛的，大部分人都处在20个新闻类型以下。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " category_id \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 250000.000000 \n",
- " 250000.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 124999.500000 \n",
- " 4.573188 \n",
- " \n",
- " \n",
- " std \n",
- " 72168.927986 \n",
- " 4.419800 \n",
- " \n",
- " \n",
- " min \n",
- " 0.000000 \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 62499.750000 \n",
- " 2.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 124999.500000 \n",
- " 3.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 187499.250000 \n",
- " 6.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 95.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id category_id\n",
- "count 250000.000000 250000.000000\n",
- "mean 124999.500000 4.573188\n",
- "std 72168.927986 4.419800\n",
- "min 0.000000 1.000000\n",
- "25% 62499.750000 2.000000\n",
- "50% 124999.500000 3.000000\n",
- "75% 187499.250000 6.000000\n",
- "max 249999.000000 95.000000"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "user_click_merge.groupby('user_id')['category_id'].nunique().reset_index().describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户查看文章的长度的分布\n",
- "\n",
- "通过统计不同用户点击新闻的平均字数，这个可以反映用户是对长文更感兴趣还是对短文更感兴趣。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图中可以发现有一小部分人看的文章平均词数非常高，也有一小部分人看的平均文章次数非常低。\n",
- "\n",
- "大多数人偏好于阅读字数在200-400字之间的新闻。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#挑出大多数人的区间仔细看看\n",
- "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以发现大多数人都是看250字以下的文章"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 250000.000000 \n",
- " 250000.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 124999.500000 \n",
- " 205.830189 \n",
- " \n",
- " \n",
- " std \n",
- " 72168.927986 \n",
- " 47.174030 \n",
- " \n",
- " \n",
- " min \n",
- " 0.000000 \n",
- " 8.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 62499.750000 \n",
- " 187.500000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 124999.500000 \n",
- " 202.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 187499.250000 \n",
- " 217.750000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 3434.500000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id words_count\n",
- "count 250000.000000 250000.000000\n",
- "mean 124999.500000 205.830189\n",
- "std 72168.927986 47.174030\n",
- "min 0.000000 8.000000\n",
- "25% 62499.750000 187.500000\n",
- "50% 124999.500000 202.000000\n",
- "75% 187499.250000 217.750000\n",
- "max 249999.000000 3434.500000"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#更加详细的参数\n",
- "user_click_merge.groupby('user_id')['words_count'].mean().reset_index().describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 用户点击新闻的时间分析"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [],
- "source": [
- "#为了更好的可视化，这里把时间进行归一化操作\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "mm = MinMaxScaler()\n",
- "user_click_merge['click_timestamp'] = mm.fit_transform(user_click_merge[['click_timestamp']])\n",
- "user_click_merge['created_at_ts'] = mm.fit_transform(user_click_merge[['created_at_ts']])\n",
- "\n",
- "user_click_merge = user_click_merge.sort_values('click_timestamp')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " click_timestamp \n",
- " click_environment \n",
- " click_deviceGroup \n",
- " click_os \n",
- " click_country \n",
- " click_region \n",
- " click_referrer_type \n",
- " rank \n",
- " click_cnts \n",
- " category_id \n",
- " created_at_ts \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 18 \n",
- " 249990 \n",
- " 162300 \n",
- " 0.000000 \n",
- " 4 \n",
- " 3 \n",
- " 20 \n",
- " 1 \n",
- " 25 \n",
- " 2 \n",
- " 5 \n",
- " 5 \n",
- " 281 \n",
- " 0.989186 \n",
- " 193 \n",
- " \n",
- " \n",
- " 2 \n",
- " 249998 \n",
- " 160974 \n",
- " 0.000002 \n",
- " 4 \n",
- " 1 \n",
- " 12 \n",
- " 1 \n",
- " 13 \n",
- " 2 \n",
- " 5 \n",
- " 5 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- " 30 \n",
- " 249985 \n",
- " 160974 \n",
- " 0.000003 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 8 \n",
- " 2 \n",
- " 8 \n",
- " 8 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- " 50 \n",
- " 249979 \n",
- " 162300 \n",
- " 0.000004 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 25 \n",
- " 2 \n",
- " 2 \n",
- " 2 \n",
- " 281 \n",
- " 0.989186 \n",
- " 193 \n",
- " \n",
- " \n",
- " 25 \n",
- " 249988 \n",
- " 160974 \n",
- " 0.000004 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 21 \n",
- " 2 \n",
- " 17 \n",
- " 17 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id click_article_id click_timestamp click_environment \\\n",
- "18 249990 162300 0.000000 4 \n",
- "2 249998 160974 0.000002 4 \n",
- "30 249985 160974 0.000003 4 \n",
- "50 249979 162300 0.000004 4 \n",
- "25 249988 160974 0.000004 4 \n",
- "\n",
- " click_deviceGroup click_os click_country click_region \\\n",
- "18 3 20 1 25 \n",
- "2 1 12 1 13 \n",
- "30 1 17 1 8 \n",
- "50 1 17 1 25 \n",
- "25 1 17 1 21 \n",
- "\n",
- " click_referrer_type rank click_cnts category_id created_at_ts \\\n",
- "18 2 5 5 281 0.989186 \n",
- "2 2 5 5 281 0.989092 \n",
- "30 2 8 8 281 0.989092 \n",
- "50 2 2 2 281 0.989186 \n",
- "25 2 17 17 281 0.989092 \n",
- "\n",
- " words_count \n",
- "18 193 \n",
- "2 259 \n",
- "30 259 \n",
- "50 193 \n",
- "25 259 "
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "user_click_merge.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [],
- "source": [
- "def mean_diff_time_func(df, col):\n",
- " df = pd.DataFrame(df, columns={col})\n",
- " df['time_shift1'] = df[col].shift(1).fillna(0)\n",
- " df['diff_time'] = abs(df[col] - df['time_shift1'])\n",
- " return df['diff_time'].mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 点击时间差的平均值\n",
- "mean_diff_click_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'click_timestamp'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(mean_diff_click_time.values, reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图可以发现不同用户点击文章的时间差是有差异的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 前后点击文章的创建时间差的平均值\n",
- "mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'created_at_ts'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(mean_diff_created_time.values, reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从图中可以发现用户先后点击文章，文章的创建时间也是有差异的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Defaulting to user installation because normal site-packages is not writeable\n",
- "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
- "Collecting gensim\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/2b/e0/fa6326251692056dc880a64eb22117e03269906ba55a6864864d24ec8b4e/gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl (24.2 MB)\n",
- "\u001b[K |████████████████████████████████| 24.2 MB 91.0 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.15.0)\n",
- "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
- "Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.5.4)\n",
- "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
- "Collecting smart-open>=1.8.1\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/e3/cf/6311dfb0aff3e295d63930dea72e3029800242cdfe0790478e33eccee2ab/smart_open-4.0.1.tar.gz (117 kB)\n",
- "\u001b[K |████████████████████████████████| 117 kB 96.7 MB/s eta 0:00:01\n",
- "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
- " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for smart-open: filename=smart_open-4.0.1-py3-none-any.whl size=108249 sha256=50eb67320a58790e8b173971aeb6af7b636d48259d7c9de759612e58e334215b\n",
- " Stored in directory: /home/admin/.cache/pip/wheels/c3/14/fc/a0e523e5d2f13d083ce0af09d4e2861d8e2ec65fc466fb1dff\n",
- "Successfully built smart-open\n",
- "Installing collected packages: smart-open, gensim\n",
- "Successfully installed gensim-3.8.3 smart-open-4.0.1\n"
- ]
- }
- ],
- "source": [
- "# 安装gensim\n",
- "!pip install gensim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [],
- "source": [
- "from gensim.models import Word2Vec\n",
- "import logging, pickle\n",
- "\n",
- "# 需要注意这里模型只迭代了一次\n",
- "def trian_item_word2vec(click_df, embed_size=16, save_name='item_w2v_emb.pkl', split_char=' '):\n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " # 只有转换成字符串才可以进行训练\n",
- " click_df['click_article_id'] = click_df['click_article_id'].astype(str)\n",
- " # 转换成句子的形式\n",
- " docs = click_df.groupby(['user_id'])['click_article_id'].apply(lambda x: list(x)).reset_index()\n",
- " docs = docs['click_article_id'].values.tolist()\n",
- "\n",
- " # 为了方便查看训练的进度，这里设定一个log信息\n",
- " logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', level=logging.INFO)\n",
- "\n",
- " # 这里的参数对训练得到的向量影响也很大,默认负采样为5\n",
- " w2v = Word2Vec(docs, size=16, sg=1, window=5, seed=2020, workers=24, min_count=1, iter=10)\n",
- " \n",
- " # 保存成字典的形式\n",
- " item_w2v_emb_dict = {k: w2v[k] for k in click_df['click_article_id']}\n",
- " \n",
- " return item_w2v_emb_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [],
- "source": [
- "item_w2v_emb_dict = trian_item_word2vec(user_click_merge)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(item_click_count[:20])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "点击次数最多的前20篇新闻，点击次数大于2500。思路：可以定义这些新闻为热门新闻， 这个也是简单的处理方式，后面我们也是根据点击次数和时间进行文章热度的一个划分。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(item_click_count[3500:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以发现很多新闻只被点击过一两次。思路：可以定义这些新闻是冷门新闻"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻共现频次：两篇新闻连续出现的次数"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 433597.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 3.184139 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 18.851753 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 2.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 2202.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#画个图直观地看一看\n",
+ "x = union_item['click_article_id']\n",
+ "y = union_item['count']\n",
+ "plt.scatter(x, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(union_item['count'].values[40000:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "大概有75000个pair至少共现一次"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章信息"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#不同类型的新闻出现的次数\n",
+ "plt.plot(user_click_merge['category_id'].value_counts().values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#出现次数比较少的新闻类型, 有些新闻类型，基本上就出现过几次\n",
+ "plt.plot(user_click_merge['category_id'].value_counts().values[150:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 1.630633e+06\n",
+ "mean 2.043012e+02\n",
+ "std 6.382198e+01\n",
+ "min 0.000000e+00\n",
+ "25% 1.720000e+02\n",
+ "50% 1.970000e+02\n",
+ "75% 2.290000e+02\n",
+ "max 6.690000e+03\n",
+ "Name: words_count, dtype: float64"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#新闻字数的描述性统计\n",
+ "user_click_merge['words_count'].describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(user_click_merge['words_count'].values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击的新闻类型的偏好\n",
+ "\n",
+ "此特征可以用于度量用户的兴趣是否广泛。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图中可以看出有一小部分用户阅读类型是极其广泛的，大部分人都处在20个新闻类型以下。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " category_id \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 250000.000000 \n",
+ " 250000.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 124999.500000 \n",
+ " 4.573188 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 72168.927986 \n",
+ " 4.419800 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 62499.750000 \n",
+ " 2.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 124999.500000 \n",
+ " 3.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 187499.250000 \n",
+ " 6.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 95.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
]"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图中可以发现有一小部分人看的文章平均词数非常高，也有一小部分人看的平均文章次数非常低。\n",
+ "\n",
+ "大多数人偏好于阅读字数在200-400字之间的新闻。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#挑出大多数人的区间仔细看看\n",
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以发现大多数人都是看250字以下的文章"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 250000.000000 \n",
+ " 250000.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 124999.500000 \n",
+ " 205.830189 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 72168.927986 \n",
+ " 47.174030 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000 \n",
+ " 8.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 62499.750000 \n",
+ " 187.500000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 124999.500000 \n",
+ " 202.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 187499.250000 \n",
+ " 217.750000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 3434.500000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " 249990 \n",
+ " 162300 \n",
+ " 0.000000 \n",
+ " 4 \n",
+ " 3 \n",
+ " 20 \n",
+ " 1 \n",
+ " 25 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 0.989186 \n",
+ " 193 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 249998 \n",
+ " 160974 \n",
+ " 0.000002 \n",
+ " 4 \n",
+ " 1 \n",
+ " 12 \n",
+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " 249985 \n",
+ " 160974 \n",
+ " 0.000003 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 8 \n",
+ " 2 \n",
+ " 8 \n",
+ " 8 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 50 \n",
+ " 249979 \n",
+ " 162300 \n",
+ " 0.000004 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 25 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 281 \n",
+ " 0.989186 \n",
+ " 193 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " 249988 \n",
+ " 160974 \n",
+ " 0.000004 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 21 \n",
+ " 2 \n",
+ " 17 \n",
+ " 17 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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pxPgYnv5kAZ++bjQ/XXuAh57fSqvCX0SAGL8LkN4THRW42tewQQP499/tIj4mim/deTXR2ukrEtEU/BHgs/PHcLK5jcdWF3P8TCvf//gMYqP1x55IpNL//gjxpUV5/OsH8lm98wh3P71eF3kXiWAK/gjy6XmjeXz5NPYcPs4tj7/Fr9+tINSm84pI71PwR5il03J45cH55A9N4csvbuP+FVtoONnid1ki0ocU/BFoeHoCK+6dwz8smcCrOw6z5PE3WVtS63dZItJHFPwRKjrKuO8vxvHyfdcxMC6aj/94A996dbcu9SgSART8Ee6q3FR++8A87pyZy5Ov7+NrK4toU/iLhDVN5xQS4mL4zzumkjowlh++tZ99NU08vnw6mcnxfpcmIr1AW/wCgJnx8G35fHPZVDaXHeO2J97iN1srdbSvSBhS8Mt5PlownJfvu460hDgefH4rc/5jDf+2qog9h3XFL5FwodMyywW1tzvW7K7mpc0VrNldTXNbOzflZ/G3N01gQnay3+WJSCc6H7/0qPqTzfx07QF+/NZ+mppbWTYjlwcX55GbluB3aSLiUfBLr6g/2cyTr5fws7VltDvHHTNy+fz1YxiTmeR3aSIRT8EvvepQ/SmeemMfKzaV09rWzh0zcvnHWyaSkaRZQCJ+UfBLn6g5foan3tjHz9YdIHVgLI99dBoLxmf6XZZIRNKlF6VPZCbH888fyOe3D8wnLSGOTz6zkfufe5eSas0AEgllCn65YhOyk1n1wDy+tHAca3ZXc9N33uRvXtjK/toTfpcmIhegoR7pUUebzvDUm6X8fF0ZZ1rbWDgxiw9NH8aiiVkMjIv2uzyRsKUxfvFd9fHT/OSdA/xqcwXVx8+QEBfN4klZ3DEzl/njMojS5R9FepSCX0JGW7tjw/6jrNp2iN/vOEz9yRaGpg7g9mnDWDYjl7wsHQwm0hMU/BKSzrS2sXrnEX79biVvFNfQ1u5YMD6TT8wewYLxmQyI1VCQyOXq8eA3syXA40A08CPn3Dc6LY8HngVmAkeBu5xzB8xsFLAL2OOtut4594WuXkvBHxmONp1hxcaD/GxdGTXHz5AYF831EzKZn5fJzZOzSU+M87tEkX6lR4PfzKKBYuBGoALYBNztnNvZYZ37gKnOuS+Y2XLgw865u7zg/61zbkqwxSv4I0tLWztr9x3lDzsO8/ruag43niYuOoo7C3J5aPF4nRpaJEiXEvzBnI9/FlDinCv1nvx5YCmws8M6S4FHvNsvAd8zM+29k27FRkdx/fhMrh+fiXOOnVWN/L/1B3mxsJyVWw9x3w3j+PicEaQMiPW7VJGwEcw8/hygvMP9Cq/tgus451qBBmCwt2y0mW0xszfMbP6FXsDM7jWzQjMrrKmpuaQOSPgwMyYPS+U/PnIVrz60gIJRafznH3Yz8+urueeZjfxiQxnVjaf9LlOk3+vtK3BVASOcc0fNbCbwP2Y22TnX2HEl59zTwNMQGOrp5ZqkHxiTmcRP/moW2yvqWbXtEH/ceYSHX97Bwy/vYPqIQSycMIQF4zOZkpNKtKaGilySYIK/Ehje4X6u13ahdSrMLAZIBY66wA6EMwDOuc1mtg8YD2gQX4IyNXcQU3MH8U+3TqL4SBOrdx7mjzuP8Nifivn26mLSEmKZl5fJgrwMFozPJCtlgN8li4S8YIJ/E5BnZqMJBPxy4GOd1lkJ3AOsA5YBa5xzzswygTrnXJuZjQHygNIeq14ihpkxITuZCdnJ3L8wj6NNZ3i7pJY3imt4a28tq7YdAmBidjI35WfxsdkjyU7Vl4DIhQQ7nfNW4LsEpnM+45z7P2b2KFDonFtpZgOAnwPTgTpguXOu1MzuAB4FWoB24GvOuVVdvZZm9cilcs6xq+o4b+6t4Y09NazffxSA+XmZ/OWckSycOETDQRL2dACXRLSDR0/y0rsVvLipnMONp8lMjmfxpCEsnJjFdeMGkxDX27u2RPqegl8EaG1rZ/XOI6zafog3i2tpOtNKXEwUc8cOZtHEIdwwcYguHylhQ8Ev0klzazubDtTx2q5qXtt9hLKjJ4HAPoF54zK4Li+D2aPT9deA9FsKfpEuOOcorT3Bml3VrNldzeayYzS3tRMbbcwdm8GymbksmjREXwLSryj4RS7BqeY2CsvqeNubHXSo4TTx3pDQwklZLJw4hJxBA/0uU6RLCn6Ry3T2NNKrdx5hze7q84aEFk0awqzRg5k8LEUXlpeQo+AX6QHOOfbVnGDN7iO8tquawrJjtLUH/r9kpcQzITuFidnJTB6WwtW5gxg5OAGdokr8ouAX6QWNp1vYUdlAUWUjRYca2FvdxN7qJppb2wEYnBjHrNHpXDt2MHPHDmZsZpK+CKTP9PTZOUUESBkQy9yxGcwdm3GuraWtnZLqJrYcrGdz2THWlx7l9zsOA5CZHM+1YwJfAteOHcyIdP1FIKFBW/wiPcg5R3ndKdaV1rJ231HW7jtKzfEzAOSmDeSWKdncMHEIM0ak6Ypj0qM01CMSIs7uJ1i3r5bX99Tw1t4aWtoccTFRzBgxiEUTs7gxP4tRGYl+lyr9nIJfJEQdP93Cxv11rC89ylt7a9l9+DgA47OSWJCXyewxg7lmVBqDEnTpSbk0Cn6RfqK87iR/2nWEV4sO8+7Beppb2zGDSdkpzB07mGtGp1MwMo3Bmj4q3VDwi/RDp1va2FZez4b9dbxTUsuWg/U0twVmDE0elsJfTMjkurEZzBip/QPyfgp+kTBwuqWNHZUNbNhfxxt7ath8MHAcQVxMFJOyk8kflsJVOYOYmpvKhOxkYqODuZKqhCsFv0gYOn66hU0H6li37yhFhxopOtRIw6kWAOJiosgfmsLVualclTuIq3NTGZOZpOsQRBAFv0gEcM5xsO4k2ysa2F5Rz/aKBnZUNnCiuQ2AxLhoJueknvdloGMJwpcO4BKJAGbGyMGJjBycyAevHgYEzjVUWtP0v18GlQ38bF0Zza37ARiUEMvVuYOYOTKN/KEpjB2SxPC0gcRomCiiaItfJMy1tLWz5/Bx3qtsYFt5Pe8ePEbxkaZzy2OjjZxBA8lNS2DE4ATGZCQyNjOJMZmJ5KYlaLion9BQj4h06fjpFoqPNFFa00Rp7QkO1p2k4tgpyo6eoP5ky7n14qKjmDg0mRsmDGH26HSmDh9EUrwGCkKRhnpEpEvJA2KZOTKNmSPT3res7kRz4Auh5gT7apvYtL+OJ9bsxTkwg7GZSUwZlsJVuYOYNjyVycNSNb20n1Hwi8h50hPjSE9Mp2BU+rm2hpMtbK2oZ8vBY+yobGB9aR3/s/UQEPirYEpOivdFks7s0emkJerI41CmoR4RuSzVjafZUl7Pu2XH2Fx2jO0VDecOOMtNG8hVOalMHpbClJxU8oemkJkcrxlFvUhDPSLS64akDODmydncPDkbgDOtbbxX0UBh2THeqwxMLT17imqAlAEx5GUlMz4rifxhgWmm47OSNUzkA23xi0ivaTzdQlFlI8VHjlN85Dh7q5soPnL83A7k6ChjTEYiE7KTmZqbyrThaUwelkKidiBfMm3xi0hISBkQy7XehWjOcs5RcewU2ysa2FXVyK6qRrYcrOe326vOrZMzaGDgspY5qVyVk8qUnBSyUwZoqKiHKPhFpE+ZGcPTExiensBtU4eea69tOsPWg/XsqmqkuLqJXVWNvL6nGu8yx6QMiGHS0BQmDU0hf1gKU3NTGZuZpHMUXQYFv4iEhIykeBbnZ7E4P+tc28nmVnZVHafoUAN7Dh9nV1UjvywsP3daitjowNHLE7KSGZ+VzITsZCYNTdapKbqh4BeRkJUQF/O+4w3a2x2ltScoOtTA7sPHKaluYsehBl7ZUcXZXZbJ8TFM8M5gmu/9lTAhWzuSz1Lwi0i/EhVljBuSxLghSSzt0H6yuZXiI4EhoqJDDRQfbuLX71by7JmywOO8g886fhnkD0shIwIvcqPgF5GwkBAXw7Thg5g2fNC5tvb2wI7knVUN7DzUyM6qRjbtr+M33sFnAEOS4899GeQPC3whjBqcGNbnKFLwi0jYiooyRgwOnHxuyZT/3ZFcf7KZnVWN574Mdh5q5O29tbR6e5IHxkYzcWjyeX8ZTMxOJiEuPCJT8/hFRAgcgLbXGyrq+KVw/HQrEDhP0YSsZKaPGMT04WnMGDmIsZlJIbMTWfP4RUQuUXxMNFNyUpmSk3qu7ewxBzurAlc821Zez++2V7FiYzkAmcnxzBuXwfy8DOblZTAkeYBf5V8SBb+IyEV0PObg7KkpArOKmthcdox3So7yRnENL2+pBGBidjILxmcyb1wGs0anh+wsIg31iIhcgfZ2x86qRt7aW8tbe2soPHCM5rZ24mKimD06nUUTh3DLVUPJSundvwZ0IRYREZ+cbG5lw/463iqu5c29NZRUN2EGs0al8+HpOdw2dSjJA2J7/HUV/CIiIaKk+ji/236Y32ytpLT2BIlx0SydnsOn5o5ifFZyj71Ojwe/mS0BHgeigR85577RaXk88CwwEzgK3OWcO+At+yrwGaAN+JJz7tWuXkvBLyLhyDnHlvJ6nttwkN9uP8TplnYWjM/kwUXjmDkyvfsn6EaPBr+ZRQPFwI1ABbAJuNs5t7PDOvcBU51zXzCz5cCHnXN3mVk+sAKYBQwD/gSMd861Xez1FPwiEu6OnWjmuY0H+ck7+6ltambeuAz+5sa8K/oCuJTgD+a0drOAEudcqXOuGXgezjtSGu/+z7zbLwGLLDC5dSnwvHPujHNuP1DiPZ+ISMRKS4zjizeM481/uIF/unUiuw83csd/r+OBFVtob+/94fdgpnPmAOUd7lcAsy+2jnOu1cwagMFe+/pOj83p/AJmdi9wL8CIESOCrV1EpF9LiIvh3gVj+cSckTz9Zilt7Y6oPjhVREjM43fOPQ08DYGhHp/LERHpUwlxMTy0eHyfvV4wQz2VwPAO93O9tguuY2YxQCqBnbzBPFZERPpQMMG/Ccgzs9FmFgcsB1Z2WmclcI93exmwxgX2Gq8ElptZvJmNBvKAjT1TuoiIXI5uh3q8Mfv7gVcJTOd8xjlXZGaPAoXOuZXAj4Gfm1kJUEfgywFvvReBnUAr8MWuZvSIiEjv0wFcIiJhoKenc4qISBhR8IuIRBgFv4hIhFHwi4hEmJDbuWtmNUDZFTxFBlDbQ+X0F5HW50jrL6jPkeJK+jzSOZcZzIohF/xXyswKg92zHS4irc+R1l9QnyNFX/VZQz0iIhFGwS8iEmHCMfif9rsAH0RanyOtv6A+R4o+6XPYjfGLiEjXwnGLX0REuqDgFxGJMGET/Ga2xMz2mFmJmX3F73ouh5kdMLP3zGyrmRV6belmttrM9nq/07x2M7MnvP5uN7MZHZ7nHm/9vWZ2T4f2md7zl3iP7f1L/by/j8+YWbWZ7ejQ1ut9vNhr+NjnR8ys0nuvt5rZrR2WfdWrf4+Z3dyh/YKfce+U6Ru89he806fjnQ79Ba99g5mN6qP+Djez181sp5kVmdmDXnvYvs9d9Dk032fnXL//IXC66H3AGCAO2Abk+13XZfTjAJDRqe2bwFe8218B/tO7fSvwe8CAOcAGrz0dKPV+p3m307xlG711zXvsLT70cQEwA9jRl3282Gv42OdHgL+7wLr53uc3Hhjtfa6ju/qMAy8Cy73bPwD+2rt9H/AD7/Zy4IU+6u9QYIZ3Oxko9voVtu9zF30Oyfe5T//T9+I/+rXAqx3ufxX4qt91XUY/DvD+4N8DDO3w4drj3X4KuLvzesDdwFMd2p/y2oYCuzu0n7deH/dzFOeHYK/38WKv4WOfLxYI5312CVwH49qLfca94KsFYrz2c+udfax3O8Zbz3x4v38D3BgJ7/MF+hyS73O4DPVc6ILw77uoez/ggD+a2WYLXIAeIMs5V+XdPgxkebcv1ueu2isu0B4K+qKPF3sNP93vDW0802FI4lL7PBiod861dmo/77m85Q3e+n3GG3aYDmwgQt7nTn2GEHyfwyX4w8U859wM4Bbgi2a2oONCF/hKD+v5t33RxxD5d/xvYCwwDagCvu1rNb3AzJKAXwEPOecaOy4L1/f5An0Oyfc5XII/LC7q7pyr9H5XAy8Ds4AjZjYUwPtd7a1+sT531Z57gfZQ0Bd9vNhr+MI5d8Q51+acawd+SOC9hkvv81FgkJnFdGo/77m85ane+r3OzGIJBOAvnHO/9prD+n2+UJ9D9X0Ol+AP5oLwIc3MEs0s+ext4CZgB+dfyP4eAmOHeO2f9GZEzAEavD9xXwVuMrM078/KmwiMBVYBjWY2x5sB8ckOz+W3vujjxV7DF2fDyfNhAu81BOpc7s3UGA3kEdiRecHPuLdV+zqwzHt853+/s31eBqzx1u9V3r/9j4FdzrnHOiwK2/f5Yn0O2ffZjx0fvbQz5VYCe9L3AQ/7Xc9l1D+GwB78bUDR2T4QGKt7DdgL/AlI99oNeNLr73tAQYfn+jRQ4v38VYf2Au+Dtw/4Hv7s6FtB4E/eFgLjlJ/piz5e7DV87PPPvT5t9/7jDu2w/sNe/XvoMPPqYp9x77Oz0fu3+CUQ77UP8O6XeMvH9FF/5xEYYtkObPV+bg3n97mLPofk+6xTNoiIRJhwGeoREZEgKfhFRCKMgl9EJMIo+EVEIoyCX0Qkwij4RUQijIJfRCTC/H+aNWm0J6VnRAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(mean_diff_click_time.values, reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图可以发现不同用户点击文章的时间差是有差异的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 前后点击文章的创建时间差的平均值\n",
+ "mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'created_at_ts'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(mean_diff_created_time.values, reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从图中可以发现用户先后点击文章，文章的创建时间也是有差异的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
+ "Collecting gensim\n",
+ " Downloading https://mirrors.aliyun.com/pypi/packages/2b/e0/fa6326251692056dc880a64eb22117e03269906ba55a6864864d24ec8b4e/gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl (24.2 MB)\n",
+ "\u001b[K |████████████████████████████████| 24.2 MB 91.0 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.15.0)\n",
+ "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
+ "Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.5.4)\n",
+ "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
+ "Collecting smart-open>=1.8.1\n",
+ " Downloading https://mirrors.aliyun.com/pypi/packages/e3/cf/6311dfb0aff3e295d63930dea72e3029800242cdfe0790478e33eccee2ab/smart_open-4.0.1.tar.gz (117 kB)\n",
+ "\u001b[K |████████████████████████████████| 117 kB 96.7 MB/s eta 0:00:01\n",
+ "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
+ " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for smart-open: filename=smart_open-4.0.1-py3-none-any.whl size=108249 sha256=50eb67320a58790e8b173971aeb6af7b636d48259d7c9de759612e58e334215b\n",
+ " Stored in directory: /home/admin/.cache/pip/wheels/c3/14/fc/a0e523e5d2f13d083ce0af09d4e2861d8e2ec65fc466fb1dff\n",
+ "Successfully built smart-open\n",
+ "Installing collected packages: smart-open, gensim\n",
+ "Successfully installed gensim-3.8.3 smart-open-4.0.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 安装gensim\n",
+ "!pip install gensim"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim.models import Word2Vec\n",
+ "import logging, pickle\n",
+ "\n",
+ "# 需要注意这里模型只迭代了一次\n",
+ "def trian_item_word2vec(click_df, embed_size=16, save_name='item_w2v_emb.pkl', split_char=' '):\n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " # 只有转换成字符串才可以进行训练\n",
+ " click_df['click_article_id'] = click_df['click_article_id'].astype(str)\n",
+ " # 转换成句子的形式\n",
+ " docs = click_df.groupby(['user_id'])['click_article_id'].apply(lambda x: list(x)).reset_index()\n",
+ " docs = docs['click_article_id'].values.tolist()\n",
+ "\n",
+ " # 为了方便查看训练的进度，这里设定一个log信息\n",
+ " logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', level=logging.INFO)\n",
+ "\n",
+ " # 这里的参数对训练得到的向量影响也很大,默认负采样为5\n",
+ " w2v = Word2Vec(docs, size=16, sg=1, window=5, seed=2020, workers=24, min_count=1, iter=10)\n",
+ " \n",
+ " # 保存成字典的形式\n",
+ " item_w2v_emb_dict = {k: w2v[k] for k in click_df['click_article_id']}\n",
+ " \n",
+ " return item_w2v_emb_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "item_w2v_emb_dict = trian_item_word2vec(user_click_merge)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
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],
- "text/plain": [
- " user_id ... click_referrer_type\n",
- "25667 190841 ... 2\n",
- "25668 190841 ... 2\n",
- "25669 190841 ... 2\n",
- "25670 190841 ... 2\n",
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- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
+ "source": [
+ "for _, user_df in sub_user_info.groupby('user_id'):\n",
+ " item_sim_list = get_item_sim_list(user_df)\n",
+ " plt.plot(item_sim_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "这里由于对词向量的训练迭代次数不是很多，所以看到的可视化结果不是很准确，可以训练更多次来观察具体的现象。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 总结\n",
+ "\n",
+ "通过数据分析的过程， 我们目前可以得到以下几点重要的信息， 这个对于我们进行后面的特征制作和分析非常有帮助：\n",
+ "1. 训练集和测试集的用户id没有重复，也就是测试集里面的用户模型是没有见过的\n",
+ "2. 训练集中用户最少的点击文章数是2， 而测试集里面用户最少的点击文章数是1\n",
+ "3. 用户对于文章存在重复点击的情况， 但这个都存在于训练集里面\n",
+ "4. 同一用户的点击环境存在不唯一的情况，后面做这部分特征的时候可以采用统计特征\n",
+ "5. 用户点击文章的次数有很大的区分度，后面可以根据这个制作衡量用户活跃度的特征\n",
+ "6. 文章被用户点击的次数也有很大的区分度，后面可以根据这个制作衡量文章热度的特征\n",
+ "7. 用户看的新闻，相关性是比较强的，所以往往我们判断用户是否对某篇文章感兴趣的时候， 在很大程度上会和他历史点击过的文章有关\n",
+ "8. 用户点击的文章字数有比较大的区别， 这个可以反映用户对于文章字数的区别\n",
+ "9. 用户点击过的文章主题也有很大的区别， 这个可以反映用户的主题偏好\n",
+ "10.不同用户点击文章的时间差也会有所区别， 这个可以反映用户对于文章时效性的偏好\n",
+ "\n",
+ "所以根据上面的一些分析，可以更好的帮助我们后面做好特征工程， 充分挖掘数据的隐含信息。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
+ "\n",
+ ""
+ ]
}
- ],
- "source": [
- "# 随机选择5个用户，查看这些用户前后查看文章的相似性\n",
- "sub_user_ids = np.random.choice(user_click_merge.user_id.unique(), size=15, replace=False)\n",
- "sub_user_info = user_click_merge[user_click_merge['user_id'].isin(sub_user_ids)]\n",
- "\n",
- "sub_user_info.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 上一个版本，这个函数使用的是赛题提供的词向量，但是由于给出的embedding并不是所有的数据的embedding，所以运行下面画图函数的时候会报keyerror的错误\n",
- "# 为了防止出现这个错误，这里修改为使用word2vec训练得到的词向量进行可视化\n",
- "def get_item_sim_list(df):\n",
- " sim_list = []\n",
- " item_list = df['click_article_id'].values\n",
- " for i in range(0, len(item_list)-1):\n",
- " emb1 = item_w2v_emb_dict[str(item_list[i])] # 需要注意的是word2vec训练时候使用的是str类型的数据\n",
- " emb2 = item_w2v_emb_dict[str(item_list[i+1])]\n",
- " sim_list.append(np.dot(emb1,emb2)/(np.linalg.norm(emb1)*(np.linalg.norm(emb2))))\n",
- " sim_list.append(0)\n",
- " return sim_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "source": [
- "for _, user_df in sub_user_info.groupby('user_id'):\n",
- " item_sim_list = get_item_sim_list(user_df)\n",
- " plt.plot(item_sim_list)"
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- "这里由于对词向量的训练迭代次数不是很多，所以看到的可视化结果不是很准确，可以训练更多次来观察具体的现象。"
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},
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- "## 总结\n",
- "\n",
- "通过数据分析的过程， 我们目前可以得到以下几点重要的信息， 这个对于我们进行后面的特征制作和分析非常有帮助：\n",
- "1. 训练集和测试集的用户id没有重复，也就是测试集里面的用户模型是没有见过的\n",
- "2. 训练集中用户最少的点击文章数是2， 而测试集里面用户最少的点击文章数是1\n",
- "3. 用户对于文章存在重复点击的情况， 但这个都存在于训练集里面\n",
- "4. 同一用户的点击环境存在不唯一的情况，后面做这部分特征的时候可以采用统计特征\n",
- "5. 用户点击文章的次数有很大的区分度，后面可以根据这个制作衡量用户活跃度的特征\n",
- "6. 文章被用户点击的次数也有很大的区分度，后面可以根据这个制作衡量文章热度的特征\n",
- "7. 用户看的新闻，相关性是比较强的，所以往往我们判断用户是否对某篇文章感兴趣的时候， 在很大程度上会和他历史点击过的文章有关\n",
- "8. 用户点击的文章字数有比较大的区别， 这个可以反映用户对于文章字数的区别\n",
- "9. 用户点击过的文章主题也有很大的区别， 这个可以反映用户的主题偏好\n",
- "10.不同用户点击文章的时间差也会有所区别， 这个可以反映用户对于文章时效性的偏好\n",
- "\n",
- "所以根据上面的一些分析，可以更好的帮助我们后面做好特征工程， 充分挖掘数据的隐含信息。"
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- "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
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-}
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git "a/docs/ch03/ch3.1/jupyter/\347\211\271\345\276\201\345\267\245\347\250\213.ipynb" "b/docs/ch03/ch3.1/jupyter/\347\211\271\345\276\201\345\267\245\347\250\213.ipynb"
index f4e21cabc..d74eed156 100644
--- "a/docs/ch03/ch3.1/jupyter/\347\211\271\345\276\201\345\267\245\347\250\213.ipynb"
+++ "b/docs/ch03/ch3.1/jupyter/\347\211\271\345\276\201\345\267\245\347\250\213.ipynb"
@@ -1,1772 +1,1772 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 制作特征和标签， 转成监督学习问题\n",
- "我们先捋一下基于原始的给定数据， 有哪些特征可以直接利用：\n",
- "1. 文章的自身特征， category_id表示这文章的类型， created_at_ts表示文章建立的时间， 这个关系着文章的时效性， words_count是文章的字数， 一般字数太长我们不太喜欢点击, 也不排除有人就喜欢读长文。\n",
- "2. 文章的内容embedding特征， 这个召回的时候用过， 这里可以选择使用， 也可以选择不用， 也可以尝试其他类型的embedding特征， 比如W2V等\n",
- "3. 用户的设备特征信息\n",
- "\n",
- "上面这些直接可以用的特征， 待做完特征工程之后， 直接就可以根据article_id或者是user_id把这些特征加入进去。 但是我们需要先基于召回的结果， 构造一些特征，然后制作标签，形成一个监督学习的数据集。
-
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传统推荐系统(左)，深度学习推荐系统(右)
@@ -119,7 +119,7 @@
在讲AUC前需要理解混淆矩阵，召回率，精确率，ROC曲线等概念
-
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+
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
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- " click_deviceGroup \n",
- " click_os \n",
- " click_country \n",
- " click_region \n",
- " click_referrer_type \n",
- " rank \n",
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- "click_environment 1112623 non-null int64\n",
- "click_deviceGroup 1112623 non-null int64\n",
- "click_os 1112623 non-null int64\n",
- "click_country 1112623 non-null int64\n",
- "click_region 1112623 non-null int64\n",
- "click_referrer_type 1112623 non-null int64\n",
- "rank 1112623 non-null int64\n",
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- "category_id 1112623 non-null int64\n",
- "created_at_ts 1112623 non-null int64\n",
- "words_count 1112623 non-null int64\n",
- "dtypes: int64(14)\n",
- "memory usage: 127.3 MB\n"
- ]
- }
- ],
- "source": [
- "#用户点击日志信息\n",
- "trn_click.info()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
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- " 1.220012e+00 \n",
- " 1.016095e+01 \n",
- " 1.631503e+01 \n",
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+ "click_deviceGroup 1112623 non-null int64\n",
+ "click_os 1112623 non-null int64\n",
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+ "click_region 1112623 non-null int64\n",
+ "click_referrer_type 1112623 non-null int64\n",
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+ "dtypes: int64(14)\n",
+ "memory usage: 127.3 MB\n"
+ ]
+ }
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- " user_id click_article_id click_timestamp click_environment \\\n",
- "count 1.112623e+06 1.112623e+06 1.112623e+06 1.112623e+06 \n",
- "mean 1.221198e+05 1.951541e+05 1.507588e+12 3.947786e+00 \n",
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- " click_referrer_type rank click_cnts category_id \\\n",
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- "mean 1.910063e+00 7.118518e+00 1.323704e+01 3.056176e+02 \n",
- "std 1.220012e+00 1.016095e+01 1.631503e+01 1.155791e+02 \n",
- "min 1.000000e+00 1.000000e+00 2.000000e+00 1.000000e+00 \n",
- "25% 1.000000e+00 2.000000e+00 4.000000e+00 2.500000e+02 \n",
- "50% 2.000000e+00 4.000000e+00 8.000000e+00 3.280000e+02 \n",
- "75% 2.000000e+00 8.000000e+00 1.600000e+01 4.100000e+02 \n",
- "max 7.000000e+00 2.410000e+02 2.410000e+02 4.600000e+02 \n",
- "\n",
- " created_at_ts words_count \n",
- "count 1.112623e+06 1.112623e+06 \n",
- "mean 1.506598e+12 2.011981e+02 \n",
- "std 8.343066e+09 5.223881e+01 \n",
- "min 1.166573e+12 0.000000e+00 \n",
- "25% 1.507220e+12 1.700000e+02 \n",
- "50% 1.507553e+12 1.970000e+02 \n",
- "75% 1.507756e+12 2.280000e+02 \n",
- "max 1.510666e+12 6.690000e+03 "
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click.describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "200000"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#训练集中的用户数量为20w\n",
- "trn_click.user_id.nunique()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T16:03:01.378461Z",
- "start_time": "2020-11-13T16:03:01.300712Z"
- }
- },
- "outputs": [
+ "source": [
+ "#用户点击日志信息\n",
+ "trn_click.info()"
+ ]
+ },
{
- "data": {
- "text/plain": [
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+ " 1.506598e+12 \n",
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+ " \n",
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+ " std \n",
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+ " 1.155791e+02 \n",
+ " 8.343066e+09 \n",
+ " 5.223881e+01 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000e+00 \n",
+ " 3.000000e+00 \n",
+ " 1.507030e+12 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.166573e+12 \n",
+ " 0.000000e+00 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 7.934700e+04 \n",
+ " 1.239090e+05 \n",
+ " 1.507297e+12 \n",
+ " 4.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.300000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.000000e+00 \n",
+ " 4.000000e+00 \n",
+ " 2.500000e+02 \n",
+ " 1.507220e+12 \n",
+ " 1.700000e+02 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.309670e+05 \n",
+ " 2.038900e+05 \n",
+ " 1.507596e+12 \n",
+ " 4.000000e+00 \n",
+ " 1.000000e+00 \n",
+ " 1.700000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.100000e+01 \n",
+ " 2.000000e+00 \n",
+ " 4.000000e+00 \n",
+ " 8.000000e+00 \n",
+ " 3.280000e+02 \n",
+ " 1.507553e+12 \n",
+ " 1.970000e+02 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 1.704010e+05 \n",
+ " 2.777120e+05 \n",
+ " 1.507841e+12 \n",
+ " 4.000000e+00 \n",
+ " 3.000000e+00 \n",
+ " 1.700000e+01 \n",
+ " 1.000000e+00 \n",
+ " 2.500000e+01 \n",
+ " 2.000000e+00 \n",
+ " 8.000000e+00 \n",
+ " 1.600000e+01 \n",
+ " 4.100000e+02 \n",
+ " 1.507756e+12 \n",
+ " 2.280000e+02 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 1.999990e+05 \n",
+ " 3.640460e+05 \n",
+ " 1.510603e+12 \n",
+ " 4.000000e+00 \n",
+ " 5.000000e+00 \n",
+ " 2.000000e+01 \n",
+ " 1.100000e+01 \n",
+ " 2.800000e+01 \n",
+ " 7.000000e+00 \n",
+ " 2.410000e+02 \n",
+ " 2.410000e+02 \n",
+ " 4.600000e+02 \n",
+ " 1.510666e+12 \n",
+ " 6.690000e+03 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure()\n",
- "plt.figure(figsize=(15, 20))\n",
- "i = 1\n",
- "for col in ['click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', \n",
- " 'click_region', 'click_referrer_type', 'rank', 'click_cnts']:\n",
- " plot_envs = plt.subplot(5, 2, i)\n",
- " i += 1\n",
- " v = trn_click[col].value_counts().reset_index()[:10]\n",
- " fig = sns.barplot(x=v['index'], y=v[col])\n",
- " for item in fig.get_xticklabels():\n",
- " item.set_rotation(90)\n",
- " plt.title(col)\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "注：此处click_cnts直方图表示的是每篇文章对应用户的点击次数累计图\n",
- "\n",
- "也可以以用户角度分析，画出每个用户点击文章次数的直方图"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4 1084627\n",
- "2 25894\n",
- "1 2102\n",
- "Name: click_environment, dtype: int64"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click['click_environment'].value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从点击环境click_environment来看，仅有2102次（占0.19%）点击环境为1；仅有25894次（占2.3%）点击环境为2；剩余（占97.6%）点击环境为4。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1 678187\n",
- "3 395558\n",
- "4 38731\n",
- "5 141\n",
- "2 6\n",
- "Name: click_deviceGroup, dtype: int64"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trn_click['click_deviceGroup'].value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从点击设备组click_deviceGroup来看，设备1占大部分（61%），设备3占36%。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 测试集用户点击日志"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " 518010.000000 \n",
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- " 518010.000000 \n",
- " 518010.000000 \n",
- " 5.180100e+05 \n",
- " 518010.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 227342.428169 \n",
- " 193803.792550 \n",
- " 1.507387e+12 \n",
- " 3.947300 \n",
- " 1.738285 \n",
- " 13.628467 \n",
- " 1.348209 \n",
- " 18.250250 \n",
- " 1.819614 \n",
- " 15.521785 \n",
- " 30.043586 \n",
- " 305.324961 \n",
- " 1.506883e+12 \n",
- " 210.966331 \n",
- " \n",
- " \n",
- " std \n",
- " 14613.907188 \n",
- " 88279.388177 \n",
- " 3.706127e+08 \n",
- " 0.323916 \n",
- " 1.020858 \n",
- " 6.625564 \n",
- " 1.703524 \n",
- " 7.060798 \n",
- " 1.082657 \n",
- " 33.957702 \n",
- " 56.868021 \n",
- " 110.411513 \n",
- " 5.816668e+09 \n",
- " 83.040065 \n",
- " \n",
- " \n",
- " min \n",
- " 200000.000000 \n",
- " 137.000000 \n",
- " 1.506959e+12 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 2.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.000000 \n",
- " 1.265812e+12 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 214926.000000 \n",
- " 128551.000000 \n",
- " 1.507026e+12 \n",
- " 4.000000 \n",
- " 1.000000 \n",
- " 12.000000 \n",
- " 1.000000 \n",
- " 13.000000 \n",
- " 1.000000 \n",
- " 4.000000 \n",
- " 10.000000 \n",
- " 252.000000 \n",
- " 1.506970e+12 \n",
- " 176.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 229109.000000 \n",
- " 199197.000000 \n",
- " 1.507308e+12 \n",
- " 4.000000 \n",
- " 1.000000 \n",
- " 17.000000 \n",
- " 1.000000 \n",
- " 21.000000 \n",
- " 2.000000 \n",
- " 8.000000 \n",
- " 19.000000 \n",
- " 323.000000 \n",
- " 1.507249e+12 \n",
- " 199.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 240182.000000 \n",
- " 272143.000000 \n",
- " 1.507666e+12 \n",
- " 4.000000 \n",
- " 3.000000 \n",
- " 17.000000 \n",
- " 1.000000 \n",
- " 25.000000 \n",
- " 2.000000 \n",
- " 18.000000 \n",
- " 35.000000 \n",
- " 399.000000 \n",
- " 1.507630e+12 \n",
- " 232.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 364043.000000 \n",
- " 1.508832e+12 \n",
- " 4.000000 \n",
- " 5.000000 \n",
- " 20.000000 \n",
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- " 28.000000 \n",
- " 7.000000 \n",
- " 938.000000 \n",
- " 938.000000 \n",
- " 460.000000 \n",
- " 1.509949e+12 \n",
- " 3082.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " click_article_id \n",
- " category_id \n",
- " created_at_ts \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 0 \n",
- " 0 \n",
- " 1513144419000 \n",
- " 168 \n",
- " \n",
- " \n",
- " 1 \n",
- " 1 \n",
- " 1 \n",
- " 1405341936000 \n",
- " 189 \n",
- " \n",
- " \n",
- " 2 \n",
- " 2 \n",
- " 1 \n",
- " 1408667706000 \n",
- " 250 \n",
- " \n",
- " \n",
- " 3 \n",
- " 3 \n",
- " 1 \n",
- " 1408468313000 \n",
- " 230 \n",
- " \n",
- " \n",
- " 4 \n",
- " 4 \n",
- " 1 \n",
- " 1407071171000 \n",
- " 162 \n",
- " \n",
- " \n",
- " 364042 \n",
- " 364042 \n",
- " 460 \n",
- " 1434034118000 \n",
- " 144 \n",
- " \n",
- " \n",
- " 364043 \n",
- " 364043 \n",
- " 460 \n",
- " 1434148472000 \n",
- " 463 \n",
- " \n",
- " \n",
- " 364044 \n",
- " 364044 \n",
- " 460 \n",
- " 1457974279000 \n",
- " 177 \n",
- " \n",
- " \n",
- " 364045 \n",
- " 364045 \n",
- " 460 \n",
- " 1515964737000 \n",
- " 126 \n",
- " \n",
- " \n",
- " 364046 \n",
- " 364046 \n",
- " 460 \n",
- " 1505811330000 \n",
- " 479 \n",
- " \n",
- " \n",
- "
\n",
- "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " click_article_id category_id created_at_ts words_count\n",
- "0 0 0 1513144419000 168\n",
- "1 1 1 1405341936000 189\n",
- "2 2 1 1408667706000 250\n",
- "3 3 1 1408468313000 230\n",
- "4 4 1 1407071171000 162\n",
- "364042 364042 460 1434034118000 144\n",
- "364043 364043 460 1434148472000 463\n",
- "364044 364044 460 1457974279000 177\n",
- "364045 364045 460 1515964737000 126\n",
- "364046 364046 460 1505811330000 479"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#新闻文章数据集浏览\n",
- "item_df.head().append(item_df.tail())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:28:13.084501Z",
- "start_time": "2020-11-13T15:28:13.062561Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "176 3485\n",
- "182 3480\n",
- "179 3463\n",
- "178 3458\n",
- "174 3456\n",
- "183 3432\n",
- "184 3427\n",
- "173 3414\n",
- "180 3403\n",
- "177 3391\n",
- "170 3387\n",
- "187 3355\n",
- "169 3352\n",
- "185 3348\n",
- "175 3346\n",
- "181 3330\n",
- "186 3328\n",
- "189 3327\n",
- "171 3327\n",
- "172 3322\n",
- "165 3308\n",
- "188 3288\n",
- "167 3269\n",
- "190 3261\n",
- "192 3257\n",
- "168 3248\n",
- "193 3225\n",
- "166 3199\n",
- "191 3182\n",
- "194 3164\n",
- " ... \n",
- "601 1\n",
- "857 1\n",
- "1977 1\n",
- "1626 1\n",
- "697 1\n",
- "1720 1\n",
- "696 1\n",
- "706 1\n",
- "592 1\n",
- "1605 1\n",
- "586 1\n",
- "582 1\n",
- "1606 1\n",
- "972 1\n",
- "716 1\n",
- "584 1\n",
- "1608 1\n",
- "715 1\n",
- "841 1\n",
- "968 1\n",
- "964 1\n",
- "587 1\n",
- "1099 1\n",
- "1355 1\n",
- "711 1\n",
- "845 1\n",
- "710 1\n",
- "965 1\n",
- "847 1\n",
- "1535 1\n",
- "Name: words_count, Length: 866, dtype: int64"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "item_df['words_count'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:28:59.029535Z",
- "start_time": "2020-11-13T15:28:58.816106Z"
- }
- },
- "outputs": [
+ "source": [
+ "plt.figure()\n",
+ "plt.figure(figsize=(15, 20))\n",
+ "i = 1\n",
+ "for col in ['click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', \n",
+ " 'click_region', 'click_referrer_type', 'rank', 'click_cnts']:\n",
+ " plot_envs = plt.subplot(5, 2, i)\n",
+ " i += 1\n",
+ " v = trn_click[col].value_counts().reset_index()[:10]\n",
+ " fig = sns.barplot(x=v['index'], y=v[col])\n",
+ " for item in fig.get_xticklabels():\n",
+ " item.set_rotation(90)\n",
+ " plt.title(col)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "461\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "注：此处click_cnts直方图表示的是每篇文章对应用户的点击次数累计图\n",
+ "\n",
+ "也可以以用户角度分析，画出每个用户点击文章次数的直方图"
+ ]
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 1084627\n",
+ "2 25894\n",
+ "1 2102\n",
+ "Name: click_environment, dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trn_click['click_environment'].value_counts()"
]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从点击环境click_environment来看，仅有2102次（占0.19%）点击环境为1；仅有25894次（占2.3%）点击环境为2；剩余（占97.6%）点击环境为4。"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "print(item_df['category_id'].nunique()) # 461个文章主题\n",
- "item_df['category_id'].hist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(364047, 4)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "item_df.shape # 364047篇文章"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻文章embedding向量表示"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
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- " \n",
- " \n",
- " article_id \n",
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- " emb_2 \n",
- " emb_3 \n",
- " emb_4 \n",
- " emb_5 \n",
- " emb_6 \n",
- " emb_7 \n",
- " emb_8 \n",
- " ... \n",
- " emb_240 \n",
- " emb_241 \n",
- " emb_242 \n",
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- " emb_244 \n",
- " emb_245 \n",
- " emb_246 \n",
- " emb_247 \n",
- " emb_248 \n",
- " emb_249 \n",
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- " 0 \n",
- " 0 \n",
- " -0.161183 \n",
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- " 0.830055 \n",
- " 0.901365 \n",
- " -0.335148 \n",
- " -0.559561 \n",
- " -0.500603 \n",
- " ... \n",
- " 0.321248 \n",
- " 0.313999 \n",
- " 0.636412 \n",
- " 0.169179 \n",
- " 0.540524 \n",
- " -0.813182 \n",
- " 0.286870 \n",
- " -0.231686 \n",
- " 0.597416 \n",
- " 0.409623 \n",
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- " 1 \n",
- " 1 \n",
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- " -0.974058 \n",
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- " -0.594137 \n",
- " 0.182828 \n",
- " 0.397090 \n",
- " -0.834364 \n",
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- " 2 \n",
- " 2 \n",
- " -0.619619 \n",
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- " -0.207360 \n",
- " -0.128861 \n",
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- " \n",
- " 3 \n",
- " 3 \n",
- " -0.740843 \n",
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- " -0.537838 \n",
- " 0.243541 \n",
- " -0.885329 \n",
- " \n",
- " \n",
- " 4 \n",
- " 4 \n",
- " -0.279052 \n",
- " -0.972315 \n",
- " 0.685374 \n",
- " 0.113056 \n",
- " 0.238315 \n",
- " 0.271913 \n",
- " -0.568816 \n",
- " 0.341194 \n",
- " -0.600554 \n",
- " ... \n",
- " 0.238286 \n",
- " 0.809268 \n",
- " 0.427521 \n",
- " -0.615932 \n",
- " -0.503697 \n",
- " 0.614450 \n",
- " -0.917760 \n",
- " -0.424061 \n",
- " 0.185484 \n",
- " -0.580292 \n",
- " \n",
- " \n",
- "
\n",
- "
5 rows × 251 columns
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " count \n",
- " \n",
- " \n",
- " \n",
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- " 289197 \n",
- " 1 \n",
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- " 36162 \n",
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- " \n",
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- " 2 \n",
- " 168401 \n",
- " 1 \n",
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- " \n",
- " 6 \n",
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- " 42567 \n",
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- " \n",
- " \n",
- "
\n",
- "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 249999 \n",
+ " 160974 \n",
+ " 1506959142820 \n",
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+ " 19 \n",
+ " 19 \n",
+ " 281 \n",
+ " 1506912747000 \n",
+ " 259 \n",
+ " \n",
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+ " 249999 \n",
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+ " 18 \n",
+ " 19 \n",
+ " 281 \n",
+ " 1506942089000 \n",
+ " 173 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 249998 \n",
+ " 160974 \n",
+ " 1506959056066 \n",
+ " 4 \n",
+ " 1 \n",
+ " 12 \n",
+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 1506912747000 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 249998 \n",
+ " 202557 \n",
+ " 1506959086066 \n",
+ " 4 \n",
+ " 1 \n",
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+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 4 \n",
+ " 5 \n",
+ " 327 \n",
+ " 1506938401000 \n",
+ " 219 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 249997 \n",
+ " 183665 \n",
+ " 1506959088613 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 15 \n",
+ " 5 \n",
+ " 7 \n",
+ " 7 \n",
+ " 301 \n",
+ " 1500895686000 \n",
+ " 256 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 311242 \n",
- " 86295 \n",
- " 74254 \n",
- " 10 \n",
- " \n",
- " \n",
- " 311243 \n",
- " 86295 \n",
- " 76268 \n",
- " 10 \n",
- " \n",
- " \n",
- " 393761 \n",
- " 103237 \n",
- " 205948 \n",
- " 10 \n",
- " \n",
- " \n",
- " 393763 \n",
- " 103237 \n",
- " 235689 \n",
- " 10 \n",
- " \n",
- " \n",
- " 576902 \n",
- " 134850 \n",
- " 69463 \n",
- " 13 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 5.180100e+05 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 518010.000000 \n",
+ " 5.180100e+05 \n",
+ " 518010.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 227342.428169 \n",
+ " 193803.792550 \n",
+ " 1.507387e+12 \n",
+ " 3.947300 \n",
+ " 1.738285 \n",
+ " 13.628467 \n",
+ " 1.348209 \n",
+ " 18.250250 \n",
+ " 1.819614 \n",
+ " 15.521785 \n",
+ " 30.043586 \n",
+ " 305.324961 \n",
+ " 1.506883e+12 \n",
+ " 210.966331 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 14613.907188 \n",
+ " 88279.388177 \n",
+ " 3.706127e+08 \n",
+ " 0.323916 \n",
+ " 1.020858 \n",
+ " 6.625564 \n",
+ " 1.703524 \n",
+ " 7.060798 \n",
+ " 1.082657 \n",
+ " 33.957702 \n",
+ " 56.868021 \n",
+ " 110.411513 \n",
+ " 5.816668e+09 \n",
+ " 83.040065 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 200000.000000 \n",
+ " 137.000000 \n",
+ " 1.506959e+12 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 2.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.265812e+12 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 214926.000000 \n",
+ " 128551.000000 \n",
+ " 1.507026e+12 \n",
+ " 4.000000 \n",
+ " 1.000000 \n",
+ " 12.000000 \n",
+ " 1.000000 \n",
+ " 13.000000 \n",
+ " 1.000000 \n",
+ " 4.000000 \n",
+ " 10.000000 \n",
+ " 252.000000 \n",
+ " 1.506970e+12 \n",
+ " 176.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 229109.000000 \n",
+ " 199197.000000 \n",
+ " 1.507308e+12 \n",
+ " 4.000000 \n",
+ " 1.000000 \n",
+ " 17.000000 \n",
+ " 1.000000 \n",
+ " 21.000000 \n",
+ " 2.000000 \n",
+ " 8.000000 \n",
+ " 19.000000 \n",
+ " 323.000000 \n",
+ " 1.507249e+12 \n",
+ " 199.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 240182.000000 \n",
+ " 272143.000000 \n",
+ " 1.507666e+12 \n",
+ " 4.000000 \n",
+ " 3.000000 \n",
+ " 17.000000 \n",
+ " 1.000000 \n",
+ " 25.000000 \n",
+ " 2.000000 \n",
+ " 18.000000 \n",
+ " 35.000000 \n",
+ " 399.000000 \n",
+ " 1.507630e+12 \n",
+ " 232.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 364043.000000 \n",
+ " 1.508832e+12 \n",
+ " 4.000000 \n",
+ " 5.000000 \n",
+ " 20.000000 \n",
+ " 11.000000 \n",
+ " 28.000000 \n",
+ " 7.000000 \n",
+ " 938.000000 \n",
+ " 938.000000 \n",
+ " 460.000000 \n",
+ " 1.509949e+12 \n",
+ " 3082.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "50000"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#测试集中的用户数量为5w\n",
+ "tst_click.user_id.nunique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:56:07.717463Z",
+ "start_time": "2020-11-13T15:56:07.693494Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tst_click.groupby('user_id')['click_article_id'].count().min() # 注意测试集里面有只点击过一次文章的用户"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
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kgZ2Y5LlJZut3PCXzVRov5qy0sAYpkHcHHkGTfDu20w4z7ZBk/ST3aD+vATwZ+N+5BCppIP8MHAPcnOS6JNcnuW6mHcxXabyYs9LwDNIH+VFVtdmAx78vcGjbR2oV4Oiq+vKAx5A0oKpaZw67ma/SeDFnpSEZpEA+LcnmVXV+vztU1bnAloOHJWku2oZyjaq6oZ1/DHDXdvUPq+r66fY1X6XxYs5KwzNIgfwY4JwkF9OMvRiaUWamHeZN0qJ7P/Ab4N/b+c8BPwFWB84G3tRRXJIkjY1BCmQ7/kuj7x+AR/XMX1tVO7YP6323o5gkSRorfT+k1w7pdjlwC81A5BOTpNGxSlXd2jP/JvjLCwXW7iYkSf1Kcs8k3pmVOjbIm/ReA+wL/Jq/viikABNZGh13TbLORF/jqvoGQJK703SzkDRikpwMPJOmTT4L+E2SU6vqdZ0GJi1hgwzztiewWVU9pKoe1k4Wx9Jo+SRwVJK/vDIrySY0fZE/1VlUkmZy96q6DtgJOKyqtga26zgmaUkbpA/yZcAfhhWIpPmrqg8muQk4JclaNA/TXg+8r6o+3m10kqZxlyT3BZ4PvKXrYCQNViBfBJyc5HiaUSyApkFe8KgkzVlV/Tfw30nWaeenHdpN0kh4B/B14NSqOiPJ/YGfdRyTtKQNUiD/sp3uyl/HVZU0gpLcG3gPsAGwfZLNgcdW1ae7jUzSZFV1DM2bLyfmLwKe211EkvoukKvq7QBJ1m7nbxhWUJLm7RDgYP56u/anwFGABbI0YpJsBHwEeHy76LvAnlV1eXdRSUtb3w/pJXlokh8C5wHnJTkryUOGF5qkeVivqo6mHXGmHfrttm5DkjSNg4HjaO74bAB8qV0mqSODjGJxIPC6qtqkqjYBXk/zxLyk0XNjknvRjlXevnLah2yl0bR+VR1cVbe20yHA+l0HJS1lg/RBXquqvjUxU1Unt0/JSxo9r6O5IrVpklNpGtvndRuSpGlck+RFNMMxAuwKXNNhPNKSN9AoFkn+P+Az7fyLaEa2kDRiqursJE8ENqMZ6u3Cqrql47AkTe1lNH2Q/5Pmrs9pwO6dRiQtcYN0sXgZzVWoY4EvAOu1yySNmCSvAtauqvOq6ifA2kle2XVcku6oqi6tqmdW1fpV9bdV9eyq+uXE+iT7dBmftBT1VSAnWRU4tqpeW1WPrKqtqmqvqvr9kOOTNDevqKprJ2baXH1Fd+FImoeduw5AWmr6KpCr6jbg9iR3H3I8khbGqkkyMdN+yXX8cmk8ZfZNJC2kQfog3wD8OMkJwI0TC6vqtQselaT5+hpwVJJPtPP/3C6TNH6q6wCkpWaQAvnYdpI0+t5EUxT/azt/AvCp7sKRNA9eQZYW2SBv0jt00IMn2Rg4DLg3zTfgA6vqgEGPI2kwVXU78PF26ov5KnUjybpV9btJy+5XVRe3s8dMsZs5Kw3RrAVykqOr6vlJfswUt3mqaosZdr8VeH075NQ6wFlJTqiq8+cesqTpmK/SWPpSku2r6jqAJJsDRwMPBaiq90yznzkrDUk/V5D3bH/uMOjBq+oq4Kr28/VJLgA2BExeaTjMV2n8vIemSH4GzdjlhwG7zbaTOSsNz6wFcpuAAM8FjqyqK+dyoiTLgS2BH0xavgewB8CyZcvmcmhJrWHna7vOnJUWUFUdn2Q14BvAOsBzquqngxzDNlZaWIO8KGQd4IQk303y6iT37nfHJGvTvFxkr4lbSBOq6sCqWlFVK9Zf31fPSwtkKPkK5qy0UJJ8JMmHk3wY2Ba4O3Ax8Op2Wb/HsY2VFtggD+m9HXh7ki2AXYBvJ7m8qrabab/2W/EXgCOqylEwpEVgvkpj4cxJ82cNegBzVhqOQYZ5m/Ab4FfANcDfzrRh+6KCTwMXVNUH53AuSfNjvkojamJ0qCRrAX9qX8o18WKfu822vzkrDU/fXSySvDLJycCJwL1oXmU70xPxAI8HXgxsm+Scdnr6nKOV1BfzVRorJwJr9MyvAXyzj/3MWWlIBrmCvDFN/6Zz+t2hqk7BAc6lLpiv0vhYvapumJipqhuSrDnbTuasNDx9X0Guqn1oXjW9QZJlE9MQY5M0R22+rp1kd4Ak6ye5X8dhSZrajUkeOTGTZCvgjx3GIy15fV9BTvJqYD/g18Dt7eICZrttK2mRJdkXWEEzpurBwGrA4TS3ZCWNlr2AY5JcSXNF+D40D9dK6sggXSz2AjarqmuGFIukhfMcmjFRzwaoqivbN21JGjFVdUaSB9N8oQW4sKpu6TImaakbpEC+DPjDsAKRtKD+XFWVpOAvT8lLGiFJtq2qk5LsNGnVg5LgsG1SdwYpkC8CTk5yPHDzxEKHlpFG0tFJPgHcI8krgJcBn+w4JkkreyJwErDjFOsKsECWOjJIgfzLdrprO0kaUVX1gSRPBq6juW37tqo6oeOwJPWoqn3bn7t3HYuklQ36Jj2SrFlVNw0vJEkLoS2ILYqlEZXkdTOt9w6t1J1BRrF4LM0be9YGliV5OPDPVfXKYQUnaTBJrqe5NTulqvqbRQxH0sxmenB22jyWNHyDdLH4EPBU4DiAqvpRkr8fRlCS5qaq1gFI8k7gKuAzNMNG7Qbct8PQJE3Sc2f2UGDPqrq2nb8nsH+HoUlLXt8vCgGoqssmLbptAWORtHCeWVX/VVXXV9V1VfVx4FldByVpSltMFMcAVfV7mmEaJXVkkAL5siSPAyrJakn2Bi4YUlyS5ufGJLslWTXJKkl2A27sOihJU1qlvWoMQJJ1GewOr6QFNkgC/gtwALAhcAXwDeBVwwhK0ry9kCZfD6Dpy3hqu0zS6Nkf+F6SY9r5nYF3dxiPtOQNMorF1TT9GKeUZJ+qeu+CRCVpXqrqEmboUmG+SqOjqg5Lciawbbtop6o6v8uYpKVuoD7Is9h5AY8labjMV2mEVNX5VfXRdrI4ljq2kAVyFvBYkobLfJUkaRoLWSA7ZqM0PsxXSZKm4RVkaWkyXyVJmkbfBXI77MzkZffrmT1m8npJ3TBfJUmau0GuIH8pyV9eU5tkc+BLE/NV9Z7JOyQ5KMlvkvxkfmFKGtDA+dpuZ85KY8J8lYZnkAL5PTSN7tpJtqK5AvWiWfY5BHjaHGOTNHdzyVcwZ6VxcgjmqzQUg4yDfHyS1WheELIO8Jyq+uks+3wnyfL5hShpUHPJ13Y/c1YaE+arNDyzFshJPsLKT7zfHfgF8OokVNVr5xNAkj2APQCWLVs2n0NJS96w87U9hzkrjQnzVZqbfq4gnzlp/qyFDKCqDgQOBFixYoVDT0nzM9R8BXNWGifmqzQ3sxbIVXUoQJK1gD9V1W3t/KrA3YYbnqRBmK+SJM3fIA/pnQis0TO/BvDNhQ1H0gIxXyVJmqNBCuTVq+qGiZn285oz7ZDkc8D3gM2SXJ7k5XMLU9KABs5XMGelcWK+SsPT9ygWwI1JHllVZwO0Q0f9caYdqmrX+QQnac4GzlcwZ6VxYr5KwzNIgbwXcEySK2leU3sfYJdhBCVp3vbCfJUkaU4GGQf5jCQPBjZrF11YVbcMJyxJ82G+SpI0d/2Mg7xtVZ2UZKdJqx7Ujqt67JBikzQg81WSpPnr5wryE4GTgB2nWFeADa40OsxXSZLmqZ9xkPdtf+4+/HAkzYf5KknS/PXTxeJ1M62vqg8uXDiS5sN8lSRp/vrpYrHODOt8baU0WsxXSZLmqZ8uFm8HSHIosGdVXdvO3xPYf6jRSRqI+SpJ0vwN8ia9LSYaW4Cq+j2w5YJHJGkhmK+SJM3RIAXyKu1VKACSrMtgLxqRtHjMV0mS5miQBnN/4HtJjmnndwbevfAhSVoA5qskSXM0yJv0DktyJrBtu2inqjp/OGFJmg/zVZKkuRvolmvbwNrISmPAfJUkaW4G6YMsSZIk3elZIEuSJEk9LJAlSZKkHhbIkiRJUg8LZEmSJKnH0AvkJE9LcmGSnyf5t2GfT9Lcma/SeDFnpeEYaoGcZFXgY8D2wObArkk2H+Y5Jc2N+SqNF3NWGp5hX0F+NPDzqrqoqv4MHAk8a8jnlDQ35qs0XsxZaUgGelHIHGwIXNYzfzmwde8GSfYA9mhnb0hy4ZBjUn/WA67uOohxlA/840IfcpOFPuA0Zs1XMGdHmDk7B2Ocr2AbO87M1zlarJwddoE8q6o6EDiw6zi0siRnVtWKruPQ6DFnR5M5q6mYr6PJfB19w+5icQWwcc/8Ru0ySaPHfJXGizkrDcmwC+QzgAcmuV+SuwIvAI4b8jklzY35Ko0Xc1YakqF2saiqW5O8Gvg6sCpwUFWdN8xzasF4S26JMV/Hnjm7xJizY818HXGpqq5jkCRJkkaGb9KTJEmSelggS5IkST0skCVJkqQeFsiaUpLDuo5BkiSpC52/KETdSzJ5WKAAT0pyD4CqeuaiByVJktQRC2RBM7j8+cCngKIpkFcA+3cZlKTBJdm9qg7uOg5JGmcO8yaSrALsCTwdeENVnZPkoqq6f8ehSRpQkl9W1bKu45DUSHI2cCzwuar6RdfxqD9eQRZVdTvwn0mOaX/+Gv/fkEZWknOnWwXcezFjkTSrewL3AL6V5FfA54CjqurKTqPSjLyCrDtI8gzg8VX15q5jkXRH7ZfYpwK/n7wKOK2qNlj8qCRNJcnZVfXI9vP/AXYFdgIuoLmq7Fv1RpAFsiSNmSSfBg6uqlOmWPfZqnphB2FJmkJvgdyzbFXgycAuVbV7N5FpJhbIkiRJQ5LkyKp6QddxaDCOgyxJkjQkMxXHSbx6PKIskJeAJKcNuP02Sb48rHgkSRIAb+86AE3NkQqWgKp6XNcxSOpfktMGydsk2wB7V9UOQwtK0pw46sx4skBeApLcUFVrt43ofsDVwEOBs4AXVVUleRrwIeAm4JSefdcCPtJuvxqwX1X9T5IDgGuq6h1Jngq8BdimHTJO0jz4pVa6U7k3M4w6s/jhqB92sVh6tgT2AjYH7g88PsnqwCeBHYGtgPv0bP8W4KSqejTwJOA/2qJ5H2CXJE8CPgzsbnEsLYwkN7Q/t0lycpLPJ/nfJEckSbvuae2ys2mGjJrYd60kByU5PckPkzyrXX5Akre1n5+a5DvtS4IkDdeXgbWr6tJJ0yXAyd2Gpul4BXnpOb2qLgdIcg6wHLgBuLiqftYuPxzYo93+KcAzk+zdzq8OLKuqC5K8AvgO8H99O5A0NFsCDwGuBE6l+VJ7Js2X2m2BnwNH9Ww/8aX2ZUnuAZye5Js0X2rPSPJdmi+1T/dLrTR8VfXyGdY5JOOIskBeem7u+Xwbs/8/EOC5VXXhFOseBlwD+FICaXj8UitJi8zbawL4X2B5kk3b+V171n0deE3Pbd0t25+bAK+nubq1fZKtFzFeaSmZ65faR7TTsqq6oF3nl1pJ6oMFsqiqP9FcfTq+7c/4m57V76R5OO/cJOcB72yL5U/TPDV/JfBy4FNtX2ZJw+eXWmlEOJTqnZNdLJaAqlq7/XkyPQ8EVNWrez5/DXjwFPv+EfjnKQ67Xc82Z9FcmZK0CKrqT0kmvtTeBHwXWKdd/U6aEWnObR/CuzjJjvR8qU3ycuCQJI9qvyBLmiNHnblz8lXTkiRJczTHoVTvX1U7OJTq6PIKsiRJ0sJw1Jk7CfsgS5IkLYzTq+rytpg9h2bUmQfTjjpTzW37w3u2fwrwb+0INSfz11FnbgJeAZwAfNRRZxafV5AlSZIWhkOp3kl4BVmSJGl4HHVmDFkgS5IkDYlDqY4nR7GQJEmSengFWZIkSephgSxJkiT1sECWJEmSelggS5IkST0skCVJkqQeFsiSJElSDwtkSZIkqYcFsiRJktTDAlmSJEnqYYEsSZIk9bBAliRJknpYIEuSJEk9LJBHUJKXJjmlZ/6GJPefZZ/lSSrJXYYfoaSZjGsOJ9ktyTe6Or/Uj1HOrySPT/KzNqZnD/NcGi6LqTFQVWt3HcNCSbIcuBhYrapu7TgcaVGMSw5X1RHAEV3HIQ1ixPLrHcBHq+qArgOZLMl+wAOq6kVdxzIOvIKskeNVcGluzB1pePrMr02A8xbq+ElWne8xNDcWyB1LsnGSY5P8Nsk1ST46xTaV5AHt5zWS7J/k0iR/SHJKkjWm2Oe5SS5J8tBZzv+EJKcluTbJZUle2i6/e5LD2rguTfLWJKu06/ZLcnjPMVa6dZXk5CTvTHJqkuuTfCPJeu3m32l/Xtvegnpse7vs1CT/meQa4B1JfpfkYT3n+NskNyVZf5C/rzRsXeZwT+69PMkvgZPa5S9LckGS3yf5epJNevZ5SpIL23P/V5JvJ/mndt3kW9ePS3JGu+0ZSR7Xs26mPJcWxDjlV5JfAPcHvtS2b3dr29JPJ7kqyRVJ3pW26J2i7dsvySFJPp7kK0luBJ6UZIMkX2j/BhcneW1PjPsl+XySw5NcB7x0mt/lacCbgV3a2H6UZOckZ03a7nVJ/qf9fEiS/05yQpvj3570b8mD23W/a/9Nef50f8txZIHcoTZJvgxcCiwHNgSOnGW3DwBbAY8D1gXeCNw+6bi7A+8Htquqn8xw/k2ArwIfAdYHHgGc067+CHB3mmR/IvASYPc+fzWAF7bb/y1wV2Dvdvnftz/vUVVrV9X32vmtgYuAewPvpPk79N4G2hU4sap+O0AM0lB1ncM9ngj8HfDUJM+iaQh3osnr7wKfa4+7HvB5YB/gXsCFbRxT/W7rAscDH263/SBwfJJ79Ww2XZ5L8zZu+VVVmwK/BHZs27ebgUOAW4EHAFsCTwH+qefYvW3fu9tlL2w/rwOcBnwJ+FH7+/8DsFeSp/Yc41k0eX0PpukiVVVfA94DHNXG9nDgOOB+Sf6uZ9MXA4f1zO9G0yavR1MfHAGQZC3gBOCzNPn/AuC/kmw+5V9wHFWVU0cT8Fjgt8BdJi1/KXBKz3zRJNcqwB+Bh09xrOXtdnsD5wMb9XH+fYAvTrF8VeDPwOY9y/4ZOLn9vB9w+BTnvks7fzLw1p71rwS+NtW2Pb/vLyfFsDXNPzRp588Ent/1fzMnp95pBHJ4Yp/79yz7KvDynvlVgJtobv2+BPhez7oAlwH/NDlumoby9Enn+x7w0vbztHnu5LQQ07jlVzt/CU3hDU3RezOwRs/2uwLf6vk9Jrd9hwCH9cxvPcU2+wAHt5/3A77T599zP3ra7nbZx4F3t58fAvweuFtPLEf2bLs2cBuwMbAL8N1Jx/oEsG/X/98s1OQV5G5tDFxa/T+sth6wOvCLGbZ5A/Cxqrq8z/NPdaz1gNVovrVPuJTm22u/ftXz+SaaxJrJZb0zVfWDdr9tkjyY5h+/4wY4v7QYus7hCb35swlwQJpuU9cCv6MphDcENujdtppWbbrzbMDK/wbAHf8dGDTPpUGMW35NtglNW3pVz/afoLniOtWxpzvfBhP7t8d4M03xPdMx+nUo8MIkoflSfHQ1V77vcOyquoHm992gjWvrSXHtBtxnHrGMFDtzd+syYFmSu/T5D8DVwJ+ATWlut0zlKcDXkvyqqr7Qx/kfPc15bqFJgPPbZcuAK9rPNwJr9mw/SELUAMsPpelm8Svg81X1pwHOIy2GrnN4Qm/+XEZzRegOt1qTPBDYqGc+vfOTXEnzb0CvZcDX+oxJmq+xyq8pXEZzBXm9GeKfqu2bfL6Lq+qBfcY3kztsV1XfT/Jn4P/QdO144aRNNp74kGRtmm4rV7ZxfbuqntznuceOV5C7dTpwFfC+JGslWT3J46fbuKpuBw4CPth22l81zUNud+vZ7DzgacDHkjxzlvMfAWyX5PlJ7pLkXkkeUVW3AUcD706yTttX+XXAxIN55wB/n2RZkrvT3O7p129p+oPNOGZl63DgOTRF8mGzbCt1oescnsp/A/skeQj85YHbndt1xwMPS/LsNA/Vvorpv+B+BXhQkhe2/z7sAmxO0ydUWgzjll+T47kK+Aawf5K/SbJKkk2TPHGA850OXJ/kTWkeQFw1yUOTPGoOsf8aWJ72gfsehwEfBW6pqlMmrXt6mof570rTF/n7VXUZzb8DD0ry4iSrtdOjJvVnHmsWyB1qC9EdaboP/JLmVucus+y2N/Bj4AyaWx3vZ9J/x6r6EbAD8Mkk289w/l8CTwde3x7rHODh7erX0Fwpvgg4haYj/kHtficARwHnAmcxQINZVTfRPHxwantb5jEzbHsZcDbNt97v9nsOabF0ncPTxPTF9phHpnmq/SfA9u26q4GdgX8HrqEpeM+kuco1+TjXtDG8vt32jcAO7TGkoRu3/JrGS2geYD2fpn/v54H7DnC+29pYH0HzDoGrgU/RPEQ/qGPan9ckObtn+WeAh/LXi2C9PgvsS/O33Ir24fmqup7mavwLaK4o/4rm73K3KY4xliYegJJGUpKDgCur6q1dxyLd2bRXki4Hdquqb3Udj6TFl2YYvN8Aj6yqn/UsPwS4fKm2v/ZB1shK89a9nWiGxpG0ANrhoX5A87T/G2geMPp+p0FJ6tK/Amf0Fseyi8WdXpLd0gwKPnma05t+FkuSd9LcuvqPqrq463ikrgwhhx9L85T/1TS3r59dVX9csIClMTKubeR0knx1mt/nzdNsfwmwJ01XKvWwi4UkSZLUwyvIkiRJUo+R6oO83nrr1fLly7sOQxopZ5111tVVtX7XcUzFnJVWZr5K42W6nB2pAnn58uWceeaZXYchjZQkk99mNjLMWWll5qs0XqbL2UXpYtEObP3DJA4wL40481UaH+arNByL1Qd5T+CCRTqXpPkxX6XxYb5KQzD0AjnJRsAzaN78ImmEma/S+DBfpeFZjD7IH6J5Rek6U61MsgewB8CyZcsW9MRbveGwBT2e1I+z/uMlXYcwHx9ihnwFc1Z3Lubr3Jmv6sJi5exQryAn2QH4TVWdNd02VXVgVa2oqhXrrz+SD/5KS0I/+QrmrDQKzFdpuIbdxeLxwDPbN7UcCWyb5PAhn1PS3Jiv0vgwX6UhGmqBXFX7VNVGVbUceAFwUlW9aJjnlDQ35qs0PsxXabh8k54kSZLUY9FeFFJVJwMnL9b5JM2d+SqND/NVWnheQZYkSZJ6WCBLkiRJPSyQJUmSpB4WyJIkdSjJg5KcmOQn7fwWSd7adVzSUmaBLElStz4J7APcAlBV59IM3SapIxbIkiR1a82qOn3Ssls7iUQSYIEsSVLXrk6yKVAASZ4HXNVtSNLStmjjIEuSpCm9CjgQeHCSK4CLgd26DUla2iyQJUnqUFVdBGyXZC1glaq6vuuYpKXOLhaSJHUoyb2SfBj4LnBykgOS3KvruKSlzAJZkqRuHQn8Fngu8Lz281GdRiQtcXaxkCSpW/etqnf2zL8ryS6dRSPJK8iSJHXsG0lekGSVdno+8PWug5KWMgtkSZK69Qrgs8DN7XQk8M9Jrk9yXaeRSUuUXSwkSepQVa3TdQySVuYVZEmSOpTkC0mensQ2WRoRfSdjkocNMxBJkpaoj9O8GORnSd6XZLOuA5KWukG+rf5XktOTvDLJ3YcWkSRJS0hVfbOqdgMeCVwCfDPJaUl2T7Jat9FJS1PfBXJV/R+ab7gbA2cl+WySJ8+0T5LV26L6R0nOS/L2ecYraUjMV6k77YtBXgr8E/BD4ACagvmEGfYxZ6UhGeghvar6WZK3AmcCHwa2TBLgzVV17BS73AxsW1U3tN+CT0ny1ar6/rwjlzSjJBsCm9CT51X1nRl2MV+lDiT5IrAZ8Blgx6q6ql11VJIzZ9jVnJWGpO8COckWwO7AM2i+0e5YVWcn2QD4HnCHArmqCrihnV2tnWq+QUuaWZL3A7sA5wO3tYsLmLZANl+lznyyqr7SuyDJ3arq5qpaMd1O5qw0PINcQf4I8Cmaq8V/nFhYVVe2V5WnlGRV4CzgAcDHquoHcw1WUt+eDWxWVTcPspP5KnXiXcBXJi37Hk0XixmZs9Jw9NUHuU3AK6rqM73F8YSq+sx0+1bVbVX1CGAj4NFJHjrp2HskOTPJmb/97W8Hi17SdC6iuZo0kNnyFcxZaaEkuU+SrYA1kmyZ5JHttA2wZj/HsI2VhqOvK8hVdVuSjZPctar+PJcTVdW1Sb4FPA34Sc/yA4EDAVasWOGtIWlh3ASck+REmn6KAFTVa/vZebp8bdeZs9LCeCrNg3kbAfsDaZdfB7x5kAPZxkoLa5AuFhcDpyY5DrhxYmFVfXC6HZKsD9zSJu4awJOB9881WEl9O66d+ma+Sourqg4FDk3y3Kr6wnTbJfnHdtvJy81ZaUgGKZB/0U6rABOvxZzt2+h9aZJ/1Xa/o6vqywNHKWkgVXVokrsCD2oXXVhVt8yym/kqdWCm4ri1J3CHAhlzVhqaQQrk86vqmN4FSXaeaYeqOhfYci6BSZq7tg/joTQvHQiwcXsVaqZRLMxXaTRlqoXmrDQ8g7xJb58+l0nq3v7AU6rqiVX19zR9Hf+z45gkzY19h6VFNusV5CTbA08HNkzy4Z5VfwPcOqzAJM3LalV14cRMVf3UV9ZKY2vKK8iShqefLhZX0rw575k0Yy1OuB74v8MIStK8nZnkU8Dh7fxuNHksaYQkWQV4XlUdPcNmpy5WPJIasxbIVfUj4EdJPtvHQz6SRsO/Aq8CJoZ1+y7wX92FI2kqVXV7kjcC0xbIVfXqRQxJEoM9pPfoJPsBm7T7heZNl/cfRmCS5q59g94H20nSaPtmkr2Bo1h5GNXfdReStLQNUiB/mqZLxVnAbcMJR9J8JDm6qp6f5MdM8WBPVW3RQViSZrZL+/NVPcsK8AKU1JFBCuQ/VNVXhxaJpIWwZ/tzh06jkNS3qrpf1zFIWtkgBfK3kvwHcCwrv7r27AWPStKcVNVV7c9Lu45FUn+SrAm8DlhWVXskeSCwmS/9kLozSIG8dftzRc+yArZduHAkLYQk13PHLhZ/oBnJ4vVVddHiRyVpGgfTdF98XDt/BXAMYIEsdaTvArmqnjTMQCQtqA8BlwOfpXmg9gXApsDZwEHANl0FJukONq2qXZLsClBVNyVx7GOpQ30XyEneNtXyqnrHwoUjaYE8s6oe3jN/YJJzqupNSd7cWVSSpvLnJGvQ3vVJsik9XRklLb5BXjV9Y890G7A9sHwIMUmav5uSPD/JKu30fOBP7TpfWyuNln2BrwEbJzkCOBF4Y7chSUvbIF0s9u+dT/IB4OsLHpGkhbAbcADNy0EK+D7wovYqlS8dkEZE+ya9ewI7AY+h6RK1Z1Vd3Wlg0hI3yEN6k60JbLRQgUhaOO1DeDtOs/qUxYxF0vQm3qTXvmr6+K7jkdTou4tFkh8nObedzgMupHkQSNKISfKgJCcm+Uk7v0WSt3Ydl6QpfTPJ3kk2TrLuxNR1UNJSNsgV5N4XD9wK/Lqqbl3geCQtjE8CbwA+AVBV5yb5LPCuTqOSNBXfpCeNmEH6IF+a5OHA/2kXfQc4dyhRSZqvNavq9EkjRfmFVhoxbR/kf6uqo7qORdJfDdLFYk/gCOBv2+mIJK8ZVmCS5uXqdqioiWGjngdc1W1Ikiarqttp7vZIGiGDdLF4ObB1Vd0IkOT9wPeAjwwjMEnz8irgQODBSa4ALqYZ2ULS6Plmkr2Bo2iGUgWgqn7XXUjS0jZIgRya8Y8n3NYum36HZGPgMODeNFeyDqyqAwYNUlL/kqwKvLKqtkuyFrBKVV3fx37mq9SNOfVBNmel4RmkQD4Y+EGSL7bzzwY+Pcs+twKvr6qzk6wDnJXkhKo6f/BQJfWjqm5L8oT2842zbd/DfJU6UFX3m+Ou5qw0JIM8pPfBJCcDT2gX7V5VP5xln6to+z1W1fVJLgA2BExeabh+mOQ44BhWvmV77HQ7mK9SN5KsCbwOWFZVeyR5ILBZVX15pv3MWWl4+i6QkzwGOK+qzm7n/ybJ1lX1gz73Xw5sCfxg0vI9gD0Ali1b1m84kma2OnANsG3PsgKmLZB7TZev7TpzVlpYBwNnAY9r56+g+XI7Y4HcyzZWWlh9j2IBfBy4oWf+hnbZrJKsDXwB2KuqrutdV1UHVtWKqlqx/vrrDxCOpOlU1e5TTC+bWJ9kn+n2nSlf22Obs9LC2rSq/h24BaCqbmKWZ3x62cZKC2+QAjlVVRMz7dA0s16BTrIaTeIeMdPtXUmLauepFpqvUif+nGQN/jos46bAzf3saM5KwzFIgXxRktcmWa2d9gQummmHNG8p+DRwQVV9cD6BSlpQd7g6Zb5KndkX+BqwcZIjgBOBN862kzkrDc8gBfK/0PSPugK4HNiatl/TDB4PvBjYNsk57fT0OUUqaSHVFMvMV2kRJXl8+/E7wE7AS4HPASuq6uQ+DmHOSkMyyCgWvwFeMN36JPtU1Xsn7XMKA/SjkrRo7pCX5qu06D4MbAV8r6oeCRw/yM7mrDQ8g4yDPJudgffOupWkoUuy7uS3cCW5X1Vd3M4e00FYklZ2S5IDgY2SfHjyyqp6bQcxSWJhC2S/xUqj40tJtp94oj3J5sDRwEMBquo9XQYnCYAdgO2Ap9IM8yZpRCxkgTxVn0ZJ3XgPTZH8DGAzmtfR7tZtSJJ6VdXVwJFJLqiqH3Udj6S/8gqydCdUVce3wz99A1gHeE5V/bTjsCRN7Y9JTgTuXVUPTbIF8MyqelfXgUlL1SBv0rNPozTiknyEle/m3B34BfDqJPZplEbTJ4E3AJ8AqKpzk3wWsECWOjLIFWT7NEqj78xJ8/ZrlEbfmlV1ejOs8V/c2lUwkgYrkO3TKI24qjoUIMlawJ+q6rZ2flXgbl3GJmlaV7dvz5t4k97zgKu6DUla2gYZB9k+jdL4OJHm6fgb2vk1aHL3cZ1FJGk6rwIOBB6c5ArgYrwAJXVq1gLZPo3SWFq9qiaKY6rqhiRrdhmQpDtq7+68sqq2a+/8rFJV13cdl7TU9XMF2T6N0vi5Mckjq+psgCRbAX/sOCZJk1TVbUme0H6+set4JDVmLZDt0yiNpb2AY5JcSTME432AXTqNSNJ0fpjkOJrRoP5SJFfVsd2FJC1tgzykZ59GaUxU1RlJHkzzQC3AhVV1S5cxSZrW6sA1wLY9ywqwQJY6MkiBbJ9GacQl2baqTkqy06RVD2qfGbDBlUZMVe0+0/ok+1TVexcrHkmDFcj2aZRG3xOBk4Adp1jnFSlpPO0MWCBLi2iQAnkv7NMojbSq2rf9OeMVKUljJbNvImkhDTIOsn0apRGX5HUzra+qDy5WLJIWTM2+iaSF1M84yPZplMbHOjOss5GVxpNXkKVF1s8VZPs0SmOiqt4OkORQYM+quradvyewf4ehSZpGknWr6neTlt2vqi5uZ4/pICxpSetnHOQ592lMchCwA/Cbqnro4OFJmqMtJopjgKr6fZItZ9vJnJU68aUk21fVdQBJNgeOBh4KUFXvmWon81Uann66WMynT+MhwEeBwwYLS9I8rZLknlX1e2iuUNHfHaNDMGelxfYemiL5GTTP+RwG7NbHfodgvkpD0U+DOec+jVX1nSTLB4pI0kLYH/hekolbszsD755tJ3NWWnxVdXyS1WhevrUO8Jyq+mkf+5mv0pD008ViqH0ak+wB7AGwbNmy+R5OElBVhyU5k7++mWunqjp/IY5tzkoLI8lHWPlC092BXwCvbh+Cf+0CnMN8leZgkHGQ59SncTZVdSBwIMCKFSt8yl5aIG1BvCBF8aTjmrPSwjhz0vxZC30C81Wam0EK5Ln2aZQkSZNU1aEASdYC/lRVt7XzqwJ36zI2aakbpMCdU59GSZI0oxOB7YAb2vk1aPojP66ziKQlbpV+N6yqw4CdgF+3005V9ZmZ9knyOeB7wGZJLk/y8vkEK2m4zFmpE6tX1URxTPt5zdl2Ml+l4Rmoi8SgfRqrateBI5LUGXNW6sSNSR5ZVWcDJNkK+ONsO5mv0vDYh1iSpG7tBRyT5Eqa10rfB9il04ikJc4CWZKkDlXVGUkeTPOSEIALq+qWLmOSljoLZEmSOpBk26o6KclOk1Y9qB0H+dhOApNkgSxJUkeeCJwE7DjFugIskKWOWCBLktSBqtq3/bl717FIWpkFsiRJHUjyupnWV9UHFysWSSuzQJYkqRvrzLDO10JLHbJAliSpA1X1doAkhwJ7VtW17fw9ad5eK6kjfb9JT5IkDcUWE8UxQFX9Htiyu3AkWSBLktStVdqrxgAkWRfv8EqdMgElSerW/sD3khzTzu8MvLvDeKQlzwJZkqQOVdVhSc4Etm0X7VRV53cZk7TUWSBLktSxtiC2KJZGhH2QJUmSpB4WyJIkSVIPC2RJkiSphwWyJEmS1MMCWZIkSeox9AI5ydOSXJjk50n+bdjnkzR35qs0XsxZaTiGWiAnWRX4GLA9sDmwa5LNh3lOSXNjvkrjxZyVhmfYV5AfDfy8qi6qqj8DRwLPGvI5Jc2N+SqNF3NWGpJhvyhkQ+CynvnLga17N0iyB7BHO3tDkguHHJP6sx5wdddBjKN84B8X+pCbLPQBpzFrvoI5O8LM2TkY43wF29hxZr7O0WLlbOdv0quqA4EDu45DK0tyZlWt6DoOjR5zdjSZs5qK+TqazNfRN+wuFlcAG/fMb9QukzR6zFdpvJiz0pAMu0A+A3hgkvsluSvwAuC4IZ9T0tyYr9J4MWelIRlqF4uqujXJq4GvA6sCB1XVecM8pxaMt+SWGPN17JmzS4w5O9bM1xGXquo6BkmSJGlk+CY9SZIkqYcFsiRJktTDAlmSJEnqYYEsSZI0REkenOQfkqw9afnTuopJM7NA1oyS7N51DJIkjaskrwX+B3gN8JMkva8Df083UWk2jmKhGSX5ZVUt6zoOSTNLcq+quqbrOCStLMmPgcdW1Q1JlgOfBz5TVQck+WFVbdlthJpK56+aVveSnDvdKuDeixmLpNkleR/wgaq6OskK4Gjg9iSrAS+pqm93G6GkHqtU1Q0AVXVJkm2AzyfZhKad1QjyCrJI8mvgqcDvJ68CTquqDRY/KknTSfLjqnpY+/lbwBur6owkDwI+W1Uruo1Q0oQkJwGvq6pzepbdBTgI2K2qVu0qNk3PK8gC+DKwdm/yTkhy8qJHI2k2d0lyl6q6FVijqs4AqKqfJrlbx7FJWtlLgFt7F7S5+5Ikn+gmJM3GK8iSNGaSvAbYEXgf8PfAPYFjgW2B+1fVizsMT5LGngWyJI2hth/jvwIPorkbeBnw/4CD2qtTkqQ5skCWpDuRJLtX1cFdxyFJ48xxkJeAJKcNuP02Sb48rHgkDdXbuw5AWkpsY++cfEhvCaiqx3Udg6SF49CM0uiwjb1z8gryEpDkhvbnNklOTvL5JP+b5Igkadc9rV12NrBTz75rJTkoyelJfjjxBqAkByR5W/v5qUm+k8T/n6TFcW+aJ+N3nGLyZSHSIrKNvXPyCvLSsyXwEOBK4FTg8UnOBD5J8wT8z4GjerZ/C3BSVb0syT2A05N8E9gHOCPJd4EPA0+vqtsX79eQljSHZpRGk23snYTfRpae06vq8jbRzgGWAw8GLq6qn1Xz1ObhPds/Bfi3JOcAJwOrA8uq6ibgFcAJwEer6heL9htIS1xVvbyqTplm3QsXOx5Jf2EbeyfhFeSl5+aez7cx+/8DAZ5bVRdOse5hNLdzfdOeJEm2sXcaXkEWwP8Cy5Ns2s7v2rPu68BrevpRbdn+3AR4Pc3tpO2TbL2I8UqSNC5sY8eQBbKoqj8BewDHtw8Q/KZn9TuB1YBzk5wHvLNN5E8De1fVlcDLgU8lWX2RQ5fulBw2SrrzsI0dT74oRJLGXPtWvb2raoeOQ5GkOwWvIEvSiHHYKEnqlg/pSdJoc9goSVpkXj2QpNHmsFGStMi8gixJo81hoyRpkXkFWZLGj8NGSdIQWSBL0phx2ChJGi6HeZMkSZJ6eAVZkiRJ6mGBLEmSJPWwQJYkSZJ6WCBLkiRJPSyQJUmSpB4WyJIkSVIPC2RJkiSpx/8PIxztHfsoEBwAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章信息数据表"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:20:34.183761Z",
+ "start_time": "2020-11-13T15:20:34.164770Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " click_article_id \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
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+ " \n",
+ " 0 \n",
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+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1408667706000 \n",
+ " 250 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 1408468313000 \n",
+ " 230 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 1 \n",
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+ " 162 \n",
+ " \n",
+ " \n",
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+ " 364043 \n",
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+ " 364046 \n",
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+ " 1505811330000 \n",
+ " 479 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:28:13.084501Z",
+ "start_time": "2020-11-13T15:28:13.062561Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "176 3485\n",
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+ "191 3182\n",
+ "194 3164\n",
+ " ... \n",
+ "601 1\n",
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+ "1626 1\n",
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+ "Name: words_count, Length: 866, dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "item_df['words_count'].value_counts()"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:28:59.029535Z",
+ "start_time": "2020-11-13T15:28:58.816106Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "461\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print(item_df['category_id'].nunique()) # 461个文章主题\n",
+ "item_df['category_id'].hist()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(364047, 4)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "item_df.shape # 364047篇文章"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章embedding向量表示"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
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5 rows × 251 columns
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+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
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+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "item_emb_df.shape"
]
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- "metadata": {},
- "output_type": "display_data"
},
{
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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 数据分析"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户重复点击"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:30:20.899771Z",
+ "start_time": "2020-11-13T15:30:20.750817Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "#####merge\n",
+ "user_click_merge = trn_click.append(tst_click)"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:30:26.290038Z",
+ "start_time": "2020-11-13T15:30:25.339579Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 30760 \n",
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+ " 36162 \n",
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+ " \n",
+ " 7 \n",
+ " 3 \n",
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+ " 8 \n",
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+ " 39894 \n",
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+ " \n",
+ " \n",
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+ " 4 \n",
+ " 42567 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:34:27.418638Z",
+ "start_time": "2020-11-13T15:34:27.372761Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 311242 \n",
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+ " 10 \n",
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+ " 69463 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:32:53.298575Z",
+ "start_time": "2020-11-13T15:32:53.285611Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 2, 4, 3, 6, 5, 10, 7, 13])"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "user_click_count['count'].unique()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1 1605541\n",
+ "2 11621\n",
+ "3 422\n",
+ "4 77\n",
+ "5 26\n",
+ "6 12\n",
+ "10 4\n",
+ "7 3\n",
+ "13 1\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#用户点击新闻次数\n",
+ "user_click_count.loc[:,'count'].value_counts() "
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### 可以看出：有1605541（约占99.2%）的用户未重复阅读过文章，仅有极少数用户重复点击过某篇文章。 这个也可以单独制作成特征"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击环境变化分析"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
},
{
- "data": {
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:39:41.961797Z",
+ "start_time": "2020-11-13T15:39:41.949829Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_envs(df, cols, r, c):\n",
+ " plt.figure()\n",
+ " plt.figure(figsize=(10, 5))\n",
+ " i = 1\n",
+ " for col in cols:\n",
+ " plt.subplot(r, c, i)\n",
+ " i += 1\n",
+ " v = df[col].value_counts().reset_index()\n",
+ " fig = sns.barplot(x=v['index'], y=v[col])\n",
+ " for item in fig.get_xticklabels():\n",
+ " item.set_rotation(90)\n",
+ " plt.title(col)\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:39:55.476626Z",
+ "start_time": "2020-11-13T15:39:48.764592Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "output_type": "display_data"
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\n",
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+ "output_type": "display_data"
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QNBXYCLig1ryKpNmSLpX0mmH6HVT2mX377bd38ntELNckrQBcChwI3FWWA21/cZSuPc/X0jc5GzE2co6NaEA3U9XdbruXY6L2Bc6yXf+qeartBWVKvAskXW37+non2ycCJwJMnz7dPYwvYiDYfkzS8ba3Bq7o0dssVb6W+JKzEeMv59iIMdLNlecjJH1T0n6S9hpaRumzAJhcW9+wtLWzL9WNiI+zvaD8vIFqfumtl+wWEW2cL+l1kkYc59wi+RoxWJKzEQ3opng+ENgK2IUnhmzsNlIHYBawiaSNJK1ElbxLXL2WtBmwNvC7WtvaklYur9cFXsbw47giYnH/AZwJPCLpPkn3S7pvlD7J14jBkpyNaEA3wzZeZHvTbg5ue6GkQ4BzgUnADNvXSDoKmF0bBrIvcJrt+ldCzwVOkPQYVZH/qfodxBExPNtrLkWf5GvEAEnORjSjm+L5Ekmbd5tcts8Bzmlp+1jL+pFt+l0CPL+b94qY6MrUVavafqCsvxhYqWz+g+37R+qffI0YLMnZiPHXTfH8YuBKSTdSPShFgEeZqi4ixtengduAz5T17wN/Alahunnwgw3FFRERsVzopnjepWdRRMRYeSXwotr6PbZ3LzcO/rahmCIiIpYbHd8waPtmqjkkHwVcWyKif6xge2Ft/YNQfUUErNFMSBHRqXIjX77Rjehj3Txh8F1UTyb6O/BYaTaQJI/oHytJWnNobLPtXwBIWotq6EZE9BlJFwJ7UJ2TLwduk3Sx7fc2GlhEtNXNVHWHApva3sL288uSwjmiv3wDOF3SlKGG8mSx7wPfbCyqiBjJWrbvA/YCTrG9HbBjwzFFxDC6GfM8D7i3V4FExLKz/XlJDwEXSVqd6sbe+6mmofpas9FFxDCeJOmZwBuAjzQdTESMrJvi+QbgQklnU822AVQn6zGPKiKWmu2vA1+XtGZZH3F6uoho3FFUczVfbHtWeVz2dQ3HFBHD6KZ4/mtZVuKJeWMjog9JejpwDLA+sKukzYGX2P5Ws5FFRCvbZ1I9EXRo/Qbgdc1FFBEj6bh4tv1xAElrlPUHehVURCyzk4Bv88RXwH8BTgdSPEf0GUkbAl+mekQ2VNNKHmp7fnNRRcRwOr5hUNLzJP0BuAa4RtLlkrboXWgRsQzWtX0GZWacMn3domZDiohhfBuYSfVN0frAT0pbRPShbmbbOBF4r+2ptqcC76O6sz8i+s+Dkp5KmYu9PKY7N/xG9Kf1bH/b9sKynASs13RQEdFeN2OeV7f9q6EV2xeWu/kjov+8l+pK1saSLqY6Eb++2ZAiYhh3SnoT1ZSSAPsBdzYYT0SMoKvZNiT9P+A7Zf1NVDNwRESfsX2FpFcAm1JNVzfH9qMNhxUR7b2VaszzF6i+LboEOLDRiCJiWN0M23gr1dWrHwI/ANYtbRHRZyQdDKxh+xrbfwLWkPTOpuOKiCXZvtn2HrbXs/0026+x/deh7ZIObzK+iFhcR8WzpEnAD22/2/YLbW9j+z227+5xfBGxdN5h+56hlZKr72gunIhYBns3HUBEPKGj4tn2IuAxSWv1OJ6IGBuTJGlopXwAzvzsEYNJo+8SEeOlmzHPDwBXSzoPeHCo0fa7xzyqiFhWPwdOl3RCWf+P0hYRg8dNBxART+imeP5hWSKi/32QqmD+r7J+HvDN5sKJiGWQK88RfaTjGwZtn9xuGa2fpF0kzZE0V9KH2mw/QNLtkq4sy9tr294i6bqyvKXzXytiYrP9mO2v2X59WU4ow69GlHyNGH+S1mnTtlFt9czW7bX9krMR42zUK8+SzrD9BklX0+arI9tbjtB3EnA88CpgPjBL0kzb17bserrtQ1r6rgMcAUwv73t56ZubFCOGkXyNGEg/kbSr7fsAJG0OnAE8D8D2Me06JWcjmtHJsI1Dy8/dluL42wJzbd8AIOk0YE+gNbHb2Rk4z/Zdpe95wC48MYl8RCwp+RoxeI6hKqD/jWpu9lOA/Tvol5yNaMCowzZs31pevg54tMxH+fgySvcNgHm19fmlrdXrJF0l6SxJk7vpK+kgSbMlzb799ttH+3Uilmv9nq+QnI1oZftsqgek/AI4CXit7Ss76JpzbEQDunlIyprAeZJ+K+kQSU8foxh+AkwrXyefB4w6jrrO9om2p9uevt56641RSBEDry/zFZKzEUMkfVnScZKOA3YA1gJuBA4pbWMh59iIMdbNDYMft70FcDDwTODXkn45SrcFwOTa+oalrX7cO20/Ula/CWzTad+IaC/5GjEQZgOX15bPUD3Bd2h9NMnZiAZ0M1XdkNuAvwF3Ak8bZd9ZwCblruEFwL7AG+s7SHpm7avmPYA/l9fnAsdIWrus7wTkEaUR3Um+RvSpoRmrJK0O/GNoRpxyI+DKHRwiORvRgI6LZ0nvBN4ArEc1bc472tzRuxjbCyUdQpWkk4AZtq+RdBQw2/ZM4N2S9gAWAncBB5S+d0k6muofB4Cjhm5siIiRJV8jBsr5wI5UDyMDWJVq/PNLR+qUnI1oRjdXnicD7+nwJobH2T4HOKel7WO114czzKdd2zOAGd28X0QAydeIQbKK7aHCGdsPSFqtk47J2Yjx182Y58OpHs+9vqQpQ0sPY4uIpVTydQ1JBwJIWq/loQsR0T8elPTCoRVJ2wAPNxhPRIygm2EbhwBHAn8HHivNBoZ96EJENEPS0MMPNgW+DawIfBd4WZNxRURb7wHOlHQL1aO4nwHs02hEETGsboZtvAfY1PadPYolIsbOa4GtgSsAbN8iac1mQ4qIdmzPkrQZ1YddgDm2H20ypogYXjfF8zzg3l4FEhFj6p+2Lcnw+N38EdFHJO1g+wJJe7Vseo4kbP+wkcAiYkTdFM83ABdKOhsYmjMS258f86giYlmdIekE4CmS3gG8FfhGwzFFxOJeAVwA7N5mm4EUzxF9qJvi+a9lWaksEdGnbH9O0quA+6i+Cv6Y7fMaDisiamwfUX4e2HQsEdG5jotn2x8HkLSa7Yd6F1JEjIVSLKdgjuhTkt470vZ8sxvRn7qZbeMlwLeANYApkl4A/Iftd/YquIjojqT7qb7ubcv2k8cxnIgY2Ug38Q6bxxHRrG6GbXwR2BmYCWD7j5L+pRdBRcTSsb0mQHly2K3Ad6imvtofeGaDoUVEi9o3uicDh9q+p6yvDRzbYGgRMYKOH5ICYHteS9OiMYwlIsbOHra/avt+2/fZ/hqwZ9NBRURbWw4VzgC276aaajIi+lA3xfM8SS8FLGlFSYcBf+5RXBGxbB6UtL+kSZJWkLQ/8GDTQUVEWyuUq80ASFqH7r4Zjohx1E1y/ifwJWADYAHwC+DgXgQVEcvsjVT5+iWqsZMXl7aI6D/HAr+TdGZZ3xv4ZIPxRMQIuplt4w6qcZNtSTrc9n+PSVQRsUxs38QIwzSSrxH9w/YpkmYDO5SmvWxf22RMETG8rsY8j2LvMTxWRPRW8jWij9i+1vZXypLCOaKPjWXxrDE8VkT0VvI1IiJiKYxl8Zw5KSMGR/I1IiJiKeTKc8TElHyNiIhYCh0Xz2XqnNa2jWqrZ7Zuj4hmJF8jIiJ6o5srzz+R9PijfSVtDvxkaN32Me06SdpF0hxJcyV9qM3290q6VtJVks6XNLW2bZGkK8sys4tYIya65GvEBJCcjRh/3RTPx1CdkNeQtA3Vlas3jdRB0iTgeGBXYHNgv3ISr/sDMN32lsBZwGdq2x62vVVZ9ugi1oiJLvkasZxLzkY0o5t5ns+WtCLVw1HWBF5r+y+jdNsWmGv7BgBJp1HNPfv4NDy2f1Xb/1JGOcFHxOiSrxETQnI2ogGjFs+Svszid+avBVwPHCIJ2+8eofsGwLza+nxguxH2fxvws9r6KmXi+IXAp2z/T5v4DgIOApgyZcoIh45Y/vV7vpYYk7MRYyPn2IgGdHLleXbL+uW9CETSm4DpwCtqzVNtL5D0LOACSVfbvr7ez/aJwIkA06dPz/RbMdH1db5CcjaiCTnHRoydUYtn2ycDSFod+IftRWV9ErDyKN0XAJNr6xuWtsVI2hH4CPAK24/U3ntB+XmDpAuBramuokVEG8nXiAklORvRgG5uGDwfWLW2virwy1H6zAI2kbSRpJWAfYHF7uiVtDVwArCH7dtq7WtLWrm8Xhd4GbVxXBExouRrxPIvORvRgI5vGARWsf3A0IrtByStNlIH2wslHQKcC0wCZti+RtJRwGzbM4HPAmsAZ0oC+Gu56/e5wAmSHqMq8j9lO4kd0Znka8RyLjkb0YxuiucHJb3Q9hUAZfqrh0frZPsc4JyWto/VXu84TL9LgOd3EV9EPCH5GjEBJGcjxl83xfN7qD653kL1aN9nAPv0IqiIWGbvIfkaEREx5rqZ53mWpM2ATUvTHNuP9iasiFgWydeIiIje6GSe5x1sXyBpr5ZNzynzxv6wR7FFRJeSrxEREb3VyZXnVwAXALu32WYgJ+OI/pF8jYiI6KFO5nk+ovw8sPfhRMSySL5GRET0VifDNt470nbbnx+7cCJiWSRfIyIiequTYRtrjrAtj+qM6C/J14iIiB7qZNjGxwEknQwcavuesr42cGxPo4uIriRfIyIiequbx3NvOXQiBrB9N7D1mEcUEWMh+RoREdED3RTPK5SrVwBIWofuHrISEeMn+RoREdED3ZxMjwV+J+nMsr438MmxDykixkDyNSIioge6ecLgKZJmAzuUpr1sX9ubsCJiWSRfIyIieqOrr3HLyTcn4IgBkHyNiIgYe92MeY6IiIiImNBSPEdEREREdCjFc0REREREh1I8R0RERER0KMVzRERERESHel48S9pF0hxJcyV9qM32lSWdXrb/XtK02rbDS/scSTv3OtaIiS75GjFYkrMR46+nxbOkScDxwK7A5sB+kjZv2e1twN22nw18Afh06bs5sC+wBbAL8NVyvIjogeRrxGBJzkY0o9dXnrcF5tq+wfY/gdOAPVv22RM4ubw+C3ilJJX202w/YvtGYG45XkT0RvI1YrAkZyMa0OvieQNgXm19fmlru4/thcC9wFM77BsRYyf5GjFYkrMRDejqCYP9SNJBwEFl9QFJc5qMZ4JaF7ij6SAGgT73lrE83NSxPNh4Sc72heRshyZ6ziZf+0LytUPjla+9Lp4XAJNr6xuWtnb7zJf0JGAt4M4O+2L7RODEMYw5uiRptu3pTccRy6zn+QrJ2X6QnF1u5Bw7ASRf+0+vh23MAjaRtJGklahuTpjZss9MYOijwuuBC2y7tO9b7hTeCNgEuKzH8UZMZMnXiMGSnI1oQE+vPNteKOkQ4FxgEjDD9jWSjgJm254JfAv4jqS5wF1UyU/Z7wzgWmAhcLDtRb2MN2IiS75GDJbkbEQzVH0AjVh6kg4qX+1FxABIzkYMjuRr/0nxHBERERHRoTyeOyIiIiKiQymeIyIiIiI6lOI5IiIiIqJDKZ5jmUk6pekYIqI9SdtKelF5vbmk90p6ddNxRUQMqoF/wmCML0mtc4gK+FdJTwGwvce4BxURbUk6AtgVeJKk84DtgF8BH5K0te1PNhpgRCxG0lrA4cBrgKcBBm4Dfgx8yvY9jQUXj8tsG9EVSVdQzQv6TaqkFvB9npg79NfNRRcRdZKuBrYCVgb+Bmxo+z5JqwK/t71lk/FFxOIknQtcAJxs+2+l7RlUD7p5pe2dmowvKhm2Ed2aDlwOfAS41/aFwMO2f53COaLvLLS9yPZDwPW27wOw/TDwWLOhRUQb02x/eqhwBrD9N9ufBqY2GFfUZNhGdMX2Y8AXJJ1Zfv6d/H8U0a/+KWm1UjxvM9RYvhpO8RzRf26W9AGqK89/B5D0dOAAYF6TgcUTcuU5lort+bb3Bn4GfLfpeCKirX8phfPQB98hK1J9DRwR/WUf4KnAryXdLeku4EJgHeANTQYWT8iY54iIiIg+IWkzYEPgUtsP1Np3sf3z5iKLIbnyHBEREdEHJL2bamaNQ4A/SdqztvmYZqKKVhmrGhEREdEf3gFsY/sBSdOAsyRNs/0lqtmtog/kynMsQdIlXe6/vaSf9iqeiBhe8jViubLC0FAN2zcB2wO7Svo8KZ77RornWILtlzYdQ0R0JvkasVz5u6SthlZKIb0bsC7w/KaCisWleI4lSHqg/Nxe0oWSzpL0v5JOlaSybZfSdgWwV63v6pJmSLpM0h+GxmtJ+pKkj5XXO0v6jaT8/xexjJKvEcuVN1M90OhxthfafjPwL82EFK0y5jlGszWwBXALcDHwMkmzgW8AOwBzgdNr+38EuMD2W8sjuy+T9Euqx43OkvRb4Djg1S1TZ0XEsku+Rgww2/NH2HbxeMYSw8uVhBjNZWVO58eAK4FpwGbAjbavczXXYX2e552AD0m6kmpuylWAKWWu2XcA5wFfsX39uP0GERNH8jUiosdy5TlG80jt9SJG/39GwOtsz2mz7fnAncD6YxRbRCwu+RoR0WO58hxL43+BaZI2Luv71badC7yrNtZy6/JzKvA+qq+Vd5W03TjGGzGRJV8j+kRmx1k+pHiOrtn+B3AQcHa5Aem22uajqR79e5Wka4Cjy4n5W8Bhtm8B3gZ8U9Iq4xx6xISTfI3oH5kdZ/mQx3NHREREjANJD9heQ9L2wJHAHcDzgMuBN9m2pF2ALwIPARcBz7K9m6TVgS+X/VcEjrT9Y0lfAu60fZSknaluBN4+N/n2TsY8R0RERIy/zI4zoDJsIyIiImL8ZXacAZUrzxERERHjL7PjDKhceY6IiIjoD5kdZwCkeI6IiIjoA5kdZzBkto2IiIiIiA7lynNERERERIdSPEdEREREdCjFc0REREREh1I8R0RERER0KMVzRERERESHUjxHRERERHQoxXNERERERIdSPEdEREREdCjFc0REREREh1I8R0RERER0KMVzRERERESHUjxHRERERHQoxfNyQNIBki6qrT8g6Vmj9JkmyZKe1PsII2I4g5q/kvaX9Ium3j+iE/2cX5JeJum6EtNrevleMbZSOC2HbK/RdAxjRdI04EZgRdsLGw4noucGJX9tnwqc2nQcEd3os/w6CviK7S81HUgrSUcCz7b9pqZj6Ue58hwDL1fPI7qXvInonQ7zaypwzVgdX9KkZT1GdCbF84CRNFnSDyXdLulOSV9ps48lPbu8XlXSsZJulnSvpIskrdqmz+sk3STpeaO8/8slXSLpHknzJB1Q2teSdEqJ62ZJH5W0Qtl2pKTv1o6x2Fdiki6UdLSkiyXdL+kXktYtu/+m/LynfLX1kvI13MWSviDpTuAoSXdJen7tPZ4m6SFJ63Xz943opSbzt5Z3b5P0V+CC0v5WSX+WdLekcyVNrfXZSdKc8t5flfRrSW8v21q/Dn+ppFll31mSXlrbNlKOR4yJQcovSdcDzwJ+Us5tK5fz6Lck3SppgaRPqBTEbc57R0o6SdLXJJ0j6UHgXyWtL+kH5W9wo6R312I8UtJZkr4r6T7ggGF+l12ADwP7lNj+KGlvSZe37PdeST8ur0+S9HVJ55Uc/3XLvyWblW13lX9T3jDc33IQpHgeICWJfgrcDEwDNgBOG6Xb54BtgJcC6wAfAB5rOe6BwKeBHW3/aYT3nwr8DPgysB6wFXBl2fxlYC2qfwxeAbwZOLDDXw3gjWX/pwErAYeV9n8pP59iew3bvyvr2wE3AE8Hjqb6O9S/XtoPON/27V3EENEzTedvzSuA5wI7S9qT6iS5F1VO/xb4fjnuusBZwOHAU4E5JY52v9s6wNnAcWXfzwNnS3pqbbfhcjximQ1aftneGPgrsHs5tz0CnAQsBJ4NbA3sBLy9duz6ee+Tpe2N5fWawCXAT4A/lt//lcB7JO1cO8aeVHn9FIYZdmX758AxwOklthcAM4GNJD23tuu/A6fU1venOh+vS1UbnAogaXXgPOB7VPm/L/BVSZu3/QsOAttZBmQBXgLcDjyppf0A4KLauqmSbwXgYeAFbY41rex3GHAtsGEH73848KM27ZOAfwKb19r+A7iwvD4S+G6b935SWb8Q+Ght+zuBn7fbt/b7/rUlhu2o/iFSWZ8NvKHp/2ZZsgwtfZC/Q32eVWv7GfC22voKwENUXye/GfhdbZuAecDbW+OmOole1vJ+vwMOKK+HzfEsWcZiGbT8Kus3URXlUBXEjwCr1vbfD/hV7fdoPe+dBJxSW9+uzT6HA98ur48EftPh3/NIauft0vY14JPl9RbA3cDKtVhOq+27BrAImAzsA/y25VgnAEc0/f/N0i658jxYJgM3u/Mb59YFVgGuH2Gf9wPH257f4fu3O9a6wIpUn/iH3Ez1ybdTf6u9fogq8UYyr75i+/el3/aSNqP6x3FmF+8f0WtN5++Qeu5MBb6kahjWPcBdVEXyBsD69X1dnfGGe5/1WTz/Ycl/A7rN8YhuDFp+tZpKdR69tbb/CVRXatsde7j3W3+ofznGh6kK85GO0amTgTdKEtUH5jNcXTFf4ti2H6D6fdcvcW3XEtf+wDOWIZZGZbD4YJkHTJH0pA7/gbgD+AewMdXXOO3sBPxc0t9s/6CD9992mPd5lCpBri1tU4AF5fWDwGq1/btJGHfRfjLV0I2/AWfZ/kcX7xPRa03n75B67syjupK0xNe3kjYBNqytq77e4haq/K+bAvy8w5giltVA5Vcb86iuPK87Qvztznut73ej7U06jG8kS+xn+1JJ/wT+D9VwkTe27DJ56IWkNaiGwtxS4vq17Vd1+N59L1eeB8tlwK3ApyStLmkVSS8bbmfbjwEzgM+XmwgmqbrhbuXabtcAuwDHS9pjlPc/FdhR0hskPUnSUyVtZXsRcAbwSUlrlrHR7wWGbhK8EvgXSVMkrUX1NVKnbqcagzbivJzFd4HXUhXQp4yyb8R4azp/2/k6cLikLeDxG3/3LtvOBp4v6TWqbu49mOE/+J4DPEfSG8u/DfsAm1ONQY0YD4OWX63x3Ar8AjhW0pMlrSBpY0mv6OL9LgPul/RBVTdDTpL0PEkvWorY/w5MU7nxv+YU4CvAo7Yvatn2alWTCqxENfb5UtvzqP4deI6kf5e0Ylle1DJ+eqCkeB4gpUjdnWpIwl+pvkLdZ5RuhwFXA7OovkL5NC3/3W3/EdgN+IakXUd4/78CrwbeV451JfCCsvldVFeYbwAuoroxYEbpdx5wOnAVcDldnFBtP0R1M8TF5eueF4+w7zzgCqpPzL/t9D0ixkPT+TtMTD8qxzxN1d33fwJ2LdvuAPYGPgPcSVUMz6a6OtZ6nDtLDO8r+34A2K0cI6LnBi2/hvFmqptpr6UaT3wW8Mwu3m9RiXUrqucj3AF8k+pm/m6dWX7eKemKWvt3gOfxxMWxuu8BR1D9Lbeh3MRv+36qq/j7Ul2J/hvV32XlNscYCEM3V0UsFyTNAG6x/dGmY4lYnpQrUPOB/W3/qul4ImL8qZrK7zbghbavq7WfBMyfKOfejHmO5YaqpxHuRTXFT0QsozLF1e+pZiV4P9XNTpc2GlRENOm/gFn1wnkiyrCNWIyk/VVNit66LNVTkMaLpKOpvhL7rO0bm44nogk9yN+XUM1GcAfVV+Kvsf3wmAUcMUAG9fw4HEk/G+b3+fAw+98EHEo1PGtCy7CNiIiIiIgO5cpzRERERESHlqsxz+uuu66nTZvWdBgR4+Lyyy+/w/Z6TcexLJKzMZEMes4mX2MiGSlfl6viedq0acyePbvpMCLGhaTWJ7oNnORsTCSDnrPJ15hIRsrXng7bkDRD0m2S/jTMdkk6TtJcSVdJemFt21skXVeWt/QyzoioJGcjBkfyNaIZvR7zfBLV03mGsyuwSVkOAr4GIGkdqom2t6N6HPQRktbuaaQRAcnZiEFyEsnXiHHX0+LZ9m+onjQznD2BU1y5FHiKpGcCOwPn2b7L9t3AeYz8D0REjIHkbMTgSL5GNKPpMc8bAPNq6/NL23DtS5B0ENUnaqZMmTJmgW3z/lPG7FgRQy7/7JubDmFZJWdjQhnwnE2+xoQyXvk68FPV2T7R9nTb09dbb2BvYo6YMJKzEYMj+RqxpKaL5wXA5Nr6hqVtuPaIaFZyNmJwJF8jeqDp4nkm8OZyR/CLgXtt3wqcC+wkae1yE8NOpS0impWcjRgcydeIHujpmGdJ3we2B9aVNJ/q7t4VAWx/HTgHeDUwF3gIOLBsu0vS0cCscqijbI90U0REjIHkbMTgSL5GNKOnxbPt/UbZbuDgYbbNAGb0Iq6IaC85GzE4kq8RzWh62EZERERExMBI8RwRERER0aEUzxERERERHUrxHBER0SBJz5F0vqQ/lfUtJX206bgior0UzxEREc36BnA48CiA7auAfRuNKCKGleI5IiKiWavZvqylbWEjkUTEqFI8R0RENOsOSRsDBpD0euDWZkOKiOH0dJ7niIiIGNXBwInAZpIWADcC+zcbUkQMJ8VzREREg2zfAOwoaXVgBdv3Nx1TRAwvwzYiIiIaJOmpko4DfgtcKOlLkp7adFwR0V6K54iIiGadBtwOvA54fXl9eqMRRcSwMmwjIiKiWc+0fXRt/ROS9mksmogYUa48R0RENOsXkvaVtEJZ3gCc23RQEdFeiueIiIhmvQP4HvBIWU4D/kPS/ZLuazSyiFhChm1EREQ0yPaaTccQEZ3LleeIiIgGSfqBpFdLyjk5YgB0nKiSnt/LQCIiIiaor1E9FOU6SZ+StGnTAUXE8Lr5lPtVSZdJeqektXoWUURExARi+5e29wdeCNwE/FLSJZIOlLRis9FFRKuOi2fb/4fqk/Fk4HJJ35P0qtH6SdpF0hxJcyV9qM32L0i6six/kXRPbdui2raZncYaEUsn+RrRjPJQlAOAtwN/AL5EVUyfN0q/5GzEOOvqhkHb10n6KDAbOA7YWpKAD9v+Yev+kiYBxwOvAuYDsyTNtH1t7Zj/t7b/u4Cta4d42PZW3cQYERVJGwBTqeW57d+MsH/yNaIBkn4EbAp8B9jd9q1l0+mSZo/QLzkb0YCOi2dJWwIHAv9G9Ul4d9tXSFof+B2wRPEMbAvMtX1DOcZpwJ7AtW32BdgPOKLz8COiHUmfBvahyrVFpdnAsMUzydeIpnzD9jn1Bkkr237E9vQR+iVnIxrQzZjnLwNXAC+wfbDtKwBs3wJ8dJg+GwDzauvzS9sSJE0FNgIuqDWvImm2pEslvaaLWCMmutcAm9p+te3dy7LHKH2SrxHN+ESbtt910C85G9GAjq48l6+GFtj+Trvtw7V3aV/gLNuLam1TbS+Q9CzgAklX276+JbaDgIMApkyZMgZhRCwXbgBWpHrgQi8sVb5CcjZiiKRnUBW7q0raGlDZ9GRgtTF+u5xjI8ZIR8Wz7UWSJktayfY/uzj+AqobDIdsWNra2Rc4uOV9F5SfN0i6kGqs1vUt+5wInAgwffp0dxFbxPLsIeBKSedTK6Btv3uEPj3P17I9ORtR2ZnqJsENgWN5oni+D/hwB/1zjo1oQDc3DN4IXFzuyH1wqNH250foMwvYRNJGVAm9L/DG1p0kbQasTe1rKklrAw/ZfkTSusDLgM90EW/ERDazLN1IvkaMI9snAydLep3tHwy3n6S3lH1bJWcjGtBN8Xx9WVYAhh4lOuKnUNsLJR0CnAtMAmbYvkbSUcBs20Mn932B02zXj/dc4ARJj5X3/FT9DuKIGJ7tkyWtBDynNM2x/egofZKvEQ0YqXAuDgWWKJ6TsxHN6KZ4vtb2mfUGSXuP1qncQXxOS9vHWtaPbNPvEiBPNYxYCpK2pzrZ3kT1VfDkcvVqpNk2kq8R/UnDbUjORoy/bmbbOLzDtoho3rHATrZfYftfqMZWfqHhmCJi6WSscUQfGfXKs6RdgVcDG0g6rrbpycDCXgUWEctkRdtzhlZs/yWP+Y0YWMNeeY6I8dfJsI1bqJ4ouAdwea39fuD/tu0REU2bLembwHfL+v5UeRwRfUTSCsDrbZ8xwm4Xj1c8ETG6UYtn238E/ijpe6PdcBQRfeO/qKalGpqa7rfAV5sLJyLasf2YpA8AwxbPtg8Zx5AiYhTd3DC4raQjgamlnwDbflYvAouIpWf7EeDzZYmI/vZLSYcBp7P4VLB3NRdSRAynm+L5W1TDNC4HFo2yb0Q0QNIZtt8g6Wra3GRke8sGwoqIke1TftYfYmIgF6ci+lA3xfO9tn/Ws0giYiwcWn7u1mgUEdEx2xs1HUNEdK6b4vlXkj4L/JDFH/d7xZhHFRFLxfat5efNTccSEZ2RtBrwXmCK7YMkbQJsavunDYcWEW10UzxvV35Or7UZ2GHswomIsSDpfpYctnEv1Ywb77N9w/hHFRHD+DbVkMiXlvUFwJlAiueIPtRx8Wz7X3sZSESMqS8C84HvUd3cuy+wMXAFMAPYvqnAImIJG9veR9J+ALYfkpS5nSP6VMfFs6SPtWu3fdTYhRMRY2QP2y+orZ8o6UrbH5T04caiioh2/ilpVcq3RZI2pjY8MiL6SzeP536wtiwCdgWm9SCmiFh2D0l6g6QVyvIG4B9lWx71G9FfjgB+DkyWdCpwPvCBZkOKiOF0M2zj2Pq6pM8B5455RBExFvYHvkT1YBQDlwJvKle38sCFiD5RnjC4NrAX8GKqYVaH2r6j0cAiYljd3DDYajVgw7EKJCLGTrkhcPdhNl80nrFExPCGnjBYHs99dtPxRMToOh62IelqSVeV5RpgDtVNSRHRZyQ9R9L5kv5U1reU9NGm44qItn4p6TBJkyWtM7Q0HVREtNfNlef6QxcWAn+3vXCM44mIsfEN4P3ACQC2r5L0PeATjUYVEe3kCYMRA6SbMc83S3oB8H9K02+Aq3oSVUQsq9VsX9Yy21U+7Eb0mTLm+UO2T286lojoTDfDNg4FTgWeVpZTJb2rV4FFxDK5o0x3NTT11euBW5sNKSJa2X6M6luiiBgQ3QzbeBuwne0HASR9Gvgd8OVeBBYRy+Rg4ERgM0kLgBupZuCIiP7zS0mHAadTTQcLgO27mgspIobTzTzPoprfecii0jZyJ2kXSXMkzZX0oTbbD5B0u6Qry/L22ra3SLquLG/pItaICUvSJOCdtncE1gM2s/1y2zd30Df5GjH+9qH6wPsbqsd0Xw7M7qRjcjZi/HVz5fnbwO8l/aisvwb41kgdykn8eOBVVI8KniVppu1rW3Y93fYhLX3XoZo4fjrVV8+Xl753dxFzxIRje5Gkl5fXD462/5Dka0QzbG+0NP2SsxHN6PjKs+3PAwcCd5XlQNtfHKXbtsBc2zfY/idwGrBnh2+5M3Ce7btKMp8H7NJpvBET3B8kzZT075L2GlpG6ZN8jWiApNUkfVTSiWV9E0m7jdaP5GxEI7q5YfDFwHW2j7N9HHC9pO1G6bYBMK+2Pr+0tXpdmT/6LEmTu+kr6SBJsyXNvv322zv9dSKWd6sAdwI7UD0sZXcWn26ynZ7nKyRnI9r4NvBP4KVlfQGdTSuZc2xEA7oZ8/w14IHa+gOlbVn9BJhme0uqT74nd9PZ9om2p9uevt56641BOBGDz/aBbZa3Dm2XdPhSHnqZ8rXElpyNWNzGtj8DPApg+yE6uKeoQznHRoyxrm4YtO2hlTK9zmhjphcAk2vrG5a2x9m+0/YjZfWbwDad9o2IpbZ3m7bka0Qz/ilpVZ6YWnJj4JGRuwDJ2YhGdFM83yDp3ZJWLMuhwA2j9JkFbCJpI0krAfsCM+s7SHpmbXUP4M/l9bnATpLWlrQ2sFNpi4hl1+6qVvI1ohlHAD8HJks6FTgf+EAH/ZKzEQ3oZraN/wSOAz5K9en4fOCgkTrYXijpEKqEnATMsH2NpKOA2bZnAu+WtAfV08/uAg4ofe+SdDTVPw4AR2XOy4gx4yUakq8R40rSy2xfTDVF3V7Ai6k+2B5q+47R+idnI5rRzeO5b6P6VNuWpMNt/3ebfucA57S0faz2+nCg7fhL2zOAGZ3GGBEdazueMvkaMa6OoxpG8TvbLwTO7vYAydmI8dfNlefR7A0sUTxHxPiTtE7rVSRJG9m+saye2UBYEbG4R8v0dBtKOq51o+13NxBTRIxiLIvnsbozOCKW3U8k7Wr7PgBJmwNnAM8DsH1Mk8FFBFBNH7kj1ZzLlzccS0R0aCyL5yXGUEZEY46hKqD/DdgUOAXYv9mQIqKujGs+TdKfbf+x6XgiojO58hyxHLJ9tqQVgV8AawKvtf2XhsOKiPYelnQ+8HTbz5O0JbCH7U4elBIR46zj4jljKCP6n6Qvs/i3QGsB1wOHSMoYyoj+9A3g/cAJALavkvQ9OnvKYESMs26uPGcMZUT/m92ynnGUEf1vNduXSYt9gbuwqWAiYmTdFM8ZQxnR52yfDCBpdeAftheV9UnAyk3GFhHDuqM8VXDoCYOvB25tNqSIGE438zxnDGXE4Dif6i7+B8r6qlS5+9LGIoqI4RwMnAhsJmkBcCO5OBXRt0YtnjOGMmIgrWJ7qHDG9gOSVmsyoIhYUvlW6J22dyzfGK1g+/6m44qI4XVy5TljKCMGz4OSXmj7CgBJ2wAPNxxTRLSwvUjSy8vrB5uOJyJGN2rxnDGUEQPpPcCZkm6hmkbyGcA+jUYUEcP5g6SZVLNWPV5A2/5hcyFFxHC6uWEwYygjBoTtWZI2o7q5F2CO7UebjCkihrUKcCewQ63NQIrniD7UTfGcMZQRfU7SDrYvkLRXy6bnlHsUcjKO6DO2Dxxpu6TDbf/3eMUTESPrpnjOGMqI/vcK4AJg9zbbciUrYjDtDaR4jugT3RTP7yFjKCP6mu0jys8Rr2RFxEDR6LtExHjpZp7njKGM6HOS3jvSdtufH69YImLMePRdImK8dDLPc8ZQRgyONUfYlhNwxGDKleeIPtLJleeMoYwYELY/DiDpZOBQ2/eU9bWBYxsMLSKGIWkd23e1tG1k+8ayemYDYUXEMFYYbYf6GMo2y1tH6y9pF0lzJM2V9KE2298r6VpJV0k6X9LU2rZFkq4sy8xuf7mICWzLocIZwPbdwNajdUq+RjTiJ5KePLQiaXPgJ0Prto8ZrmNyNmL8dTJsY6nHUJYHqRwPvAqYD8ySNNP2tbXd/gBMt/2QpP8CPsMTNyI+bHur0WKMiCWsIGntUjQjaR1Gyffka0RjjqEqoP+N6r6iU4D9R+uUnI1oRifDNpZlDOW2wFzbNwBIOg3YE3g8sW3/qrb/pcCbOogpIkZ2LPA7SUNf9+4NfHKUPsnXiAbYPlvSilQPHlsTeK3tv3TQNTkb0YBOHs+9LGMoNwDm1dbnA9uNsP/bgJ/V1leRNBtYCHzK9v+0dpB0EHAQwJQpU0YJJ2JisH1KyZ2hJ5bt1XI1qp2e5yskZyOGSPoyi1+EWgu4Hjik3JD/7lEOkXNsRAO6med5iTGUkkYdQ9kpSW8CplPdoDhkqu0Fkp4FXCDpatvX1/vZPhE4EWD69OmZTSCiKMXyaAXzUlnafC1xJWcjKrNb1i/v1RvlHBsxdropnrseQwksACbX1jcsbYuRtCPwEeAVth8Zare9oPy8QdKFVDc8LXEyjogxkXyNGEe2TwaQtDrwD9uLyvokYOUODpGcjWjAqLNt1AyNoTxa0tHAJVQ3HoxkFrCJpI0krQTsCyx2R2+5en0CsIft22rta0taubxeF3gZPbqKFhFA8jWiKecDq9bWVwV+2UG/5GxEA7p5wmDXYyhtL5R0CHAuMAmYYfsaSUcBs23PBD4LrEH16G+Av9reA3gucIKkx6iK/E91MGYzIpZS8jWiMavYfmBoxfYDklYbrVNyNqIZ3QzbWKoxlLbPAc5paftY7fWOw/S7BHh+N+8VEcsm+RrRiAclvdD2FQCStgEe7qRjcjZi/HVVPEdERMSYew/VleFbqB7F/QyemIs5IvpMiueIiIgG2Z4laTOqB6QAzLH9aJMxRcTwUjxHREQ0QNIOti+QtFfLpueUeZ5/2EhgETGiFM8RERHNeAVwAbB7m20GUjxH9KEUzxEREQ2wfUT5eWDTsURE51I8R0RENEDSe0fabvvz4xVLRHQuxXNEREQz1hxhWx6FHdGnUjxHREQ0wPbHASSdDBxq+56yvjbVU30jog9183juiIiIGHtbDhXOALbvBrZuLpyIGEmK54iIiGatUK42AyBpHfLNcETfSnJGREQ061jgd5LOLOt7A59sMJ6IGEGK54iIiAbZPkXSbGCH0rSX7WubjCkihpfiOSIiomGlWE7BHDEAMuY5IiIiIqJDKZ4jIiIiIjqU4jkiIiIiokMpniMiIiIiOpTiOSIiIiKiQz0vniXtImmOpLmSPtRm+8qSTi/bfy9pWm3b4aV9jqSdex1rxESXfI0YLMnZiPHX0+JZ0iTgeGBXYHNgP0mbt+z2NuBu288GvgB8uvTdHNgX2ALYBfhqOV5E9EDyNWKwJGcjmtHrK8/bAnNt32D7n8BpwJ4t++wJnFxenwW8UpJK+2m2H7F9IzC3HC8ieiP5GjFYkrMRDej1Q1I2AObV1ucD2w23j+2Fku4FnlraL23pu0HrG0g6CDiorD4gac7YhB5dWBe4o+kgBoE+95axPNzUsTwY45CvkJztE8nZDk30nE2+9oXka4fGK18H/gmDtk8ETmw6jolM0mzb05uOIwZDcrZ5ydnoVPK1ecnX/tPrYRsLgMm19Q1LW9t9JD0JWAu4s8O+ETF2kq8RgyU5G9GAXhfPs4BNJG0kaSWqmxNmtuwzExi6zv564ALbLu37ljuFNwI2AS7rcbwRE1nyNWKwJGcjGtDTYRtlfNUhwLnAJGCG7WskHQXMtj0T+BbwHUlzgbuokp+y3xnAtcBC4GDbi3oZbyy1fKW3HEi+TijJ2eVAcnbCSL72GVUfQCMiIiIiYjR5wmBERERERIdSPEdEREREdCjFc0REREREh1I8R0RERPQJSZtJeqWkNVrad2kqplhciucYM5IObDqGiIiIQSXp3cCPgXcBf5JUf9z6Mc1EFa1SPMdY+njTAUREZyT9rOkYImIJ7wC2sf0aYHvg/0k6tGxTU0HF4gb+8dwxviRdNdwm4OnjGUtEjEzSC4fbBGw1jqFERGdWsP0AgO2bJG0PnCVpKime+0aK5+jW04Gdgbtb2gVcMv7hRMQIZgG/pv1J9ynjG0pEdODvkrayfSWA7Qck7QbMAJ7faGTxuBTP0a2fAmsMJXadpAvHPZqIGMmfgf+wfV3rBknzGognIkb2ZqonPj7O9kLgzZJOaCakaJUnDEZELKckvR642vacNtteY/t/xj+qiIjBlhsGIyKWU7bPAtRu2ivgH03EFBEx6FI8R0QspzLtVUTE2EvxHEuQ1NWNf5K2l/TTXsUTEUst015F9JGcX5cPuWEwlmD7pU3HEBFjItNeRfSRnF+XD7nyHEuQ9ED5ub2kCyWdJel/JZ0qSWXbLqXtCmCvWt/VJc2QdJmkPwx9TSzpS5I+Vl7vLOk3kvL/X0Rv/V3SVkMrpZDeDViXTHsVMe5yfl0+5MpzjGZrYAvgFuBi4GWSZgPfAHYA5gKn1/b/CHCB7bdKegpwmaRfAocDsyT9FjgOeLXtx8bv14iYkDLtVUT/yvl1QOWTSYzmMtvzSyJeCUwDNgNutH2dq7kOv1vbfyfgQ5KuBC4EVgGm2H6IavzlecBXbF8/br9BxARVcvdvw2y7eLzjiYjF5Pw6oHLlOUbzSO31Ikb/f0bA69rNK0v1NfGdwPpjFFtERMSgyvl1QOXKcyyN/wWmSdq4rO9X23Yu8K7a2K2ty8+pwPuovqbaVdJ24xhvRETEIMj5dQCkeI6u2f4HcBBwdrmh4bba5qOBFYGrJF0DHF0S/VvAYbZvAd4GfFPSKuMcesRyJ1NfRSw/cn4dDHk8d0TEBFKmqzvM9m4NhxIRMZBy5TkiYoBl6quIiPGVGwYjIpYfmfoqIqLHciUhImL5kamvIiJ6LFeeIyKWH5n6KiKix3LlOSJi+ZapryIixlCK54iI5VimvoqIGFuZqi4iIiIiokO58hwRERER0aEUzxERERERHUrxHBERERHRoRTPEREREREdSvEcEREREdGhFM8RERERER1K8RwRERER0aH/D1Uqpj6vKafdAAAAAElFTkSuQmCC\n",
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\n",
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+ "output_type": "display_data"
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\n",
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\n",
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+ "output_type": "display_data"
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\n",
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\n",
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\n",
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+ "output_type": "display_data"
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 分析用户点击环境变化是否明显，这里随机采样10个用户分析这些用户的点击环境分布\n",
+ "sample_user_ids = np.random.choice(tst_click['user_id'].unique(), size=10, replace=False)\n",
+ "sample_users = user_click_merge[user_click_merge['user_id'].isin(sample_user_ids)]\n",
+ "cols = ['click_environment','click_deviceGroup', 'click_os', 'click_country', 'click_region','click_referrer_type']\n",
+ "for _, user_df in sample_users.groupby('user_id'):\n",
+ " plot_envs(user_df, cols, 2, 3)"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 分析用户点击环境变化是否明显，这里随机采样10个用户分析这些用户的点击环境分布\n",
- "sample_user_ids = np.random.choice(tst_click['user_id'].unique(), size=10, replace=False)\n",
- "sample_users = user_click_merge[user_click_merge['user_id'].isin(sample_user_ids)]\n",
- "cols = ['click_environment','click_deviceGroup', 'click_os', 'click_country', 'click_region','click_referrer_type']\n",
- "for _, user_df in sample_users.groupby('user_id'):\n",
- " plot_envs(user_df, cols, 2, 3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户点击新闻数量的分布"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:40:04.296033Z",
- "start_time": "2020-11-13T15:40:03.980868Z"
- }
- },
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "[]"
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性"
]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": "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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击新闻数量的分布"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "user_click_item_count = sorted(user_click_merge.groupby('user_id')['click_article_id'].count(), reverse=True)\n",
- "plt.plot(user_click_item_count)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以根据用户的点击文章次数看出用户的活跃度"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#点击次数在前50的用户\n",
- "plt.plot(user_click_item_count[:50])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "点击次数排前50的用户的点击次数都在100次以上。思路：我们可以定义点击次数大于等于100次的用户为活跃用户，这是一种简单的处理思路， 判断用户活跃度，更加全面的是再结合上点击时间，后面我们会基于点击次数和点击时间两个方面来判断用户活跃度。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#点击次数排名在[25000:50000]之间\n",
- "plt.plot(user_click_item_count[25000:50000])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出点击次数小于等于两次的用户非常的多，这些用户可以认为是非活跃用户"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻点击次数分析"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:42:14.526476Z",
- "start_time": "2020-11-13T15:42:14.463642Z"
- }
- },
- "outputs": [],
- "source": [
- "item_click_count = sorted(user_click_merge.groupby('click_article_id')['user_id'].count(), reverse=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T15:42:16.198000Z",
- "start_time": "2020-11-13T15:42:16.044455Z"
- }
- },
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "[]"
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T15:40:04.296033Z",
+ "start_time": "2020-11-13T15:40:03.980868Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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HziNJasOED92klA5FxMeAH9CYXvnNlNKTE30eSVJ7OjJGn1K6F7i3E/ctSRqfYlavlCSNzaCXpMIZ9JJUOINekgrXE6tXRsQo8NxJ3vws4OUJbE4d2OfTg30+PZxKn9+SUuqvqtQTQX8qImKonWU6S2KfTw/2+fQwGX126EaSCmfQS1LhSgj69d1uQBfY59ODfT49dLzPtR+jlySdWAmv6CVJJ2DQS1Lhah30EfGBiHgmIoYj4sZut2e8ImJLRDweEY9GxFAuWxgR90fEs/nnglweEXFr7utjEXFx0/2szfWfjYi1TeV/kO9/ON920r/IMiK+GRE7IuKJprKO97HVObrY589GxEi+1o9GxJVNx27K7X8mIt7fVD7m4zsvAb4xl9+ZlwMnIqbn/eF8fMUkdZmIWBYRD0XEUxHxZER8IpcXe61P0Ofeu9YppVr+o7EE8i+B84BpwM+AC7rdrnH2YQtw1nFlXwBuzNs3Ap/P21cC/wUEsBrYmMsXApvzzwV5e0E+9pNcN/Jtr+hCHy8DLgaemMw+tjpHF/v8WeDvxqh7QX7sTgdW5sf0lBM9voHvAtfk7a8Cf5m3/wr4at6+BrhzEvu8BLg4b58J/CL3rdhrfYI+99y1ntT/9BP8S74U+EHT/k3ATd1u1zj7sIXfDPpngCVND6Rn8vbXgI8cXw/4CPC1pvKv5bIlwM+byo+pN8n9XMGxodfxPrY6Rxf73Oo//zGPWxrf43Bpq8d3DrmXgb5cfrTekdvm7b5cL7p0ze8B3ns6XOsx+txz17rOQzdjfQn5QJfacrIScF9EbIrGl6UDLE4pbcvbLwGL83ar/p6ofOsY5b1gMvrY6hzd9LE8TPHNpuGF8fZ5EbAzpXTouPJj7isf35XrT6o8jHARsJHT5Fof12fosWtd56AvwbtSShcDVwDXR8RlzQdT4+m66Pmvk9HHHvk93ga8Ffh9YBvwxa62pkMiYg7wfeCTKaXdzcdKvdZj9LnnrnWdg772X0KeUhrJP3cA/wFcAmyPiCUA+eeOXL1Vf09UvnSM8l4wGX1sdY6uSCltTym9mVI6DHydxrWG8ff5FWB+RPQdV37MfeXj83L9SRERU2kE3rdTSnfn4qKv9Vh97sVrXeegr/WXkEfE7Ig488g28D7gCRp9ODLTYC2NcT9y+bV5tsJqYFd+u/oD4H0RsSC/RXwfjXG8bcDuiFidZydc23Rf3TYZfWx1jq44EkTZn9K41tBo5zV5FsVKYBWNDx3HfHznV6wPAVfn2x//+zvS56uBB3P9jsu//9uBp1NKNzcdKvZat+pzT17rbnxoMYEfflxJ45PuXwKf7nZ7xtn282h8uv4z4Mkj7acxzvYA8CzwP8DCXB7AV3JfHwcGm+7rL4Dh/O+jTeWD+UH2S+DLdOGDOeA7NN6+HqQxxnjdZPSx1Tm62Od/y316LP8nXdJU/9O5/c/QNDOq1eM7P3Z+kn8X3wOm5/IZeX84Hz9vEvv8LhpDJo8Bj+Z/V5Z8rU/Q55671i6BIEmFq/PQjSSpDQa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKtz/A1/NmoIeUlAfAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "user_click_item_count = sorted(user_click_merge.groupby('user_id')['click_article_id'].count(), reverse=True)\n",
+ "plt.plot(user_click_item_count)"
]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
},
{
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以根据用户的点击文章次数看出用户的活跃度"
]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[:100])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以看出点击次数最多的前100篇新闻，点击次数大于1000次"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[:20])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "点击次数最多的前20篇新闻，点击次数大于2500。思路：可以定义这些新闻为热门新闻， 这个也是简单的处理方式，后面我们也是根据点击次数和时间进行文章热度的一个划分。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(item_click_count[3500:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以发现很多新闻只被点击过一两次。思路：可以定义这些新闻是冷门新闻"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻共现频次：两篇新闻连续出现的次数"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 433597.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 3.184139 \n",
- " \n",
- " \n",
- " std \n",
- " 18.851753 \n",
- " \n",
- " \n",
- " min \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 2.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 2202.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " count\n",
- "count 433597.000000\n",
- "mean 3.184139\n",
- "std 18.851753\n",
- "min 1.000000\n",
- "25% 1.000000\n",
- "50% 1.000000\n",
- "75% 2.000000\n",
- "max 2202.000000"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "tmp = user_click_merge.sort_values('click_timestamp')\n",
- "tmp['next_item'] = tmp.groupby(['user_id'])['click_article_id'].transform(lambda x:x.shift(-1))\n",
- "union_item = tmp.groupby(['click_article_id','next_item'])['click_timestamp'].agg({'count'}).reset_index().sort_values('count', ascending=False)\n",
- "union_item[['count']].describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "由统计数据可以看出，平均共现次数3.18，最高为2202。\n",
- "\n",
- "说明用户看的新闻，相关性是比较强的。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#画个图直观地看一看\n",
- "x = union_item['click_article_id']\n",
- "y = union_item['count']\n",
- "plt.scatter(x, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(union_item['count'].values[40000:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "大概有75000个pair至少共现一次"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 新闻文章信息"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#不同类型的新闻出现的次数\n",
- "plt.plot(user_click_merge['category_id'].value_counts().values)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#出现次数比较少的新闻类型, 有些新闻类型，基本上就出现过几次\n",
- "plt.plot(user_click_merge['category_id'].value_counts().values[150:])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "count 1.630633e+06\n",
- "mean 2.043012e+02\n",
- "std 6.382198e+01\n",
- "min 0.000000e+00\n",
- "25% 1.720000e+02\n",
- "50% 1.970000e+02\n",
- "75% 2.290000e+02\n",
- "max 6.690000e+03\n",
- "Name: words_count, dtype: float64"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#新闻字数的描述性统计\n",
- "user_click_merge['words_count'].describe()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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7JHcf4+5jSkpKMlkXyaGwjTgQ6W7C+vssndE7BjwIrHT3u+Ieeg5oHYEzDng2rvy6YBTPhUBN0Aw0HbjUzIYEHbiXBmUiIj1PkPXD9outTxp1LgK+DCwzs7eCsh8AdwBTzGw8UA58NnhsKnAlUAbsA64HcPddZnYrsDCod4u778rFSoj0ZOoflVxKmfTdfTbJm5kvSVDfgRuSLOsh4KFMAhSJqu7WxKZ9U/egM3IlobD9JBWR3FDSl27t0xPn8KO/Lit2GHnV1SPoq347m5E3vkBFbT0X3j6Tia+uzUlc0j0p6Uu3tqi8mkfnbSx2GPmRo19bSzfXADBv/S6219bz82mrcrNg6VRYz69Q0u+CnXUNrKusK3YYeaHWnZ5HZ8x2zY7aejbt2pd2/da3O2x9M+mM3pEkPvTzV6hvbGHDHZ8sdigiBdXS4jS2tNC/T+9ih1IwF9w+EyDj73vY+sd0pJ+Gij317DvQdEh5fWNLTl8nrD8Hk2loaubjd73GnLKdxQ4Fd2djVfpHYZ2pqK2nvrGZO6ev4qZnit9fkOgAvbnFWZvDX5lVdQ3sqW9MWa/1Pf7uU0s47UfT2j2mHxLdQ49P+i0tzrenvMWSTbuzXsb5t83k6nv/nrugUrCwHRokUV61j7KKOm5+7u1ih8LjCzZx8Z2zWFTe9VM/zr99Jtc9uIB7Z63l8QXF6y/obCv49cvvcMkvX6OsIv3E39JJVj7vpy9z8S9mpVzGxXfO4uUVO3hm8SEn00s30eOTfmVdA8+8uYV/e6S0S8t5Z0d2R1XVew906XUlpr6xOeGvrVaLN1YDsLZiL7PX7OTvXfz1sWBDuM8bLN0QW9+K2vqcLbN6X+ojfYCV22pTV5LQ6vFJv5C++oeFXHH3G+3Kzrl1Bm8GCakQKmrrmb+uql3ZvgNNjLzxBaYs3JTkWV2zpqIurQ7tnXUNzFmbXTL+x1/M4oz/nc6BpsRNaq0/jhznSw/O54sPzG/3eFNzC9OWb8tLZ2Zzi9PSkvvltv7i66zZL5NXTbbqSzfvzmApqV+zuzbzvP5OJTVp7vjSEda3QUk/h15ZVZHwKGjF1sIdGf3Tb2bzuUnz2pXtqG0A4L5Xy3L6WvFf7o/98rWU9T/3u7l84ffzU9ZLpHJPbB1+OWN1wsdbR0gky70TX13Lfzz6JtPf3pHV63fm3T+YylX3zs75ctuadxKsUzYtgMnem6t+m5umy27SKpnQ7n0HuO6hBfz7o11rEUgkbG+Lkn4BFHKPXxEkxzBaW7m3y8vYUr0/YXlrwknW8bq1Jva8XXlqblu+pThNHsU4qg7Lkfyq7bU8vWhzTpZ1oDn2C7KsouvbaKuwDpFV0g+8vGIH33xicdv92vpGPnv/3IzG5SYV0g9/Y9U+FpUnbnpKpzO5GEd2yd7JQsayrWY/10ycU7D+mkSdpvHNWenKVRJK9pqdLX5nXQPXTJzTrg+iucUZ/4eFLFh/sP/kqdJN/DjNgQGX//oNvvvUkvSCTlv+munCQkk/8K+PlPLsW1vb7k9btp0FG3Zxz8w1XV52OFN+bCTGpyfOKXYYOdL5FyuX+91Jr6+jtLy6qCNYsjnhJ9u3oOPOIpv38vH5Gyktr+aRueVtZVV1DcxcVcENj73ZVva9p5fyhzkbsow0e63vZ0iPz3IqMkm/Yk8DD81e37YB/3nRZjZX52ZcdyqF2JC27N7PU6Xpd9Tu3tf5UWp8Snls/sa2NnWINZG8s2NPcb4gSV4z3YOpbA669h9obh9C25mW6dvb0NRuHHz13gM0NDUfUq+2vrFtlFI6sWbyGTyRo+GnhfzYl2zazazVFakrdlHIDsbzKjJJH+CW51cwu2wnTc0tfOepJVwzcW7Sut3tRKnP/W4u33t6aad14tfoU/elf4T/g78s42t/WtR2/4q7X+fSX72eaYh5leo725Ud1F3JOo8zSBTvv+Ul3vvjl9run3PrDL784IJD6r3vxy9x0R2vpFxeNknqzY27M39SkY299+9c//DC1BVzJJff+rBmkB6f9Dt+2ePPoq3Yk3qMcy6OAArRoVOZYQfuup2ZdVhVxbVft44GKoZkO+NUn1Pr87L5OPfUtz8/oPXzzGRZjc2Hxh3flh0v3fHyUJjEcsjm2wPbQPJ5oB+2HxGRm3vH3dPqWMnldp3uovL9XUq21iu31VJWUce2mv0YxiffdzzHHXlYyuX97rXCT9Gb7D1Kt407JzvxtmXl9+scpSaHQnl1dQX1jc1U7Gngug+MPOTxsI64yaXoJX0OJr90Pt5czJCX6XZU6C97xxPKnizdxEvfurh9pQTrEKZT8Qv5nrW16YcgKRcjSXXntPiVuKaii0eXMHLoACA/O/Cw7j96fPNOR+5xw906+VDy0ba3dPNuxv52NvWNzcxZu5PP3D+Hpub0Jm2bv66K55duTfp4LuNNNBQxLNtv8iP97J6XjicWbuJ7cUMDW7Jo3sm1Qg4DzGXrTpj6ypoSnK2Wj+jCcHAQr0cn/cbmFr7/5847N1PJZZv+T/62giWba1i+pYZvP7mEhRuqqaxLr338c5Pm8fXHFqeu2FkcOa4XJqmSYFuTTJap+qm4k4Da3p8QfJuLM4Aq81cNwVt1iPiY2n79d8eNP0Mpk76ZPWRmFWa2PK7saDObYWZrgv9DgnIzs3vMrMzMlprZuXHPGRfUX2Nm4/KzOu29saaS19+p7FAajk81nV8bGS0vy8cSSdRkEJa2zmw7cg9WzEEMWQzZzEZnO6jOpmjIt5BsCjl18PvYA1eug3SO9P8AXN6h7EZgpruPBmYG9wGuAEYHfxOAiRDbSQA3AxcA5wM3t+4oCi3WvFPgjtwOy3Kgl7XOFZP/jSzTV+iOm31hr04UNO8U8ei1sH0YudsiwpRTe8W9iWG7ulU+pUz67v460HFs2VhgcnB7MnB1XPkjHjMPGGxmxwOXATPcfZe7VwMzOHRHkjONzS389PkV1O4/dCreH/xlGdOWb097Wcm+XMu31LTdTjaVQas1FXtiy0rw2KLyav44r5ym5hZun7oyrZjmrq3ivFtn8IXfH5xYLZ3vUlXdAR6cvT7l6yT6Ym6o2sfehiY+cVfyidUa4/onlmzazYwViSc3a2nxdtdpbU4yE9ik19ceMoFd0jb9VEM2g+fdn4OLgnd2GbyNVfu4a8Y7BTtivG3qyrT7hdKxqHwXj84rZ/66Kh5fsJHqvQc45YcvtqtzX9x7OPLGF7h3Vhkjb3yhrSz+81yxtZYf/GUZ98yMTfY3a3Ulzy1J3jeVD+uTDE+OXV+5vF1ZbX0TD/99fcav8VTpJv66eAt3vLgqLzOu5lK2o3eGufu24PZ2YFhw+wQg/rTQzUFZsvK8+NuSrTwwe33CYYfV+xr5j0cXJXhWe6naLf/pNwdnVfz0xDmdXkJtSulmfnHN2QeX7dAr2N1+84m3ACgZ2K9du3FnPh8k+zlrq1LUbK+uoYlbn1+R0XPiTXx1LWs6uWjH1GXbGPv+2Mc6NrjoTKL3ZdHGaibGJY7ZZTv58Kklh9S7feoqetkq1v0s9eXpUnbkBp9npucnJFxWJ6N3vjp5IWUVdVxz7nBGHHNEl16nsx1Z60NlFXVMf3sHn3zf8Rkte9Lraxkz8mjOHdH+B/enO5yweOf0xCemdVZn1uoKrnxvLJ4r72k/Mmzltlr+3+OLuersd2UUb7amLtvG1/70Jg9cN4aPnzGs3WOt8/Z86cKT2m1AP/nbCq6/aFRGrxN/YuTFo4fywVOGZh90nnW5I9djhzQ527WZ2QQzKzWz0srKju3x6WntlW/MyRFQ7n72te84ar/cRCMJiiXZUWpziqPXpgQnICXS8Uiosyaujm9LGN6lzk70qm+MTa1QyOaXVJ9LIrdPXZXWWdmdXbgmmUI0Wabr7a2xX+SrthduFtRsPo9Cyjbp7wiabQj+t06OsQU4Ma7e8KAsWfkh3H2Su49x9zElJYce/aUjF9+3fH5usRPEcvt6Oe3ITVLeK0+JLBeL7ZUquDz00fRKkNkL9X0v1JDNbNYnjO3jqdYjl29nyHN+1kn/OaB1BM444Nm48uuCUTwXAjVBM9B04FIzGxJ04F4alOXFwSsOZe9Hf40NVsrlNVLjvwwdE0aYjo6STmoWgi9ztuP0cxpDJy/a+isp5U4oJL78YHYXtelMLhLob2auaddPkHUspJcLcvlpdXytsE2tnLJN38weBz4CDDWzzcRG4dwBTDGz8UA58Nmg+lTgSqAM2AdcD+Duu8zsVqD1dLhb3D1vFyFtfYvTuWDGN59YzODD+6a13K2797O9tp7+fQ7dV8Z3CKVzWcKOnUsdZ3JMpmPTy/x1VZx94uCkG/W9s8qyunBIou002zxWs7+RQf0PbmpvrGl/ycRcfCkaklxGMR86G7LZ0vYrIL1lrd+5l1HBWaEdvbVpd9vt196p5JRjB1Lf2Myg/n3YujvxxWQy1fGz6Cib9/X7Ty9l+ZYa6hqSNw3915Nvtc3nVLmngW9PeYu+vQ5+r34545129V9ZtYOjDu/H0QP68cqqCq7/4Mh2jy8qr+a04wYxsH/7lNbY0tL2+P0FmjYk3U78uoYm6hub6W3G4CP6FmznkDLpu/vnkzx0SYK6DtyQZDkPAQ9lFF2WMnnv4ufQT+WDncx+2PrLAEh4Qlj8l3TmqkOnir0x7opPnYXf8UpBn5s0j8+fPyJp/XQ64jpKtslms1HuqW/k7J+8xISLT24r++2srly2MXF0qeZgz+0Z1q1DNg99Pw6erZvee/XR/3s16SCAh/++oe32uIcOnZEzrOoamtqN8EnkLx2m8Hjmzc6n9PjqH9pfxjB+QEJtfSOfnjiHD59awuSvnt+u3u9eWwfEdpqvHXLOzkG5TLjpbmtn3XywseMnV53JuA47snzpkWfkhuzXFBCb777VtprUs3smsynB5QLX7NiT9fISSXakks37WhvMUPm3Ag/T6yinQyiTHOlX7Klnb0P6c+HHa2puyfoqbTm5uls31tp53tppW3RZbGqFuGZAqx6Z9EMrXx2hlt82yVaJOi5TaW3myFWfRRi6PtqmdOjwdpx/20z2Hshu9M4dL67iH38xK6t47py+mvKq3F3btfvK/luQ2+9P5htpNt+tbPXIpB+GxJA3CVbOzHLbfJFkYdm06R888zh5nRD+MOtU23z6RtuRfUeZdnr/PcNzLjrK9HoKPUoONv5ij94pZL+/kn4B5etzzfUGk3x+m/QmNWv/nOCxHH0m2S4ml5tES1vzjnHmzYkHoWX6mXT1Mwxjk2ahJPvllYlcjkzL7pSbwn2APXI+/TDm/Dunr87qiksdJUysBdpgsvlStR7pd9amXoiElY+L4nQWd1OLc/3DC9iyez8//uczUy6z48/7uRke+YdtWGAhFWoCvFz56+It7aZxAR3pd1kYZ8pbsH4XCzfE5ujpyvcz0ar1yvGnmGyYXqp2x+8+teSQsdWZTiz35MKNjLzxBXbUJu7szvVnW5HkdTqTTqf08i01zFpdyTs76vjCA6nHwnd8a7/ycGajdQrZJhxWuX4L7ns1NqdQsia8ZFJto9968i0emN1+fp9Cfnw9M+kXO4ACy/WRfi7b9Fufku5P3j8vig3dSzZJVvxiSjekf6pH/PPip4FI9jrJLN28u+12Lo+uu7qsB2evb5vvSGK6MvFZfWMzj86NnXtTvS+z81yyedXWc2mamlvyftDaM5N+CI/043WteefQdZtdtpMDOT45KdFbmNUp+WmM3onfaSWa1ybZl/ea++cmLE8lfsK9TPPCVb89mFg7nfqii236mT7/b0u2siTuZK4oif9OjLzxBX4VnNg1Ls1fS80tfsj36vT/mcaetuG3mX0Y2eSfhRuq2V5Tzyk/fJHHcjgLQCI9NOkXO4LccHeeKt3U7szG37+e+bSv2fjZi4dOv/yzF1clqNm5m4KTzlJ9JgeaWnh8wcaDnaRxX7Qzbp7Wdjvbzzb+i/hS3JTPXRlK2lkuyHSxap7putaDh7tnruGC219OebZxq3f/YCo/m3rotr0nOMck47mrstykNgTDbp9dnN9zWnpkR26IJqxMKN3wSsur+d7TS3lh2ba2sgM5nDu9M79/Izc7lxeDaxekOvr5zStr+M0rB8/Ujc+B9Y1dX+dkr55sLv905LJZreP7k4t1jopEm9aO2syGsP6xw7z68Qq1P259mXxfR7iHJv1wZ/1UUz+0HuXWBUcar67OborpYknU8bW3k7mFHltQztRl7S9s85kkTTevvVOZ9kRctz6/gv/48LsZdFgfdidpl11bWUfN/kbOfNeRaS0z3vqdya8t8MzizqcV6OjNjbszfn2JaT1C3p5Fp3w6/uvJt5i37mD/Uev2d98Xz6W+sfmQefrnr9/F5Wcdl3BZnU1V3TZRZJ7Tl4W5/XvMmDFeWlqaumIHf5xXzv/EzYXT3Rw9oB9v/s8nmLlyB+MnZ77+El6t8+zkYgZJifneZadlNcdUoZw2bBBjRg7hxitO5/tPL2379ZvM6ccNYtq3Lu7Sa5rZIncfk+ixHnmk390b9fcdaGLfgaaMr4wl4adkn3vbanIz42i+rN6xh9U79vCn+el10K7antu5tDrqmR25xQ6gi+obWzjjf6fz4OzCdNqKdGePzsvvaJdi+NR9+Rt+2yOTftgvTCwi0pk3N+5O2g/VVT0y6Svli0h39/5bZuRluT0z6Svri4gk1COT/hH9ehc7BBGRUOqRSf+kYxJfc1REJOp6ZNLvOG2piIjE9Mik39CU/OxPEZEoK3jSN7PLzWy1mZWZ2Y35eI1ehbwigYhIN1LQpG9mvYF7gSuAM4DPm9kZuX6dQYf1zfUiRUR6hEIf6Z8PlLn7Onc/ADwBjC1wDCIikVXopH8CsCnu/uagLKeymTFRuq+vf/SUYocgRVb6o4/ndfkXnXJMXpdfSKGbcM3MJgATAEaMGJHVMs4dMYQ3/+cTAPTtbQw6rC/fnvIWV551PPsbmxnQvzd7G5rZUVvP2ScOZsmm3Vx+1nG8/s5OVm6rZe66Kq45bzj/fPa7OGZAP/YfaGbIgH4sKq+mvrGZYUf2Z099Ez99YSVPTLiQvr178cjcDazctodLTj+Wh+es51PnDOe04waxdfd+Tj/uSAYd1ofP/G4u/fv04oeffA+1+5uoa2ji8rOOo3Z/I4s37uaS9xzLvHVVnDx0IBffOSvhupUM6s9Prz6LX0xbxTXnncj2mv2cMORwzh91DEs376a8ah/nnTSEqroGqvfFpgx+Y81OBh/RlzUVddx+9XtZvrWGmv2NrN+5lwXrd/HlC0/io6cfy4U/m8mlZwzjhWXbGNCvDx87/ViufO/x9O1tjBo6gA1V+ygZ2J85a3dy0SlDWbalhmFHHsbwIYcDsP9AM1V7G3h3yUD21DdhBof17c26yr2cP+rotnXYU9/I715bx/S3t/PhU0sYVTKAC0YdQ/8+vTj2yP7sbWhmQ9Vehg85nJdXVPDx9xxLbX0TFbX1nPmuo9i5t4F+vXvR0NTMUYf3o2RQf/7t4pM5ol9vKvY00NTcwjED+7Nh515Wb99DyaD+HHtkfyr3NHDhycfQN3huU7Ozo7ae4486nDUVexh8eD8cZ/AR/dhZ18DKbbUMHdif2v2NNDY7v3r5Hc5615HMXFnRdlWlZD5z3nD+9R9PpqmlhfrGZo476nAG9u+De2z5VXUN9O/bm8P79uZAUwurttdyy/MrOOfEIexvbKa2vpFTjx3EuScNbltmRW0DTy7cxIINuzh/1NGs2lbLNeedyLAj+3P9RaN4a9NuNu3ax+hhA9td4Ssbf/rXC7jolKFt919esYMj+vVmUXk1HzntWO5/bS1ba/azOG5K6NOPG8SW6v3saWjiiH69OXpAP1panK999BR+9NflfOiUoazfuZd/PvtdnHbcQLburueD7z6G+sYWRg0dQMmg/uw70MTijbsp37WPy84YRv8+vRl4WB96B/1022r28+i8ck46ZgC1+xs50NxCv969GDqwP7/9wjl8/bHFDOrfh5e+fTG79h5g2+56ZpftpHcv41/OOYHRwwayYmstzy3ZyvgPjWLwEf0Y2L8Pjc0t7G9s5sjD+rJ8Sw0jjjmC8p37eHReORecfDSfOnc4ALX1jRzWpzf9+vRiT30je+qb2FFbz8klA6nd38jh/XrT1Oys3rGHl97e3jbJ2hcvGMFXPzSKgf370KeXsal6P/PXVXHqcYOYtmw7o4cN5KcvxC5cdMqxA7n2H07kCxdkl/9SKejUymb2AeDH7n5ZcP8mAHf/WaL62U6tLCISZZ1NrVzo5p2FwGgzG2Vm/YBrgecKHIOISGQVtHnH3ZvM7OvAdKA38JC7v13IGEREoqzgbfruPhWYWujXFRGRHnpGroiIJKakLyISIUr6IiIRoqQvIhIhSvoiIhFS0JOzMmVmlUB5FxYxFNiZo3ByRTGlL4xxKab0hTGuqMR0kruXJHog1Em/q8ysNNlZacWimNIXxrgUU/rCGJdiUvOOiEikKOmLiERIT0/6k4odQAKKKX1hjEsxpS+McUU+ph7dpi8iIu319CN9ERGJ0+2TfqoLrZtZfzN7Mnh8vpmNDElc3zazFWa21MxmmtlJxY4prt6nzczNLO8jCtKJycw+G7xXb5vZY/mOKZ24zGyEmc0ys8XBZ3hlnuN5yMwqzGx5ksfNzO4J4l1qZufmM54M4vpiEM8yM5tjZmcXO6a4ev9gZk1mdk0YYjKzj5jZW8F2/lregnH3bvtHbHrmtcDJQD9gCXBGhzpfA+4Pbl8LPBmSuD4KHBHc/s98x5VOTEG9QcDrwDxgTLFjAkYDi4Ehwf1jQ/L5TQL+M7h9BrAhzzFdDJwLLE/y+JXAi4ABFwLz8/0+pRnXB+M+uysKEVeqmOI+41eIzfh7TbFjAgYDK4ARwf28befd/Ug/nQutjwUmB7efBi4xMyt2XO4+y933BXfnAcOLHVPgVuDnQH2e40k3pn8D7nX3agB3rwhJXA60Xoz5KGBrPgNy99eBXZ1UGQs84jHzgMFmdnw+Y0onLnef0/rZUZjtPJ33CuAbwJ+BQmxP6cT0BeAZd98Y1M9bXN096adzofW2Ou7eBNQA+b7KcaYXgB9P7Cgtn1LGFDQJnOjuL+Q5lrRjAk4FTjWzv5vZPDO7PCRx/Rj4kpltJna0+I0CxNWZTLe5YijEdp6SmZ0A/AswsdixxDkVGGJmr5rZIjO7Ll8vFLoLo0eNmX0JGAN8uMhx9ALuAr5SzDgS6EOsiecjxI4SXzez97r77mIGBXwe+IO7/zK49vMfzewsd28pclyhZGYfJZb0P1TsWIBfA//t7i35/9Gftj7AecAlwOHAXDOb5+7v5OOFurMtwIlx94cHZYnqbDazPsR+ileFIC7M7OPAD4EPu3tDkWMaBJwFvBp8EY4DnjOzq9w9X1enT+d92kysHbgRWG9m7xDbCSzMU0zpxjUeuBzA3eea2WHE5lApSHNBAmltc8VgZu8DHgCucPd8f/fSMQZ4ItjOhwJXmlmTu/+1iDFtBqrcfS+w18xeB84Gcp70897Rk+fOkT7AOmAUBzvczuxQ5wbad+ROCUlc5xDrLBwdlveqQ/1XyX9Hbjrv0+XA5OD2UGJNGMeEIK4Xga8Et99DrE3f8hzXSJJ3BH6S9h25CwqxXaUR1wigDPhgoeJJFVOHen+gAB25abxP7wFmBtveEcBy4Kx8xNGtj/Q9yYXWzewWoNTdnwMeJPbTu4xYR8q1IYnrTmAg8FRwxLHR3a8qckwFlWZM04FLzWwF0Ax8z/N8tJhmXN8Bfm9m/0WsU/crHnx788HMHifWxDU06Ee4GegbxHs/sX6FK4kl2H3A9fmKJcO4/pdYH9p9wXbe5HmeXCyNmAouVUzuvtLMpgFLgRbgAXfvdMhp1rHkcTsVEZGQ6e6jd0REJANK+iIiEaKkLyISIUr6IiIRoqQvIhIS6U4WF1c/48kINXpHRCQkzOxioI7YPEpnpag7GpgCfMzdq83sWE9jzh4d6YuIhIQnmJjNzN5tZtOCOXneMLPTg4eymoxQSV9EJNwmAd9w9/OA7wL3BeVZTUbYrc/IFRHpycxsILFrErSeuQ/QP/if1WSESvoiIuHVC9jt7u9P8FhWkxGqeUdEJKTcvZZYQv8MtF0Ws/WSk38ldpSPmQ0l1tyzLtUylfRFREIimJhtLnCamW02s/HAF4HxZrYEeJuDV3GbDlQFkxHOIs3JCDVkU0QkQnSkLyISIUr6IiIRoqQvIhIhSvoiIhGipC8iEiFK+iIiEaKkLyISIUr6IiIR8v8Bhwm8q0Q0/foAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(user_click_merge['words_count'].values)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户点击的新闻类型的偏好\n",
- "\n",
- "此特征可以用于度量用户的兴趣是否广泛。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图中可以看出有一小部分用户阅读类型是极其广泛的，大部分人都处在20个新闻类型以下。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " category_id \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 250000.000000 \n",
- " 250000.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 124999.500000 \n",
- " 4.573188 \n",
- " \n",
- " \n",
- " std \n",
- " 72168.927986 \n",
- " 4.419800 \n",
- " \n",
- " \n",
- " min \n",
- " 0.000000 \n",
- " 1.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 62499.750000 \n",
- " 2.000000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 124999.500000 \n",
- " 3.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 187499.250000 \n",
- " 6.000000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 95.000000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id category_id\n",
- "count 250000.000000 250000.000000\n",
- "mean 124999.500000 4.573188\n",
- "std 72168.927986 4.419800\n",
- "min 0.000000 1.000000\n",
- "25% 62499.750000 2.000000\n",
- "50% 124999.500000 3.000000\n",
- "75% 187499.250000 6.000000\n",
- "max 249999.000000 95.000000"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "user_click_merge.groupby('user_id')['category_id'].nunique().reset_index().describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户查看文章的长度的分布\n",
- "\n",
- "通过统计不同用户点击新闻的平均字数，这个可以反映用户是对长文更感兴趣还是对短文更感兴趣。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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CcJc7Yma2gJ1WCEh6UdLPJO2SNJRq50jaIWlvel6R6pJ0r6RhSbslXTYXA5jOy28e78RuzMzOSHNxJPCvI2JjRAym+TuBRyNiA/Bomge4HtiQHpuB++Zg302tO2cRAEf9zWJmZk3Nx+mgG4EH0vQDwIcz9a9F3RPAckmr52H/AKxZXg+Bn+x7fb52YWZ2xjvdEAjgB5KelrQ51VZFxME0/WtgVZpeA7yUWXd/qs2LSrlEpSTSpQEzM5tC5TTXvzIiDkg6D9gh6efZhRERkmZ0e04Kk80A559//ml17h3LF3F8bPy0tmFmlmendSQQEQfS8yHge8DlwMuN0zzp+VBqfgBYl1l9bapN3uaWiBiMiMGBgYHT6R79PSWOj/n2IDOzZmYdApIWS1ramAauAZ4BtgGbUrNNwPfT9Dbg1nSX0BXAG5nTRvNiWX8Ph39zYj53YWZ2Rjud00GrgO+pftK9AvxtRPwfSTuBhyTdBuwDPprabwduAIaBo8DHTmPfbfmd1cv4zk/2c6Jao7eS27dEmJnN2qxDICJeAC6Zov4qcPUU9QBun+3+ZuN3N6zk60/sY+eLh/nARSs7uWszszNCrv97fOWGlZRL4okXXu12V8zMFqRch8BZvRV+e9VSdr54uNtdMTNbkHIdAgBXvfs8dr74mj8+wsxsCrkPgZveu5bxWvD1f9zX7a6YmS04uQ+B9SsX82/+5Wq2/MMLDB96q9vdMTNbUHIfAgB3//57WNJX4Y//508YeWu0290xM1swChECA0v7+OtbLmXf4aNc9dkf8sUfDvOrV492u1tmZl2nWMDfvDU4OBhDQ0Nztr3hQ0f49MPP8qN/HgHgwoHFvP/Cc3n36mWcvaiHpf0V1q04i7UrFtHfU56z/ZqZdZKkpzMf79+6bZFCoOGFkSM89vND/MPeV3h632scGX37dw6cu7iXlUv6WNpfYflZvSxbVGFZfw+Lesss6ilzVm+ZJX0VlvRXWNJXYWl/D4v7yizurdDfUz7ZrvFdx2ZmnTKTEDjdTxE9I104sIQLB5bw73/3Qmq1YOTIKG8dr/LGsRP86vBRXjp8jINvHOfVVN//2lHeOljlzeNjHB8bZ2y8/eDsq5Q4q7dMf0/90Vcppen0XCnT11Oir1Kit1Kitzx5vkTPyYforZSolOrTPeUSlfTcU1aqp+n0Udo95RLlkqiURCW1acyXHFBmhVfIEMgqlcSqZf2sWlaff+9vnTPtOtXxGkfHxjlyvMpbx6scGR3jzeNVjp0Y58holdGxcY6NjXP0xDjHTtSfj4+NM1qtcWwsTY/VePXICU5Uaxyv1udPjNcYq9YYrdan55tEPRxKpZMhUU7T5ZPzSvOZegqQxnx50nRJp6bL2elJy+rT9e+DLqW2pcx6pZIoiYnrnWzDhJo0se3J7YmT2yypvs7JR4mJ20zbO9WXbHtSfxr7y+xLIH9xhZ2hCh8Cs1Epl1hWLrGsv2fe9jFeC8bGa+lRnz6RwqE6fmpZtRaMVWuM1YJqpn21Vn8erwXVtCw7Xa3FlPP1dWqM12C8NrFd47lWa2y/xvGxqZePR32+8ajFqTaN5bUaJ9ud6aQUZo1wyQRaI0jKU9ZPBY0a06mdJi0vlUBkg2piQE4IsmwoZuqNfk4Mu8Zy3raNcunt25vwyLZJ7Zr9Z6F+FMrb/qPRODrtKZ9q52DtHIfAAlX/h1UuzAXqbHDUGs816tMxMViy4VHLPEcwYb4WnAydWnByOxOWTdrOqel6+0jbPbXNU+tGaneqfmq+sa2IRr/JhF96Tv2NODXWWqZeC+rLJvw8aoxWg/FI28v2PSYG7FRji5jqZ9ftV39q2VCYfEpz8inPSln0lEr0VDLLS/V674TTphNPtfZW6vM9lfqy3kybvp5sm/LJtn09pXRdsJKLa34OAVsQSiVRQhQk8xaUmCIUTwbKFKHSqGWP9Bq1RjBWa6eOPGsnjzhPbXcsc2TaOOqtZrbV7Gi1Oh6M1epHw9mj3caR8fGxGtXx6sR67dTR82j11BH1XNwT05uu+S3urZy8UWRZf4UVZ/WyuK/C4r4KS/srLFvUw/JFPZy7pJd3nL2I1cv76assjF92h4BZwUmnTi0VRaTTkyeqtQmnWsfG67UT1Rqj1fH03HiMn5w+nq71HT1R5eiJcX5zosrR0XHeGh1j5Mgoew8d4TejVX4zOj7l9T0Jzlvax4Url/A7q5dxybqzufKilZy7pK/jPwuHgJkVjqSTd9jNt+Nj47x5bIzXj43xylujHHj9GAdeP8b+146x9+W3+Nun9rH1/9aQ4IPvGuA/Xb2BS89fMe/9anAImJnNo8bt4ect6+ddq5a+bfl4Ldi9/3Uefe4QD+78FTd96R/5H/92I79/yTs60r9CfGyEmdlCVS6JS89fwSev/W0e/+QHuWTt2fyXb+/m4BvHOrJ/h4CZ2QKxtL+Hv7rpEo6NjfO/dx/syD4dAmZmC8hF5y1hcW+Z//d6Z74Iq+MhIOk6Sc9LGpZ0Z6f3b2a20K1c2scrRzrzsfcdDQFJZeALwPXAxcAtki7uZB/MzBa6pf2VKT/Ycj50+kjgcmA4Il6IiBPAg8CNHe6DmdmCtqSvwpHjnQmBTt8iugZ4KTO/H3hftoGkzcBmgPPPP79zPTMzWyCuvGglx8bGO7KvBfc+gYjYAmyB+vcJdLk7ZmYdd8dVGzq2r06fDjoArMvMr001MzPrgk6HwE5gg6QLJPUCNwPbOtwHMzNLOno6KCKqku4AHgHKwNaI2NPJPpiZ2SkdvyYQEduB7Z3er5mZvZ3fMWxmVmAOATOzAnMImJkVmEPAzKzAFHPxRZvzRNIIsO80NrESeGWOunOmKNqYizZe8JiL4nTG/FsRMdBOwwUdAqdL0lBEDHa7H51UtDEXbbzgMRdFp8bs00FmZgXmEDAzK7C8h8CWbnegC4o25qKNFzzmoujImHN9TcDMzFrL+5GAmZm14BAwMyuwXIZAHr7MXtKLkn4maZekoVQ7R9IOSXvT84pUl6R703h3S7oss51Nqf1eSZsy9fem7Q+nddWFMW6VdEjSM5navI+x2T66OOZPSTqQXutdkm7ILLsr9f95Sddm6lP+jqePaX8y1b+VPrIdSX1pfjgtX9+h8a6T9LikZyXtkfTxVM/t69xizAvzdY6IXD2of0T1L4ALgV7gp8DF3e7XLMbxIrByUu2vgDvT9J3AX6bpG4C/AwRcATyZ6ucAL6TnFWl6RVr2VGqrtO71XRjj7wGXAc90cozN9tHFMX8K+OQUbS9Ov799wAXp97rc6ncceAi4OU1/CfjjNP0fgS+l6ZuBb3VovKuBy9L0UuCf07hy+zq3GPOCfJ07+o++Qy/A+4FHMvN3AXd1u1+zGMeLvD0EngdWZ37Rnk/TXwZumdwOuAX4cqb+5VRbDfw8U5/QrsPjXM/EP4jzPsZm++jimJv9cZjwu0v9ezje3+x3PP0RfAWopPrJdo1103QltVMXXu/vAx8qwus8xZgX5Oucx9NBU32Z/Zou9eV0BPADSU9L2pxqqyLiYJr+NbAqTTcbc6v6/inqC0EnxthsH910Rzr9sTVz2mKmYz4XeD0iqpPqE7aVlr+R2ndMOjVxKfAkBXmdJ40ZFuDrnMcQyIsrI+Iy4Hrgdkm/l10Y9ajP9f29nRjjAvk53ge8E9gIHAQ+29XezANJS4DvAJ+IiDezy/L6Ok8x5gX5OucxBHLxZfYRcSA9HwK+B1wOvCxpNUB6PpSaNxtzq/raKeoLQSfG2GwfXRERL0fEeETUgK9Qf61h5mN+FVguqTKpPmFbafnZqf28k9RD/Y/hNyLiu6mc69d5qjEv1Nc5jyFwxn+ZvaTFkpY2poFrgGeoj6NxV8Qm6ucaSfVb050VVwBvpMPgR4BrJK1Ih57XUD93eBB4U9IV6U6KWzPb6rZOjLHZPrqi8Ycq+Qj11xrq/bw53fFxAbCB+kXQKX/H0/92HwduSutP/vk1xnwT8FhqP6/Sz/5+4LmI+FxmUW5f52ZjXrCvczculHTgQswN1K/I/wL4s273Zxb9v5D6nQA/BfY0xkD93N6jwF7g74FzUl3AF9J4fwYMZrb174Dh9PhYpj6Yfgl/Afw13blI+E3qh8Vj1M9r3taJMTbbRxfH/PU0pt3pH/HqTPs/S/1/nswdXM1+x9PvzlPpZ/G/gL5U70/zw2n5hR0a75XUT8PsBnalxw15fp1bjHlBvs7+2AgzswLL4+kgMzNrk0PAzKzAHAJmZgXmEDAzKzCHgJlZgTkEzMwKzCFgZlZg/x8GJdlC0PTKeQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图中可以发现有一小部分人看的文章平均词数非常高，也有一小部分人看的平均文章次数非常低。\n",
- "\n",
- "大多数人偏好于阅读字数在200-400字之间的新闻。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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Ov8sRETkjNh+OTpubm93GjRv9LuOw3uFx3vKVR1m5IMPPPnoZRTDYR0RCyMyedc41H2+ZrlA9jsqSBLdevYbn9vbyr7olgYgUIYX7Cby3uYnltaV88V+26uSqiBQdhfsJxKMR/vq6tezuGua+jbqwSUSKi8L9JN56dh3NS6v42q+3s/2ALmwSkeKhcD8JM+OL715HNue46a4NdA6O+V2SiMiUKNxP4ZyF5fz4I2+kY3CMz9z/vN/liIhMicJ9Ci5cUsWnrlzNIy+184cdGvsuIvOfwn2Kbn7TchZVpPhvP99CNpf3uxwRkZNSuE9RKh7lC9evY2fHEHc+scvvckRETkrhfhrevmYBV66t56u/3s6GXd1+lyMickIK99NgZnzxhnVUpuPcdNcG/tCi/ncRmZ8U7qepvjzFD29+IxGDD9y1gef29vhdkojIMRTuZ2DtonL+3yffwsLyFDfdtYGXD+oCJxGZXxTuZ2hxdQl3/ceLyDt413ef4AdP6iSriMwfCvdpOHthGT/72GWUJmJ84ZdbueWejbz4ap/fZYmIKNyna3V9GU9/7go++taV/GbrQa799hP8ZIOewSoi/lK4z4B4NMJnrzqHB//yTSyvLeXWn73AF375Irm8/w9CEZFwUrjPoHWNFTz0iTfzjrX1/ODJ3dx89x8ZGde94EVk7incZ1gqHuV7H3gDf7n+LB7b3sG7/+5Jfv7cfvI6iheROaRwnwVmxl+942y+++8uYG/3MJ+8dxO3/PBZPdFJROaMwn0WXfe6RWz5m3dy40WL+e22g1z3nSfY1z3sd1kiEgIK91kWiRj/69++jv9+3Vr2dA1xxTce5+u/2a6jeBGZVQr3OXLzm5bz0CfewkXLqvjOIy28+SuP8otN+3FOffEiMvMU7nPorAUZfvjhN/LV97wOAz7xk0185J6N6qoRkRmncJ9jkYjx3ubFPPHZ9Xz8bSt55KV21n/9Mb73+A4GRrN+lyciAaFw90kiFuHT7zyHn3/8cs5rrOB/PvQSb/vaY9z3x31+lyYiAWDzoc+3ubnZbdy40e8yfPX4yx18+aGX2NrWTyIW4YpzFvDuCxp5+5p6IhHzuzwRmYfM7FnnXPPxlsXmuhg5vj9bXcflK2u4d+M+HtnWzqPb23loywFW12e45rwGVtZliEaMiBnRiBGLGJGIETUjHjXOX1JJMhb1+2OIyDyhI/d5anwiz70b9/Gjp/awfQr3i88kYzRUpHhvcxMfuGQZ6YSCXiToTnbkrnCf55xz7OseYWwiR845cnlHPo83nSeXh92dQ2xu7WXTvl5efLWfTDLGu85fxJ83L+b1TRWYqVtHJIgU7iHyh5ZO7npyF4+/3EE251hcneaqcxfy7guaWLuo3O/yRGQGKdxD6GD/KL/c/CoPbz3IM7u6AVhRW8p1r1/EO9bWs7q+jERMg6VEipnCPeQO9BWC/qd/amX7wQGcg+rSBG89u46r1zVw0bIqKksSfpcpIqdpWuFuZouBe4B6wAF3OOe+ZWZ/A3wE6PBW/Zxz7lfeNrcBNwM54L865359sp+hcJ87e7uGeWpnJ7/d1s5j29vJ5hzJWIRV9RkuX1lLRUmc5qXVvGFpFVENwRSZ16Yb7g1Ag3PuT2ZWBjwL3AD8OTDonPvaUeuvBf4ZuBhYBPwWWO2cO+GdshTu/ugaHGPTvl5+93IHT+7ooqV98PCykkSUt52zgDefVUvzsipW1mV0YlZknpnWOHfnXBvQ5k0PmNk2oPEkm1wP/MQ5NwbsMrMWCkH/1GlXLrOqJpPkijX1XLGmHoB83tE7kuXhrQd4/OUOnmzp5F+ebwOgvjxJVUmCRCxCPBqhPBXj8rNqWVVfxiUrqjXGXmSeOa2LmMxsGXAB8AxwOfBfzOyDwEbgr5xzPRSC/+lJm7VynC8DM7sFuAVgyZIlZ1K7zLBIxKguTfC+i5bwvouWkM87nt/fxx93dbOptZfsRJ5sLk8259h+YIBHt3cc3nZNQzlvOquGt6yu49xFFVSXqg9fxE9TPqFqZhngceBLzrmfmVk90EmhH/5/UOi6+bCZfRd42jn3I2+7O4GHnHP3n+i91S1TfPJ5x8GBUTbv6+O5fT387uVOWtoHyOYK/z+tayynMp0gGYuQSkS5eFk1y2tLWdeo4BeZKdO+/YCZxYGfAj92zv0MwDl3cNLy/w086M3uBxZP2rzJa5MAiUSMhoo0DRVprlq3kNuuhqGxCX7/Sgcv7O9j075eRrI5ekfGae8fO9y9E4sYNZkE5ak4l66sYdWCDJeurGV5balO4IrMoFOGuxXOot0JbHPOfWNSe4PXHw/wbmCLN/0A8E9m9g0KJ1RXARtmtGqZl0qTMa5a18BV6xqOaM/lHS3tg3QNjvHUzi7a+8fY3zvCD5/ew6E/HONR4w1Lq6hMJwr30IkYUYOIvXYPnUjEiEagPBXnbecsYGVdRn8FiJzAVI7cLwc+ALxgZpu8ts8B7zez8yl0y+wG/jOAc+5FM7sP2ApMAB8/2UgZCb5oxDh7YRlQxmVn1R5un8jl2ds9zNM7u2lpH+TpnV10D40XbrHg8P515PPOu91C4XYM3cPj/N1jO4gYrKjLUJqMUV0S55IVNTRWpWleWs3CipR/H1hkHtBFTFJ02vtHebGtn6d2dLG3a5iRbI4dHYO09owcXicWMZKxCKXJGOcvrmRRZZo3LK3ivMYKKtJxqnTELwGgK1Ql8JxzdA6Oc6BvlCdaOhkcyzKazdM1OMbm1j7a+kYYzeYPr7+kuoSVdaUsr81w4dJKqkoSrKrPsKBMR/xSPHQ/dwk8M6OuLEldWZLzmiqOWZ7N5Xl6ZxcdA2Mc7B9j875e9vUM8+SOLu56ctfh9WpKE5QmYywoS9K8rJrSRJRkPEIyFmVpTQnnLqqgLBUjGYvooi6Z1xTuEgrxaIQ3r6o7pr1/NEtr9wg9w+Ns2tfL/t4RRsZzvHxwgO/9bgcn+sO2Ih2neWkVNZkETVUlrG0op7YsSWU6zpLqEj09S3yncJdQK0/FWbsoDsDlk072QqGrZyLvGJ/IMzQ+wba2AXZ3DjE4NsGOjkFeaO3jxVf7OdA/esR2VSVx6stTlCZjNFWlWdtQTnk6TioeIZOMc15jBfXlSR35y6xSuIucgHmPMIxHI15XTYo/W33s0X/fSJbtBwYYHMtysH+MZ/f00DeSZXB0gqd2dPGLTa8e570pnPBNxFhZl2H1wgyliRhlqRiNVWkWlKVIJ6I0edMip0vhLjJNFek4Fy+vPjz//otfu52Gc46e4SxjEzlGs3l6h8cPh/9oNkf/yARbXu3jwefbGBnPMTaRP+b9V9dnWFiRpiwVo74sxaLKFMl4lGQsQmNlmtcvriST1K+yHEn/R4jMIjM76kKrUi5YUnXC9UezOfZ2D9M9NM7A6ATb2vp5bm8PPcNZWruHeaSvnZHssZeNVKTjZJIxMskYCytSNFSkSMQixCIR4jGjsTLN6voyFpQlKU3GqM0kdUVwwCncReaRVDzK6vqyw/NXrq0/Ynk+7xgcn2A0m2NorHDid0fHIAf6Rhkez9E/kmVfzwhb2/qZ8G7yNpLNkcsfeWY4EY2wqDLFqvoyMskYqXiEukySRZVpajJJKtJxmqrS1Jen9CVQpBTuIkUkEjHKU3HKU3Eog+W1pafcxjnH7q5h9nYP0zM0zsBoltbeEXZ1DLGrc4jRiRwj43m6hsaOGR1UliycA0jFo2SSMZbWlLC8tpSSROELoSIdZ2FFirPry4hF9djG+UThLhJwZsby2tJTfhFkc3kO9I3SO5ylc7Bw/58t+/voHhpnJJtjYHSCBza/ysDoxHG3T8YipBNRakoTLK/NsLAiSToeJR2PUluWZHFVCY1VaeLRiPdcAKOqJEFcXwqzQuEuIkDhWoDF1SUsrj7xOs4572RwvnDXz+Fx9nQNT/oLIMeBvlF2dw3xp709jGZzjGRzJ7xewAwWlqdYVJkmk4xRmoxSVZJgSXUJTVUlpOIRUvEo1aUJltaUUJJQZE2V9pSITJmZHfUw9ZOfIIbCDeC6h8Z5pX2A7qFxsrk84xOFV8fAGK09IxzoH6V3JEtrzzA9w1m6h8aP+17VpQnS8SgliULgN1amaaouIeENWS0MW43SUJFmYUXq8PqpeDR05w4U7iIyq6KR124NMVWdg2N0DY4zms0xms3RPjDG7s4hDg4UThwPjU3QM5Tl9y2ddAyMTek9y1Mx6stTrKgrJRGLkvC6h8pSMRaWp1hYkaKyJE4yFiERLdx2orIkTmW68HjJYqNwF5F5pzaTpDYztS8D5xy5vCObc4zn8vSPZGnrG6Wtb4S+kSwj4zmGxwtdSK09I+zuHGbc++thbCLPwGj2uNcXHGIGS6tLiEcjxKKFcwWxiBGLRKgqjVNXlqS6JEFFSeHJY4lYhKT3pVGRTtBUlaY2kyRizOlVyQp3ESlqZkYsasSikCZKRTrO4uqSKW9/6EKztr4R+kcmGM/lyU68dk6hrW+Uvd3DTOQKt6OYyOeZyDmyuTy7OofYsKub3pHsCc8rTFb4YogUvhyiRiYV46ZLl/Gf3rxiGnvg+BTuIhJqhy40m85TvXJ5x+DoBGO5HGPZPOO5PH0jWfpGsuzpHKJvZIJcPk82f+ivjDy5fOHk9Ol0V50OhbuIyDRFI0ZFSRyIH7vw7DkvB4DiO0sgIiKnpHAXEQkghbuISAAp3EVEAkjhLiISQAp3EZEAUriLiASQwl1EJIDMTeWa2dkuwqwD2HOGm9cCnTNYTlBovxxL++RY2ifHKqZ9stQ5d+xT25kn4T4dZrbROdfsdx3zjfbLsbRPjqV9cqyg7BN1y4iIBJDCXUQkgIIQ7nf4XcA8pf1yLO2TY2mfHCsQ+6To+9xFRORYQThyFxGRoyjcRUQCqKjD3cyuMrPtZtZiZrf6Xc9MM7O7zKzdzLZMaqs2s4fN7BXv3yqv3czs296+eN7MLpy0zU3e+q+Y2U2T2t9gZi9423zb5vIBj2fIzBab2aNmttXMXjSzT3jtod0vZpYysw1mttnbJ1/w2peb2TPe57jXzBJee9Kbb/GWL5v0Xrd57dvN7J2T2ovyd83Momb2nJk96M2HZ58454ryBUSBHcAKIAFsBtb6XdcMf8a3ABcCWya1fQW41Zu+FfiyN30N8BBgwCXAM157NbDT+7fKm67ylm3w1jVv26v9/sxT2CcNwIXedBnwMrA2zPvFqzPjTceBZ7z67wNu9Nr/AfioN/0x4B+86RuBe73ptd7vURJY7v1+RYv5dw34FPBPwIPefGj2STEfuV8MtDjndjrnxoGfANf7XNOMcs79Dug+qvl64G5v+m7ghknt97iCp4FKM2sA3gk87Jzrds71AA8DV3nLyp1zT7vC/8X3THqvecs51+ac+5M3PQBsAxoJ8X7xPtugN3voWW8OWA/c77UfvU8O7av7gSu8v06uB37inBtzzu0CWij8nhXl75qZNQHXAt/35o0Q7ZNiDvdGYN+k+VavLejqnXNt3vQBoN6bPtH+OFl763Hai4b3p/MFFI5UQ71fvO6HTUA7hS+qHUCvc27CW2Xy5zj82b3lfUANp7+v5rtvAp8B8t58DSHaJ8Uc7qHnHVmGciyrmWWAnwKfdM71T14Wxv3inMs5584HmigcVZ7jb0X+MrPrgHbn3LN+1+KXYg73/cDiSfNNXlvQHfS6DvD+bffaT7Q/TtbedJz2ec/M4hSC/cfOuZ95zaHfLwDOuV7gUeBSCl1QMW/R5M9x+LN7yyuALk5/X81nlwPvMrPdFLpM1gPfIkz7xO9O/zN9ATEKJ8GW89oJjXP9rmsWPucyjjyh+lWOPHH4FW/6Wo48cbjBa68GdlE4aVjlTVd7y44+cXiN3593CvvDKPSDf/Oo9tDuF6AOqPSm08DvgeuA/8ORJw8/5k1/nCNPHt7nTZ/LkScPd1I4cVjUv2vAW3nthGpo9onvBUzzP9o1FEZL7AA+73c9s/D5/hloA7IU+vRuptAP+K/AK8BvJwWSAX/r7YsXgOZJ7/NhCieCWoAPTWpvBrZ423wX74rl+fwC3kShy+V5YJP3uibM+wV4HfCct0+2AH/tta+g8EXV4oVa0mtPefMt3vIVk97r897n3s6kUULF/Lt2VLiHZp/o9gMiIgFUzH3uIiJyAgp3EZEAUriLiASQwl1EJIAU7iIiAaRwFxEJIIW7iEgA/X/oUOvlK5thTAAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#挑出大多数人的区间仔细看看\n",
- "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "可以发现大多数人都是看250字以下的文章"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " count \n",
- " 250000.000000 \n",
- " 250000.000000 \n",
- " \n",
- " \n",
- " mean \n",
- " 124999.500000 \n",
- " 205.830189 \n",
- " \n",
- " \n",
- " std \n",
- " 72168.927986 \n",
- " 47.174030 \n",
- " \n",
- " \n",
- " min \n",
- " 0.000000 \n",
- " 8.000000 \n",
- " \n",
- " \n",
- " 25% \n",
- " 62499.750000 \n",
- " 187.500000 \n",
- " \n",
- " \n",
- " 50% \n",
- " 124999.500000 \n",
- " 202.000000 \n",
- " \n",
- " \n",
- " 75% \n",
- " 187499.250000 \n",
- " 217.750000 \n",
- " \n",
- " \n",
- " max \n",
- " 249999.000000 \n",
- " 3434.500000 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id words_count\n",
- "count 250000.000000 250000.000000\n",
- "mean 124999.500000 205.830189\n",
- "std 72168.927986 47.174030\n",
- "min 0.000000 8.000000\n",
- "25% 62499.750000 187.500000\n",
- "50% 124999.500000 202.000000\n",
- "75% 187499.250000 217.750000\n",
- "max 249999.000000 3434.500000"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#更加详细的参数\n",
- "user_click_merge.groupby('user_id')['words_count'].mean().reset_index().describe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 用户点击新闻的时间分析"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [],
- "source": [
- "#为了更好的可视化，这里把时间进行归一化操作\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "mm = MinMaxScaler()\n",
- "user_click_merge['click_timestamp'] = mm.fit_transform(user_click_merge[['click_timestamp']])\n",
- "user_click_merge['created_at_ts'] = mm.fit_transform(user_click_merge[['created_at_ts']])\n",
- "\n",
- "user_click_merge = user_click_merge.sort_values('click_timestamp')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " user_id \n",
- " click_article_id \n",
- " click_timestamp \n",
- " click_environment \n",
- " click_deviceGroup \n",
- " click_os \n",
- " click_country \n",
- " click_region \n",
- " click_referrer_type \n",
- " rank \n",
- " click_cnts \n",
- " category_id \n",
- " created_at_ts \n",
- " words_count \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 18 \n",
- " 249990 \n",
- " 162300 \n",
- " 0.000000 \n",
- " 4 \n",
- " 3 \n",
- " 20 \n",
- " 1 \n",
- " 25 \n",
- " 2 \n",
- " 5 \n",
- " 5 \n",
- " 281 \n",
- " 0.989186 \n",
- " 193 \n",
- " \n",
- " \n",
- " 2 \n",
- " 249998 \n",
- " 160974 \n",
- " 0.000002 \n",
- " 4 \n",
- " 1 \n",
- " 12 \n",
- " 1 \n",
- " 13 \n",
- " 2 \n",
- " 5 \n",
- " 5 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- " 30 \n",
- " 249985 \n",
- " 160974 \n",
- " 0.000003 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 8 \n",
- " 2 \n",
- " 8 \n",
- " 8 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- " 50 \n",
- " 249979 \n",
- " 162300 \n",
- " 0.000004 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 25 \n",
- " 2 \n",
- " 2 \n",
- " 2 \n",
- " 281 \n",
- " 0.989186 \n",
- " 193 \n",
- " \n",
- " \n",
- " 25 \n",
- " 249988 \n",
- " 160974 \n",
- " 0.000004 \n",
- " 4 \n",
- " 1 \n",
- " 17 \n",
- " 1 \n",
- " 21 \n",
- " 2 \n",
- " 17 \n",
- " 17 \n",
- " 281 \n",
- " 0.989092 \n",
- " 259 \n",
- " \n",
- " \n",
- "
\n",
- "
]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
],
- "text/plain": [
- " user_id click_article_id click_timestamp click_environment \\\n",
- "18 249990 162300 0.000000 4 \n",
- "2 249998 160974 0.000002 4 \n",
- "30 249985 160974 0.000003 4 \n",
- "50 249979 162300 0.000004 4 \n",
- "25 249988 160974 0.000004 4 \n",
- "\n",
- " click_deviceGroup click_os click_country click_region \\\n",
- "18 3 20 1 25 \n",
- "2 1 12 1 13 \n",
- "30 1 17 1 8 \n",
- "50 1 17 1 25 \n",
- "25 1 17 1 21 \n",
- "\n",
- " click_referrer_type rank click_cnts category_id created_at_ts \\\n",
- "18 2 5 5 281 0.989186 \n",
- "2 2 5 5 281 0.989092 \n",
- "30 2 8 8 281 0.989092 \n",
- "50 2 2 2 281 0.989186 \n",
- "25 2 17 17 281 0.989092 \n",
- "\n",
- " words_count \n",
- "18 193 \n",
- "2 259 \n",
- "30 259 \n",
- "50 193 \n",
- "25 259 "
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "user_click_merge.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [],
- "source": [
- "def mean_diff_time_func(df, col):\n",
- " df = pd.DataFrame(df, columns={col})\n",
- " df['time_shift1'] = df[col].shift(1).fillna(0)\n",
- " df['diff_time'] = abs(df[col] - df['time_shift1'])\n",
- " return df['diff_time'].mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 点击时间差的平均值\n",
- "mean_diff_click_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'click_timestamp'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(mean_diff_click_time.values, reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从上图可以发现不同用户点击文章的时间差是有差异的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 前后点击文章的创建时间差的平均值\n",
- "mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'created_at_ts'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(sorted(mean_diff_created_time.values, reverse=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "从图中可以发现用户先后点击文章，文章的创建时间也是有差异的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Defaulting to user installation because normal site-packages is not writeable\n",
- "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
- "Collecting gensim\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/2b/e0/fa6326251692056dc880a64eb22117e03269906ba55a6864864d24ec8b4e/gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl (24.2 MB)\n",
- "\u001b[K |████████████████████████████████| 24.2 MB 91.0 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.15.0)\n",
- "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
- "Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.5.4)\n",
- "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
- "Collecting smart-open>=1.8.1\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/e3/cf/6311dfb0aff3e295d63930dea72e3029800242cdfe0790478e33eccee2ab/smart_open-4.0.1.tar.gz (117 kB)\n",
- "\u001b[K |████████████████████████████████| 117 kB 96.7 MB/s eta 0:00:01\n",
- "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
- " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for smart-open: filename=smart_open-4.0.1-py3-none-any.whl size=108249 sha256=50eb67320a58790e8b173971aeb6af7b636d48259d7c9de759612e58e334215b\n",
- " Stored in directory: /home/admin/.cache/pip/wheels/c3/14/fc/a0e523e5d2f13d083ce0af09d4e2861d8e2ec65fc466fb1dff\n",
- "Successfully built smart-open\n",
- "Installing collected packages: smart-open, gensim\n",
- "Successfully installed gensim-3.8.3 smart-open-4.0.1\n"
- ]
- }
- ],
- "source": [
- "# 安装gensim\n",
- "!pip install gensim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [],
- "source": [
- "from gensim.models import Word2Vec\n",
- "import logging, pickle\n",
- "\n",
- "# 需要注意这里模型只迭代了一次\n",
- "def trian_item_word2vec(click_df, embed_size=16, save_name='item_w2v_emb.pkl', split_char=' '):\n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " # 只有转换成字符串才可以进行训练\n",
- " click_df['click_article_id'] = click_df['click_article_id'].astype(str)\n",
- " # 转换成句子的形式\n",
- " docs = click_df.groupby(['user_id'])['click_article_id'].apply(lambda x: list(x)).reset_index()\n",
- " docs = docs['click_article_id'].values.tolist()\n",
- "\n",
- " # 为了方便查看训练的进度，这里设定一个log信息\n",
- " logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', level=logging.INFO)\n",
- "\n",
- " # 这里的参数对训练得到的向量影响也很大,默认负采样为5\n",
- " w2v = Word2Vec(docs, size=16, sg=1, window=5, seed=2020, workers=24, min_count=1, iter=10)\n",
- " \n",
- " # 保存成字典的形式\n",
- " item_w2v_emb_dict = {k: w2v[k] for k in click_df['click_article_id']}\n",
- " \n",
- " return item_w2v_emb_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [],
- "source": [
- "item_w2v_emb_dict = trian_item_word2vec(user_click_merge)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(item_click_count[:20])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "点击次数最多的前20篇新闻，点击次数大于2500。思路：可以定义这些新闻为热门新闻， 这个也是简单的处理方式，后面我们也是根据点击次数和时间进行文章热度的一个划分。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(item_click_count[3500:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以发现很多新闻只被点击过一两次。思路：可以定义这些新闻是冷门新闻"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻共现频次：两篇新闻连续出现的次数"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 433597.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 3.184139 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 18.851753 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 2.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 2202.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#画个图直观地看一看\n",
+ "x = union_item['click_article_id']\n",
+ "y = union_item['count']\n",
+ "plt.scatter(x, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(union_item['count'].values[40000:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "大概有75000个pair至少共现一次"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 新闻文章信息"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#不同类型的新闻出现的次数\n",
+ "plt.plot(user_click_merge['category_id'].value_counts().values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#出现次数比较少的新闻类型, 有些新闻类型，基本上就出现过几次\n",
+ "plt.plot(user_click_merge['category_id'].value_counts().values[150:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 1.630633e+06\n",
+ "mean 2.043012e+02\n",
+ "std 6.382198e+01\n",
+ "min 0.000000e+00\n",
+ "25% 1.720000e+02\n",
+ "50% 1.970000e+02\n",
+ "75% 2.290000e+02\n",
+ "max 6.690000e+03\n",
+ "Name: words_count, dtype: float64"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#新闻字数的描述性统计\n",
+ "user_click_merge['words_count'].describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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7JHcf4+5jSkpKMlkXyaGwjTgQ6W7C+vssndE7BjwIrHT3u+Ieeg5oHYEzDng2rvy6YBTPhUBN0Aw0HbjUzIYEHbiXBmUiIj1PkPXD9outTxp1LgK+DCwzs7eCsh8AdwBTzGw8UA58NnhsKnAlUAbsA64HcPddZnYrsDCod4u778rFSoj0ZOoflVxKmfTdfTbJm5kvSVDfgRuSLOsh4KFMAhSJqu7WxKZ9U/egM3IlobD9JBWR3FDSl27t0xPn8KO/Lit2GHnV1SPoq347m5E3vkBFbT0X3j6Tia+uzUlc0j0p6Uu3tqi8mkfnbSx2GPmRo19bSzfXADBv/S6219bz82mrcrNg6VRYz69Q0u+CnXUNrKusK3YYeaHWnZ5HZ8x2zY7aejbt2pd2/da3O2x9M+mM3pEkPvTzV6hvbGHDHZ8sdigiBdXS4jS2tNC/T+9ih1IwF9w+EyDj73vY+sd0pJ+Gij317DvQdEh5fWNLTl8nrD8Hk2loaubjd73GnLKdxQ4Fd2djVfpHYZ2pqK2nvrGZO6ev4qZnit9fkOgAvbnFWZvDX5lVdQ3sqW9MWa/1Pf7uU0s47UfT2j2mHxLdQ49P+i0tzrenvMWSTbuzXsb5t83k6nv/nrugUrCwHRokUV61j7KKOm5+7u1ih8LjCzZx8Z2zWFTe9VM/zr99Jtc9uIB7Z63l8QXF6y/obCv49cvvcMkvX6OsIv3E39JJVj7vpy9z8S9mpVzGxXfO4uUVO3hm8SEn00s30eOTfmVdA8+8uYV/e6S0S8t5Z0d2R1XVew906XUlpr6xOeGvrVaLN1YDsLZiL7PX7OTvXfz1sWBDuM8bLN0QW9+K2vqcLbN6X+ojfYCV22pTV5LQ6vFJv5C++oeFXHH3G+3Kzrl1Bm8GCakQKmrrmb+uql3ZvgNNjLzxBaYs3JTkWV2zpqIurQ7tnXUNzFmbXTL+x1/M4oz/nc6BpsRNaq0/jhznSw/O54sPzG/3eFNzC9OWb8tLZ2Zzi9PSkvvltv7i66zZL5NXTbbqSzfvzmApqV+zuzbzvP5OJTVp7vjSEda3QUk/h15ZVZHwKGjF1sIdGf3Tb2bzuUnz2pXtqG0A4L5Xy3L6WvFf7o/98rWU9T/3u7l84ffzU9ZLpHJPbB1+OWN1wsdbR0gky70TX13Lfzz6JtPf3pHV63fm3T+YylX3zs75ctuadxKsUzYtgMnem6t+m5umy27SKpnQ7n0HuO6hBfz7o11rEUgkbG+Lkn4BFHKPXxEkxzBaW7m3y8vYUr0/YXlrwknW8bq1Jva8XXlqblu+pThNHsU4qg7Lkfyq7bU8vWhzTpZ1oDn2C7KsouvbaKuwDpFV0g+8vGIH33xicdv92vpGPnv/3IzG5SYV0g9/Y9U+FpUnbnpKpzO5GEd2yd7JQsayrWY/10ycU7D+mkSdpvHNWenKVRJK9pqdLX5nXQPXTJzTrg+iucUZ/4eFLFh/sP/kqdJN/DjNgQGX//oNvvvUkvSCTlv+munCQkk/8K+PlPLsW1vb7k9btp0FG3Zxz8w1XV52OFN+bCTGpyfOKXYYOdL5FyuX+91Jr6+jtLy6qCNYsjnhJ9u3oOPOIpv38vH5Gyktr+aRueVtZVV1DcxcVcENj73ZVva9p5fyhzkbsow0e63vZ0iPz3IqMkm/Yk8DD81e37YB/3nRZjZX52ZcdyqF2JC27N7PU6Xpd9Tu3tf5UWp8Snls/sa2NnWINZG8s2NPcb4gSV4z3YOpbA669h9obh9C25mW6dvb0NRuHHz13gM0NDUfUq+2vrFtlFI6sWbyGTyRo+GnhfzYl2zazazVFakrdlHIDsbzKjJJH+CW51cwu2wnTc0tfOepJVwzcW7Sut3tRKnP/W4u33t6aad14tfoU/elf4T/g78s42t/WtR2/4q7X+fSX72eaYh5leo725Ud1F3JOo8zSBTvv+Ul3vvjl9run3PrDL784IJD6r3vxy9x0R2vpFxeNknqzY27M39SkY299+9c//DC1BVzJJff+rBmkB6f9Dt+2ePPoq3Yk3qMcy6OAArRoVOZYQfuup2ZdVhVxbVft44GKoZkO+NUn1Pr87L5OPfUtz8/oPXzzGRZjc2Hxh3flh0v3fHyUJjEcsjm2wPbQPJ5oB+2HxGRm3vH3dPqWMnldp3uovL9XUq21iu31VJWUce2mv0YxiffdzzHHXlYyuX97rXCT9Gb7D1Kt407JzvxtmXl9+scpSaHQnl1dQX1jc1U7Gngug+MPOTxsI64yaXoJX0OJr90Pt5czJCX6XZU6C97xxPKnizdxEvfurh9pQTrEKZT8Qv5nrW16YcgKRcjSXXntPiVuKaii0eXMHLoACA/O/Cw7j96fPNOR+5xw906+VDy0ba3dPNuxv52NvWNzcxZu5PP3D+Hpub0Jm2bv66K55duTfp4LuNNNBQxLNtv8iP97J6XjicWbuJ7cUMDW7Jo3sm1Qg4DzGXrTpj6ypoSnK2Wj+jCcHAQr0cn/cbmFr7/5847N1PJZZv+T/62giWba1i+pYZvP7mEhRuqqaxLr338c5Pm8fXHFqeu2FkcOa4XJqmSYFuTTJap+qm4k4Da3p8QfJuLM4Aq81cNwVt1iPiY2n79d8eNP0Mpk76ZPWRmFWa2PK7saDObYWZrgv9DgnIzs3vMrMzMlprZuXHPGRfUX2Nm4/KzOu29saaS19+p7FAajk81nV8bGS0vy8cSSdRkEJa2zmw7cg9WzEEMWQzZzEZnO6jOpmjIt5BsCjl18PvYA1eug3SO9P8AXN6h7EZgpruPBmYG9wGuAEYHfxOAiRDbSQA3AxcA5wM3t+4oCi3WvFPgjtwOy3Kgl7XOFZP/jSzTV+iOm31hr04UNO8U8ei1sH0YudsiwpRTe8W9iWG7ulU+pUz67v460HFs2VhgcnB7MnB1XPkjHjMPGGxmxwOXATPcfZe7VwMzOHRHkjONzS389PkV1O4/dCreH/xlGdOWb097Wcm+XMu31LTdTjaVQas1FXtiy0rw2KLyav44r5ym5hZun7oyrZjmrq3ivFtn8IXfH5xYLZ3vUlXdAR6cvT7l6yT6Ym6o2sfehiY+cVfyidUa4/onlmzazYwViSc3a2nxdtdpbU4yE9ik19ceMoFd0jb9VEM2g+fdn4OLgnd2GbyNVfu4a8Y7BTtivG3qyrT7hdKxqHwXj84rZ/66Kh5fsJHqvQc45YcvtqtzX9x7OPLGF7h3Vhkjb3yhrSz+81yxtZYf/GUZ98yMTfY3a3Ulzy1J3jeVD+uTDE+OXV+5vF1ZbX0TD/99fcav8VTpJv66eAt3vLgqLzOu5lK2o3eGufu24PZ2YFhw+wQg/rTQzUFZsvK8+NuSrTwwe33CYYfV+xr5j0cXJXhWe6naLf/pNwdnVfz0xDmdXkJtSulmfnHN2QeX7dAr2N1+84m3ACgZ2K9du3FnPh8k+zlrq1LUbK+uoYlbn1+R0XPiTXx1LWs6uWjH1GXbGPv+2Mc6NrjoTKL3ZdHGaibGJY7ZZTv58Kklh9S7feoqetkq1v0s9eXpUnbkBp9npucnJFxWJ6N3vjp5IWUVdVxz7nBGHHNEl16nsx1Z60NlFXVMf3sHn3zf8Rkte9Lraxkz8mjOHdH+B/enO5yweOf0xCemdVZn1uoKrnxvLJ4r72k/Mmzltlr+3+OLuersd2UUb7amLtvG1/70Jg9cN4aPnzGs3WOt8/Z86cKT2m1AP/nbCq6/aFRGrxN/YuTFo4fywVOGZh90nnW5I9djhzQ527WZ2QQzKzWz0srKju3x6WntlW/MyRFQ7n72te84ar/cRCMJiiXZUWpziqPXpgQnICXS8Uiosyaujm9LGN6lzk70qm+MTa1QyOaXVJ9LIrdPXZXWWdmdXbgmmUI0Wabr7a2xX+SrthduFtRsPo9Cyjbp7wiabQj+t06OsQU4Ma7e8KAsWfkh3H2Su49x9zElJYce/aUjF9+3fH5usRPEcvt6Oe3ITVLeK0+JLBeL7ZUquDz00fRKkNkL9X0v1JDNbNYnjO3jqdYjl29nyHN+1kn/OaB1BM444Nm48uuCUTwXAjVBM9B04FIzGxJ04F4alOXFwSsOZe9Hf40NVsrlNVLjvwwdE0aYjo6STmoWgi9ztuP0cxpDJy/a+isp5U4oJL78YHYXtelMLhLob2auaddPkHUspJcLcvlpdXytsE2tnLJN38weBz4CDDWzzcRG4dwBTDGz8UA58Nmg+lTgSqAM2AdcD+Duu8zsVqD1dLhb3D1vFyFtfYvTuWDGN59YzODD+6a13K2797O9tp7+fQ7dV8Z3CKVzWcKOnUsdZ3JMpmPTy/x1VZx94uCkG/W9s8qyunBIou002zxWs7+RQf0PbmpvrGl/ycRcfCkaklxGMR86G7LZ0vYrIL1lrd+5l1HBWaEdvbVpd9vt196p5JRjB1Lf2Myg/n3YujvxxWQy1fGz6Cib9/X7Ty9l+ZYa6hqSNw3915Nvtc3nVLmngW9PeYu+vQ5+r34545129V9ZtYOjDu/H0QP68cqqCq7/4Mh2jy8qr+a04wYxsH/7lNbY0tL2+P0FmjYk3U78uoYm6hub6W3G4CP6FmznkDLpu/vnkzx0SYK6DtyQZDkPAQ9lFF2WMnnv4ufQT+WDncx+2PrLAEh4Qlj8l3TmqkOnir0x7opPnYXf8UpBn5s0j8+fPyJp/XQ64jpKtslms1HuqW/k7J+8xISLT24r++2srly2MXF0qeZgz+0Z1q1DNg99Pw6erZvee/XR/3s16SCAh/++oe32uIcOnZEzrOoamtqN8EnkLx2m8Hjmzc6n9PjqH9pfxjB+QEJtfSOfnjiHD59awuSvnt+u3u9eWwfEdpqvHXLOzkG5TLjpbmtn3XywseMnV53JuA47snzpkWfkhuzXFBCb777VtprUs3smsynB5QLX7NiT9fISSXakks37WhvMUPm3Ag/T6yinQyiTHOlX7Klnb0P6c+HHa2puyfoqbTm5uls31tp53tppW3RZbGqFuGZAqx6Z9EMrXx2hlt82yVaJOi5TaW3myFWfRRi6PtqmdOjwdpx/20z2Hshu9M4dL67iH38xK6t47py+mvKq3F3btfvK/luQ2+9P5htpNt+tbPXIpB+GxJA3CVbOzHLbfJFkYdm06R888zh5nRD+MOtU23z6RtuRfUeZdnr/PcNzLjrK9HoKPUoONv5ij94pZL+/kn4B5etzzfUGk3x+m/QmNWv/nOCxHH0m2S4ml5tES1vzjnHmzYkHoWX6mXT1Mwxjk2ahJPvllYlcjkzL7pSbwn2APXI+/TDm/Dunr87qiksdJUysBdpgsvlStR7pd9amXoiElY+L4nQWd1OLc/3DC9iyez8//uczUy6z48/7uRke+YdtWGAhFWoCvFz56+It7aZxAR3pd1kYZ8pbsH4XCzfE5ujpyvcz0ar1yvGnmGyYXqp2x+8+teSQsdWZTiz35MKNjLzxBXbUJu7szvVnW5HkdTqTTqf08i01zFpdyTs76vjCA6nHwnd8a7/ycGajdQrZJhxWuX4L7ns1NqdQsia8ZFJto9968i0emN1+fp9Cfnw9M+kXO4ACy/WRfi7b9Fufku5P3j8vig3dSzZJVvxiSjekf6pH/PPip4FI9jrJLN28u+12Lo+uu7qsB2evb5vvSGK6MvFZfWMzj86NnXtTvS+z81yyedXWc2mamlvyftDaM5N+CI/043WteefQdZtdtpMDOT45KdFbmNUp+WmM3onfaSWa1ybZl/ea++cmLE8lfsK9TPPCVb89mFg7nfqii236mT7/b0u2siTuZK4oif9OjLzxBX4VnNg1Ls1fS80tfsj36vT/mcaetuG3mX0Y2eSfhRuq2V5Tzyk/fJHHcjgLQCI9NOkXO4LccHeeKt3U7szG37+e+bSv2fjZi4dOv/yzF1clqNm5m4KTzlJ9JgeaWnh8wcaDnaRxX7Qzbp7Wdjvbzzb+i/hS3JTPXRlK2lkuyHSxap7putaDh7tnruGC219OebZxq3f/YCo/m3rotr0nOMck47mrstykNgTDbp9dnN9zWnpkR26IJqxMKN3wSsur+d7TS3lh2ba2sgM5nDu9M79/Izc7lxeDaxekOvr5zStr+M0rB8/Ujc+B9Y1dX+dkr55sLv905LJZreP7k4t1jopEm9aO2syGsP6xw7z68Qq1P259mXxfR7iHJv1wZ/1UUz+0HuXWBUcar67OborpYknU8bW3k7mFHltQztRl7S9s85kkTTevvVOZ9kRctz6/gv/48LsZdFgfdidpl11bWUfN/kbOfNeRaS0z3vqdya8t8MzizqcV6OjNjbszfn2JaT1C3p5Fp3w6/uvJt5i37mD/Uev2d98Xz6W+sfmQefrnr9/F5Wcdl3BZnU1V3TZRZJ7Tl4W5/XvMmDFeWlqaumIHf5xXzv/EzYXT3Rw9oB9v/s8nmLlyB+MnZ77+El6t8+zkYgZJifneZadlNcdUoZw2bBBjRg7hxitO5/tPL2379ZvM6ccNYtq3Lu7Sa5rZIncfk+ixHnmk390b9fcdaGLfgaaMr4wl4adkn3vbanIz42i+rN6xh9U79vCn+el10K7antu5tDrqmR25xQ6gi+obWzjjf6fz4OzCdNqKdGePzsvvaJdi+NR9+Rt+2yOTftgvTCwi0pk3N+5O2g/VVT0y6Svli0h39/5bZuRluT0z6Svri4gk1COT/hH9ehc7BBGRUOqRSf+kYxJfc1REJOp6ZNLvOG2piIjE9Mik39CU/OxPEZEoK3jSN7PLzWy1mZWZ2Y35eI1ehbwigYhIN1LQpG9mvYF7gSuAM4DPm9kZuX6dQYf1zfUiRUR6hEIf6Z8PlLn7Onc/ADwBjC1wDCIikVXopH8CsCnu/uagLKeymTFRuq+vf/SUYocgRVb6o4/ndfkXnXJMXpdfSKGbcM3MJgATAEaMGJHVMs4dMYQ3/+cTAPTtbQw6rC/fnvIWV551PPsbmxnQvzd7G5rZUVvP2ScOZsmm3Vx+1nG8/s5OVm6rZe66Kq45bzj/fPa7OGZAP/YfaGbIgH4sKq+mvrGZYUf2Z099Ez99YSVPTLiQvr178cjcDazctodLTj+Wh+es51PnDOe04waxdfd+Tj/uSAYd1ofP/G4u/fv04oeffA+1+5uoa2ji8rOOo3Z/I4s37uaS9xzLvHVVnDx0IBffOSvhupUM6s9Prz6LX0xbxTXnncj2mv2cMORwzh91DEs376a8ah/nnTSEqroGqvfFpgx+Y81OBh/RlzUVddx+9XtZvrWGmv2NrN+5lwXrd/HlC0/io6cfy4U/m8mlZwzjhWXbGNCvDx87/ViufO/x9O1tjBo6gA1V+ygZ2J85a3dy0SlDWbalhmFHHsbwIYcDsP9AM1V7G3h3yUD21DdhBof17c26yr2cP+rotnXYU9/I715bx/S3t/PhU0sYVTKAC0YdQ/8+vTj2yP7sbWhmQ9Vehg85nJdXVPDx9xxLbX0TFbX1nPmuo9i5t4F+vXvR0NTMUYf3o2RQf/7t4pM5ol9vKvY00NTcwjED+7Nh515Wb99DyaD+HHtkfyr3NHDhycfQN3huU7Ozo7ae4486nDUVexh8eD8cZ/AR/dhZ18DKbbUMHdif2v2NNDY7v3r5Hc5615HMXFnRdlWlZD5z3nD+9R9PpqmlhfrGZo476nAG9u+De2z5VXUN9O/bm8P79uZAUwurttdyy/MrOOfEIexvbKa2vpFTjx3EuScNbltmRW0DTy7cxIINuzh/1NGs2lbLNeedyLAj+3P9RaN4a9NuNu3ax+hhA9td4Ssbf/rXC7jolKFt919esYMj+vVmUXk1HzntWO5/bS1ba/azOG5K6NOPG8SW6v3saWjiiH69OXpAP1panK999BR+9NflfOiUoazfuZd/PvtdnHbcQLburueD7z6G+sYWRg0dQMmg/uw70MTijbsp37WPy84YRv8+vRl4WB96B/1022r28+i8ck46ZgC1+xs50NxCv969GDqwP7/9wjl8/bHFDOrfh5e+fTG79h5g2+56ZpftpHcv41/OOYHRwwayYmstzy3ZyvgPjWLwEf0Y2L8Pjc0t7G9s5sjD+rJ8Sw0jjjmC8p37eHReORecfDSfOnc4ALX1jRzWpzf9+vRiT30je+qb2FFbz8klA6nd38jh/XrT1Oys3rGHl97e3jbJ2hcvGMFXPzSKgf370KeXsal6P/PXVXHqcYOYtmw7o4cN5KcvxC5cdMqxA7n2H07kCxdkl/9SKejUymb2AeDH7n5ZcP8mAHf/WaL62U6tLCISZZ1NrVzo5p2FwGgzG2Vm/YBrgecKHIOISGQVtHnH3ZvM7OvAdKA38JC7v13IGEREoqzgbfruPhWYWujXFRGRHnpGroiIJKakLyISIUr6IiIRoqQvIhIhSvoiIhFS0JOzMmVmlUB5FxYxFNiZo3ByRTGlL4xxKab0hTGuqMR0kruXJHog1Em/q8ysNNlZacWimNIXxrgUU/rCGJdiUvOOiEikKOmLiERIT0/6k4odQAKKKX1hjEsxpS+McUU+ph7dpi8iIu319CN9ERGJ0+2TfqoLrZtZfzN7Mnh8vpmNDElc3zazFWa21MxmmtlJxY4prt6nzczNLO8jCtKJycw+G7xXb5vZY/mOKZ24zGyEmc0ys8XBZ3hlnuN5yMwqzGx5ksfNzO4J4l1qZufmM54M4vpiEM8yM5tjZmcXO6a4ev9gZk1mdk0YYjKzj5jZW8F2/lregnH3bvtHbHrmtcDJQD9gCXBGhzpfA+4Pbl8LPBmSuD4KHBHc/s98x5VOTEG9QcDrwDxgTLFjAkYDi4Ehwf1jQ/L5TQL+M7h9BrAhzzFdDJwLLE/y+JXAi4ABFwLz8/0+pRnXB+M+uysKEVeqmOI+41eIzfh7TbFjAgYDK4ARwf28befd/Ug/nQutjwUmB7efBi4xMyt2XO4+y933BXfnAcOLHVPgVuDnQH2e40k3pn8D7nX3agB3rwhJXA60Xoz5KGBrPgNy99eBXZ1UGQs84jHzgMFmdnw+Y0onLnef0/rZUZjtPJ33CuAbwJ+BQmxP6cT0BeAZd98Y1M9bXN096adzofW2Ou7eBNQA+b7KcaYXgB9P7Cgtn1LGFDQJnOjuL+Q5lrRjAk4FTjWzv5vZPDO7PCRx/Rj4kpltJna0+I0CxNWZTLe5YijEdp6SmZ0A/AswsdixxDkVGGJmr5rZIjO7Ll8vFLoLo0eNmX0JGAN8uMhx9ALuAr5SzDgS6EOsiecjxI4SXzez97r77mIGBXwe+IO7/zK49vMfzewsd28pclyhZGYfJZb0P1TsWIBfA//t7i35/9Gftj7AecAlwOHAXDOb5+7v5OOFurMtwIlx94cHZYnqbDazPsR+ileFIC7M7OPAD4EPu3tDkWMaBJwFvBp8EY4DnjOzq9w9X1enT+d92kysHbgRWG9m7xDbCSzMU0zpxjUeuBzA3eea2WHE5lApSHNBAmltc8VgZu8DHgCucPd8f/fSMQZ4ItjOhwJXmlmTu/+1iDFtBqrcfS+w18xeB84Gcp70897Rk+fOkT7AOmAUBzvczuxQ5wbad+ROCUlc5xDrLBwdlveqQ/1XyX9Hbjrv0+XA5OD2UGJNGMeEIK4Xga8Et99DrE3f8hzXSJJ3BH6S9h25CwqxXaUR1wigDPhgoeJJFVOHen+gAB25abxP7wFmBtveEcBy4Kx8xNGtj/Q9yYXWzewWoNTdnwMeJPbTu4xYR8q1IYnrTmAg8FRwxLHR3a8qckwFlWZM04FLzWwF0Ax8z/N8tJhmXN8Bfm9m/0WsU/crHnx788HMHifWxDU06Ee4GegbxHs/sX6FK4kl2H3A9fmKJcO4/pdYH9p9wXbe5HmeXCyNmAouVUzuvtLMpgFLgRbgAXfvdMhp1rHkcTsVEZGQ6e6jd0REJANK+iIiEaKkLyISIUr6IiIRoqQvIhIS6U4WF1c/48kINXpHRCQkzOxioI7YPEpnpag7GpgCfMzdq83sWE9jzh4d6YuIhIQnmJjNzN5tZtOCOXneMLPTg4eymoxQSV9EJNwmAd9w9/OA7wL3BeVZTUbYrc/IFRHpycxsILFrErSeuQ/QP/if1WSESvoiIuHVC9jt7u9P8FhWkxGqeUdEJKTcvZZYQv8MtF0Ws/WSk38ldpSPmQ0l1tyzLtUylfRFREIimJhtLnCamW02s/HAF4HxZrYEeJuDV3GbDlQFkxHOIs3JCDVkU0QkQnSkLyISIUr6IiIRoqQvIhIhSvoiIhGipC8iEiFK+iIiEaKkLyISIUr6IiIR8v8Bhwm8q0Q0/foAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(user_click_merge['words_count'].values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户点击的新闻类型的偏好\n",
+ "\n",
+ "此特征可以用于度量用户的兴趣是否广泛。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图中可以看出有一小部分用户阅读类型是极其广泛的，大部分人都处在20个新闻类型以下。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " category_id \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 250000.000000 \n",
+ " 250000.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 124999.500000 \n",
+ " 4.573188 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 72168.927986 \n",
+ " 4.419800 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 62499.750000 \n",
+ " 2.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 124999.500000 \n",
+ " 3.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 187499.250000 \n",
+ " 6.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 95.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
]"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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CcJc7Yma2gJ1WCEh6UdLPJO2SNJRq50jaIWlvel6R6pJ0r6RhSbslXTYXA5jOy28e78RuzMzOSHNxJPCvI2JjRAym+TuBRyNiA/Bomge4HtiQHpuB++Zg302tO2cRAEf9zWJmZk3Nx+mgG4EH0vQDwIcz9a9F3RPAckmr52H/AKxZXg+Bn+x7fb52YWZ2xjvdEAjgB5KelrQ51VZFxME0/WtgVZpeA7yUWXd/qs2LSrlEpSTSpQEzM5tC5TTXvzIiDkg6D9gh6efZhRERkmZ0e04Kk80A559//ml17h3LF3F8bPy0tmFmlmendSQQEQfS8yHge8DlwMuN0zzp+VBqfgBYl1l9bapN3uaWiBiMiMGBgYHT6R79PSWOj/n2IDOzZmYdApIWS1ramAauAZ4BtgGbUrNNwPfT9Dbg1nSX0BXAG5nTRvNiWX8Ph39zYj53YWZ2Rjud00GrgO+pftK9AvxtRPwfSTuBhyTdBuwDPprabwduAIaBo8DHTmPfbfmd1cv4zk/2c6Jao7eS27dEmJnN2qxDICJeAC6Zov4qcPUU9QBun+3+ZuN3N6zk60/sY+eLh/nARSs7uWszszNCrv97fOWGlZRL4okXXu12V8zMFqRch8BZvRV+e9VSdr54uNtdMTNbkHIdAgBXvfs8dr74mj8+wsxsCrkPgZveu5bxWvD1f9zX7a6YmS04uQ+B9SsX82/+5Wq2/MMLDB96q9vdMTNbUHIfAgB3//57WNJX4Y//508YeWu0290xM1swChECA0v7+OtbLmXf4aNc9dkf8sUfDvOrV492u1tmZl2nWMDfvDU4OBhDQ0Nztr3hQ0f49MPP8qN/HgHgwoHFvP/Cc3n36mWcvaiHpf0V1q04i7UrFtHfU56z/ZqZdZKkpzMf79+6bZFCoOGFkSM89vND/MPeV3h632scGX37dw6cu7iXlUv6WNpfYflZvSxbVGFZfw+Lesss6ilzVm+ZJX0VlvRXWNJXYWl/D4v7yizurdDfUz7ZrvFdx2ZmnTKTEDjdTxE9I104sIQLB5bw73/3Qmq1YOTIKG8dr/LGsRP86vBRXjp8jINvHOfVVN//2lHeOljlzeNjHB8bZ2y8/eDsq5Q4q7dMf0/90Vcppen0XCnT11Oir1Kit1Kitzx5vkTPyYforZSolOrTPeUSlfTcU1aqp+n0Udo95RLlkqiURCW1acyXHFBmhVfIEMgqlcSqZf2sWlaff+9vnTPtOtXxGkfHxjlyvMpbx6scGR3jzeNVjp0Y58holdGxcY6NjXP0xDjHTtSfj4+NM1qtcWwsTY/VePXICU5Uaxyv1udPjNcYq9YYrdan55tEPRxKpZMhUU7T5ZPzSvOZegqQxnx50nRJp6bL2elJy+rT9e+DLqW2pcx6pZIoiYnrnWzDhJo0se3J7YmT2yypvs7JR4mJ20zbO9WXbHtSfxr7y+xLIH9xhZ2hCh8Cs1Epl1hWLrGsv2fe9jFeC8bGa+lRnz6RwqE6fmpZtRaMVWuM1YJqpn21Vn8erwXVtCw7Xa3FlPP1dWqM12C8NrFd47lWa2y/xvGxqZePR32+8ajFqTaN5bUaJ9ud6aQUZo1wyQRaI0jKU9ZPBY0a06mdJi0vlUBkg2piQE4IsmwoZuqNfk4Mu8Zy3raNcunt25vwyLZJ7Zr9Z6F+FMrb/qPRODrtKZ9q52DtHIfAAlX/h1UuzAXqbHDUGs816tMxMViy4VHLPEcwYb4WnAydWnByOxOWTdrOqel6+0jbPbXNU+tGaneqfmq+sa2IRr/JhF96Tv2NODXWWqZeC+rLJvw8aoxWg/FI28v2PSYG7FRji5jqZ9ftV39q2VCYfEpz8inPSln0lEr0VDLLS/V674TTphNPtfZW6vM9lfqy3kybvp5sm/LJtn09pXRdsJKLa34OAVsQSiVRQhQk8xaUmCIUTwbKFKHSqGWP9Bq1RjBWa6eOPGsnjzhPbXcsc2TaOOqtZrbV7Gi1Oh6M1epHw9mj3caR8fGxGtXx6sR67dTR82j11BH1XNwT05uu+S3urZy8UWRZf4UVZ/WyuK/C4r4KS/srLFvUw/JFPZy7pJd3nL2I1cv76assjF92h4BZwUmnTi0VRaTTkyeqtQmnWsfG67UT1Rqj1fH03HiMn5w+nq71HT1R5eiJcX5zosrR0XHeGh1j5Mgoew8d4TejVX4zOj7l9T0Jzlvax4Url/A7q5dxybqzufKilZy7pK/jPwuHgJkVjqSTd9jNt+Nj47x5bIzXj43xylujHHj9GAdeP8b+146x9+W3+Nun9rH1/9aQ4IPvGuA/Xb2BS89fMe/9anAImJnNo8bt4ect6+ddq5a+bfl4Ldi9/3Uefe4QD+78FTd96R/5H/92I79/yTs60r9CfGyEmdlCVS6JS89fwSev/W0e/+QHuWTt2fyXb+/m4BvHOrJ/h4CZ2QKxtL+Hv7rpEo6NjfO/dx/syD4dAmZmC8hF5y1hcW+Z//d6Z74Iq+MhIOk6Sc9LGpZ0Z6f3b2a20K1c2scrRzrzsfcdDQFJZeALwPXAxcAtki7uZB/MzBa6pf2VKT/Ycj50+kjgcmA4Il6IiBPAg8CNHe6DmdmCtqSvwpHjnQmBTt8iugZ4KTO/H3hftoGkzcBmgPPPP79zPTMzWyCuvGglx8bGO7KvBfc+gYjYAmyB+vcJdLk7ZmYdd8dVGzq2r06fDjoArMvMr001MzPrgk6HwE5gg6QLJPUCNwPbOtwHMzNLOno6KCKqku4AHgHKwNaI2NPJPpiZ2SkdvyYQEduB7Z3er5mZvZ3fMWxmVmAOATOzAnMImJkVmEPAzKzAFHPxRZvzRNIIsO80NrESeGWOunOmKNqYizZe8JiL4nTG/FsRMdBOwwUdAqdL0lBEDHa7H51UtDEXbbzgMRdFp8bs00FmZgXmEDAzK7C8h8CWbnegC4o25qKNFzzmoujImHN9TcDMzFrL+5GAmZm14BAwMyuwXIZAHr7MXtKLkn4maZekoVQ7R9IOSXvT84pUl6R703h3S7oss51Nqf1eSZsy9fem7Q+nddWFMW6VdEjSM5navI+x2T66OOZPSTqQXutdkm7ILLsr9f95Sddm6lP+jqePaX8y1b+VPrIdSX1pfjgtX9+h8a6T9LikZyXtkfTxVM/t69xizAvzdY6IXD2of0T1L4ALgV7gp8DF3e7XLMbxIrByUu2vgDvT9J3AX6bpG4C/AwRcATyZ6ucAL6TnFWl6RVr2VGqrtO71XRjj7wGXAc90cozN9tHFMX8K+OQUbS9Ov799wAXp97rc6ncceAi4OU1/CfjjNP0fgS+l6ZuBb3VovKuBy9L0UuCf07hy+zq3GPOCfJ07+o++Qy/A+4FHMvN3AXd1u1+zGMeLvD0EngdWZ37Rnk/TXwZumdwOuAX4cqb+5VRbDfw8U5/QrsPjXM/EP4jzPsZm++jimJv9cZjwu0v9ezje3+x3PP0RfAWopPrJdo1103QltVMXXu/vAx8qwus8xZgX5Oucx9NBU32Z/Zou9eV0BPADSU9L2pxqqyLiYJr+NbAqTTcbc6v6/inqC0EnxthsH910Rzr9sTVz2mKmYz4XeD0iqpPqE7aVlr+R2ndMOjVxKfAkBXmdJ40ZFuDrnMcQyIsrI+Iy4Hrgdkm/l10Y9ajP9f29nRjjAvk53ge8E9gIHAQ+29XezANJS4DvAJ+IiDezy/L6Ok8x5gX5OucxBHLxZfYRcSA9HwK+B1wOvCxpNUB6PpSaNxtzq/raKeoLQSfG2GwfXRERL0fEeETUgK9Qf61h5mN+FVguqTKpPmFbafnZqf28k9RD/Y/hNyLiu6mc69d5qjEv1Nc5jyFwxn+ZvaTFkpY2poFrgGeoj6NxV8Qm6ucaSfVb050VVwBvpMPgR4BrJK1Ih57XUD93eBB4U9IV6U6KWzPb6rZOjLHZPrqi8Ycq+Qj11xrq/bw53fFxAbCB+kXQKX/H0/92HwduSutP/vk1xnwT8FhqP6/Sz/5+4LmI+FxmUW5f52ZjXrCvczculHTgQswN1K/I/wL4s273Zxb9v5D6nQA/BfY0xkD93N6jwF7g74FzUl3AF9J4fwYMZrb174Dh9PhYpj6Yfgl/Afw13blI+E3qh8Vj1M9r3taJMTbbRxfH/PU0pt3pH/HqTPs/S/1/nswdXM1+x9PvzlPpZ/G/gL5U70/zw2n5hR0a75XUT8PsBnalxw15fp1bjHlBvs7+2AgzswLL4+kgMzNrk0PAzKzAHAJmZgXmEDAzKzCHgJlZgTkEzMwKzCFgZlZg/x8GJdlC0PTKeQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图中可以发现有一小部分人看的文章平均词数非常高，也有一小部分人看的平均文章次数非常低。\n",
+ "\n",
+ "大多数人偏好于阅读字数在200-400字之间的新闻。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#挑出大多数人的区间仔细看看\n",
+ "plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "可以发现大多数人都是看250字以下的文章"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 250000.000000 \n",
+ " 250000.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 124999.500000 \n",
+ " 205.830189 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 72168.927986 \n",
+ " 47.174030 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.000000 \n",
+ " 8.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 62499.750000 \n",
+ " 187.500000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 124999.500000 \n",
+ " 202.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 187499.250000 \n",
+ " 217.750000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 249999.000000 \n",
+ " 3434.500000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " user_id \n",
+ " click_article_id \n",
+ " click_timestamp \n",
+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
+ " click_region \n",
+ " click_referrer_type \n",
+ " rank \n",
+ " click_cnts \n",
+ " category_id \n",
+ " created_at_ts \n",
+ " words_count \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " 249990 \n",
+ " 162300 \n",
+ " 0.000000 \n",
+ " 4 \n",
+ " 3 \n",
+ " 20 \n",
+ " 1 \n",
+ " 25 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 0.989186 \n",
+ " 193 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 249998 \n",
+ " 160974 \n",
+ " 0.000002 \n",
+ " 4 \n",
+ " 1 \n",
+ " 12 \n",
+ " 1 \n",
+ " 13 \n",
+ " 2 \n",
+ " 5 \n",
+ " 5 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " 249985 \n",
+ " 160974 \n",
+ " 0.000003 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 8 \n",
+ " 2 \n",
+ " 8 \n",
+ " 8 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ " 50 \n",
+ " 249979 \n",
+ " 162300 \n",
+ " 0.000004 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 25 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 281 \n",
+ " 0.989186 \n",
+ " 193 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " 249988 \n",
+ " 160974 \n",
+ " 0.000004 \n",
+ " 4 \n",
+ " 1 \n",
+ " 17 \n",
+ " 1 \n",
+ " 21 \n",
+ " 2 \n",
+ " 17 \n",
+ " 17 \n",
+ " 281 \n",
+ " 0.989092 \n",
+ " 259 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(mean_diff_click_time.values, reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从上图可以发现不同用户点击文章的时间差是有差异的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 前后点击文章的创建时间差的平均值\n",
+ "mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp', 'created_at_ts'].apply(lambda x: mean_diff_time_func(x, 'created_at_ts'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sorted(mean_diff_created_time.values, reverse=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "从图中可以发现用户先后点击文章，文章的创建时间也是有差异的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
+ "Collecting gensim\n",
+ " Downloading https://mirrors.aliyun.com/pypi/packages/2b/e0/fa6326251692056dc880a64eb22117e03269906ba55a6864864d24ec8b4e/gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl (24.2 MB)\n",
+ "\u001b[K |████████████████████████████████| 24.2 MB 91.0 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.15.0)\n",
+ "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
+ "Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.5.4)\n",
+ "Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.6/site-packages (from gensim) (1.19.1)\n",
+ "Collecting smart-open>=1.8.1\n",
+ " Downloading https://mirrors.aliyun.com/pypi/packages/e3/cf/6311dfb0aff3e295d63930dea72e3029800242cdfe0790478e33eccee2ab/smart_open-4.0.1.tar.gz (117 kB)\n",
+ "\u001b[K |████████████████████████████████| 117 kB 96.7 MB/s eta 0:00:01\n",
+ "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
+ " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for smart-open: filename=smart_open-4.0.1-py3-none-any.whl size=108249 sha256=50eb67320a58790e8b173971aeb6af7b636d48259d7c9de759612e58e334215b\n",
+ " Stored in directory: /home/admin/.cache/pip/wheels/c3/14/fc/a0e523e5d2f13d083ce0af09d4e2861d8e2ec65fc466fb1dff\n",
+ "Successfully built smart-open\n",
+ "Installing collected packages: smart-open, gensim\n",
+ "Successfully installed gensim-3.8.3 smart-open-4.0.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 安装gensim\n",
+ "!pip install gensim"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim.models import Word2Vec\n",
+ "import logging, pickle\n",
+ "\n",
+ "# 需要注意这里模型只迭代了一次\n",
+ "def trian_item_word2vec(click_df, embed_size=16, save_name='item_w2v_emb.pkl', split_char=' '):\n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " # 只有转换成字符串才可以进行训练\n",
+ " click_df['click_article_id'] = click_df['click_article_id'].astype(str)\n",
+ " # 转换成句子的形式\n",
+ " docs = click_df.groupby(['user_id'])['click_article_id'].apply(lambda x: list(x)).reset_index()\n",
+ " docs = docs['click_article_id'].values.tolist()\n",
+ "\n",
+ " # 为了方便查看训练的进度，这里设定一个log信息\n",
+ " logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', level=logging.INFO)\n",
+ "\n",
+ " # 这里的参数对训练得到的向量影响也很大,默认负采样为5\n",
+ " w2v = Word2Vec(docs, size=16, sg=1, window=5, seed=2020, workers=24, min_count=1, iter=10)\n",
+ " \n",
+ " # 保存成字典的形式\n",
+ " item_w2v_emb_dict = {k: w2v[k] for k in click_df['click_article_id']}\n",
+ " \n",
+ " return item_w2v_emb_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "item_w2v_emb_dict = trian_item_word2vec(user_click_merge)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " click_environment \n",
+ " click_deviceGroup \n",
+ " click_os \n",
+ " click_country \n",
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],
- "text/plain": [
- " user_id ... click_referrer_type\n",
- "25667 190841 ... 2\n",
- "25668 190841 ... 2\n",
- "25669 190841 ... 2\n",
- "25670 190841 ... 2\n",
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- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
+ "source": [
+ "for _, user_df in sub_user_info.groupby('user_id'):\n",
+ " item_sim_list = get_item_sim_list(user_df)\n",
+ " plt.plot(item_sim_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "这里由于对词向量的训练迭代次数不是很多，所以看到的可视化结果不是很准确，可以训练更多次来观察具体的现象。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 总结\n",
+ "\n",
+ "通过数据分析的过程， 我们目前可以得到以下几点重要的信息， 这个对于我们进行后面的特征制作和分析非常有帮助：\n",
+ "1. 训练集和测试集的用户id没有重复，也就是测试集里面的用户模型是没有见过的\n",
+ "2. 训练集中用户最少的点击文章数是2， 而测试集里面用户最少的点击文章数是1\n",
+ "3. 用户对于文章存在重复点击的情况， 但这个都存在于训练集里面\n",
+ "4. 同一用户的点击环境存在不唯一的情况，后面做这部分特征的时候可以采用统计特征\n",
+ "5. 用户点击文章的次数有很大的区分度，后面可以根据这个制作衡量用户活跃度的特征\n",
+ "6. 文章被用户点击的次数也有很大的区分度，后面可以根据这个制作衡量文章热度的特征\n",
+ "7. 用户看的新闻，相关性是比较强的，所以往往我们判断用户是否对某篇文章感兴趣的时候， 在很大程度上会和他历史点击过的文章有关\n",
+ "8. 用户点击的文章字数有比较大的区别， 这个可以反映用户对于文章字数的区别\n",
+ "9. 用户点击过的文章主题也有很大的区别， 这个可以反映用户的主题偏好\n",
+ "10.不同用户点击文章的时间差也会有所区别， 这个可以反映用户对于文章时效性的偏好\n",
+ "\n",
+ "所以根据上面的一些分析，可以更好的帮助我们后面做好特征工程， 充分挖掘数据的隐含信息。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
+ "\n",
+ ""
+ ]
}
- ],
- "source": [
- "# 随机选择5个用户，查看这些用户前后查看文章的相似性\n",
- "sub_user_ids = np.random.choice(user_click_merge.user_id.unique(), size=15, replace=False)\n",
- "sub_user_info = user_click_merge[user_click_merge['user_id'].isin(sub_user_ids)]\n",
- "\n",
- "sub_user_info.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 上一个版本，这个函数使用的是赛题提供的词向量，但是由于给出的embedding并不是所有的数据的embedding，所以运行下面画图函数的时候会报keyerror的错误\n",
- "# 为了防止出现这个错误，这里修改为使用word2vec训练得到的词向量进行可视化\n",
- "def get_item_sim_list(df):\n",
- " sim_list = []\n",
- " item_list = df['click_article_id'].values\n",
- " for i in range(0, len(item_list)-1):\n",
- " emb1 = item_w2v_emb_dict[str(item_list[i])] # 需要注意的是word2vec训练时候使用的是str类型的数据\n",
- " emb2 = item_w2v_emb_dict[str(item_list[i+1])]\n",
- " sim_list.append(np.dot(emb1,emb2)/(np.linalg.norm(emb1)*(np.linalg.norm(emb2))))\n",
- " sim_list.append(0)\n",
- " return sim_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "source": [
- "for _, user_df in sub_user_info.groupby('user_id'):\n",
- " item_sim_list = get_item_sim_list(user_df)\n",
- " plt.plot(item_sim_list)"
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- "这里由于对词向量的训练迭代次数不是很多，所以看到的可视化结果不是很准确，可以训练更多次来观察具体的现象。"
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},
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- "## 总结\n",
- "\n",
- "通过数据分析的过程， 我们目前可以得到以下几点重要的信息， 这个对于我们进行后面的特征制作和分析非常有帮助：\n",
- "1. 训练集和测试集的用户id没有重复，也就是测试集里面的用户模型是没有见过的\n",
- "2. 训练集中用户最少的点击文章数是2， 而测试集里面用户最少的点击文章数是1\n",
- "3. 用户对于文章存在重复点击的情况， 但这个都存在于训练集里面\n",
- "4. 同一用户的点击环境存在不唯一的情况，后面做这部分特征的时候可以采用统计特征\n",
- "5. 用户点击文章的次数有很大的区分度，后面可以根据这个制作衡量用户活跃度的特征\n",
- "6. 文章被用户点击的次数也有很大的区分度，后面可以根据这个制作衡量文章热度的特征\n",
- "7. 用户看的新闻，相关性是比较强的，所以往往我们判断用户是否对某篇文章感兴趣的时候， 在很大程度上会和他历史点击过的文章有关\n",
- "8. 用户点击的文章字数有比较大的区别， 这个可以反映用户对于文章字数的区别\n",
- "9. 用户点击过的文章主题也有很大的区别， 这个可以反映用户的主题偏好\n",
- "10.不同用户点击文章的时间差也会有所区别， 这个可以反映用户对于文章时效性的偏好\n",
- "\n",
- "所以根据上面的一些分析，可以更好的帮助我们后面做好特征工程， 充分挖掘数据的隐含信息。"
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- "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
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-}
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/jupyter/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.ipynb" "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/jupyter/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
index 3a4bccd4e..08bc05222 100644
--- "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/jupyter/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
+++ "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/jupyter/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.ipynb"
@@ -1,2107 +1,2107 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 多路召回\n",
- "\n",
- "所谓的“多路召回”策略，就是指采用不同的策略、特征或简单模型，分别召回一部分候选集，然后把候选集混合在一起供后续排序模型使用，可以明显的看出，“多路召回策略”是在“计算速度”和“召回率”之间进行权衡的结果。其中，各种简单策略保证候选集的快速召回，从不同角度设计的策略保证召回率接近理想的状态，不至于损伤排序效果。如下图是多路召回的一个示意图，在多路召回中，每个策略之间毫不相关，所以一般可以写并发多线程同时进行，这样可以更加高效。\n",
- "\n",
- ".wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
- "Model: \"model\"\n",
- "__________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- "user_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_article_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "category_id (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_environment (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_deviceGroup (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_os (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_country (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_region (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_referrer_type (InputLayer [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "is_cat_hab (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_user_id (Embedding) (None, 1, 32) 1600032 user_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_seq_emb_hist_click_artic multiple 525664 click_article_id[0][0] \n",
- " hist_click_article_id[0][0] \n",
- " click_article_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_category_id (Embeddi (None, 1, 32) 7776 category_id[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_environment (E (None, 1, 32) 128 click_environment[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_deviceGroup (E (None, 1, 32) 160 click_deviceGroup[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_os (Embedding) (None, 1, 32) 288 click_os[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_country (Embed (None, 1, 32) 384 click_country[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_region (Embedd (None, 1, 32) 928 click_region[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_click_referrer_type (None, 1, 32) 256 click_referrer_type[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "sparse_emb_is_cat_hab (Embeddin (None, 1, 32) 64 is_cat_hab[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask (NoMask) (None, 1, 32) 0 sparse_emb_user_id[0][0] \n",
- " sparse_seq_emb_hist_click_article\n",
- " sparse_emb_category_id[0][0] \n",
- " sparse_emb_click_environment[0][0\n",
- " sparse_emb_click_deviceGroup[0][0\n",
- " sparse_emb_click_os[0][0] \n",
- " sparse_emb_click_country[0][0] \n",
- " sparse_emb_click_region[0][0] \n",
- " sparse_emb_click_referrer_type[0]\n",
- " sparse_emb_is_cat_hab[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "hist_click_article_id (InputLay [(None, 50)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "concatenate (Concatenate) (None, 1, 320) 0 no_mask[0][0] \n",
- " no_mask[1][0] \n",
- " no_mask[2][0] \n",
- " no_mask[3][0] \n",
- " no_mask[4][0] \n",
- " no_mask[5][0] \n",
- " no_mask[6][0] \n",
- " no_mask[7][0] \n",
- " no_mask[8][0] \n",
- " no_mask[9][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_1 (NoMask) (None, 1, 320) 0 concatenate[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "attention_sequence_pooling_laye (None, 1, 32) 13961 sparse_seq_emb_hist_click_article\n",
- " sparse_seq_emb_hist_click_article\n",
- "__________________________________________________________________________________________________\n",
- "concatenate_1 (Concatenate) (None, 1, 352) 0 no_mask_1[0][0] \n",
- " attention_sequence_pooling_layer[\n",
- "__________________________________________________________________________________________________\n",
- "sim0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "time_diff0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "word_diff0 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_max (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_min (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_sum (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "sim_mean (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "score (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "rank (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "click_size (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "time_diff_mean (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "active_level (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "user_time_hob1 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "user_time_hob2 (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "words_hbo (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "words_count (InputLayer) [(None, 1)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "flatten (Flatten) (None, 352) 0 concatenate_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_3 (NoMask) (None, 1) 0 sim0[0][0] \n",
- " time_diff0[0][0] \n",
- " word_diff0[0][0] \n",
- " sim_max[0][0] \n",
- " sim_min[0][0] \n",
- " sim_sum[0][0] \n",
- " sim_mean[0][0] \n",
- " score[0][0] \n",
- " rank[0][0] \n",
- " click_size[0][0] \n",
- " time_diff_mean[0][0] \n",
- " active_level[0][0] \n",
- " user_time_hob1[0][0] \n",
- " user_time_hob2[0][0] \n",
- " words_hbo[0][0] \n",
- " words_count[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_2 (NoMask) (None, 352) 0 flatten[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "concatenate_2 (Concatenate) (None, 16) 0 no_mask_3[0][0] \n",
- " no_mask_3[1][0] \n",
- " no_mask_3[2][0] \n",
- " no_mask_3[3][0] \n",
- " no_mask_3[4][0] \n",
- " no_mask_3[5][0] \n",
- " no_mask_3[6][0] \n",
- " no_mask_3[7][0] \n",
- " no_mask_3[8][0] \n",
- " no_mask_3[9][0] \n",
- " no_mask_3[10][0] \n",
- " no_mask_3[11][0] \n",
- " no_mask_3[12][0] \n",
- " no_mask_3[13][0] \n",
- " no_mask_3[14][0] \n",
- " no_mask_3[15][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 352) 0 no_mask_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten_2 (Flatten) (None, 16) 0 concatenate_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "no_mask_4 (NoMask) multiple 0 flatten_1[0][0] \n",
- " flatten_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "concatenate_3 (Concatenate) (None, 368) 0 no_mask_4[0][0] \n",
- " no_mask_4[1][0] \n",
- "__________________________________________________________________________________________________\n",
- "dnn_1 (DNN) (None, 80) 89880 concatenate_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense (Dense) (None, 1) 80 dnn_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "prediction_layer (PredictionLay (None, 1) 1 dense[0][0] \n",
- "==================================================================================================\n",
- "Total params: 2,239,602\n",
- "Trainable params: 2,239,362\n",
- "Non-trainable params: 240\n",
- "__________________________________________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "# 建立模型\n",
- "model = DIN(dnn_feature_columns, behavior_fea)\n",
- "\n",
- "# 查看模型结构\n",
- "model.summary()\n",
- "\n",
- "# 模型编译\n",
- "model.compile('adam', 'binary_crossentropy',metrics=['binary_crossentropy', tf.keras.metrics.AUC()])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:28:43.885773Z",
- "start_time": "2020-11-18T04:26:48.746787Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:26.195838Z",
+ "start_time": "2020-11-18T04:21:46.115002Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1]\tvalid_0's ndcg@1: 0.909975\tvalid_0's ndcg@2: 0.963068\tvalid_0's ndcg@3: 0.96533\tvalid_0's ndcg@4: 0.965729\tvalid_0's ndcg@5: 0.965864\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9143\tvalid_0's ndcg@2: 0.964711\tvalid_0's ndcg@3: 0.966961\tvalid_0's ndcg@4: 0.967338\tvalid_0's ndcg@5: 0.967483\n",
+ "[3]\tvalid_0's ndcg@1: 0.9181\tvalid_0's ndcg@2: 0.966114\tvalid_0's ndcg@3: 0.968289\tvalid_0's ndcg@4: 0.968773\tvalid_0's ndcg@5: 0.96887\n",
+ "[4]\tvalid_0's ndcg@1: 0.925575\tvalid_0's ndcg@2: 0.969093\tvalid_0's ndcg@3: 0.971193\tvalid_0's ndcg@4: 0.971603\tvalid_0's ndcg@5: 0.97169\n",
+ "[5]\tvalid_0's ndcg@1: 0.9267\tvalid_0's ndcg@2: 0.969635\tvalid_0's ndcg@3: 0.97166\tvalid_0's ndcg@4: 0.972037\tvalid_0's ndcg@5: 0.972133\n",
+ "[6]\tvalid_0's ndcg@1: 0.927\tvalid_0's ndcg@2: 0.969682\tvalid_0's ndcg@3: 0.971757\tvalid_0's ndcg@4: 0.972134\tvalid_0's ndcg@5: 0.972231\n",
+ "[7]\tvalid_0's ndcg@1: 0.928825\tvalid_0's ndcg@2: 0.970451\tvalid_0's ndcg@3: 0.972476\tvalid_0's ndcg@4: 0.97282\tvalid_0's ndcg@5: 0.972927\n",
+ "[8]\tvalid_0's ndcg@1: 0.930025\tvalid_0's ndcg@2: 0.970988\tvalid_0's ndcg@3: 0.972951\tvalid_0's ndcg@4: 0.973295\tvalid_0's ndcg@5: 0.973402\n",
+ "[9]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971347\tvalid_0's ndcg@3: 0.973384\tvalid_0's ndcg@4: 0.973707\tvalid_0's ndcg@5: 0.973794\n",
+ "[10]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.971385\tvalid_0's ndcg@3: 0.973372\tvalid_0's ndcg@4: 0.973717\tvalid_0's ndcg@5: 0.973794\n",
+ "[11]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.971433\tvalid_0's ndcg@3: 0.973333\tvalid_0's ndcg@4: 0.973699\tvalid_0's ndcg@5: 0.973767\n",
+ "[12]\tvalid_0's ndcg@1: 0.93145\tvalid_0's ndcg@2: 0.971656\tvalid_0's ndcg@3: 0.973493\tvalid_0's ndcg@4: 0.973881\tvalid_0's ndcg@5: 0.973949\n",
+ "[13]\tvalid_0's ndcg@1: 0.932525\tvalid_0's ndcg@2: 0.971927\tvalid_0's ndcg@3: 0.973839\tvalid_0's ndcg@4: 0.974227\tvalid_0's ndcg@5: 0.974304\n",
+ "[14]\tvalid_0's ndcg@1: 0.932575\tvalid_0's ndcg@2: 0.971898\tvalid_0's ndcg@3: 0.973823\tvalid_0's ndcg@4: 0.974243\tvalid_0's ndcg@5: 0.97432\n",
+ "[15]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972239\tvalid_0's ndcg@3: 0.974189\tvalid_0's ndcg@4: 0.974587\tvalid_0's ndcg@5: 0.974665\n",
+ "[16]\tvalid_0's ndcg@1: 0.933475\tvalid_0's ndcg@2: 0.972309\tvalid_0's ndcg@3: 0.974209\tvalid_0's ndcg@4: 0.974596\tvalid_0's ndcg@5: 0.974674\n",
+ "[17]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972369\tvalid_0's ndcg@3: 0.974307\tvalid_0's ndcg@4: 0.974684\tvalid_0's ndcg@5: 0.974761\n",
+ "[18]\tvalid_0's ndcg@1: 0.9339\tvalid_0's ndcg@2: 0.972497\tvalid_0's ndcg@3: 0.974372\tvalid_0's ndcg@4: 0.974749\tvalid_0's ndcg@5: 0.974836\n",
+ "[19]\tvalid_0's ndcg@1: 0.9345\tvalid_0's ndcg@2: 0.972845\tvalid_0's ndcg@3: 0.974645\tvalid_0's ndcg@4: 0.974979\tvalid_0's ndcg@5: 0.975085\n",
+ "[20]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.97484\tvalid_0's ndcg@4: 0.975174\tvalid_0's ndcg@5: 0.975271\n",
+ "[21]\tvalid_0's ndcg@1: 0.935\tvalid_0's ndcg@2: 0.973092\tvalid_0's ndcg@3: 0.97488\tvalid_0's ndcg@4: 0.975192\tvalid_0's ndcg@5: 0.975289\n",
+ "[22]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.974988\tvalid_0's ndcg@4: 0.975289\tvalid_0's ndcg@5: 0.975386\n",
+ "[23]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974824\tvalid_0's ndcg@4: 0.975136\tvalid_0's ndcg@5: 0.975223\n",
+ "[24]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973274\tvalid_0's ndcg@3: 0.975087\tvalid_0's ndcg@4: 0.975388\tvalid_0's ndcg@5: 0.975475\n",
+ "[25]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973345\tvalid_0's ndcg@3: 0.97512\tvalid_0's ndcg@4: 0.975443\tvalid_0's ndcg@5: 0.97553\n",
+ "[26]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.975\tvalid_0's ndcg@4: 0.975313\tvalid_0's ndcg@5: 0.9754\n",
+ "[27]\tvalid_0's ndcg@1: 0.935175\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.974983\tvalid_0's ndcg@4: 0.975295\tvalid_0's ndcg@5: 0.975382\n",
+ "[28]\tvalid_0's ndcg@1: 0.935425\tvalid_0's ndcg@2: 0.973328\tvalid_0's ndcg@3: 0.975041\tvalid_0's ndcg@4: 0.975374\tvalid_0's ndcg@5: 0.975471\n",
+ "[29]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973225\tvalid_0's ndcg@3: 0.974963\tvalid_0's ndcg@4: 0.975297\tvalid_0's ndcg@5: 0.975403\n",
+ "[30]\tvalid_0's ndcg@1: 0.9353\tvalid_0's ndcg@2: 0.973235\tvalid_0's ndcg@3: 0.97501\tvalid_0's ndcg@4: 0.975311\tvalid_0's ndcg@5: 0.975418\n",
+ "[31]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973361\tvalid_0's ndcg@3: 0.975099\tvalid_0's ndcg@4: 0.975422\tvalid_0's ndcg@5: 0.975528\n",
+ "[32]\tvalid_0's ndcg@1: 0.9364\tvalid_0's ndcg@2: 0.973641\tvalid_0's ndcg@3: 0.975391\tvalid_0's ndcg@4: 0.975714\tvalid_0's ndcg@5: 0.97582\n",
+ "[33]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973751\tvalid_0's ndcg@3: 0.975501\tvalid_0's ndcg@4: 0.975824\tvalid_0's ndcg@5: 0.975931\n",
+ "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.973902\tvalid_0's ndcg@3: 0.975677\tvalid_0's ndcg@4: 0.975989\tvalid_0's ndcg@5: 0.976095\n",
+ "[35]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974105\tvalid_0's ndcg@3: 0.975892\tvalid_0's ndcg@4: 0.976194\tvalid_0's ndcg@5: 0.9763\n",
+ "[36]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974184\tvalid_0's ndcg@3: 0.975984\tvalid_0's ndcg@4: 0.976296\tvalid_0's ndcg@5: 0.976402\n",
+ "[37]\tvalid_0's ndcg@1: 0.93845\tvalid_0's ndcg@2: 0.974366\tvalid_0's ndcg@3: 0.976166\tvalid_0's ndcg@4: 0.976467\tvalid_0's ndcg@5: 0.976574\n",
+ "[38]\tvalid_0's ndcg@1: 0.938925\tvalid_0's ndcg@2: 0.974557\tvalid_0's ndcg@3: 0.976332\tvalid_0's ndcg@4: 0.976655\tvalid_0's ndcg@5: 0.976751\n",
+ "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974471\tvalid_0's ndcg@3: 0.976234\tvalid_0's ndcg@4: 0.976557\tvalid_0's ndcg@5: 0.976653\n",
+ "[40]\tvalid_0's ndcg@1: 0.938325\tvalid_0's ndcg@2: 0.974335\tvalid_0's ndcg@3: 0.97611\tvalid_0's ndcg@4: 0.976433\tvalid_0's ndcg@5: 0.97653\n",
+ "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.974669\tvalid_0's ndcg@3: 0.976431\tvalid_0's ndcg@4: 0.976743\tvalid_0's ndcg@5: 0.97683\n",
+ "[42]\tvalid_0's ndcg@1: 0.939375\tvalid_0's ndcg@2: 0.974833\tvalid_0's ndcg@3: 0.976546\tvalid_0's ndcg@4: 0.976858\tvalid_0's ndcg@5: 0.976945\n",
+ "[43]\tvalid_0's ndcg@1: 0.939625\tvalid_0's ndcg@2: 0.974878\tvalid_0's ndcg@3: 0.976628\tvalid_0's ndcg@4: 0.97694\tvalid_0's ndcg@5: 0.977027\n",
+ "[44]\tvalid_0's ndcg@1: 0.9395\tvalid_0's ndcg@2: 0.974832\tvalid_0's ndcg@3: 0.97657\tvalid_0's ndcg@4: 0.976893\tvalid_0's ndcg@5: 0.97698\n",
+ "[45]\tvalid_0's ndcg@1: 0.939775\tvalid_0's ndcg@2: 0.974949\tvalid_0's ndcg@3: 0.976674\tvalid_0's ndcg@4: 0.976997\tvalid_0's ndcg@5: 0.977084\n",
+ "[46]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.974945\tvalid_0's ndcg@3: 0.976708\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977107\n",
+ "[47]\tvalid_0's ndcg@1: 0.94005\tvalid_0's ndcg@2: 0.975004\tvalid_0's ndcg@3: 0.976766\tvalid_0's ndcg@4: 0.977078\tvalid_0's ndcg@5: 0.977175\n",
+ "[48]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
+ "[49]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
+ "[50]\tvalid_0's ndcg@1: 0.9405\tvalid_0's ndcg@2: 0.975264\tvalid_0's ndcg@3: 0.976989\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
+ "[51]\tvalid_0's ndcg@1: 0.941125\tvalid_0's ndcg@2: 0.975526\tvalid_0's ndcg@3: 0.977226\tvalid_0's ndcg@4: 0.977528\tvalid_0's ndcg@5: 0.977605\n",
+ "[52]\tvalid_0's ndcg@1: 0.941\tvalid_0's ndcg@2: 0.97548\tvalid_0's ndcg@3: 0.977193\tvalid_0's ndcg@4: 0.977484\tvalid_0's ndcg@5: 0.977561\n",
+ "[53]\tvalid_0's ndcg@1: 0.9411\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.977259\tvalid_0's ndcg@4: 0.977539\tvalid_0's ndcg@5: 0.977616\n",
+ "[54]\tvalid_0's ndcg@1: 0.9412\tvalid_0's ndcg@2: 0.975712\tvalid_0's ndcg@3: 0.977299\tvalid_0's ndcg@4: 0.97759\tvalid_0's ndcg@5: 0.977667\n",
+ "[55]\tvalid_0's ndcg@1: 0.94155\tvalid_0's ndcg@2: 0.975841\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977719\tvalid_0's ndcg@5: 0.977797\n",
+ "[56]\tvalid_0's ndcg@1: 0.941825\tvalid_0's ndcg@2: 0.975943\tvalid_0's ndcg@3: 0.97753\tvalid_0's ndcg@4: 0.977821\tvalid_0's ndcg@5: 0.977898\n",
+ "[57]\tvalid_0's ndcg@1: 0.9416\tvalid_0's ndcg@2: 0.975891\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977741\tvalid_0's ndcg@5: 0.977818\n",
+ "[58]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977494\tvalid_0's ndcg@4: 0.977795\tvalid_0's ndcg@5: 0.977873\n",
+ "[59]\tvalid_0's ndcg@1: 0.942025\tvalid_0's ndcg@2: 0.975985\tvalid_0's ndcg@3: 0.977547\tvalid_0's ndcg@4: 0.977881\tvalid_0's ndcg@5: 0.977958\n",
+ "[60]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975994\tvalid_0's ndcg@3: 0.977569\tvalid_0's ndcg@4: 0.977892\tvalid_0's ndcg@5: 0.977969\n",
+ "[61]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977559\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.97796\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[62]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976027\tvalid_0's ndcg@3: 0.97764\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.978028\n",
+ "[63]\tvalid_0's ndcg@1: 0.942125\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977622\tvalid_0's ndcg@4: 0.977912\tvalid_0's ndcg@5: 0.977999\n",
+ "[64]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977793\tvalid_0's ndcg@4: 0.978105\tvalid_0's ndcg@5: 0.978192\n",
+ "[65]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976227\tvalid_0's ndcg@3: 0.977802\tvalid_0's ndcg@4: 0.978125\tvalid_0's ndcg@5: 0.978212\n",
+ "[66]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976132\tvalid_0's ndcg@3: 0.977695\tvalid_0's ndcg@4: 0.978018\tvalid_0's ndcg@5: 0.978105\n",
+ "[67]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976092\tvalid_0's ndcg@3: 0.977679\tvalid_0's ndcg@4: 0.978002\tvalid_0's ndcg@5: 0.978089\n",
+ "[68]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976148\tvalid_0's ndcg@3: 0.977698\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.978108\n",
+ "[69]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976123\tvalid_0's ndcg@3: 0.977686\tvalid_0's ndcg@4: 0.978009\tvalid_0's ndcg@5: 0.978096\n",
+ "[70]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976222\tvalid_0's ndcg@3: 0.977785\tvalid_0's ndcg@4: 0.978097\tvalid_0's ndcg@5: 0.978184\n",
+ "[71]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976188\tvalid_0's ndcg@3: 0.977763\tvalid_0's ndcg@4: 0.978075\tvalid_0's ndcg@5: 0.978162\n",
+ "[72]\tvalid_0's ndcg@1: 0.9427\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977809\tvalid_0's ndcg@4: 0.978121\tvalid_0's ndcg@5: 0.978208\n",
+ "[73]\tvalid_0's ndcg@1: 0.9428\tvalid_0's ndcg@2: 0.976255\tvalid_0's ndcg@3: 0.977843\tvalid_0's ndcg@4: 0.978155\tvalid_0's ndcg@5: 0.978242\n",
+ "[74]\tvalid_0's ndcg@1: 0.94295\tvalid_0's ndcg@2: 0.97631\tvalid_0's ndcg@3: 0.977898\tvalid_0's ndcg@4: 0.97821\tvalid_0's ndcg@5: 0.978297\n",
+ "[75]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976329\tvalid_0's ndcg@3: 0.977941\tvalid_0's ndcg@4: 0.978232\tvalid_0's ndcg@5: 0.978319\n",
+ "[76]\tvalid_0's ndcg@1: 0.9433\tvalid_0's ndcg@2: 0.976471\tvalid_0's ndcg@3: 0.978059\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978437\n",
+ "[77]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976416\tvalid_0's ndcg@3: 0.977991\tvalid_0's ndcg@4: 0.978314\tvalid_0's ndcg@5: 0.978381\n",
+ "[78]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976657\tvalid_0's ndcg@3: 0.978194\tvalid_0's ndcg@4: 0.978517\tvalid_0's ndcg@5: 0.978585\n",
+ "[79]\tvalid_0's ndcg@1: 0.94365\tvalid_0's ndcg@2: 0.976663\tvalid_0's ndcg@3: 0.978188\tvalid_0's ndcg@4: 0.978501\tvalid_0's ndcg@5: 0.978578\n",
+ "[80]\tvalid_0's ndcg@1: 0.943725\tvalid_0's ndcg@2: 0.976628\tvalid_0's ndcg@3: 0.978203\tvalid_0's ndcg@4: 0.978515\tvalid_0's ndcg@5: 0.978593\n",
+ "[81]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97672\tvalid_0's ndcg@3: 0.978295\tvalid_0's ndcg@4: 0.978607\tvalid_0's ndcg@5: 0.978685\n",
+ "[82]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978397\tvalid_0's ndcg@4: 0.97872\tvalid_0's ndcg@5: 0.978787\n",
+ "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976788\tvalid_0's ndcg@3: 0.978375\tvalid_0's ndcg@4: 0.978698\tvalid_0's ndcg@5: 0.978766\n",
+ "[84]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.97679\tvalid_0's ndcg@3: 0.97839\tvalid_0's ndcg@4: 0.978702\tvalid_0's ndcg@5: 0.97878\n",
+ "[85]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.976809\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978723\tvalid_0's ndcg@5: 0.9788\n",
+ "[86]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976939\tvalid_0's ndcg@3: 0.978502\tvalid_0's ndcg@4: 0.978814\tvalid_0's ndcg@5: 0.978891\n",
+ "[87]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.976976\tvalid_0's ndcg@3: 0.978551\tvalid_0's ndcg@4: 0.978852\tvalid_0's ndcg@5: 0.97893\n",
+ "[88]\tvalid_0's ndcg@1: 0.944925\tvalid_0's ndcg@2: 0.977102\tvalid_0's ndcg@3: 0.978677\tvalid_0's ndcg@4: 0.978968\tvalid_0's ndcg@5: 0.979045\n",
+ "[89]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978758\tvalid_0's ndcg@4: 0.979048\tvalid_0's ndcg@5: 0.979126\n",
+ "[90]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.978735\tvalid_0's ndcg@4: 0.979026\tvalid_0's ndcg@5: 0.979104\n",
+ "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978858\tvalid_0's ndcg@4: 0.979138\tvalid_0's ndcg@5: 0.979215\n",
+ "[92]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.977267\tvalid_0's ndcg@3: 0.978905\tvalid_0's ndcg@4: 0.979174\tvalid_0's ndcg@5: 0.979251\n",
+ "[93]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977193\tvalid_0's ndcg@3: 0.978818\tvalid_0's ndcg@4: 0.979098\tvalid_0's ndcg@5: 0.979176\n",
+ "[94]\tvalid_0's ndcg@1: 0.94545\tvalid_0's ndcg@2: 0.97728\tvalid_0's ndcg@3: 0.97888\tvalid_0's ndcg@4: 0.97916\tvalid_0's ndcg@5: 0.979238\n",
+ "[95]\tvalid_0's ndcg@1: 0.9458\tvalid_0's ndcg@2: 0.977394\tvalid_0's ndcg@3: 0.979006\tvalid_0's ndcg@4: 0.979286\tvalid_0's ndcg@5: 0.979364\n",
+ "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979114\tvalid_0's ndcg@4: 0.979394\tvalid_0's ndcg@5: 0.979472\n",
+ "[97]\tvalid_0's ndcg@1: 0.946475\tvalid_0's ndcg@2: 0.977659\tvalid_0's ndcg@3: 0.979259\tvalid_0's ndcg@4: 0.979539\tvalid_0's ndcg@5: 0.979616\n",
+ "[98]\tvalid_0's ndcg@1: 0.94675\tvalid_0's ndcg@2: 0.97776\tvalid_0's ndcg@3: 0.97936\tvalid_0's ndcg@4: 0.979651\tvalid_0's ndcg@5: 0.979719\n",
+ "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
+ "[100]\tvalid_0's ndcg@1: 0.9468\tvalid_0's ndcg@2: 0.977794\tvalid_0's ndcg@3: 0.979369\tvalid_0's ndcg@4: 0.979671\tvalid_0's ndcg@5: 0.979739\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
+ "[1]\tvalid_0's ndcg@1: 0.909075\tvalid_0's ndcg@2: 0.963019\tvalid_0's ndcg@3: 0.965069\tvalid_0's ndcg@4: 0.965543\tvalid_0's ndcg@5: 0.965601\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9123\tvalid_0's ndcg@2: 0.964273\tvalid_0's ndcg@3: 0.966248\tvalid_0's ndcg@4: 0.966722\tvalid_0's ndcg@5: 0.966789\n",
+ "[3]\tvalid_0's ndcg@1: 0.915075\tvalid_0's ndcg@2: 0.965691\tvalid_0's ndcg@3: 0.967466\tvalid_0's ndcg@4: 0.967854\tvalid_0's ndcg@5: 0.967922\n",
+ "[4]\tvalid_0's ndcg@1: 0.91845\tvalid_0's ndcg@2: 0.967047\tvalid_0's ndcg@3: 0.968735\tvalid_0's ndcg@4: 0.969133\tvalid_0's ndcg@5: 0.969201\n",
+ "[5]\tvalid_0's ndcg@1: 0.92355\tvalid_0's ndcg@2: 0.968961\tvalid_0's ndcg@3: 0.970674\tvalid_0's ndcg@4: 0.97104\tvalid_0's ndcg@5: 0.971098\n",
+ "[6]\tvalid_0's ndcg@1: 0.9253\tvalid_0's ndcg@2: 0.969607\tvalid_0's ndcg@3: 0.971345\tvalid_0's ndcg@4: 0.971689\tvalid_0's ndcg@5: 0.971747\n",
+ "[7]\tvalid_0's ndcg@1: 0.926225\tvalid_0's ndcg@2: 0.969933\tvalid_0's ndcg@3: 0.971708\tvalid_0's ndcg@4: 0.972031\tvalid_0's ndcg@5: 0.972079\n",
+ "[8]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.970104\tvalid_0's ndcg@3: 0.971804\tvalid_0's ndcg@4: 0.972116\tvalid_0's ndcg@5: 0.972184\n",
+ "[9]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970682\tvalid_0's ndcg@3: 0.972307\tvalid_0's ndcg@4: 0.972598\tvalid_0's ndcg@5: 0.972675\n",
+ "[10]\tvalid_0's ndcg@1: 0.92775\tvalid_0's ndcg@2: 0.970653\tvalid_0's ndcg@3: 0.972316\tvalid_0's ndcg@4: 0.972617\tvalid_0's ndcg@5: 0.972685\n",
+ "[11]\tvalid_0's ndcg@1: 0.9283\tvalid_0's ndcg@2: 0.97084\tvalid_0's ndcg@3: 0.97254\tvalid_0's ndcg@4: 0.97281\tvalid_0's ndcg@5: 0.972887\n",
+ "[12]\tvalid_0's ndcg@1: 0.9287\tvalid_0's ndcg@2: 0.971051\tvalid_0's ndcg@3: 0.972701\tvalid_0's ndcg@4: 0.97297\tvalid_0's ndcg@5: 0.973048\n",
+ "[13]\tvalid_0's ndcg@1: 0.9297\tvalid_0's ndcg@2: 0.971389\tvalid_0's ndcg@3: 0.973001\tvalid_0's ndcg@4: 0.973313\tvalid_0's ndcg@5: 0.9734\n",
+ "[14]\tvalid_0's ndcg@1: 0.92955\tvalid_0's ndcg@2: 0.971444\tvalid_0's ndcg@3: 0.972994\tvalid_0's ndcg@4: 0.973284\tvalid_0's ndcg@5: 0.973371\n",
+ "[15]\tvalid_0's ndcg@1: 0.930225\tvalid_0's ndcg@2: 0.97174\tvalid_0's ndcg@3: 0.973253\tvalid_0's ndcg@4: 0.973543\tvalid_0's ndcg@5: 0.97363\n",
+ "[16]\tvalid_0's ndcg@1: 0.930425\tvalid_0's ndcg@2: 0.971798\tvalid_0's ndcg@3: 0.973298\tvalid_0's ndcg@4: 0.97361\tvalid_0's ndcg@5: 0.973698\n",
+ "[17]\tvalid_0's ndcg@1: 0.93125\tvalid_0's ndcg@2: 0.971992\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973903\tvalid_0's ndcg@5: 0.97398\n",
+ "[18]\tvalid_0's ndcg@1: 0.931925\tvalid_0's ndcg@2: 0.972257\tvalid_0's ndcg@3: 0.973845\tvalid_0's ndcg@4: 0.974146\tvalid_0's ndcg@5: 0.974224\n",
+ "[19]\tvalid_0's ndcg@1: 0.932375\tvalid_0's ndcg@2: 0.972376\tvalid_0's ndcg@3: 0.974038\tvalid_0's ndcg@4: 0.974318\tvalid_0's ndcg@5: 0.974376\n",
+ "[20]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.972269\tvalid_0's ndcg@3: 0.973907\tvalid_0's ndcg@4: 0.974187\tvalid_0's ndcg@5: 0.974245\n",
+ "[21]\tvalid_0's ndcg@1: 0.932725\tvalid_0's ndcg@2: 0.972568\tvalid_0's ndcg@3: 0.974181\tvalid_0's ndcg@4: 0.974471\tvalid_0's ndcg@5: 0.974529\n",
+ "[22]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972735\tvalid_0's ndcg@3: 0.974298\tvalid_0's ndcg@4: 0.974599\tvalid_0's ndcg@5: 0.974657\n",
+ "[23]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972642\tvalid_0's ndcg@3: 0.974255\tvalid_0's ndcg@4: 0.974545\tvalid_0's ndcg@5: 0.974594\n",
+ "[24]\tvalid_0's ndcg@1: 0.933175\tvalid_0's ndcg@2: 0.972734\tvalid_0's ndcg@3: 0.974347\tvalid_0's ndcg@4: 0.974638\tvalid_0's ndcg@5: 0.974686\n",
+ "[25]\tvalid_0's ndcg@1: 0.9331\tvalid_0's ndcg@2: 0.972754\tvalid_0's ndcg@3: 0.974366\tvalid_0's ndcg@4: 0.974636\tvalid_0's ndcg@5: 0.974674\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[26]\tvalid_0's ndcg@1: 0.933275\tvalid_0's ndcg@2: 0.972787\tvalid_0's ndcg@3: 0.974424\tvalid_0's ndcg@4: 0.974694\tvalid_0's ndcg@5: 0.974732\n",
+ "[27]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972809\tvalid_0's ndcg@3: 0.974434\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.974732\n",
+ "[28]\tvalid_0's ndcg@1: 0.933625\tvalid_0's ndcg@2: 0.972932\tvalid_0's ndcg@3: 0.974557\tvalid_0's ndcg@4: 0.974826\tvalid_0's ndcg@5: 0.974855\n",
+ "[29]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972937\tvalid_0's ndcg@3: 0.974587\tvalid_0's ndcg@4: 0.974856\tvalid_0's ndcg@5: 0.974885\n",
+ "[30]\tvalid_0's ndcg@1: 0.93355\tvalid_0's ndcg@2: 0.972873\tvalid_0's ndcg@3: 0.974523\tvalid_0's ndcg@4: 0.974792\tvalid_0's ndcg@5: 0.974821\n",
+ "[31]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973065\tvalid_0's ndcg@3: 0.974753\tvalid_0's ndcg@4: 0.975022\tvalid_0's ndcg@5: 0.975051\n",
+ "[32]\tvalid_0's ndcg@1: 0.93435\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974815\tvalid_0's ndcg@4: 0.975084\tvalid_0's ndcg@5: 0.975113\n",
+ "[33]\tvalid_0's ndcg@1: 0.934475\tvalid_0's ndcg@2: 0.97323\tvalid_0's ndcg@3: 0.974855\tvalid_0's ndcg@4: 0.975135\tvalid_0's ndcg@5: 0.975164\n",
+ "[34]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973113\tvalid_0's ndcg@3: 0.974738\tvalid_0's ndcg@4: 0.975028\tvalid_0's ndcg@5: 0.975057\n",
+ "[35]\tvalid_0's ndcg@1: 0.93455\tvalid_0's ndcg@2: 0.973258\tvalid_0's ndcg@3: 0.97487\tvalid_0's ndcg@4: 0.975172\tvalid_0's ndcg@5: 0.975201\n",
+ "[36]\tvalid_0's ndcg@1: 0.9344\tvalid_0's ndcg@2: 0.973265\tvalid_0's ndcg@3: 0.974828\tvalid_0's ndcg@4: 0.975129\tvalid_0's ndcg@5: 0.975158\n",
+ "[37]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973438\tvalid_0's ndcg@3: 0.975013\tvalid_0's ndcg@4: 0.975304\tvalid_0's ndcg@5: 0.975323\n",
+ "[38]\tvalid_0's ndcg@1: 0.934975\tvalid_0's ndcg@2: 0.973541\tvalid_0's ndcg@3: 0.975066\tvalid_0's ndcg@4: 0.975367\tvalid_0's ndcg@5: 0.975386\n",
+ "[39]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973667\tvalid_0's ndcg@3: 0.975192\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975502\n",
+ "[40]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973624\tvalid_0's ndcg@3: 0.975174\tvalid_0's ndcg@4: 0.975454\tvalid_0's ndcg@5: 0.975473\n",
+ "[41]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973686\tvalid_0's ndcg@3: 0.975223\tvalid_0's ndcg@4: 0.975503\tvalid_0's ndcg@5: 0.975522\n",
+ "[42]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973716\tvalid_0's ndcg@3: 0.975266\tvalid_0's ndcg@4: 0.975546\tvalid_0's ndcg@5: 0.975565\n",
+ "[43]\tvalid_0's ndcg@1: 0.93615\tvalid_0's ndcg@2: 0.974022\tvalid_0's ndcg@3: 0.975534\tvalid_0's ndcg@4: 0.975814\tvalid_0's ndcg@5: 0.975843\n",
+ "[44]\tvalid_0's ndcg@1: 0.936225\tvalid_0's ndcg@2: 0.974112\tvalid_0's ndcg@3: 0.975562\tvalid_0's ndcg@4: 0.975853\tvalid_0's ndcg@5: 0.975882\n",
+ "[45]\tvalid_0's ndcg@1: 0.9365\tvalid_0's ndcg@2: 0.974167\tvalid_0's ndcg@3: 0.975654\tvalid_0's ndcg@4: 0.975945\tvalid_0's ndcg@5: 0.975974\n",
+ "[46]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974206\tvalid_0's ndcg@3: 0.975694\tvalid_0's ndcg@4: 0.975995\tvalid_0's ndcg@5: 0.976024\n",
+ "[47]\tvalid_0's ndcg@1: 0.93685\tvalid_0's ndcg@2: 0.974311\tvalid_0's ndcg@3: 0.975786\tvalid_0's ndcg@4: 0.976077\tvalid_0's ndcg@5: 0.976106\n",
+ "[48]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974408\tvalid_0's ndcg@3: 0.975845\tvalid_0's ndcg@4: 0.976147\tvalid_0's ndcg@5: 0.976185\n",
+ "[49]\tvalid_0's ndcg@1: 0.936975\tvalid_0's ndcg@2: 0.974342\tvalid_0's ndcg@3: 0.975829\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.976159\n",
+ "[50]\tvalid_0's ndcg@1: 0.9371\tvalid_0's ndcg@2: 0.974388\tvalid_0's ndcg@3: 0.97585\tvalid_0's ndcg@4: 0.976152\tvalid_0's ndcg@5: 0.976191\n",
+ "[51]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974329\tvalid_0's ndcg@3: 0.975841\tvalid_0's ndcg@4: 0.976121\tvalid_0's ndcg@5: 0.97616\n",
+ "[52]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974578\tvalid_0's ndcg@3: 0.976078\tvalid_0's ndcg@4: 0.976369\tvalid_0's ndcg@5: 0.976407\n",
+ "[53]\tvalid_0's ndcg@1: 0.9378\tvalid_0's ndcg@2: 0.974615\tvalid_0's ndcg@3: 0.976115\tvalid_0's ndcg@4: 0.976405\tvalid_0's ndcg@5: 0.976444\n",
+ "[54]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974689\tvalid_0's ndcg@3: 0.976214\tvalid_0's ndcg@4: 0.976483\tvalid_0's ndcg@5: 0.976521\n",
+ "[55]\tvalid_0's ndcg@1: 0.938225\tvalid_0's ndcg@2: 0.974803\tvalid_0's ndcg@3: 0.976303\tvalid_0's ndcg@4: 0.976572\tvalid_0's ndcg@5: 0.976611\n",
+ "[56]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.9748\tvalid_0's ndcg@3: 0.976275\tvalid_0's ndcg@4: 0.976555\tvalid_0's ndcg@5: 0.976594\n",
+ "[57]\tvalid_0's ndcg@1: 0.938525\tvalid_0's ndcg@2: 0.974914\tvalid_0's ndcg@3: 0.976414\tvalid_0's ndcg@4: 0.976683\tvalid_0's ndcg@5: 0.976722\n",
+ "[58]\tvalid_0's ndcg@1: 0.93875\tvalid_0's ndcg@2: 0.975028\tvalid_0's ndcg@3: 0.976503\tvalid_0's ndcg@4: 0.976773\tvalid_0's ndcg@5: 0.976811\n",
+ "[59]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975198\tvalid_0's ndcg@3: 0.976648\tvalid_0's ndcg@4: 0.976918\tvalid_0's ndcg@5: 0.976956\n",
+ "[60]\tvalid_0's ndcg@1: 0.939025\tvalid_0's ndcg@2: 0.975177\tvalid_0's ndcg@3: 0.976615\tvalid_0's ndcg@4: 0.976884\tvalid_0's ndcg@5: 0.976923\n",
+ "[61]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975205\tvalid_0's ndcg@3: 0.976642\tvalid_0's ndcg@4: 0.976912\tvalid_0's ndcg@5: 0.97695\n",
+ "[62]\tvalid_0's ndcg@1: 0.93965\tvalid_0's ndcg@2: 0.975424\tvalid_0's ndcg@3: 0.976836\tvalid_0's ndcg@4: 0.977116\tvalid_0's ndcg@5: 0.977155\n",
+ "[63]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.976996\tvalid_0's ndcg@4: 0.977276\tvalid_0's ndcg@5: 0.977315\n",
+ "[64]\tvalid_0's ndcg@1: 0.940375\tvalid_0's ndcg@2: 0.975723\tvalid_0's ndcg@3: 0.977123\tvalid_0's ndcg@4: 0.977392\tvalid_0's ndcg@5: 0.977431\n",
+ "[65]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977154\tvalid_0's ndcg@4: 0.977423\tvalid_0's ndcg@5: 0.977462\n",
+ "[66]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975744\tvalid_0's ndcg@3: 0.977156\tvalid_0's ndcg@4: 0.977426\tvalid_0's ndcg@5: 0.977464\n",
+ "[67]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.97576\tvalid_0's ndcg@3: 0.977172\tvalid_0's ndcg@4: 0.977431\tvalid_0's ndcg@5: 0.977469\n",
+ "[68]\tvalid_0's ndcg@1: 0.940675\tvalid_0's ndcg@2: 0.975849\tvalid_0's ndcg@3: 0.977249\tvalid_0's ndcg@4: 0.977508\tvalid_0's ndcg@5: 0.977546\n",
+ "[69]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.976017\tvalid_0's ndcg@3: 0.977454\tvalid_0's ndcg@4: 0.977724\tvalid_0's ndcg@5: 0.977762\n",
+ "[70]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.977362\tvalid_0's ndcg@4: 0.977631\tvalid_0's ndcg@5: 0.97767\n",
+ "[71]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.97735\tvalid_0's ndcg@4: 0.97763\tvalid_0's ndcg@5: 0.977668\n",
+ "[72]\tvalid_0's ndcg@1: 0.941325\tvalid_0's ndcg@2: 0.976058\tvalid_0's ndcg@3: 0.97747\tvalid_0's ndcg@4: 0.977739\tvalid_0's ndcg@5: 0.977778\n",
+ "[73]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977476\tvalid_0's ndcg@4: 0.977756\tvalid_0's ndcg@5: 0.977795\n",
+ "[74]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.97759\tvalid_0's ndcg@4: 0.97788\tvalid_0's ndcg@5: 0.977919\n",
+ "[75]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.977602\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.977921\n",
+ "[76]\tvalid_0's ndcg@1: 0.94195\tvalid_0's ndcg@2: 0.976273\tvalid_0's ndcg@3: 0.977685\tvalid_0's ndcg@4: 0.977965\tvalid_0's ndcg@5: 0.978004\n",
+ "[77]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.97627\tvalid_0's ndcg@3: 0.97767\tvalid_0's ndcg@4: 0.97795\tvalid_0's ndcg@5: 0.977989\n",
+ "[78]\tvalid_0's ndcg@1: 0.94235\tvalid_0's ndcg@2: 0.976452\tvalid_0's ndcg@3: 0.977839\tvalid_0's ndcg@4: 0.978119\tvalid_0's ndcg@5: 0.978158\n",
+ "[79]\tvalid_0's ndcg@1: 0.94265\tvalid_0's ndcg@2: 0.976562\tvalid_0's ndcg@3: 0.977937\tvalid_0's ndcg@4: 0.978228\tvalid_0's ndcg@5: 0.978267\n",
+ "[80]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976667\tvalid_0's ndcg@3: 0.978067\tvalid_0's ndcg@4: 0.978347\tvalid_0's ndcg@5: 0.978385\n",
+ "[81]\tvalid_0's ndcg@1: 0.94305\tvalid_0's ndcg@2: 0.97671\tvalid_0's ndcg@3: 0.978098\tvalid_0's ndcg@4: 0.978378\tvalid_0's ndcg@5: 0.978416\n",
+ "[82]\tvalid_0's ndcg@1: 0.943175\tvalid_0's ndcg@2: 0.97674\tvalid_0's ndcg@3: 0.978115\tvalid_0's ndcg@4: 0.978417\tvalid_0's ndcg@5: 0.978456\n",
+ "[83]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976752\tvalid_0's ndcg@3: 0.97814\tvalid_0's ndcg@4: 0.978441\tvalid_0's ndcg@5: 0.97848\n",
+ "[84]\tvalid_0's ndcg@1: 0.943375\tvalid_0's ndcg@2: 0.976767\tvalid_0's ndcg@3: 0.978179\tvalid_0's ndcg@4: 0.978481\tvalid_0's ndcg@5: 0.97852\n",
+ "[85]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976721\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978437\tvalid_0's ndcg@5: 0.978475\n",
+ "[86]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976792\tvalid_0's ndcg@3: 0.978204\tvalid_0's ndcg@4: 0.978506\tvalid_0's ndcg@5: 0.978535\n",
+ "[87]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.976851\tvalid_0's ndcg@3: 0.978239\tvalid_0's ndcg@4: 0.97854\tvalid_0's ndcg@5: 0.978569\n",
+ "[88]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976882\tvalid_0's ndcg@3: 0.978282\tvalid_0's ndcg@4: 0.978572\tvalid_0's ndcg@5: 0.978611\n",
+ "[89]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.976915\tvalid_0's ndcg@3: 0.97834\tvalid_0's ndcg@4: 0.97863\tvalid_0's ndcg@5: 0.978669\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[90]\tvalid_0's ndcg@1: 0.943925\tvalid_0's ndcg@2: 0.976986\tvalid_0's ndcg@3: 0.978398\tvalid_0's ndcg@4: 0.978689\tvalid_0's ndcg@5: 0.978728\n",
+ "[91]\tvalid_0's ndcg@1: 0.943875\tvalid_0's ndcg@2: 0.976999\tvalid_0's ndcg@3: 0.978399\tvalid_0's ndcg@4: 0.978679\tvalid_0's ndcg@5: 0.978717\n",
+ "[92]\tvalid_0's ndcg@1: 0.94395\tvalid_0's ndcg@2: 0.977058\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978711\tvalid_0's ndcg@5: 0.97876\n",
+ "[93]\tvalid_0's ndcg@1: 0.944075\tvalid_0's ndcg@2: 0.977104\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978759\tvalid_0's ndcg@5: 0.978807\n",
+ "[94]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977125\tvalid_0's ndcg@3: 0.978513\tvalid_0's ndcg@4: 0.978793\tvalid_0's ndcg@5: 0.978841\n",
+ "[95]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977153\tvalid_0's ndcg@3: 0.97854\tvalid_0's ndcg@4: 0.97882\tvalid_0's ndcg@5: 0.978869\n",
+ "[96]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977144\tvalid_0's ndcg@3: 0.978531\tvalid_0's ndcg@4: 0.978811\tvalid_0's ndcg@5: 0.97886\n",
+ "[97]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977221\tvalid_0's ndcg@3: 0.978584\tvalid_0's ndcg@4: 0.978864\tvalid_0's ndcg@5: 0.978912\n",
+ "[98]\tvalid_0's ndcg@1: 0.944575\tvalid_0's ndcg@2: 0.977289\tvalid_0's ndcg@3: 0.978651\tvalid_0's ndcg@4: 0.978942\tvalid_0's ndcg@5: 0.97899\n",
+ "[99]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977341\tvalid_0's ndcg@3: 0.978691\tvalid_0's ndcg@4: 0.978993\tvalid_0's ndcg@5: 0.979032\n",
+ "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
+ "[1]\tvalid_0's ndcg@1: 0.911575\tvalid_0's ndcg@2: 0.964384\tvalid_0's ndcg@3: 0.966321\tvalid_0's ndcg@4: 0.966623\tvalid_0's ndcg@5: 0.966671\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9136\tvalid_0's ndcg@2: 0.965257\tvalid_0's ndcg@3: 0.967107\tvalid_0's ndcg@4: 0.967398\tvalid_0's ndcg@5: 0.967456\n",
+ "[3]\tvalid_0's ndcg@1: 0.917425\tvalid_0's ndcg@2: 0.966732\tvalid_0's ndcg@3: 0.968545\tvalid_0's ndcg@4: 0.968814\tvalid_0's ndcg@5: 0.968882\n",
+ "[4]\tvalid_0's ndcg@1: 0.9222\tvalid_0's ndcg@2: 0.968558\tvalid_0's ndcg@3: 0.970383\tvalid_0's ndcg@4: 0.970619\tvalid_0's ndcg@5: 0.970668\n",
+ "[5]\tvalid_0's ndcg@1: 0.925875\tvalid_0's ndcg@2: 0.969914\tvalid_0's ndcg@3: 0.971714\tvalid_0's ndcg@4: 0.971972\tvalid_0's ndcg@5: 0.972021\n",
+ "[6]\tvalid_0's ndcg@1: 0.926875\tvalid_0's ndcg@2: 0.970425\tvalid_0's ndcg@3: 0.972112\tvalid_0's ndcg@4: 0.972371\tvalid_0's ndcg@5: 0.972419\n",
+ "[7]\tvalid_0's ndcg@1: 0.927475\tvalid_0's ndcg@2: 0.970631\tvalid_0's ndcg@3: 0.972306\tvalid_0's ndcg@4: 0.972586\tvalid_0's ndcg@5: 0.972634\n",
+ "[8]\tvalid_0's ndcg@1: 0.93015\tvalid_0's ndcg@2: 0.971649\tvalid_0's ndcg@3: 0.973287\tvalid_0's ndcg@4: 0.973567\tvalid_0's ndcg@5: 0.973625\n",
+ "[9]\tvalid_0's ndcg@1: 0.9312\tvalid_0's ndcg@2: 0.972084\tvalid_0's ndcg@3: 0.973684\tvalid_0's ndcg@4: 0.973964\tvalid_0's ndcg@5: 0.974022\n",
+ "[10]\tvalid_0's ndcg@1: 0.93225\tvalid_0's ndcg@2: 0.972456\tvalid_0's ndcg@3: 0.974081\tvalid_0's ndcg@4: 0.974361\tvalid_0's ndcg@5: 0.974409\n",
+ "[11]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972704\tvalid_0's ndcg@3: 0.974379\tvalid_0's ndcg@4: 0.974648\tvalid_0's ndcg@5: 0.974696\n",
+ "[12]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974574\tvalid_0's ndcg@4: 0.974832\tvalid_0's ndcg@5: 0.974881\n",
+ "[13]\tvalid_0's ndcg@1: 0.93415\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.97482\tvalid_0's ndcg@4: 0.975079\tvalid_0's ndcg@5: 0.975127\n",
+ "[14]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973671\tvalid_0's ndcg@3: 0.975246\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975531\n",
+ "[15]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.973877\tvalid_0's ndcg@3: 0.975452\tvalid_0's ndcg@4: 0.975699\tvalid_0's ndcg@5: 0.975748\n",
+ "[16]\tvalid_0's ndcg@1: 0.935825\tvalid_0's ndcg@2: 0.973917\tvalid_0's ndcg@3: 0.975442\tvalid_0's ndcg@4: 0.975712\tvalid_0's ndcg@5: 0.97576\n",
+ "[17]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.97411\tvalid_0's ndcg@3: 0.975697\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975995\n",
+ "[18]\tvalid_0's ndcg@1: 0.936925\tvalid_0's ndcg@2: 0.974292\tvalid_0's ndcg@3: 0.975867\tvalid_0's ndcg@4: 0.976114\tvalid_0's ndcg@5: 0.976163\n",
+ "[19]\tvalid_0's ndcg@1: 0.937525\tvalid_0's ndcg@2: 0.974545\tvalid_0's ndcg@3: 0.976095\tvalid_0's ndcg@4: 0.976342\tvalid_0's ndcg@5: 0.976391\n",
+ "[20]\tvalid_0's ndcg@1: 0.937775\tvalid_0's ndcg@2: 0.974653\tvalid_0's ndcg@3: 0.976203\tvalid_0's ndcg@4: 0.976429\tvalid_0's ndcg@5: 0.976487\n",
+ "[21]\tvalid_0's ndcg@1: 0.938825\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976597\tvalid_0's ndcg@4: 0.976823\tvalid_0's ndcg@5: 0.976881\n",
+ "[22]\tvalid_0's ndcg@1: 0.93885\tvalid_0's ndcg@2: 0.975097\tvalid_0's ndcg@3: 0.976609\tvalid_0's ndcg@4: 0.976846\tvalid_0's ndcg@5: 0.976895\n",
+ "[23]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976733\tvalid_0's ndcg@4: 0.976959\tvalid_0's ndcg@5: 0.977008\n",
+ "[24]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976721\tvalid_0's ndcg@4: 0.976947\tvalid_0's ndcg@5: 0.977005\n",
+ "[25]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975421\tvalid_0's ndcg@3: 0.976909\tvalid_0's ndcg@4: 0.977124\tvalid_0's ndcg@5: 0.977182\n",
+ "[26]\tvalid_0's ndcg@1: 0.9393\tvalid_0's ndcg@2: 0.975342\tvalid_0's ndcg@3: 0.976804\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977078\n",
+ "[27]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975323\tvalid_0's ndcg@3: 0.976798\tvalid_0's ndcg@4: 0.977014\tvalid_0's ndcg@5: 0.977062\n",
+ "[28]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975308\tvalid_0's ndcg@3: 0.976783\tvalid_0's ndcg@4: 0.977009\tvalid_0's ndcg@5: 0.977057\n",
+ "[29]\tvalid_0's ndcg@1: 0.94\tvalid_0's ndcg@2: 0.975569\tvalid_0's ndcg@3: 0.977056\tvalid_0's ndcg@4: 0.977282\tvalid_0's ndcg@5: 0.977331\n",
+ "[30]\tvalid_0's ndcg@1: 0.940325\tvalid_0's ndcg@2: 0.975673\tvalid_0's ndcg@3: 0.977173\tvalid_0's ndcg@4: 0.977399\tvalid_0's ndcg@5: 0.977447\n",
+ "[31]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975731\tvalid_0's ndcg@3: 0.977243\tvalid_0's ndcg@4: 0.977469\tvalid_0's ndcg@5: 0.977518\n",
+ "[32]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
+ "[33]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977241\tvalid_0's ndcg@4: 0.977457\tvalid_0's ndcg@5: 0.977505\n",
+ "[34]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
+ "[35]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975868\tvalid_0's ndcg@3: 0.977343\tvalid_0's ndcg@4: 0.977558\tvalid_0's ndcg@5: 0.977606\n",
+ "[36]\tvalid_0's ndcg@1: 0.94115\tvalid_0's ndcg@2: 0.976056\tvalid_0's ndcg@3: 0.977506\tvalid_0's ndcg@4: 0.977722\tvalid_0's ndcg@5: 0.97777\n",
+ "[37]\tvalid_0's ndcg@1: 0.9414\tvalid_0's ndcg@2: 0.976133\tvalid_0's ndcg@3: 0.977595\tvalid_0's ndcg@4: 0.977811\tvalid_0's ndcg@5: 0.977859\n",
+ "[38]\tvalid_0's ndcg@1: 0.94175\tvalid_0's ndcg@2: 0.976278\tvalid_0's ndcg@3: 0.977715\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.97799\n",
+ "[39]\tvalid_0's ndcg@1: 0.942075\tvalid_0's ndcg@2: 0.976366\tvalid_0's ndcg@3: 0.977841\tvalid_0's ndcg@4: 0.978056\tvalid_0's ndcg@5: 0.978105\n",
+ "[40]\tvalid_0's ndcg@1: 0.94215\tvalid_0's ndcg@2: 0.976409\tvalid_0's ndcg@3: 0.977872\tvalid_0's ndcg@4: 0.978087\tvalid_0's ndcg@5: 0.978136\n",
+ "[41]\tvalid_0's ndcg@1: 0.94245\tvalid_0's ndcg@2: 0.97652\tvalid_0's ndcg@3: 0.977983\tvalid_0's ndcg@4: 0.978198\tvalid_0's ndcg@5: 0.978246\n",
+ "[42]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
+ "[43]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
+ "[44]\tvalid_0's ndcg@1: 0.94285\tvalid_0's ndcg@2: 0.976636\tvalid_0's ndcg@3: 0.978111\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978386\n",
+ "[45]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.9768\tvalid_0's ndcg@3: 0.978262\tvalid_0's ndcg@4: 0.978488\tvalid_0's ndcg@5: 0.978537\n",
+ "[46]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
+ "[47]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97836\tvalid_0's ndcg@4: 0.978576\tvalid_0's ndcg@5: 0.978634\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[48]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.978373\tvalid_0's ndcg@4: 0.978577\tvalid_0's ndcg@5: 0.978636\n",
+ "[49]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
+ "[50]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97702\tvalid_0's ndcg@3: 0.97852\tvalid_0's ndcg@4: 0.978746\tvalid_0's ndcg@5: 0.978794\n",
+ "[51]\tvalid_0's ndcg@1: 0.9441\tvalid_0's ndcg@2: 0.97705\tvalid_0's ndcg@3: 0.97855\tvalid_0's ndcg@4: 0.978787\tvalid_0's ndcg@5: 0.978836\n",
+ "[52]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977121\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978846\tvalid_0's ndcg@5: 0.978894\n",
+ "[53]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977081\tvalid_0's ndcg@3: 0.978618\tvalid_0's ndcg@4: 0.978834\tvalid_0's ndcg@5: 0.978882\n",
+ "[54]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977071\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978824\tvalid_0's ndcg@5: 0.978873\n",
+ "[55]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977143\tvalid_0's ndcg@3: 0.978668\tvalid_0's ndcg@4: 0.978883\tvalid_0's ndcg@5: 0.978931\n",
+ "[56]\tvalid_0's ndcg@1: 0.9444\tvalid_0's ndcg@2: 0.977177\tvalid_0's ndcg@3: 0.978702\tvalid_0's ndcg@4: 0.978906\tvalid_0's ndcg@5: 0.978955\n",
+ "[57]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977263\tvalid_0's ndcg@3: 0.978788\tvalid_0's ndcg@4: 0.979003\tvalid_0's ndcg@5: 0.979051\n",
+ "[58]\tvalid_0's ndcg@1: 0.9448\tvalid_0's ndcg@2: 0.977293\tvalid_0's ndcg@3: 0.978843\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979096\n",
+ "[59]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977472\tvalid_0's ndcg@3: 0.978997\tvalid_0's ndcg@4: 0.979202\tvalid_0's ndcg@5: 0.97925\n",
+ "[60]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.97763\tvalid_0's ndcg@3: 0.979118\tvalid_0's ndcg@4: 0.979322\tvalid_0's ndcg@5: 0.979371\n",
+ "[61]\tvalid_0's ndcg@1: 0.945725\tvalid_0's ndcg@2: 0.977682\tvalid_0's ndcg@3: 0.979194\tvalid_0's ndcg@4: 0.979399\tvalid_0's ndcg@5: 0.979447\n",
+ "[62]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977812\tvalid_0's ndcg@3: 0.979312\tvalid_0's ndcg@4: 0.979495\tvalid_0's ndcg@5: 0.979543\n",
+ "[63]\tvalid_0's ndcg@1: 0.946\tvalid_0's ndcg@2: 0.977878\tvalid_0's ndcg@3: 0.97934\tvalid_0's ndcg@4: 0.979523\tvalid_0's ndcg@5: 0.979572\n",
+ "[64]\tvalid_0's ndcg@1: 0.946525\tvalid_0's ndcg@2: 0.978056\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979714\tvalid_0's ndcg@5: 0.979762\n",
+ "[65]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.978105\tvalid_0's ndcg@3: 0.979592\tvalid_0's ndcg@4: 0.979775\tvalid_0's ndcg@5: 0.979823\n",
+ "[66]\tvalid_0's ndcg@1: 0.9465\tvalid_0's ndcg@2: 0.978046\tvalid_0's ndcg@3: 0.979534\tvalid_0's ndcg@4: 0.979706\tvalid_0's ndcg@5: 0.979755\n",
+ "[67]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.978127\tvalid_0's ndcg@3: 0.979614\tvalid_0's ndcg@4: 0.979776\tvalid_0's ndcg@5: 0.979824\n",
+ "[68]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.979608\tvalid_0's ndcg@4: 0.97978\tvalid_0's ndcg@5: 0.979828\n",
+ "[69]\tvalid_0's ndcg@1: 0.946875\tvalid_0's ndcg@2: 0.978216\tvalid_0's ndcg@3: 0.979679\tvalid_0's ndcg@4: 0.979851\tvalid_0's ndcg@5: 0.9799\n",
+ "[70]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.978194\tvalid_0's ndcg@3: 0.979682\tvalid_0's ndcg@4: 0.979854\tvalid_0's ndcg@5: 0.979902\n",
+ "[71]\tvalid_0's ndcg@1: 0.947025\tvalid_0's ndcg@2: 0.978209\tvalid_0's ndcg@3: 0.979721\tvalid_0's ndcg@4: 0.979893\tvalid_0's ndcg@5: 0.979942\n",
+ "[72]\tvalid_0's ndcg@1: 0.9472\tvalid_0's ndcg@2: 0.978273\tvalid_0's ndcg@3: 0.979773\tvalid_0's ndcg@4: 0.979956\tvalid_0's ndcg@5: 0.980005\n",
+ "[73]\tvalid_0's ndcg@1: 0.947475\tvalid_0's ndcg@2: 0.978391\tvalid_0's ndcg@3: 0.979878\tvalid_0's ndcg@4: 0.980061\tvalid_0's ndcg@5: 0.980109\n",
+ "[74]\tvalid_0's ndcg@1: 0.94715\tvalid_0's ndcg@2: 0.978271\tvalid_0's ndcg@3: 0.979758\tvalid_0's ndcg@4: 0.979941\tvalid_0's ndcg@5: 0.97999\n",
+ "[75]\tvalid_0's ndcg@1: 0.947275\tvalid_0's ndcg@2: 0.978333\tvalid_0's ndcg@3: 0.979808\tvalid_0's ndcg@4: 0.979991\tvalid_0's ndcg@5: 0.980039\n",
+ "[76]\tvalid_0's ndcg@1: 0.9474\tvalid_0's ndcg@2: 0.97841\tvalid_0's ndcg@3: 0.979873\tvalid_0's ndcg@4: 0.980045\tvalid_0's ndcg@5: 0.980093\n",
+ "[77]\tvalid_0's ndcg@1: 0.94745\tvalid_0's ndcg@2: 0.97846\tvalid_0's ndcg@3: 0.979898\tvalid_0's ndcg@4: 0.98007\tvalid_0's ndcg@5: 0.980118\n",
+ "[78]\tvalid_0's ndcg@1: 0.94775\tvalid_0's ndcg@2: 0.978555\tvalid_0's ndcg@3: 0.980005\tvalid_0's ndcg@4: 0.980177\tvalid_0's ndcg@5: 0.980226\n",
+ "[79]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
+ "[80]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
+ "[81]\tvalid_0's ndcg@1: 0.948175\tvalid_0's ndcg@2: 0.978744\tvalid_0's ndcg@3: 0.980169\tvalid_0's ndcg@4: 0.980352\tvalid_0's ndcg@5: 0.98039\n",
+ "[82]\tvalid_0's ndcg@1: 0.948375\tvalid_0's ndcg@2: 0.97888\tvalid_0's ndcg@3: 0.980255\tvalid_0's ndcg@4: 0.980438\tvalid_0's ndcg@5: 0.980477\n",
+ "[83]\tvalid_0's ndcg@1: 0.94825\tvalid_0's ndcg@2: 0.978834\tvalid_0's ndcg@3: 0.980209\tvalid_0's ndcg@4: 0.980392\tvalid_0's ndcg@5: 0.980431\n",
+ "[84]\tvalid_0's ndcg@1: 0.948275\tvalid_0's ndcg@2: 0.978844\tvalid_0's ndcg@3: 0.980219\tvalid_0's ndcg@4: 0.980402\tvalid_0's ndcg@5: 0.98044\n",
+ "[85]\tvalid_0's ndcg@1: 0.948475\tvalid_0's ndcg@2: 0.978917\tvalid_0's ndcg@3: 0.980292\tvalid_0's ndcg@4: 0.980475\tvalid_0's ndcg@5: 0.980514\n",
+ "[86]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979102\tvalid_0's ndcg@3: 0.980477\tvalid_0's ndcg@4: 0.98066\tvalid_0's ndcg@5: 0.980699\n",
+ "[87]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979086\tvalid_0's ndcg@3: 0.980474\tvalid_0's ndcg@4: 0.980657\tvalid_0's ndcg@5: 0.980695\n",
+ "[88]\tvalid_0's ndcg@1: 0.949025\tvalid_0's ndcg@2: 0.979136\tvalid_0's ndcg@3: 0.980499\tvalid_0's ndcg@4: 0.980682\tvalid_0's ndcg@5: 0.98072\n",
+ "[89]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979285\tvalid_0's ndcg@3: 0.98061\tvalid_0's ndcg@4: 0.980793\tvalid_0's ndcg@5: 0.980832\n",
+ "[90]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
+ "[91]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
+ "[92]\tvalid_0's ndcg@1: 0.9494\tvalid_0's ndcg@2: 0.97929\tvalid_0's ndcg@3: 0.98064\tvalid_0's ndcg@4: 0.980823\tvalid_0's ndcg@5: 0.980862\n",
+ "[93]\tvalid_0's ndcg@1: 0.949375\tvalid_0's ndcg@2: 0.979297\tvalid_0's ndcg@3: 0.980634\tvalid_0's ndcg@4: 0.980817\tvalid_0's ndcg@5: 0.980856\n",
+ "[94]\tvalid_0's ndcg@1: 0.949525\tvalid_0's ndcg@2: 0.979336\tvalid_0's ndcg@3: 0.980686\tvalid_0's ndcg@4: 0.980869\tvalid_0's ndcg@5: 0.980908\n",
+ "[95]\tvalid_0's ndcg@1: 0.949825\tvalid_0's ndcg@2: 0.979416\tvalid_0's ndcg@3: 0.980791\tvalid_0's ndcg@4: 0.980974\tvalid_0's ndcg@5: 0.981012\n",
+ "[96]\tvalid_0's ndcg@1: 0.94975\tvalid_0's ndcg@2: 0.979404\tvalid_0's ndcg@3: 0.980779\tvalid_0's ndcg@4: 0.980951\tvalid_0's ndcg@5: 0.98099\n",
+ "[97]\tvalid_0's ndcg@1: 0.950025\tvalid_0's ndcg@2: 0.979537\tvalid_0's ndcg@3: 0.980874\tvalid_0's ndcg@4: 0.981057\tvalid_0's ndcg@5: 0.981096\n",
+ "[98]\tvalid_0's ndcg@1: 0.9501\tvalid_0's ndcg@2: 0.979564\tvalid_0's ndcg@3: 0.980889\tvalid_0's ndcg@4: 0.981083\tvalid_0's ndcg@5: 0.981122\n",
+ "[99]\tvalid_0's ndcg@1: 0.950275\tvalid_0's ndcg@2: 0.979629\tvalid_0's ndcg@3: 0.980967\tvalid_0's ndcg@4: 0.98115\tvalid_0's ndcg@5: 0.981188\n",
+ "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
+ "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.96382\tvalid_0's ndcg@3: 0.965707\tvalid_0's ndcg@4: 0.966009\tvalid_0's ndcg@5: 0.966086\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.91415\tvalid_0's ndcg@2: 0.965492\tvalid_0's ndcg@3: 0.967254\tvalid_0's ndcg@4: 0.967556\tvalid_0's ndcg@5: 0.967604\n",
+ "[3]\tvalid_0's ndcg@1: 0.916025\tvalid_0's ndcg@2: 0.966389\tvalid_0's ndcg@3: 0.967976\tvalid_0's ndcg@4: 0.968278\tvalid_0's ndcg@5: 0.968355\n",
+ "[4]\tvalid_0's ndcg@1: 0.919\tvalid_0's ndcg@2: 0.967392\tvalid_0's ndcg@3: 0.96903\tvalid_0's ndcg@4: 0.969364\tvalid_0's ndcg@5: 0.969431\n",
+ "[5]\tvalid_0's ndcg@1: 0.921125\tvalid_0's ndcg@2: 0.968192\tvalid_0's ndcg@3: 0.969855\tvalid_0's ndcg@4: 0.970156\tvalid_0's ndcg@5: 0.970224\n",
+ "[6]\tvalid_0's ndcg@1: 0.921675\tvalid_0's ndcg@2: 0.968411\tvalid_0's ndcg@3: 0.970111\tvalid_0's ndcg@4: 0.97037\tvalid_0's ndcg@5: 0.970437\n",
+ "[7]\tvalid_0's ndcg@1: 0.9237\tvalid_0's ndcg@2: 0.969332\tvalid_0's ndcg@3: 0.970882\tvalid_0's ndcg@4: 0.97113\tvalid_0's ndcg@5: 0.971217\n",
+ "[8]\tvalid_0's ndcg@1: 0.925775\tvalid_0's ndcg@2: 0.970129\tvalid_0's ndcg@3: 0.971642\tvalid_0's ndcg@4: 0.971922\tvalid_0's ndcg@5: 0.97199\n",
+ "[9]\tvalid_0's ndcg@1: 0.926775\tvalid_0's ndcg@2: 0.970435\tvalid_0's ndcg@3: 0.971985\tvalid_0's ndcg@4: 0.972276\tvalid_0's ndcg@5: 0.972334\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[10]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970761\tvalid_0's ndcg@3: 0.972311\tvalid_0's ndcg@4: 0.972612\tvalid_0's ndcg@5: 0.97267\n",
+ "[11]\tvalid_0's ndcg@1: 0.928975\tvalid_0's ndcg@2: 0.97131\tvalid_0's ndcg@3: 0.972798\tvalid_0's ndcg@4: 0.973089\tvalid_0's ndcg@5: 0.973166\n",
+ "[12]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971505\tvalid_0's ndcg@3: 0.972968\tvalid_0's ndcg@4: 0.973259\tvalid_0's ndcg@5: 0.973326\n",
+ "[13]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971426\tvalid_0's ndcg@3: 0.972939\tvalid_0's ndcg@4: 0.97324\tvalid_0's ndcg@5: 0.973318\n",
+ "[14]\tvalid_0's ndcg@1: 0.929775\tvalid_0's ndcg@2: 0.971621\tvalid_0's ndcg@3: 0.973121\tvalid_0's ndcg@4: 0.973412\tvalid_0's ndcg@5: 0.97348\n",
+ "[15]\tvalid_0's ndcg@1: 0.9304\tvalid_0's ndcg@2: 0.971868\tvalid_0's ndcg@3: 0.97338\tvalid_0's ndcg@4: 0.97365\tvalid_0's ndcg@5: 0.973717\n",
+ "[16]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.972096\tvalid_0's ndcg@3: 0.973558\tvalid_0's ndcg@4: 0.973849\tvalid_0's ndcg@5: 0.973926\n",
+ "[17]\tvalid_0's ndcg@1: 0.93105\tvalid_0's ndcg@2: 0.972108\tvalid_0's ndcg@3: 0.973583\tvalid_0's ndcg@4: 0.973884\tvalid_0's ndcg@5: 0.973952\n",
+ "[18]\tvalid_0's ndcg@1: 0.931725\tvalid_0's ndcg@2: 0.972373\tvalid_0's ndcg@3: 0.97386\tvalid_0's ndcg@4: 0.974129\tvalid_0's ndcg@5: 0.974207\n",
+ "[19]\tvalid_0's ndcg@1: 0.932175\tvalid_0's ndcg@2: 0.972681\tvalid_0's ndcg@3: 0.974068\tvalid_0's ndcg@4: 0.974348\tvalid_0's ndcg@5: 0.974406\n",
+ "[20]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.973019\tvalid_0's ndcg@3: 0.974382\tvalid_0's ndcg@4: 0.974673\tvalid_0's ndcg@5: 0.974731\n",
+ "[21]\tvalid_0's ndcg@1: 0.933075\tvalid_0's ndcg@2: 0.97306\tvalid_0's ndcg@3: 0.974423\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.97477\n",
+ "[22]\tvalid_0's ndcg@1: 0.93375\tvalid_0's ndcg@2: 0.973262\tvalid_0's ndcg@3: 0.974649\tvalid_0's ndcg@4: 0.974929\tvalid_0's ndcg@5: 0.975007\n",
+ "[23]\tvalid_0's ndcg@1: 0.933675\tvalid_0's ndcg@2: 0.973219\tvalid_0's ndcg@3: 0.974606\tvalid_0's ndcg@4: 0.974886\tvalid_0's ndcg@5: 0.974973\n",
+ "[24]\tvalid_0's ndcg@1: 0.934\tvalid_0's ndcg@2: 0.97337\tvalid_0's ndcg@3: 0.974745\tvalid_0's ndcg@4: 0.975014\tvalid_0's ndcg@5: 0.975101\n",
+ "[25]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973674\tvalid_0's ndcg@3: 0.975062\tvalid_0's ndcg@4: 0.975342\tvalid_0's ndcg@5: 0.97541\n",
+ "[26]\tvalid_0's ndcg@1: 0.93495\tvalid_0's ndcg@2: 0.973721\tvalid_0's ndcg@3: 0.975096\tvalid_0's ndcg@4: 0.975365\tvalid_0's ndcg@5: 0.975452\n",
+ "[27]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.974082\tvalid_0's ndcg@3: 0.975444\tvalid_0's ndcg@4: 0.975713\tvalid_0's ndcg@5: 0.975781\n",
+ "[28]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973875\tvalid_0's ndcg@3: 0.975275\tvalid_0's ndcg@4: 0.975512\tvalid_0's ndcg@5: 0.975599\n",
+ "[29]\tvalid_0's ndcg@1: 0.935925\tvalid_0's ndcg@2: 0.974159\tvalid_0's ndcg@3: 0.975522\tvalid_0's ndcg@4: 0.975759\tvalid_0's ndcg@5: 0.975836\n",
+ "[30]\tvalid_0's ndcg@1: 0.9362\tvalid_0's ndcg@2: 0.974214\tvalid_0's ndcg@3: 0.975589\tvalid_0's ndcg@4: 0.975847\tvalid_0's ndcg@5: 0.975924\n",
+ "[31]\tvalid_0's ndcg@1: 0.93625\tvalid_0's ndcg@2: 0.974216\tvalid_0's ndcg@3: 0.975629\tvalid_0's ndcg@4: 0.975876\tvalid_0's ndcg@5: 0.975944\n",
+ "[32]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974427\tvalid_0's ndcg@3: 0.975814\tvalid_0's ndcg@4: 0.97603\tvalid_0's ndcg@5: 0.976107\n",
+ "[33]\tvalid_0's ndcg@1: 0.936775\tvalid_0's ndcg@2: 0.974505\tvalid_0's ndcg@3: 0.975855\tvalid_0's ndcg@4: 0.976081\tvalid_0's ndcg@5: 0.976158\n",
+ "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974643\tvalid_0's ndcg@3: 0.975993\tvalid_0's ndcg@4: 0.976219\tvalid_0's ndcg@5: 0.976296\n",
+ "[35]\tvalid_0's ndcg@1: 0.937675\tvalid_0's ndcg@2: 0.974805\tvalid_0's ndcg@3: 0.97618\tvalid_0's ndcg@4: 0.976406\tvalid_0's ndcg@5: 0.976484\n",
+ "[36]\tvalid_0's ndcg@1: 0.9382\tvalid_0's ndcg@2: 0.974983\tvalid_0's ndcg@3: 0.976371\tvalid_0's ndcg@4: 0.976597\tvalid_0's ndcg@5: 0.976674\n",
+ "[37]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.974974\tvalid_0's ndcg@3: 0.976349\tvalid_0's ndcg@4: 0.976586\tvalid_0's ndcg@5: 0.976663\n",
+ "[38]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.975143\tvalid_0's ndcg@3: 0.976518\tvalid_0's ndcg@4: 0.976776\tvalid_0's ndcg@5: 0.976844\n",
+ "[39]\tvalid_0's ndcg@1: 0.938575\tvalid_0's ndcg@2: 0.975106\tvalid_0's ndcg@3: 0.976481\tvalid_0's ndcg@4: 0.976739\tvalid_0's ndcg@5: 0.976807\n",
+ "[40]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.97519\tvalid_0's ndcg@3: 0.976528\tvalid_0's ndcg@4: 0.976775\tvalid_0's ndcg@5: 0.976853\n",
+ "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975347\tvalid_0's ndcg@3: 0.976697\tvalid_0's ndcg@4: 0.976934\tvalid_0's ndcg@5: 0.977001\n",
+ "[42]\tvalid_0's ndcg@1: 0.939825\tvalid_0's ndcg@2: 0.975599\tvalid_0's ndcg@3: 0.976961\tvalid_0's ndcg@4: 0.977198\tvalid_0's ndcg@5: 0.977266\n",
+ "[43]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975639\tvalid_0's ndcg@3: 0.976977\tvalid_0's ndcg@4: 0.977214\tvalid_0's ndcg@5: 0.977282\n",
+ "[44]\tvalid_0's ndcg@1: 0.9398\tvalid_0's ndcg@2: 0.975605\tvalid_0's ndcg@3: 0.976955\tvalid_0's ndcg@4: 0.977192\tvalid_0's ndcg@5: 0.97726\n",
+ "[45]\tvalid_0's ndcg@1: 0.9401\tvalid_0's ndcg@2: 0.9757\tvalid_0's ndcg@3: 0.977075\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
+ "[46]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975845\tvalid_0's ndcg@3: 0.977183\tvalid_0's ndcg@4: 0.97742\tvalid_0's ndcg@5: 0.977497\n",
+ "[47]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975854\tvalid_0's ndcg@3: 0.977204\tvalid_0's ndcg@4: 0.97743\tvalid_0's ndcg@5: 0.977508\n",
+ "[48]\tvalid_0's ndcg@1: 0.940575\tvalid_0's ndcg@2: 0.975923\tvalid_0's ndcg@3: 0.977273\tvalid_0's ndcg@4: 0.977488\tvalid_0's ndcg@5: 0.977556\n",
+ "[49]\tvalid_0's ndcg@1: 0.9407\tvalid_0's ndcg@2: 0.975922\tvalid_0's ndcg@3: 0.977297\tvalid_0's ndcg@4: 0.977501\tvalid_0's ndcg@5: 0.977588\n",
+ "[50]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977322\tvalid_0's ndcg@4: 0.977505\tvalid_0's ndcg@5: 0.977592\n",
+ "[51]\tvalid_0's ndcg@1: 0.9406\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.97725\tvalid_0's ndcg@4: 0.977422\tvalid_0's ndcg@5: 0.977509\n",
+ "[52]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975997\tvalid_0's ndcg@3: 0.977422\tvalid_0's ndcg@4: 0.977594\tvalid_0's ndcg@5: 0.977691\n",
+ "[53]\tvalid_0's ndcg@1: 0.940925\tvalid_0's ndcg@2: 0.975989\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977538\tvalid_0's ndcg@5: 0.977644\n",
+ "[54]\tvalid_0's ndcg@1: 0.94125\tvalid_0's ndcg@2: 0.976062\tvalid_0's ndcg@3: 0.977487\tvalid_0's ndcg@4: 0.977659\tvalid_0's ndcg@5: 0.977756\n",
+ "[55]\tvalid_0's ndcg@1: 0.94145\tvalid_0's ndcg@2: 0.976183\tvalid_0's ndcg@3: 0.97757\tvalid_0's ndcg@4: 0.977742\tvalid_0's ndcg@5: 0.977839\n",
+ "[56]\tvalid_0's ndcg@1: 0.941475\tvalid_0's ndcg@2: 0.976176\tvalid_0's ndcg@3: 0.977576\tvalid_0's ndcg@4: 0.977748\tvalid_0's ndcg@5: 0.977845\n",
+ "[57]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976139\tvalid_0's ndcg@3: 0.977539\tvalid_0's ndcg@4: 0.977712\tvalid_0's ndcg@5: 0.977808\n",
+ "[58]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.97625\tvalid_0's ndcg@3: 0.97765\tvalid_0's ndcg@4: 0.977822\tvalid_0's ndcg@5: 0.977919\n",
+ "[59]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.976253\tvalid_0's ndcg@3: 0.977653\tvalid_0's ndcg@4: 0.977836\tvalid_0's ndcg@5: 0.977932\n",
+ "[60]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977634\tvalid_0's ndcg@4: 0.977817\tvalid_0's ndcg@5: 0.977914\n",
+ "[61]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.976333\tvalid_0's ndcg@3: 0.977745\tvalid_0's ndcg@4: 0.977918\tvalid_0's ndcg@5: 0.978005\n",
+ "[62]\tvalid_0's ndcg@1: 0.941975\tvalid_0's ndcg@2: 0.976345\tvalid_0's ndcg@3: 0.977757\tvalid_0's ndcg@4: 0.97794\tvalid_0's ndcg@5: 0.978027\n",
+ "[63]\tvalid_0's ndcg@1: 0.9423\tvalid_0's ndcg@2: 0.976496\tvalid_0's ndcg@3: 0.977871\tvalid_0's ndcg@4: 0.978065\tvalid_0's ndcg@5: 0.978152\n",
+ "[64]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976632\tvalid_0's ndcg@3: 0.977995\tvalid_0's ndcg@4: 0.978188\tvalid_0's ndcg@5: 0.978275\n",
+ "[65]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976629\tvalid_0's ndcg@3: 0.977979\tvalid_0's ndcg@4: 0.978173\tvalid_0's ndcg@5: 0.97826\n",
+ "[66]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976685\tvalid_0's ndcg@3: 0.978035\tvalid_0's ndcg@4: 0.978229\tvalid_0's ndcg@5: 0.978316\n",
+ "[67]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976678\tvalid_0's ndcg@3: 0.978041\tvalid_0's ndcg@4: 0.978224\tvalid_0's ndcg@5: 0.97832\n",
+ "[68]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976694\tvalid_0's ndcg@3: 0.978044\tvalid_0's ndcg@4: 0.978227\tvalid_0's ndcg@5: 0.978324\n",
+ "[69]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976834\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978329\tvalid_0's ndcg@5: 0.978426\n",
+ "[70]\tvalid_0's ndcg@1: 0.943025\tvalid_0's ndcg@2: 0.976827\tvalid_0's ndcg@3: 0.978152\tvalid_0's ndcg@4: 0.978324\tvalid_0's ndcg@5: 0.978431\n",
+ "[71]\tvalid_0's ndcg@1: 0.9432\tvalid_0's ndcg@2: 0.976923\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978504\n",
+ "[72]\tvalid_0's ndcg@1: 0.943225\tvalid_0's ndcg@2: 0.976917\tvalid_0's ndcg@3: 0.978254\tvalid_0's ndcg@4: 0.978405\tvalid_0's ndcg@5: 0.978511\n",
+ "[73]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976936\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978409\tvalid_0's ndcg@5: 0.978496\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[74]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976957\tvalid_0's ndcg@3: 0.97827\tvalid_0's ndcg@4: 0.978431\tvalid_0's ndcg@5: 0.978528\n",
+ "[75]\tvalid_0's ndcg@1: 0.943075\tvalid_0's ndcg@2: 0.976861\tvalid_0's ndcg@3: 0.978199\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978457\n",
+ "[76]\tvalid_0's ndcg@1: 0.94335\tvalid_0's ndcg@2: 0.976963\tvalid_0's ndcg@3: 0.978288\tvalid_0's ndcg@4: 0.978471\tvalid_0's ndcg@5: 0.978568\n",
+ "[77]\tvalid_0's ndcg@1: 0.94345\tvalid_0's ndcg@2: 0.977031\tvalid_0's ndcg@3: 0.978331\tvalid_0's ndcg@4: 0.978514\tvalid_0's ndcg@5: 0.978611\n",
+ "[78]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.977088\tvalid_0's ndcg@3: 0.97835\tvalid_0's ndcg@4: 0.978533\tvalid_0's ndcg@5: 0.97863\n",
+ "[79]\tvalid_0's ndcg@1: 0.943625\tvalid_0's ndcg@2: 0.977096\tvalid_0's ndcg@3: 0.978396\tvalid_0's ndcg@4: 0.978579\tvalid_0's ndcg@5: 0.978676\n",
+ "[80]\tvalid_0's ndcg@1: 0.943825\tvalid_0's ndcg@2: 0.977154\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978651\tvalid_0's ndcg@5: 0.978748\n",
+ "[81]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.97846\tvalid_0's ndcg@4: 0.978633\tvalid_0's ndcg@5: 0.978729\n",
+ "[82]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.977361\tvalid_0's ndcg@3: 0.978673\tvalid_0's ndcg@4: 0.978845\tvalid_0's ndcg@5: 0.978933\n",
+ "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977324\tvalid_0's ndcg@3: 0.978624\tvalid_0's ndcg@4: 0.978796\tvalid_0's ndcg@5: 0.978893\n",
+ "[84]\tvalid_0's ndcg@1: 0.94405\tvalid_0's ndcg@2: 0.977253\tvalid_0's ndcg@3: 0.978565\tvalid_0's ndcg@4: 0.978737\tvalid_0's ndcg@5: 0.978834\n",
+ "[85]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977283\tvalid_0's ndcg@3: 0.978633\tvalid_0's ndcg@4: 0.978795\tvalid_0's ndcg@5: 0.978882\n",
+ "[86]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.97745\tvalid_0's ndcg@3: 0.978763\tvalid_0's ndcg@4: 0.978924\tvalid_0's ndcg@5: 0.979011\n",
+ "[87]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.977419\tvalid_0's ndcg@3: 0.978756\tvalid_0's ndcg@4: 0.978918\tvalid_0's ndcg@5: 0.979005\n",
+ "[88]\tvalid_0's ndcg@1: 0.944825\tvalid_0's ndcg@2: 0.977554\tvalid_0's ndcg@3: 0.978867\tvalid_0's ndcg@4: 0.979039\tvalid_0's ndcg@5: 0.979126\n",
+ "[89]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977767\tvalid_0's ndcg@3: 0.979079\tvalid_0's ndcg@4: 0.979262\tvalid_0's ndcg@5: 0.97934\n",
+ "[90]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977773\tvalid_0's ndcg@3: 0.979073\tvalid_0's ndcg@4: 0.979256\tvalid_0's ndcg@5: 0.979334\n",
+ "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977792\tvalid_0's ndcg@3: 0.979092\tvalid_0's ndcg@4: 0.979275\tvalid_0's ndcg@5: 0.979352\n",
+ "[92]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977776\tvalid_0's ndcg@3: 0.979088\tvalid_0's ndcg@4: 0.979261\tvalid_0's ndcg@5: 0.979348\n",
+ "[93]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977757\tvalid_0's ndcg@3: 0.979082\tvalid_0's ndcg@4: 0.979244\tvalid_0's ndcg@5: 0.979331\n",
+ "[94]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977761\tvalid_0's ndcg@3: 0.979061\tvalid_0's ndcg@4: 0.979223\tvalid_0's ndcg@5: 0.97931\n",
+ "[95]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977798\tvalid_0's ndcg@3: 0.979086\tvalid_0's ndcg@4: 0.979258\tvalid_0's ndcg@5: 0.979345\n",
+ "[96]\tvalid_0's ndcg@1: 0.945825\tvalid_0's ndcg@2: 0.977955\tvalid_0's ndcg@3: 0.97923\tvalid_0's ndcg@4: 0.979413\tvalid_0's ndcg@5: 0.9795\n",
+ "[97]\tvalid_0's ndcg@1: 0.945925\tvalid_0's ndcg@2: 0.97796\tvalid_0's ndcg@3: 0.97926\tvalid_0's ndcg@4: 0.979443\tvalid_0's ndcg@5: 0.979531\n",
+ "[98]\tvalid_0's ndcg@1: 0.9464\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.97942\tvalid_0's ndcg@4: 0.979625\tvalid_0's ndcg@5: 0.979702\n",
+ "[99]\tvalid_0's ndcg@1: 0.94655\tvalid_0's ndcg@2: 0.978191\tvalid_0's ndcg@3: 0.979479\tvalid_0's ndcg@4: 0.979683\tvalid_0's ndcg@5: 0.97977\n",
+ "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
+ "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.963031\tvalid_0's ndcg@3: 0.965281\tvalid_0's ndcg@4: 0.965819\tvalid_0's ndcg@5: 0.965887\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's ndcg@1: 0.9141\tvalid_0's ndcg@2: 0.964748\tvalid_0's ndcg@3: 0.96681\tvalid_0's ndcg@4: 0.967316\tvalid_0's ndcg@5: 0.967394\n",
+ "[3]\tvalid_0's ndcg@1: 0.915925\tvalid_0's ndcg@2: 0.9655\tvalid_0's ndcg@3: 0.967575\tvalid_0's ndcg@4: 0.968028\tvalid_0's ndcg@5: 0.968105\n",
+ "[4]\tvalid_0's ndcg@1: 0.91915\tvalid_0's ndcg@2: 0.966943\tvalid_0's ndcg@3: 0.968968\tvalid_0's ndcg@4: 0.969334\tvalid_0's ndcg@5: 0.969373\n",
+ "[5]\tvalid_0's ndcg@1: 0.920625\tvalid_0's ndcg@2: 0.967598\tvalid_0's ndcg@3: 0.969498\tvalid_0's ndcg@4: 0.969896\tvalid_0's ndcg@5: 0.969944\n",
+ "[6]\tvalid_0's ndcg@1: 0.922625\tvalid_0's ndcg@2: 0.968336\tvalid_0's ndcg@3: 0.970261\tvalid_0's ndcg@4: 0.970659\tvalid_0's ndcg@5: 0.970688\n",
+ "[7]\tvalid_0's ndcg@1: 0.923625\tvalid_0's ndcg@2: 0.968768\tvalid_0's ndcg@3: 0.970656\tvalid_0's ndcg@4: 0.971043\tvalid_0's ndcg@5: 0.971072\n",
+ "[8]\tvalid_0's ndcg@1: 0.925825\tvalid_0's ndcg@2: 0.969612\tvalid_0's ndcg@3: 0.971462\tvalid_0's ndcg@4: 0.97186\tvalid_0's ndcg@5: 0.971879\n",
+ "[9]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.969899\tvalid_0's ndcg@3: 0.971711\tvalid_0's ndcg@4: 0.97211\tvalid_0's ndcg@5: 0.972129\n",
+ "[10]\tvalid_0's ndcg@1: 0.927775\tvalid_0's ndcg@2: 0.97041\tvalid_0's ndcg@3: 0.972185\tvalid_0's ndcg@4: 0.972594\tvalid_0's ndcg@5: 0.972614\n",
+ "[11]\tvalid_0's ndcg@1: 0.92885\tvalid_0's ndcg@2: 0.970838\tvalid_0's ndcg@3: 0.972588\tvalid_0's ndcg@4: 0.973008\tvalid_0's ndcg@5: 0.973028\n",
+ "[12]\tvalid_0's ndcg@1: 0.930325\tvalid_0's ndcg@2: 0.971367\tvalid_0's ndcg@3: 0.973129\tvalid_0's ndcg@4: 0.973549\tvalid_0's ndcg@5: 0.973569\n",
+ "[13]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971631\tvalid_0's ndcg@3: 0.973443\tvalid_0's ndcg@4: 0.973842\tvalid_0's ndcg@5: 0.973871\n",
+ "[14]\tvalid_0's ndcg@1: 0.931525\tvalid_0's ndcg@2: 0.971778\tvalid_0's ndcg@3: 0.973616\tvalid_0's ndcg@4: 0.973993\tvalid_0's ndcg@5: 0.974022\n",
+ "[15]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.9717\tvalid_0's ndcg@3: 0.973475\tvalid_0's ndcg@4: 0.973852\tvalid_0's ndcg@5: 0.973872\n",
+ "[16]\tvalid_0's ndcg@1: 0.931775\tvalid_0's ndcg@2: 0.971902\tvalid_0's ndcg@3: 0.973702\tvalid_0's ndcg@4: 0.97409\tvalid_0's ndcg@5: 0.974109\n",
+ "[17]\tvalid_0's ndcg@1: 0.931425\tvalid_0's ndcg@2: 0.971805\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973967\tvalid_0's ndcg@5: 0.973986\n",
+ "[18]\tvalid_0's ndcg@1: 0.931575\tvalid_0's ndcg@2: 0.971876\tvalid_0's ndcg@3: 0.973651\tvalid_0's ndcg@4: 0.974027\tvalid_0's ndcg@5: 0.974047\n",
+ "[19]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.97208\tvalid_0's ndcg@3: 0.973805\tvalid_0's ndcg@4: 0.974192\tvalid_0's ndcg@5: 0.974212\n",
+ "[20]\tvalid_0's ndcg@1: 0.932075\tvalid_0's ndcg@2: 0.972092\tvalid_0's ndcg@3: 0.973829\tvalid_0's ndcg@4: 0.974217\tvalid_0's ndcg@5: 0.974236\n",
+ "[21]\tvalid_0's ndcg@1: 0.932675\tvalid_0's ndcg@2: 0.972282\tvalid_0's ndcg@3: 0.974057\tvalid_0's ndcg@4: 0.974444\tvalid_0's ndcg@5: 0.974454\n",
+ "[22]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972358\tvalid_0's ndcg@3: 0.974146\tvalid_0's ndcg@4: 0.974533\tvalid_0's ndcg@5: 0.974543\n",
+ "[23]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972478\tvalid_0's ndcg@3: 0.974253\tvalid_0's ndcg@4: 0.974651\tvalid_0's ndcg@5: 0.974661\n",
+ "[24]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972539\tvalid_0's ndcg@3: 0.974351\tvalid_0's ndcg@4: 0.974739\tvalid_0's ndcg@5: 0.974749\n",
+ "[25]\tvalid_0's ndcg@1: 0.93475\tvalid_0's ndcg@2: 0.973\tvalid_0's ndcg@3: 0.974788\tvalid_0's ndcg@4: 0.975197\tvalid_0's ndcg@5: 0.975206\n",
+ "[26]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.97312\tvalid_0's ndcg@3: 0.974895\tvalid_0's ndcg@4: 0.975315\tvalid_0's ndcg@5: 0.975325\n",
+ "[27]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.974865\tvalid_0's ndcg@4: 0.975264\tvalid_0's ndcg@5: 0.975273\n",
+ "[28]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974939\tvalid_0's ndcg@4: 0.975327\tvalid_0's ndcg@5: 0.975336\n",
+ "[29]\tvalid_0's ndcg@1: 0.935475\tvalid_0's ndcg@2: 0.973315\tvalid_0's ndcg@3: 0.975128\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975492\n",
+ "[30]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973522\tvalid_0's ndcg@3: 0.975297\tvalid_0's ndcg@4: 0.975663\tvalid_0's ndcg@5: 0.975673\n",
+ "[31]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973506\tvalid_0's ndcg@3: 0.975281\tvalid_0's ndcg@4: 0.975658\tvalid_0's ndcg@5: 0.975668\n",
+ "[32]\tvalid_0's ndcg@1: 0.93675\tvalid_0's ndcg@2: 0.973833\tvalid_0's ndcg@3: 0.975595\tvalid_0's ndcg@4: 0.975961\tvalid_0's ndcg@5: 0.975971\n",
+ "[33]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.973763\tvalid_0's ndcg@3: 0.975488\tvalid_0's ndcg@4: 0.975865\tvalid_0's ndcg@5: 0.975874\n",
+ "[34]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973893\tvalid_0's ndcg@3: 0.975568\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975966\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[35]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974059\tvalid_0's ndcg@3: 0.975722\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.97613\n",
+ "[36]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
+ "[37]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
+ "[38]\tvalid_0's ndcg@1: 0.938725\tvalid_0's ndcg@2: 0.974672\tvalid_0's ndcg@3: 0.97636\tvalid_0's ndcg@4: 0.976715\tvalid_0's ndcg@5: 0.976725\n",
+ "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974676\tvalid_0's ndcg@3: 0.976364\tvalid_0's ndcg@4: 0.976697\tvalid_0's ndcg@5: 0.976707\n",
+ "[40]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.974867\tvalid_0's ndcg@3: 0.97653\tvalid_0's ndcg@4: 0.976874\tvalid_0's ndcg@5: 0.976884\n",
+ "[41]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975042\tvalid_0's ndcg@3: 0.976705\tvalid_0's ndcg@4: 0.97705\tvalid_0's ndcg@5: 0.977059\n",
+ "[42]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976784\tvalid_0's ndcg@4: 0.977129\tvalid_0's ndcg@5: 0.977138\n",
+ "[43]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.97517\tvalid_0's ndcg@3: 0.97687\tvalid_0's ndcg@4: 0.977215\tvalid_0's ndcg@5: 0.977225\n",
+ "[44]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.97534\tvalid_0's ndcg@3: 0.977015\tvalid_0's ndcg@4: 0.97736\tvalid_0's ndcg@5: 0.97737\n",
+ "[45]\tvalid_0's ndcg@1: 0.94055\tvalid_0's ndcg@2: 0.975409\tvalid_0's ndcg@3: 0.977059\tvalid_0's ndcg@4: 0.977403\tvalid_0's ndcg@5: 0.977413\n",
+ "[46]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975415\tvalid_0's ndcg@3: 0.97704\tvalid_0's ndcg@4: 0.977396\tvalid_0's ndcg@5: 0.977405\n",
+ "[47]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975363\tvalid_0's ndcg@3: 0.977013\tvalid_0's ndcg@4: 0.977357\tvalid_0's ndcg@5: 0.977367\n",
+ "[48]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975388\tvalid_0's ndcg@3: 0.977025\tvalid_0's ndcg@4: 0.97737\tvalid_0's ndcg@5: 0.977379\n",
+ "[49]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975447\tvalid_0's ndcg@3: 0.977097\tvalid_0's ndcg@4: 0.977409\tvalid_0's ndcg@5: 0.977419\n",
+ "[50]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975666\tvalid_0's ndcg@3: 0.977303\tvalid_0's ndcg@4: 0.977615\tvalid_0's ndcg@5: 0.977625\n",
+ "[51]\tvalid_0's ndcg@1: 0.94135\tvalid_0's ndcg@2: 0.975751\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.97771\tvalid_0's ndcg@5: 0.97772\n",
+ "[52]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.975717\tvalid_0's ndcg@3: 0.977355\tvalid_0's ndcg@4: 0.977688\tvalid_0's ndcg@5: 0.977698\n",
+ "[53]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.975713\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977699\tvalid_0's ndcg@5: 0.977718\n",
+ "[54]\tvalid_0's ndcg@1: 0.94185\tvalid_0's ndcg@2: 0.975857\tvalid_0's ndcg@3: 0.977557\tvalid_0's ndcg@4: 0.977869\tvalid_0's ndcg@5: 0.977889\n",
+ "[55]\tvalid_0's ndcg@1: 0.941925\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.9776\tvalid_0's ndcg@4: 0.977891\tvalid_0's ndcg@5: 0.97791\n",
+ "[56]\tvalid_0's ndcg@1: 0.942325\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977719\tvalid_0's ndcg@4: 0.978032\tvalid_0's ndcg@5: 0.978051\n",
+ "[57]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977772\tvalid_0's ndcg@4: 0.978073\tvalid_0's ndcg@5: 0.978093\n",
+ "[58]\tvalid_0's ndcg@1: 0.9425\tvalid_0's ndcg@2: 0.976081\tvalid_0's ndcg@3: 0.977806\tvalid_0's ndcg@4: 0.978108\tvalid_0's ndcg@5: 0.978127\n",
+ "[59]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977788\tvalid_0's ndcg@4: 0.978079\tvalid_0's ndcg@5: 0.978098\n",
+ "[60]\tvalid_0's ndcg@1: 0.942375\tvalid_0's ndcg@2: 0.976067\tvalid_0's ndcg@3: 0.977779\tvalid_0's ndcg@4: 0.97807\tvalid_0's ndcg@5: 0.978089\n",
+ "[61]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976043\tvalid_0's ndcg@3: 0.97773\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.97804\n",
+ "[62]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976117\tvalid_0's ndcg@3: 0.977792\tvalid_0's ndcg@4: 0.978093\tvalid_0's ndcg@5: 0.978112\n",
+ "[63]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977881\tvalid_0's ndcg@4: 0.978182\tvalid_0's ndcg@5: 0.978201\n",
+ "[64]\tvalid_0's ndcg@1: 0.942925\tvalid_0's ndcg@2: 0.976254\tvalid_0's ndcg@3: 0.977966\tvalid_0's ndcg@4: 0.978268\tvalid_0's ndcg@5: 0.978287\n",
+ "[65]\tvalid_0's ndcg@1: 0.9431\tvalid_0's ndcg@2: 0.97635\tvalid_0's ndcg@3: 0.978025\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978357\n",
+ "[66]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976445\tvalid_0's ndcg@3: 0.978132\tvalid_0's ndcg@4: 0.978445\tvalid_0's ndcg@5: 0.978464\n",
+ "[67]\tvalid_0's ndcg@1: 0.943275\tvalid_0's ndcg@2: 0.976399\tvalid_0's ndcg@3: 0.978074\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978416\n",
+ "[68]\tvalid_0's ndcg@1: 0.943325\tvalid_0's ndcg@2: 0.976401\tvalid_0's ndcg@3: 0.978089\tvalid_0's ndcg@4: 0.978412\tvalid_0's ndcg@5: 0.978431\n",
+ "[69]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976578\tvalid_0's ndcg@3: 0.97819\tvalid_0's ndcg@4: 0.978546\tvalid_0's ndcg@5: 0.978565\n",
+ "[70]\tvalid_0's ndcg@1: 0.944025\tvalid_0's ndcg@2: 0.976707\tvalid_0's ndcg@3: 0.97832\tvalid_0's ndcg@4: 0.978675\tvalid_0's ndcg@5: 0.978694\n",
+ "[71]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976772\tvalid_0's ndcg@3: 0.978384\tvalid_0's ndcg@4: 0.97874\tvalid_0's ndcg@5: 0.978759\n",
+ "[72]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978409\tvalid_0's ndcg@4: 0.978765\tvalid_0's ndcg@5: 0.978784\n",
+ "[73]\tvalid_0's ndcg@1: 0.94445\tvalid_0's ndcg@2: 0.976864\tvalid_0's ndcg@3: 0.978464\tvalid_0's ndcg@4: 0.97883\tvalid_0's ndcg@5: 0.978849\n",
+ "[74]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
+ "[75]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
+ "[76]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.97696\tvalid_0's ndcg@3: 0.978535\tvalid_0's ndcg@4: 0.978901\tvalid_0's ndcg@5: 0.978921\n",
+ "[77]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
+ "[78]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
+ "[79]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976907\tvalid_0's ndcg@3: 0.978507\tvalid_0's ndcg@4: 0.978863\tvalid_0's ndcg@5: 0.978882\n",
+ "[80]\tvalid_0's ndcg@1: 0.94455\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97851\tvalid_0's ndcg@4: 0.978865\tvalid_0's ndcg@5: 0.978885\n",
+ "[81]\tvalid_0's ndcg@1: 0.944725\tvalid_0's ndcg@2: 0.97695\tvalid_0's ndcg@3: 0.978575\tvalid_0's ndcg@4: 0.978919\tvalid_0's ndcg@5: 0.978948\n",
+ "[82]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.977103\tvalid_0's ndcg@3: 0.978765\tvalid_0's ndcg@4: 0.97911\tvalid_0's ndcg@5: 0.979129\n",
+ "[83]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977066\tvalid_0's ndcg@3: 0.978716\tvalid_0's ndcg@4: 0.979071\tvalid_0's ndcg@5: 0.97909\n",
+ "[84]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.97715\tvalid_0's ndcg@3: 0.978775\tvalid_0's ndcg@4: 0.97912\tvalid_0's ndcg@5: 0.979139\n",
+ "[85]\tvalid_0's ndcg@1: 0.945025\tvalid_0's ndcg@2: 0.977092\tvalid_0's ndcg@3: 0.978692\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979067\n",
+ "[86]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977172\tvalid_0's ndcg@3: 0.97876\tvalid_0's ndcg@4: 0.979115\tvalid_0's ndcg@5: 0.979135\n",
+ "[87]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.97879\tvalid_0's ndcg@4: 0.979156\tvalid_0's ndcg@5: 0.979166\n",
+ "[88]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.978815\tvalid_0's ndcg@4: 0.979149\tvalid_0's ndcg@5: 0.979168\n",
+ "[89]\tvalid_0's ndcg@1: 0.94555\tvalid_0's ndcg@2: 0.977333\tvalid_0's ndcg@3: 0.978933\tvalid_0's ndcg@4: 0.979267\tvalid_0's ndcg@5: 0.979277\n",
+ "[90]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977462\tvalid_0's ndcg@3: 0.979062\tvalid_0's ndcg@4: 0.979396\tvalid_0's ndcg@5: 0.979406\n",
+ "[91]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977481\tvalid_0's ndcg@3: 0.979081\tvalid_0's ndcg@4: 0.979414\tvalid_0's ndcg@5: 0.979424\n",
+ "[92]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977437\tvalid_0's ndcg@3: 0.97905\tvalid_0's ndcg@4: 0.979384\tvalid_0's ndcg@5: 0.979393\n",
+ "[93]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977421\tvalid_0's ndcg@3: 0.979046\tvalid_0's ndcg@4: 0.97938\tvalid_0's ndcg@5: 0.97939\n",
+ "[94]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977431\tvalid_0's ndcg@3: 0.979068\tvalid_0's ndcg@4: 0.979391\tvalid_0's ndcg@5: 0.979401\n",
+ "[95]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977449\tvalid_0's ndcg@3: 0.979074\tvalid_0's ndcg@4: 0.979408\tvalid_0's ndcg@5: 0.979418\n",
+ "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979127\tvalid_0's ndcg@4: 0.979461\tvalid_0's ndcg@5: 0.97947\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[97]\tvalid_0's ndcg@1: 0.946375\tvalid_0's ndcg@2: 0.977622\tvalid_0's ndcg@3: 0.979222\tvalid_0's ndcg@4: 0.979577\tvalid_0's ndcg@5: 0.979577\n",
+ "[98]\tvalid_0's ndcg@1: 0.946625\tvalid_0's ndcg@2: 0.977714\tvalid_0's ndcg@3: 0.979339\tvalid_0's ndcg@4: 0.979673\tvalid_0's ndcg@5: 0.979673\n",
+ "[99]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.977739\tvalid_0's ndcg@3: 0.979352\tvalid_0's ndcg@4: 0.979685\tvalid_0's ndcg@5: 0.979685\n",
+ "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_rank_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id','label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 训练集与验证集的用户分组\n",
+ " train_idx.sort_values(by=['user_id'], inplace=True)\n",
+ " g_train = train_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id'], inplace=True)\n",
+ " g_val = valid_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ " \n",
+ " # 定义模型\n",
+ " lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) \n",
+ " # 训练模型\n",
+ " lgb_ranker.fit(train_idx[lgb_cols], train_idx['label'], group=g_train,\n",
+ " eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], eval_group= [g_val], \n",
+ " eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = lgb_ranker.predict(valid_idx[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
+ " \n",
+ " # 对输出结果进行归一化\n",
+ " valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += lgb_ranker.predict(tst_user_item_feats_df_rank_model[lgb_cols], lgb_ranker.best_iteration_)\n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_ranker_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_ranker_feats.csv', index=False)"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/2\n",
- "290964/290964 [==============================] - 55s 189us/sample - loss: 0.4209 - binary_crossentropy: 0.4206 - auc: 0.7842\n",
- "Epoch 2/2\n",
- "290964/290964 [==============================] - 52s 178us/sample - loss: 0.3630 - binary_crossentropy: 0.3618 - auc: 0.8478\n"
- ]
- }
- ],
- "source": [
- "# 模型训练\n",
- "if offline:\n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=10, validation_data=(x_val, y_val) , batch_size=256)\n",
- "else:\n",
- " # 也可以使用上面的语句用自己采样出来的验证集\n",
- " # history = model.fit(x_trn, y_trn, verbose=1, epochs=3, validation_split=0.3, batch_size=256)\n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, batch_size=256)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:29:20.436591Z",
- "start_time": "2020-11-18T04:28:58.102057Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:52.604397Z",
+ "start_time": "2020-11-18T04:22:43.253034Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "# 单模型生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_ranker')"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "500000/500000 [==============================] - 20s 39us/sample\n"
- ]
- }
- ],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df_din_model['pred_score'] = model.predict(x_tst, verbose=1, batch_size=256)\n",
- "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'din_rank_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:29:34.985535Z",
- "start_time": "2020-11-18T04:29:26.264531Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "submit(rank_results, topk=5, model_name='din')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-15T06:15:49.490705Z",
- "start_time": "2020-11-15T06:15:49.473794Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:38:53.760383Z",
- "start_time": "2020-11-18T04:29:51.737721Z"
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LGB分类模型"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 232681 samples, validate on 58283 samples\n",
- "Epoch 1/2\n",
- "232681/232681 [==============================] - 44s 189us/sample - loss: 0.2864 - binary_crossentropy: 0.2846 - auc: 0.9008 - val_loss: 0.2830 - val_binary_crossentropy: 0.2813 - val_auc: 0.9072\n",
- "Epoch 2/2\n",
- "232681/232681 [==============================] - 44s 187us/sample - loss: 0.2832 - binary_crossentropy: 0.2816 - auc: 0.9034 - val_loss: 0.2846 - val_binary_crossentropy: 0.2830 - val_auc: 0.9053\n",
- "58283/58283 [==============================] - 2s 36us/sample\n",
- "500000/500000 [==============================] - 19s 37us/sample\n",
- "Train on 232798 samples, validate on 58166 samples\n",
- "Epoch 1/2\n",
- "232798/232798 [==============================] - 43s 184us/sample - loss: 0.2818 - binary_crossentropy: 0.2802 - auc: 0.9051 - val_loss: 0.2968 - val_binary_crossentropy: 0.2953 - val_auc: 0.9062\n",
- "Epoch 2/2\n",
- "232798/232798 [==============================] - 44s 187us/sample - loss: 0.2796 - binary_crossentropy: 0.2782 - auc: 0.9069 - val_loss: 0.2820 - val_binary_crossentropy: 0.2806 - val_auc: 0.9071\n",
- "58166/58166 [==============================] - 2s 38us/sample\n",
- "500000/500000 [==============================] - 18s 37us/sample\n",
- "Train on 232847 samples, validate on 58117 samples\n",
- "Epoch 1/2\n",
- "232847/232847 [==============================] - 43s 185us/sample - loss: 0.2786 - binary_crossentropy: 0.2773 - auc: 0.9080 - val_loss: 0.2761 - val_binary_crossentropy: 0.2749 - val_auc: 0.9113\n",
- "Epoch 2/2\n",
- "232847/232847 [==============================] - 39s 166us/sample - loss: 0.2766 - binary_crossentropy: 0.2754 - auc: 0.9097 - val_loss: 0.2872 - val_binary_crossentropy: 0.2862 - val_auc: 0.9090\n",
- "58117/58117 [==============================] - 2s 34us/sample\n",
- "500000/500000 [==============================] - 17s 33us/sample\n",
- "Train on 232716 samples, validate on 58248 samples\n",
- "Epoch 1/2\n",
- "232716/232716 [==============================] - 39s 169us/sample - loss: 0.2763 - binary_crossentropy: 0.2753 - auc: 0.9100 - val_loss: 0.2739 - val_binary_crossentropy: 0.2730 - val_auc: 0.9116\n",
- "Epoch 2/2\n",
- "232716/232716 [==============================] - 39s 168us/sample - loss: 0.2743 - binary_crossentropy: 0.2735 - auc: 0.9119 - val_loss: 0.2859 - val_binary_crossentropy: 0.2851 - val_auc: 0.9090\n",
- "58248/58248 [==============================] - 2s 35us/sample\n",
- "500000/500000 [==============================] - 17s 34us/sample\n",
- "Train on 232814 samples, validate on 58150 samples\n",
- "Epoch 1/2\n",
- "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2747 - binary_crossentropy: 0.2739 - auc: 0.9115 - val_loss: 0.2702 - val_binary_crossentropy: 0.2695 - val_auc: 0.9163\n",
- "Epoch 2/2\n",
- "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2725 - binary_crossentropy: 0.2719 - auc: 0.9132 - val_loss: 0.2751 - val_binary_crossentropy: 0.2745 - val_auc: 0.9151\n",
- "58150/58150 [==============================] - 2s 34us/sample\n",
- "500000/500000 [==============================] - 17s 34us/sample\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_din_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "dense_fea = [x for x in dense_fea if x != 'label']\n",
- "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 准备训练数据\n",
- " x_trn, dnn_feature_columns = get_din_feats_columns(train_idx, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_trn = train_idx['label'].values\n",
- "\n",
- " # 准备验证数据\n",
- " x_val, dnn_feature_columns = get_din_feats_columns(valid_idx, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_val = valid_idx['label'].values\n",
- " \n",
- " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, validation_data=(x_val, y_val) , batch_size=256)\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = model.predict(x_val, verbose=1, batch_size=256) \n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += model.predict(x_tst, verbose=1, batch_size=256)[:, 0] \n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_din_cls_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_din_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_din_model['pred_score'] = tst_user_item_feats_df_din_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_din_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_din_model['pred_rank'] = tst_user_item_feats_df_din_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_din_cls_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 模型融合"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 加权融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:27.351996Z",
- "start_time": "2020-11-18T04:44:26.561275Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取多个模型的排序结果文件\n",
- "lgb_ranker = pd.read_csv(save_path + 'lgb_ranker_score.csv')\n",
- "lgb_cls = pd.read_csv(save_path + 'lgb_cls_score.csv')\n",
- "din_ranker = pd.read_csv(save_path + 'din_rank_score.csv')\n",
- "\n",
- "# 这里也可以换成交叉验证输出的测试结果进行加权融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:31.593981Z",
- "start_time": "2020-11-18T04:44:31.589439Z"
- }
- },
- "outputs": [],
- "source": [
- "rank_model = {'lgb_ranker': lgb_ranker, \n",
- " 'lgb_cls': lgb_cls, \n",
- " 'din_ranker': din_ranker}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:36.135860Z",
- "start_time": "2020-11-18T04:44:36.130577Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_ensumble_predict_topk(rank_model, topk=5):\n",
- " final_recall = rank_model['lgb_cls'].append(rank_model['din_ranker'])\n",
- " rank_model['lgb_ranker']['pred_score'] = rank_model['lgb_ranker']['pred_score'].transform(lambda x: norm_sim(x))\n",
- " \n",
- " final_recall = final_recall.append(rank_model['lgb_ranker'])\n",
- " final_recall = final_recall.groupby(['user_id', 'click_article_id'])['pred_score'].sum().reset_index()\n",
- " \n",
- " submit(final_recall, topk=topk, model_name='ensemble_fuse')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:51.659270Z",
- "start_time": "2020-11-18T04:44:40.445659Z"
- }
- },
- "outputs": [],
- "source": [
- "get_ensumble_predict_topk(rank_model)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Staking"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:44:58.025992Z",
- "start_time": "2020-11-18T04:44:56.146962Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取多个模型的交叉验证生成的结果文件\n",
- "# 训练集\n",
- "trn_lgb_ranker_feats = pd.read_csv(save_path + 'trn_lgb_ranker_feats.csv')\n",
- "trn_lgb_cls_feats = pd.read_csv(save_path + 'trn_lgb_cls_feats.csv')\n",
- "trn_din_cls_feats = pd.read_csv(save_path + 'trn_din_cls_feats.csv')\n",
- "\n",
- "# 测试集\n",
- "tst_lgb_ranker_feats = pd.read_csv(save_path + 'tst_lgb_ranker_feats.csv')\n",
- "tst_lgb_cls_feats = pd.read_csv(save_path + 'tst_lgb_cls_feats.csv')\n",
- "tst_din_cls_feats = pd.read_csv(save_path + 'tst_din_cls_feats.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:07.701862Z",
- "start_time": "2020-11-18T04:45:07.644335Z"
- }
- },
- "outputs": [],
- "source": [
- "# 将多个模型输出的特征进行拼接\n",
- "\n",
- "finall_trn_ranker_feats = trn_lgb_ranker_feats[['user_id', 'click_article_id', 'label']]\n",
- "finall_tst_ranker_feats = tst_lgb_ranker_feats[['user_id', 'click_article_id']]\n",
- "\n",
- "for idx, trn_model in enumerate([trn_lgb_ranker_feats, trn_lgb_cls_feats, trn_din_cls_feats]):\n",
- " for feat in [ 'pred_score', 'pred_rank']:\n",
- " col_name = feat + '_' + str(idx)\n",
- " finall_trn_ranker_feats[col_name] = trn_model[feat]\n",
- "\n",
- "for idx, tst_model in enumerate([tst_lgb_ranker_feats, tst_lgb_cls_feats, tst_din_cls_feats]):\n",
- " for feat in [ 'pred_score', 'pred_rank']:\n",
- " col_name = feat + '_' + str(idx)\n",
- " finall_tst_ranker_feats[col_name] = tst_model[feat]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:15.044242Z",
- "start_time": "2020-11-18T04:45:13.138252Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义一个逻辑回归模型再次拟合交叉验证产生的特征对测试集进行预测\n",
- "# 这里需要注意的是，在做交叉验证的时候可以构造多一些与输出预测值相关的特征，来丰富这里简单模型的特征\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "\n",
- "feat_cols = ['pred_score_0', 'pred_rank_0', 'pred_score_1', 'pred_rank_1', 'pred_score_2', 'pred_rank_2']\n",
- "\n",
- "trn_x = finall_trn_ranker_feats[feat_cols]\n",
- "trn_y = finall_trn_ranker_feats['label']\n",
- "\n",
- "tst_x = finall_tst_ranker_feats[feat_cols]\n",
- "\n",
- "# 定义模型\n",
- "lr = LogisticRegression()\n",
- "\n",
- "# 模型训练\n",
- "lr.fit(trn_x, trn_y)\n",
- "\n",
- "# 模型预测\n",
- "finall_tst_ranker_feats['pred_score'] = lr.predict_proba(tst_x)[:, 1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:45:29.018764Z",
- "start_time": "2020-11-18T04:45:19.423130Z"
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:22:58.259730Z",
+ "start_time": "2020-11-18T04:22:58.254297Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型及参数的定义\n",
+ "lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=500, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:11.258774Z",
+ "start_time": "2020-11-18T04:23:00.861936Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型训练\n",
+ "if offline:\n",
+ " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'],\n",
+ " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
+ " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
+ "else:\n",
+ " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:19.591396Z",
+ "start_time": "2020-11-18T04:23:13.813850Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df['pred_score'] = lgb_Classfication.predict_proba(tst_user_item_feats_df[lgb_cols])[:,1]\n",
+ "\n",
+ "# 将这里的排序结果保存一份，用户后面的模型融合\n",
+ "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_cls_score.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:23:32.352931Z",
+ "start_time": "2020-11-18T04:23:22.346609Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_cls')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:11.241196Z",
+ "start_time": "2020-11-18T04:23:41.377394Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1]\tvalid_0's auc: 0.764896\tvalid_0's binary_logloss: 0.522153\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.767857\tvalid_0's binary_logloss: 0.52057\n",
+ "[3]\tvalid_0's auc: 0.783096\tvalid_0's binary_logloss: 0.519584\n",
+ "[4]\tvalid_0's auc: 0.784354\tvalid_0's binary_logloss: 0.518485\n",
+ "[5]\tvalid_0's auc: 0.790554\tvalid_0's binary_logloss: 0.516886\n",
+ "[6]\tvalid_0's auc: 0.791954\tvalid_0's binary_logloss: 0.515334\n",
+ "[7]\tvalid_0's auc: 0.794257\tvalid_0's binary_logloss: 0.514032\n",
+ "[8]\tvalid_0's auc: 0.795222\tvalid_0's binary_logloss: 0.512516\n",
+ "[9]\tvalid_0's auc: 0.795417\tvalid_0's binary_logloss: 0.511671\n",
+ "[10]\tvalid_0's auc: 0.795913\tvalid_0's binary_logloss: 0.510226\n",
+ "[11]\tvalid_0's auc: 0.798222\tvalid_0's binary_logloss: 0.508858\n",
+ "[12]\tvalid_0's auc: 0.79825\tvalid_0's binary_logloss: 0.507928\n",
+ "[13]\tvalid_0's auc: 0.798842\tvalid_0's binary_logloss: 0.50708\n",
+ "[14]\tvalid_0's auc: 0.798935\tvalid_0's binary_logloss: 0.505752\n",
+ "[15]\tvalid_0's auc: 0.799543\tvalid_0's binary_logloss: 0.504388\n",
+ "[16]\tvalid_0's auc: 0.800844\tvalid_0's binary_logloss: 0.503126\n",
+ "[17]\tvalid_0's auc: 0.800855\tvalid_0's binary_logloss: 0.501809\n",
+ "[18]\tvalid_0's auc: 0.801653\tvalid_0's binary_logloss: 0.500676\n",
+ "[19]\tvalid_0's auc: 0.801518\tvalid_0's binary_logloss: 0.49987\n",
+ "[20]\tvalid_0's auc: 0.801662\tvalid_0's binary_logloss: 0.498625\n",
+ "[21]\tvalid_0's auc: 0.802093\tvalid_0's binary_logloss: 0.498113\n",
+ "[22]\tvalid_0's auc: 0.803071\tvalid_0's binary_logloss: 0.496933\n",
+ "[23]\tvalid_0's auc: 0.803222\tvalid_0's binary_logloss: 0.495864\n",
+ "[24]\tvalid_0's auc: 0.802927\tvalid_0's binary_logloss: 0.494691\n",
+ "[25]\tvalid_0's auc: 0.802581\tvalid_0's binary_logloss: 0.493543\n",
+ "[26]\tvalid_0's auc: 0.802965\tvalid_0's binary_logloss: 0.492444\n",
+ "[27]\tvalid_0's auc: 0.80298\tvalid_0's binary_logloss: 0.491336\n",
+ "[28]\tvalid_0's auc: 0.803226\tvalid_0's binary_logloss: 0.490275\n",
+ "[29]\tvalid_0's auc: 0.803436\tvalid_0's binary_logloss: 0.489126\n",
+ "[30]\tvalid_0's auc: 0.803796\tvalid_0's binary_logloss: 0.48802\n",
+ "[31]\tvalid_0's auc: 0.803601\tvalid_0's binary_logloss: 0.486988\n",
+ "[32]\tvalid_0's auc: 0.804416\tvalid_0's binary_logloss: 0.485972\n",
+ "[33]\tvalid_0's auc: 0.804529\tvalid_0's binary_logloss: 0.484939\n",
+ "[34]\tvalid_0's auc: 0.804534\tvalid_0's binary_logloss: 0.483927\n",
+ "[35]\tvalid_0's auc: 0.804819\tvalid_0's binary_logloss: 0.483271\n",
+ "[36]\tvalid_0's auc: 0.804774\tvalid_0's binary_logloss: 0.482273\n",
+ "[37]\tvalid_0's auc: 0.805237\tvalid_0's binary_logloss: 0.481639\n",
+ "[38]\tvalid_0's auc: 0.805546\tvalid_0's binary_logloss: 0.480959\n",
+ "[39]\tvalid_0's auc: 0.805598\tvalid_0's binary_logloss: 0.479955\n",
+ "[40]\tvalid_0's auc: 0.806011\tvalid_0's binary_logloss: 0.47903\n",
+ "[41]\tvalid_0's auc: 0.806664\tvalid_0's binary_logloss: 0.478439\n",
+ "[42]\tvalid_0's auc: 0.807021\tvalid_0's binary_logloss: 0.477798\n",
+ "[43]\tvalid_0's auc: 0.80726\tvalid_0's binary_logloss: 0.476829\n",
+ "[44]\tvalid_0's auc: 0.807157\tvalid_0's binary_logloss: 0.475976\n",
+ "[45]\tvalid_0's auc: 0.807788\tvalid_0's binary_logloss: 0.475056\n",
+ "[46]\tvalid_0's auc: 0.80805\tvalid_0's binary_logloss: 0.474446\n",
+ "[47]\tvalid_0's auc: 0.808097\tvalid_0's binary_logloss: 0.473576\n",
+ "[48]\tvalid_0's auc: 0.80815\tvalid_0's binary_logloss: 0.472676\n",
+ "[49]\tvalid_0's auc: 0.808304\tvalid_0's binary_logloss: 0.471918\n",
+ "[50]\tvalid_0's auc: 0.808749\tvalid_0's binary_logloss: 0.471481\n",
+ "[51]\tvalid_0's auc: 0.808972\tvalid_0's binary_logloss: 0.471104\n",
+ "[52]\tvalid_0's auc: 0.809326\tvalid_0's binary_logloss: 0.470289\n",
+ "[53]\tvalid_0's auc: 0.809472\tvalid_0's binary_logloss: 0.469508\n",
+ "[54]\tvalid_0's auc: 0.809505\tvalid_0's binary_logloss: 0.46869\n",
+ "[55]\tvalid_0's auc: 0.809594\tvalid_0's binary_logloss: 0.467885\n",
+ "[56]\tvalid_0's auc: 0.809847\tvalid_0's binary_logloss: 0.467356\n",
+ "[57]\tvalid_0's auc: 0.810262\tvalid_0's binary_logloss: 0.466531\n",
+ "[58]\tvalid_0's auc: 0.810407\tvalid_0's binary_logloss: 0.46573\n",
+ "[59]\tvalid_0's auc: 0.810618\tvalid_0's binary_logloss: 0.465205\n",
+ "[60]\tvalid_0's auc: 0.81066\tvalid_0's binary_logloss: 0.464435\n",
+ "[61]\tvalid_0's auc: 0.810638\tvalid_0's binary_logloss: 0.463721\n",
+ "[62]\tvalid_0's auc: 0.810658\tvalid_0's binary_logloss: 0.462982\n",
+ "[63]\tvalid_0's auc: 0.811106\tvalid_0's binary_logloss: 0.462246\n",
+ "[64]\tvalid_0's auc: 0.811313\tvalid_0's binary_logloss: 0.461748\n",
+ "[65]\tvalid_0's auc: 0.811351\tvalid_0's binary_logloss: 0.461038\n",
+ "[66]\tvalid_0's auc: 0.811433\tvalid_0's binary_logloss: 0.460323\n",
+ "[67]\tvalid_0's auc: 0.81158\tvalid_0's binary_logloss: 0.459662\n",
+ "[68]\tvalid_0's auc: 0.811561\tvalid_0's binary_logloss: 0.458988\n",
+ "[69]\tvalid_0's auc: 0.811748\tvalid_0's binary_logloss: 0.458592\n",
+ "[70]\tvalid_0's auc: 0.811919\tvalid_0's binary_logloss: 0.457934\n",
+ "[71]\tvalid_0's auc: 0.812073\tvalid_0's binary_logloss: 0.457508\n",
+ "[72]\tvalid_0's auc: 0.812273\tvalid_0's binary_logloss: 0.457038\n",
+ "[73]\tvalid_0's auc: 0.812561\tvalid_0's binary_logloss: 0.456439\n",
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+ "[75]\tvalid_0's auc: 0.812757\tvalid_0's binary_logloss: 0.455173\n",
+ "[76]\tvalid_0's auc: 0.812923\tvalid_0's binary_logloss: 0.454533\n",
+ "[77]\tvalid_0's auc: 0.81295\tvalid_0's binary_logloss: 0.45392\n",
+ "[78]\tvalid_0's auc: 0.813073\tvalid_0's binary_logloss: 0.453517\n",
+ "[79]\tvalid_0's auc: 0.813202\tvalid_0's binary_logloss: 0.452932\n",
+ "[80]\tvalid_0's auc: 0.813611\tvalid_0's binary_logloss: 0.452285\n",
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+ "[82]\tvalid_0's auc: 0.814468\tvalid_0's binary_logloss: 0.451455\n",
+ "[83]\tvalid_0's auc: 0.814656\tvalid_0's binary_logloss: 0.450885\n",
+ "[84]\tvalid_0's auc: 0.814755\tvalid_0's binary_logloss: 0.450308\n",
+ "[85]\tvalid_0's auc: 0.814824\tvalid_0's binary_logloss: 0.449739\n",
+ "[86]\tvalid_0's auc: 0.81499\tvalid_0's binary_logloss: 0.449348\n",
+ "[87]\tvalid_0's auc: 0.815232\tvalid_0's binary_logloss: 0.448759\n",
+ "[88]\tvalid_0's auc: 0.815452\tvalid_0's binary_logloss: 0.44823\n",
+ "[89]\tvalid_0's auc: 0.815593\tvalid_0's binary_logloss: 0.447861\n",
+ "[90]\tvalid_0's auc: 0.815591\tvalid_0's binary_logloss: 0.447323\n",
+ "[91]\tvalid_0's auc: 0.815672\tvalid_0's binary_logloss: 0.446796\n",
+ "[92]\tvalid_0's auc: 0.815875\tvalid_0's binary_logloss: 0.446472\n",
+ "[93]\tvalid_0's auc: 0.815984\tvalid_0's binary_logloss: 0.445961\n",
+ "[94]\tvalid_0's auc: 0.816026\tvalid_0's binary_logloss: 0.445439\n",
+ "[95]\tvalid_0's auc: 0.816172\tvalid_0's binary_logloss: 0.444909\n",
+ "[96]\tvalid_0's auc: 0.816321\tvalid_0's binary_logloss: 0.444413\n",
+ "[97]\tvalid_0's auc: 0.816751\tvalid_0's binary_logloss: 0.44405\n",
+ "[98]\tvalid_0's auc: 0.817226\tvalid_0's binary_logloss: 0.443626\n",
+ "[99]\tvalid_0's auc: 0.817286\tvalid_0's binary_logloss: 0.443136\n",
+ "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
+ "[1]\tvalid_0's auc: 0.771584\tvalid_0's binary_logloss: 0.527139\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.775446\tvalid_0's binary_logloss: 0.525462\n",
+ "[3]\tvalid_0's auc: 0.790092\tvalid_0's binary_logloss: 0.524461\n",
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+ "[7]\tvalid_0's auc: 0.800753\tvalid_0's binary_logloss: 0.5187\n",
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+ "[11]\tvalid_0's auc: 0.804972\tvalid_0's binary_logloss: 0.513321\n",
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+ "[15]\tvalid_0's auc: 0.805622\tvalid_0's binary_logloss: 0.508708\n",
+ "[16]\tvalid_0's auc: 0.806974\tvalid_0's binary_logloss: 0.507384\n",
+ "[17]\tvalid_0's auc: 0.807045\tvalid_0's binary_logloss: 0.506017\n",
+ "[18]\tvalid_0's auc: 0.807265\tvalid_0's binary_logloss: 0.504853\n",
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+ "[31]\tvalid_0's auc: 0.810086\tvalid_0's binary_logloss: 0.490633\n"
+ ]
+ },
+ {
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+ "text": [
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+ "[80]\tvalid_0's auc: 0.819958\tvalid_0's binary_logloss: 0.454765\n",
+ "[81]\tvalid_0's auc: 0.820115\tvalid_0's binary_logloss: 0.45436\n",
+ "[82]\tvalid_0's auc: 0.820536\tvalid_0's binary_logloss: 0.453965\n",
+ "[83]\tvalid_0's auc: 0.820649\tvalid_0's binary_logloss: 0.453383\n",
+ "[84]\tvalid_0's auc: 0.820663\tvalid_0's binary_logloss: 0.452804\n",
+ "[85]\tvalid_0's auc: 0.820809\tvalid_0's binary_logloss: 0.452167\n",
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+ "[88]\tvalid_0's auc: 0.821243\tvalid_0's binary_logloss: 0.45061\n",
+ "[89]\tvalid_0's auc: 0.821404\tvalid_0's binary_logloss: 0.450215\n",
+ "[90]\tvalid_0's auc: 0.821488\tvalid_0's binary_logloss: 0.449656\n",
+ "[91]\tvalid_0's auc: 0.821538\tvalid_0's binary_logloss: 0.449107\n",
+ "[92]\tvalid_0's auc: 0.82172\tvalid_0's binary_logloss: 0.448752\n",
+ "[93]\tvalid_0's auc: 0.821809\tvalid_0's binary_logloss: 0.448188\n",
+ "[94]\tvalid_0's auc: 0.82184\tvalid_0's binary_logloss: 0.447659\n",
+ "[95]\tvalid_0's auc: 0.821971\tvalid_0's binary_logloss: 0.447108\n",
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+ "[97]\tvalid_0's auc: 0.82247\tvalid_0's binary_logloss: 0.446244\n",
+ "[98]\tvalid_0's auc: 0.822951\tvalid_0's binary_logloss: 0.445812\n",
+ "[99]\tvalid_0's auc: 0.822991\tvalid_0's binary_logloss: 0.445329\n",
+ "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
+ "[1]\tvalid_0's auc: 0.769525\tvalid_0's binary_logloss: 0.526256\n",
+ "Training until validation scores don't improve for 50 rounds\n",
+ "[2]\tvalid_0's auc: 0.775857\tvalid_0's binary_logloss: 0.524594\n",
+ "[3]\tvalid_0's auc: 0.785307\tvalid_0's binary_logloss: 0.523606\n",
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+ "[88]\tvalid_0's auc: 0.817594\tvalid_0's binary_logloss: 0.450926\n",
+ "[89]\tvalid_0's auc: 0.817771\tvalid_0's binary_logloss: 0.450553\n",
+ "[90]\tvalid_0's auc: 0.81789\tvalid_0's binary_logloss: 0.449985\n",
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+ "[93]\tvalid_0's auc: 0.818334\tvalid_0's binary_logloss: 0.448527\n",
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+ "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
+ "[1]\tvalid_0's auc: 0.770032\tvalid_0's binary_logloss: 0.527241\n",
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+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
+ "[1]\tvalid_0's auc: 0.768646\tvalid_0's binary_logloss: 0.527167\n",
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+ "[2]\tvalid_0's auc: 0.779902\tvalid_0's binary_logloss: 0.525481\n",
+ "[3]\tvalid_0's auc: 0.789868\tvalid_0's binary_logloss: 0.524485\n",
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+ "[99]\tvalid_0's auc: 0.819016\tvalid_0's binary_logloss: 0.446669\n",
+ "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n",
+ "Did not meet early stopping. Best iteration is:\n",
+ "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_rank_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 模型及参数的定义\n",
+ " lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) \n",
+ " # 训练模型\n",
+ " lgb_Classfication.fit(train_idx[lgb_cols], train_idx['label'],eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], \n",
+ " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = lgb_Classfication.predict_proba(valid_idx[lgb_cols], \n",
+ " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
+ " \n",
+ " # 对输出结果进行归一化 分类模型输出的值本身就是一个概率值不需要进行归一化\n",
+ " # valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += lgb_Classfication.predict_proba(tst_user_item_feats_df_rank_model[lgb_cols], \n",
+ " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_cls_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_cls_feats.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:23.074237Z",
+ "start_time": "2020-11-18T04:24:13.812284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_cls')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DIN模型"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 用户的历史点击行为列表\n",
+ "这个是为后面的DIN模型服务的"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:24:30.508213Z",
+ "start_time": "2020-11-18T04:24:27.426372Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "if offline:\n",
+ " all_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
+ "else:\n",
+ " trn_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
+ " tst_data = pd.read_csv('./data_raw/testA_click_log.csv')\n",
+ " all_data = trn_data.append(tst_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:25:28.082071Z",
+ "start_time": "2020-11-18T04:24:33.649524Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "hist_click =all_data[['user_id', 'click_article_id']].groupby('user_id').agg({list}).reset_index()\n",
+ "his_behavior_df = pd.DataFrame()\n",
+ "his_behavior_df['user_id'] = hist_click['user_id']\n",
+ "his_behavior_df['hist_click_article_id'] = hist_click['click_article_id']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:25:52.925866Z",
+ "start_time": "2020-11-18T04:25:52.863922Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "trn_user_item_feats_df_din_model = trn_user_item_feats_df.copy()\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_din_model = val_user_item_feats_df.copy()\n",
+ "else: \n",
+ " val_user_item_feats_df_din_model = None\n",
+ " \n",
+ "tst_user_item_feats_df_din_model = tst_user_item_feats_df.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:00.070681Z",
+ "start_time": "2020-11-18T04:25:56.417197Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "trn_user_item_feats_df_din_model = trn_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_din_model = val_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
+ "else:\n",
+ " val_user_item_feats_df_din_model = None\n",
+ "\n",
+ "tst_user_item_feats_df_din_model = tst_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### DIN模型简介\n",
+ "我们下面尝试使用DIN模型， DIN的全称是Deep Interest Network， 这是阿里2018年基于前面的深度学习模型无法表达用户多样化的兴趣而提出的一个模型， 它可以通过考虑【给定的候选广告】和【用户的历史行为】的相关性，来计算用户兴趣的表示向量。具体来说就是通过引入局部激活单元，通过软搜索历史行为的相关部分来关注相关的用户兴趣，并采用加权和来获得有关候选广告的用户兴趣的表示。与候选广告相关性较高的行为会获得较高的激活权重，并支配着用户兴趣。该表示向量在不同广告上有所不同，大大提高了模型的表达能力。所以该模型对于此次新闻推荐的任务也比较适合， 我们在这里通过当前的候选文章与用户历史点击文章的相关性来计算用户对于文章的兴趣。 该模型的结构如下：\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "我们这里直接调包来使用这个模型， 关于这个模型的详细细节部分我们会在下一期的推荐系统组队学习中给出。下面说一下该模型如何具体使用：deepctr的函数原型如下：\n",
+ "> def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,\n",
+ "> dnn_hidden_units=(200, 80), dnn_activation='relu', att_hidden_size=(80, 40), att_activation=\"dice\",\n",
+ "> att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024,\n",
+ "> task='binary'):\n",
+ "> \n",
+ "> * dnn_feature_columns: 特征列， 包含数据所有特征的列表\n",
+ "> * history_feature_list: 用户历史行为列， 反应用户历史行为的特征的列表\n",
+ "> * dnn_use_bn: 是否使用BatchNormalization\n",
+ "> * dnn_hidden_units: 全连接层网络的层数和每一层神经元的个数， 一个列表或者元组\n",
+ "> * dnn_activation_relu: 全连接网络的激活单元类型\n",
+ "> * att_hidden_size: 注意力层的全连接网络的层数和每一层神经元的个数\n",
+ "> * att_activation: 注意力层的激活单元类型\n",
+ "> * att_weight_normalization: 是否归一化注意力得分\n",
+ "> * l2_reg_dnn: 全连接网络的正则化系数\n",
+ "> * l2_reg_embedding: embedding向量的正则化稀疏\n",
+ "> * dnn_dropout: 全连接网络的神经元的失活概率\n",
+ "> * task: 任务， 可以是分类， 也可是是回归\n",
+ "\n",
+ "在具体使用的时候， 我们必须要传入特征列和历史行为列， 但是再传入之前， 我们需要进行一下特征列的预处理。具体如下：\n",
+ "\n",
+ "1. 首先，我们要处理数据集， 得到数据， 由于我们是基于用户过去的行为去预测用户是否点击当前文章， 所以我们需要把数据的特征列划分成数值型特征， 离散型特征和历史行为特征列三部分， 对于每一部分， DIN模型的处理会有不同\n",
+ " 1. 对于离散型特征， 在我们的数据集中就是那些类别型的特征， 比如user_id这种， 这种类别型特征， 我们首先要经过embedding处理得到每个特征的低维稠密型表示， 既然要经过embedding， 那么我们就需要为每一列的类别特征的取值建立一个字典，并指明embedding维度， 所以在使用deepctr的DIN模型准备数据的时候， 我们需要通过SparseFeat函数指明这些类别型特征, 这个函数的传入参数就是列名， 列的唯一取值(建立字典用)和embedding维度。\n",
+ " 2. 对于用户历史行为特征列， 比如文章id， 文章的类别等这种， 同样的我们需要先经过embedding处理， 只不过和上面不一样的地方是，对于这种特征， 我们在得到每个特征的embedding表示之后， 还需要通过一个Attention_layer计算用户的历史行为和当前候选文章的相关性以此得到当前用户的embedding向量， 这个向量就可以基于当前的候选文章与用户过去点击过得历史文章的相似性的程度来反应用户的兴趣， 并且随着用户的不同的历史点击来变化，去动态的模拟用户兴趣的变化过程。这类特征对于每个用户都是一个历史行为序列， 对于每个用户， 历史行为序列长度会不一样， 可能有的用户点击的历史文章多，有的点击的历史文章少， 所以我们还需要把这个长度统一起来， 在为DIN模型准备数据的时候， 我们首先要通过SparseFeat函数指明这些类别型特征， 然后还需要通过VarLenSparseFeat函数再进行序列填充， 使得每个用户的历史序列一样长， 所以这个函数参数中会有个maxlen，来指明序列的最大长度是多少。\n",
+ " 3. 对于连续型特征列， 我们只需要用DenseFeat函数来指明列名和维度即可。\n",
+ "2. 处理完特征列之后， 我们把相应的数据与列进行对应，就得到了最后的数据。\n",
+ "\n",
+ "下面根据具体的代码感受一下， 逻辑是这样， 首先我们需要写一个数据准备函数， 在这里面就是根据上面的具体步骤准备数据， 得到数据和特征列， 然后就是建立DIN模型并训练， 最后基于模型进行测试。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:08.405211Z",
+ "start_time": "2020-11-18T04:26:04.887013Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 导入deepctr\n",
+ "from deepctr.models import DIN\n",
+ "from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, get_feature_names\n",
+ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "from tensorflow.keras import backend as K\n",
+ "from tensorflow.keras.layers import *\n",
+ "from tensorflow.keras.models import *\n",
+ "from tensorflow.keras.callbacks import * \n",
+ "import tensorflow as tf\n",
+ "\n",
+ "import os\n",
+ "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
+ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:13.485712Z",
+ "start_time": "2020-11-18T04:26:13.476042Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 数据准备函数\n",
+ "def get_din_feats_columns(df, dense_fea, sparse_fea, behavior_fea, his_behavior_fea, emb_dim=32, max_len=100):\n",
+ " \"\"\"\n",
+ " 数据准备函数:\n",
+ " df: 数据集\n",
+ " dense_fea: 数值型特征列\n",
+ " sparse_fea: 离散型特征列\n",
+ " behavior_fea: 用户的候选行为特征列\n",
+ " his_behavior_fea: 用户的历史行为特征列\n",
+ " embedding_dim: embedding的维度， 这里为了简单， 统一把离散型特征列采用一样的隐向量维度\n",
+ " max_len: 用户序列的最大长度\n",
+ " \"\"\"\n",
+ " \n",
+ " sparse_feature_columns = [SparseFeat(feat, vocabulary_size=df[feat].nunique() + 1, embedding_dim=emb_dim) for feat in sparse_fea]\n",
+ " \n",
+ " dense_feature_columns = [DenseFeat(feat, 1, ) for feat in dense_fea]\n",
+ " \n",
+ " var_feature_columns = [VarLenSparseFeat(SparseFeat(feat, vocabulary_size=df['click_article_id'].nunique() + 1,\n",
+ " embedding_dim=emb_dim, embedding_name='click_article_id'), maxlen=max_len) for feat in hist_behavior_fea]\n",
+ " \n",
+ " dnn_feature_columns = sparse_feature_columns + dense_feature_columns + var_feature_columns\n",
+ " \n",
+ " # 建立x, x是一个字典的形式\n",
+ " x = {}\n",
+ " for name in get_feature_names(dnn_feature_columns):\n",
+ " if name in his_behavior_fea:\n",
+ " # 这是历史行为序列\n",
+ " his_list = [l for l in df[name]]\n",
+ " x[name] = pad_sequences(his_list, maxlen=max_len, padding='post') # 二维数组\n",
+ " else:\n",
+ " x[name] = df[name].values\n",
+ " \n",
+ " return x, dnn_feature_columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:18.783217Z",
+ "start_time": "2020-11-18T04:26:18.776795Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 把特征分开\n",
+ "sparse_fea = ['user_id', 'click_article_id', 'category_id', 'click_environment', 'click_deviceGroup', \n",
+ " 'click_os', 'click_country', 'click_region', 'click_referrer_type', 'is_cat_hab']\n",
+ "\n",
+ "behavior_fea = ['click_article_id']\n",
+ "\n",
+ "hist_behavior_fea = ['hist_click_article_id']\n",
+ "\n",
+ "dense_fea = ['sim0', 'time_diff0', 'word_diff0', 'sim_max', 'sim_min', 'sim_sum', 'sim_mean', 'score',\n",
+ " 'rank','click_size','time_diff_mean','active_level','user_time_hob1','user_time_hob2',\n",
+ " 'words_hbo','words_count']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:25.469810Z",
+ "start_time": "2020-11-18T04:26:24.779347Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# dense特征进行归一化, 神经网络训练都需要将数值进行归一化处理\n",
+ "mm = MinMaxScaler()\n",
+ "\n",
+ "# 下面是做一些特殊处理，当在其他的地方出现无效值的时候，不处理无法进行归一化，刚开始可以先把他注释掉，在运行了下面的代码\n",
+ "# 之后如果发现报错，应该先去想办法处理如何不出现inf之类的值\n",
+ "# trn_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
+ "# tst_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
+ "\n",
+ "for feat in dense_fea:\n",
+ " trn_user_item_feats_df_din_model[feat] = mm.fit_transform(trn_user_item_feats_df_din_model[[feat]])\n",
+ " \n",
+ " if val_user_item_feats_df_din_model is not None:\n",
+ " val_user_item_feats_df_din_model[feat] = mm.fit_transform(val_user_item_feats_df_din_model[[feat]])\n",
+ " \n",
+ " tst_user_item_feats_df_din_model[feat] = mm.fit_transform(tst_user_item_feats_df_din_model[[feat]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:36.727753Z",
+ "start_time": "2020-11-18T04:26:28.854705Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 准备训练数据\n",
+ "x_trn, dnn_feature_columns = get_din_feats_columns(trn_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ "y_trn = trn_user_item_feats_df_din_model['label'].values\n",
+ "\n",
+ "if offline:\n",
+ " # 准备验证数据\n",
+ " x_val, dnn_feature_columns = get_din_feats_columns(val_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_val = val_user_item_feats_df_din_model['label'].values\n",
+ " \n",
+ "dense_fea = [x for x in dense_fea if x != 'label']\n",
+ "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:26:45.146318Z",
+ "start_time": "2020-11-18T04:26:40.423914Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:255: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
+ "Model: \"model\"\n",
+ "__________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "==================================================================================================\n",
+ "user_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_article_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "category_id (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_environment (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_deviceGroup (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_os (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_country (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_region (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_referrer_type (InputLayer [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "is_cat_hab (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_user_id (Embedding) (None, 1, 32) 1600032 user_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_seq_emb_hist_click_artic multiple 525664 click_article_id[0][0] \n",
+ " hist_click_article_id[0][0] \n",
+ " click_article_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_category_id (Embeddi (None, 1, 32) 7776 category_id[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_environment (E (None, 1, 32) 128 click_environment[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_deviceGroup (E (None, 1, 32) 160 click_deviceGroup[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_os (Embedding) (None, 1, 32) 288 click_os[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_country (Embed (None, 1, 32) 384 click_country[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_region (Embedd (None, 1, 32) 928 click_region[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_click_referrer_type (None, 1, 32) 256 click_referrer_type[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "sparse_emb_is_cat_hab (Embeddin (None, 1, 32) 64 is_cat_hab[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask (NoMask) (None, 1, 32) 0 sparse_emb_user_id[0][0] \n",
+ " sparse_seq_emb_hist_click_article\n",
+ " sparse_emb_category_id[0][0] \n",
+ " sparse_emb_click_environment[0][0\n",
+ " sparse_emb_click_deviceGroup[0][0\n",
+ " sparse_emb_click_os[0][0] \n",
+ " sparse_emb_click_country[0][0] \n",
+ " sparse_emb_click_region[0][0] \n",
+ " sparse_emb_click_referrer_type[0]\n",
+ " sparse_emb_is_cat_hab[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "hist_click_article_id (InputLay [(None, 50)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate (Concatenate) (None, 1, 320) 0 no_mask[0][0] \n",
+ " no_mask[1][0] \n",
+ " no_mask[2][0] \n",
+ " no_mask[3][0] \n",
+ " no_mask[4][0] \n",
+ " no_mask[5][0] \n",
+ " no_mask[6][0] \n",
+ " no_mask[7][0] \n",
+ " no_mask[8][0] \n",
+ " no_mask[9][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_1 (NoMask) (None, 1, 320) 0 concatenate[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "attention_sequence_pooling_laye (None, 1, 32) 13961 sparse_seq_emb_hist_click_article\n",
+ " sparse_seq_emb_hist_click_article\n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_1 (Concatenate) (None, 1, 352) 0 no_mask_1[0][0] \n",
+ " attention_sequence_pooling_layer[\n",
+ "__________________________________________________________________________________________________\n",
+ "sim0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "time_diff0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "word_diff0 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_max (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_min (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_sum (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "sim_mean (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "score (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "rank (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "click_size (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "time_diff_mean (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "active_level (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "user_time_hob1 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "user_time_hob2 (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "words_hbo (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "words_count (InputLayer) [(None, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten (Flatten) (None, 352) 0 concatenate_1[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_3 (NoMask) (None, 1) 0 sim0[0][0] \n",
+ " time_diff0[0][0] \n",
+ " word_diff0[0][0] \n",
+ " sim_max[0][0] \n",
+ " sim_min[0][0] \n",
+ " sim_sum[0][0] \n",
+ " sim_mean[0][0] \n",
+ " score[0][0] \n",
+ " rank[0][0] \n",
+ " click_size[0][0] \n",
+ " time_diff_mean[0][0] \n",
+ " active_level[0][0] \n",
+ " user_time_hob1[0][0] \n",
+ " user_time_hob2[0][0] \n",
+ " words_hbo[0][0] \n",
+ " words_count[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_2 (NoMask) (None, 352) 0 flatten[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_2 (Concatenate) (None, 16) 0 no_mask_3[0][0] \n",
+ " no_mask_3[1][0] \n",
+ " no_mask_3[2][0] \n",
+ " no_mask_3[3][0] \n",
+ " no_mask_3[4][0] \n",
+ " no_mask_3[5][0] \n",
+ " no_mask_3[6][0] \n",
+ " no_mask_3[7][0] \n",
+ " no_mask_3[8][0] \n",
+ " no_mask_3[9][0] \n",
+ " no_mask_3[10][0] \n",
+ " no_mask_3[11][0] \n",
+ " no_mask_3[12][0] \n",
+ " no_mask_3[13][0] \n",
+ " no_mask_3[14][0] \n",
+ " no_mask_3[15][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten_1 (Flatten) (None, 352) 0 no_mask_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten_2 (Flatten) (None, 16) 0 concatenate_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "no_mask_4 (NoMask) multiple 0 flatten_1[0][0] \n",
+ " flatten_2[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "concatenate_3 (Concatenate) (None, 368) 0 no_mask_4[0][0] \n",
+ " no_mask_4[1][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "dnn_1 (DNN) (None, 80) 89880 concatenate_3[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "dense (Dense) (None, 1) 80 dnn_1[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "prediction_layer (PredictionLay (None, 1) 1 dense[0][0] \n",
+ "==================================================================================================\n",
+ "Total params: 2,239,602\n",
+ "Trainable params: 2,239,362\n",
+ "Non-trainable params: 240\n",
+ "__________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 建立模型\n",
+ "model = DIN(dnn_feature_columns, behavior_fea)\n",
+ "\n",
+ "# 查看模型结构\n",
+ "model.summary()\n",
+ "\n",
+ "# 模型编译\n",
+ "model.compile('adam', 'binary_crossentropy',metrics=['binary_crossentropy', tf.keras.metrics.AUC()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:28:43.885773Z",
+ "start_time": "2020-11-18T04:26:48.746787Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/2\n",
+ "290964/290964 [==============================] - 55s 189us/sample - loss: 0.4209 - binary_crossentropy: 0.4206 - auc: 0.7842\n",
+ "Epoch 2/2\n",
+ "290964/290964 [==============================] - 52s 178us/sample - loss: 0.3630 - binary_crossentropy: 0.3618 - auc: 0.8478\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 模型训练\n",
+ "if offline:\n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=10, validation_data=(x_val, y_val) , batch_size=256)\n",
+ "else:\n",
+ " # 也可以使用上面的语句用自己采样出来的验证集\n",
+ " # history = model.fit(x_trn, y_trn, verbose=1, epochs=3, validation_split=0.3, batch_size=256)\n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, batch_size=256)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:29:20.436591Z",
+ "start_time": "2020-11-18T04:28:58.102057Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "500000/500000 [==============================] - 20s 39us/sample\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = model.predict(x_tst, verbose=1, batch_size=256)\n",
+ "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'din_rank_score.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:29:34.985535Z",
+ "start_time": "2020-11-18T04:29:26.264531Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score']]\n",
+ "submit(rank_results, topk=5, model_name='din')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-15T06:15:49.490705Z",
+ "start_time": "2020-11-15T06:15:49.473794Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:38:53.760383Z",
+ "start_time": "2020-11-18T04:29:51.737721Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 232681 samples, validate on 58283 samples\n",
+ "Epoch 1/2\n",
+ "232681/232681 [==============================] - 44s 189us/sample - loss: 0.2864 - binary_crossentropy: 0.2846 - auc: 0.9008 - val_loss: 0.2830 - val_binary_crossentropy: 0.2813 - val_auc: 0.9072\n",
+ "Epoch 2/2\n",
+ "232681/232681 [==============================] - 44s 187us/sample - loss: 0.2832 - binary_crossentropy: 0.2816 - auc: 0.9034 - val_loss: 0.2846 - val_binary_crossentropy: 0.2830 - val_auc: 0.9053\n",
+ "58283/58283 [==============================] - 2s 36us/sample\n",
+ "500000/500000 [==============================] - 19s 37us/sample\n",
+ "Train on 232798 samples, validate on 58166 samples\n",
+ "Epoch 1/2\n",
+ "232798/232798 [==============================] - 43s 184us/sample - loss: 0.2818 - binary_crossentropy: 0.2802 - auc: 0.9051 - val_loss: 0.2968 - val_binary_crossentropy: 0.2953 - val_auc: 0.9062\n",
+ "Epoch 2/2\n",
+ "232798/232798 [==============================] - 44s 187us/sample - loss: 0.2796 - binary_crossentropy: 0.2782 - auc: 0.9069 - val_loss: 0.2820 - val_binary_crossentropy: 0.2806 - val_auc: 0.9071\n",
+ "58166/58166 [==============================] - 2s 38us/sample\n",
+ "500000/500000 [==============================] - 18s 37us/sample\n",
+ "Train on 232847 samples, validate on 58117 samples\n",
+ "Epoch 1/2\n",
+ "232847/232847 [==============================] - 43s 185us/sample - loss: 0.2786 - binary_crossentropy: 0.2773 - auc: 0.9080 - val_loss: 0.2761 - val_binary_crossentropy: 0.2749 - val_auc: 0.9113\n",
+ "Epoch 2/2\n",
+ "232847/232847 [==============================] - 39s 166us/sample - loss: 0.2766 - binary_crossentropy: 0.2754 - auc: 0.9097 - val_loss: 0.2872 - val_binary_crossentropy: 0.2862 - val_auc: 0.9090\n",
+ "58117/58117 [==============================] - 2s 34us/sample\n",
+ "500000/500000 [==============================] - 17s 33us/sample\n",
+ "Train on 232716 samples, validate on 58248 samples\n",
+ "Epoch 1/2\n",
+ "232716/232716 [==============================] - 39s 169us/sample - loss: 0.2763 - binary_crossentropy: 0.2753 - auc: 0.9100 - val_loss: 0.2739 - val_binary_crossentropy: 0.2730 - val_auc: 0.9116\n",
+ "Epoch 2/2\n",
+ "232716/232716 [==============================] - 39s 168us/sample - loss: 0.2743 - binary_crossentropy: 0.2735 - auc: 0.9119 - val_loss: 0.2859 - val_binary_crossentropy: 0.2851 - val_auc: 0.9090\n",
+ "58248/58248 [==============================] - 2s 35us/sample\n",
+ "500000/500000 [==============================] - 17s 34us/sample\n",
+ "Train on 232814 samples, validate on 58150 samples\n",
+ "Epoch 1/2\n",
+ "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2747 - binary_crossentropy: 0.2739 - auc: 0.9115 - val_loss: 0.2702 - val_binary_crossentropy: 0.2695 - val_auc: 0.9163\n",
+ "Epoch 2/2\n",
+ "232814/232814 [==============================] - 40s 170us/sample - loss: 0.2725 - binary_crossentropy: 0.2719 - auc: 0.9132 - val_loss: 0.2751 - val_binary_crossentropy: 0.2745 - val_auc: 0.9151\n",
+ "58150/58150 [==============================] - 2s 34us/sample\n",
+ "500000/500000 [==============================] - 17s 34us/sample\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
+ "# 这一部分与前面的单独训练和验证是分开的\n",
+ "def get_kfold_users(trn_df, n=5):\n",
+ " user_ids = trn_df['user_id'].unique()\n",
+ " user_set = [user_ids[i::n] for i in range(n)]\n",
+ " return user_set\n",
+ "\n",
+ "k_fold = 5\n",
+ "trn_df = trn_user_item_feats_df_din_model\n",
+ "user_set = get_kfold_users(trn_df, n=k_fold)\n",
+ "\n",
+ "score_list = []\n",
+ "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
+ "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
+ "\n",
+ "dense_fea = [x for x in dense_fea if x != 'label']\n",
+ "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ "\n",
+ "# 五折交叉验证，并将中间结果保存用于staking\n",
+ "for n_fold, valid_user in enumerate(user_set):\n",
+ " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
+ " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
+ " \n",
+ " # 准备训练数据\n",
+ " x_trn, dnn_feature_columns = get_din_feats_columns(train_idx, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_trn = train_idx['label'].values\n",
+ "\n",
+ " # 准备验证数据\n",
+ " x_val, dnn_feature_columns = get_din_feats_columns(valid_idx, dense_fea, \n",
+ " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
+ " y_val = valid_idx['label'].values\n",
+ " \n",
+ " history = model.fit(x_trn, y_trn, verbose=1, epochs=2, validation_data=(x_val, y_val) , batch_size=256)\n",
+ " \n",
+ " # 预测验证集结果\n",
+ " valid_idx['pred_score'] = model.predict(x_val, verbose=1, batch_size=256) \n",
+ " \n",
+ " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
+ " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
+ " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
+ " \n",
+ " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
+ " if not offline:\n",
+ " sub_preds += model.predict(x_tst, verbose=1, batch_size=256)[:, 0] \n",
+ " \n",
+ "score_df_ = pd.concat(score_list, axis=0)\n",
+ "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
+ "# 保存训练集交叉验证产生的新特征\n",
+ "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_din_cls_feats.csv', index=False)\n",
+ " \n",
+ "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = sub_preds / k_fold\n",
+ "tst_user_item_feats_df_din_model['pred_score'] = tst_user_item_feats_df_din_model['pred_score'].transform(lambda x: norm_sim(x))\n",
+ "tst_user_item_feats_df_din_model.sort_values(by=['user_id', 'pred_score'])\n",
+ "tst_user_item_feats_df_din_model['pred_rank'] = tst_user_item_feats_df_din_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ "\n",
+ "# 保存测试集交叉验证的新特征\n",
+ "tst_user_item_feats_df_din_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_din_cls_feats.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 模型融合"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 加权融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:27.351996Z",
+ "start_time": "2020-11-18T04:44:26.561275Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取多个模型的排序结果文件\n",
+ "lgb_ranker = pd.read_csv(save_path + 'lgb_ranker_score.csv')\n",
+ "lgb_cls = pd.read_csv(save_path + 'lgb_cls_score.csv')\n",
+ "din_ranker = pd.read_csv(save_path + 'din_rank_score.csv')\n",
+ "\n",
+ "# 这里也可以换成交叉验证输出的测试结果进行加权融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:31.593981Z",
+ "start_time": "2020-11-18T04:44:31.589439Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "rank_model = {'lgb_ranker': lgb_ranker, \n",
+ " 'lgb_cls': lgb_cls, \n",
+ " 'din_ranker': din_ranker}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:36.135860Z",
+ "start_time": "2020-11-18T04:44:36.130577Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_ensumble_predict_topk(rank_model, topk=5):\n",
+ " final_recall = rank_model['lgb_cls'].append(rank_model['din_ranker'])\n",
+ " rank_model['lgb_ranker']['pred_score'] = rank_model['lgb_ranker']['pred_score'].transform(lambda x: norm_sim(x))\n",
+ " \n",
+ " final_recall = final_recall.append(rank_model['lgb_ranker'])\n",
+ " final_recall = final_recall.groupby(['user_id', 'click_article_id'])['pred_score'].sum().reset_index()\n",
+ " \n",
+ " submit(final_recall, topk=topk, model_name='ensemble_fuse')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:51.659270Z",
+ "start_time": "2020-11-18T04:44:40.445659Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "get_ensumble_predict_topk(rank_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Staking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:44:58.025992Z",
+ "start_time": "2020-11-18T04:44:56.146962Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取多个模型的交叉验证生成的结果文件\n",
+ "# 训练集\n",
+ "trn_lgb_ranker_feats = pd.read_csv(save_path + 'trn_lgb_ranker_feats.csv')\n",
+ "trn_lgb_cls_feats = pd.read_csv(save_path + 'trn_lgb_cls_feats.csv')\n",
+ "trn_din_cls_feats = pd.read_csv(save_path + 'trn_din_cls_feats.csv')\n",
+ "\n",
+ "# 测试集\n",
+ "tst_lgb_ranker_feats = pd.read_csv(save_path + 'tst_lgb_ranker_feats.csv')\n",
+ "tst_lgb_cls_feats = pd.read_csv(save_path + 'tst_lgb_cls_feats.csv')\n",
+ "tst_din_cls_feats = pd.read_csv(save_path + 'tst_din_cls_feats.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:07.701862Z",
+ "start_time": "2020-11-18T04:45:07.644335Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 将多个模型输出的特征进行拼接\n",
+ "\n",
+ "finall_trn_ranker_feats = trn_lgb_ranker_feats[['user_id', 'click_article_id', 'label']]\n",
+ "finall_tst_ranker_feats = tst_lgb_ranker_feats[['user_id', 'click_article_id']]\n",
+ "\n",
+ "for idx, trn_model in enumerate([trn_lgb_ranker_feats, trn_lgb_cls_feats, trn_din_cls_feats]):\n",
+ " for feat in [ 'pred_score', 'pred_rank']:\n",
+ " col_name = feat + '_' + str(idx)\n",
+ " finall_trn_ranker_feats[col_name] = trn_model[feat]\n",
+ "\n",
+ "for idx, tst_model in enumerate([tst_lgb_ranker_feats, tst_lgb_cls_feats, tst_din_cls_feats]):\n",
+ " for feat in [ 'pred_score', 'pred_rank']:\n",
+ " col_name = feat + '_' + str(idx)\n",
+ " finall_tst_ranker_feats[col_name] = tst_model[feat]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:15.044242Z",
+ "start_time": "2020-11-18T04:45:13.138252Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义一个逻辑回归模型再次拟合交叉验证产生的特征对测试集进行预测\n",
+ "# 这里需要注意的是，在做交叉验证的时候可以构造多一些与输出预测值相关的特征，来丰富这里简单模型的特征\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "\n",
+ "feat_cols = ['pred_score_0', 'pred_rank_0', 'pred_score_1', 'pred_rank_1', 'pred_score_2', 'pred_rank_2']\n",
+ "\n",
+ "trn_x = finall_trn_ranker_feats[feat_cols]\n",
+ "trn_y = finall_trn_ranker_feats['label']\n",
+ "\n",
+ "tst_x = finall_tst_ranker_feats[feat_cols]\n",
+ "\n",
+ "# 定义模型\n",
+ "lr = LogisticRegression()\n",
+ "\n",
+ "# 模型训练\n",
+ "lr.fit(trn_x, trn_y)\n",
+ "\n",
+ "# 模型预测\n",
+ "finall_tst_ranker_feats['pred_score'] = lr.predict_proba(tst_x)[:, 1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:45:29.018764Z",
+ "start_time": "2020-11-18T04:45:19.423130Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = finall_tst_ranker_feats[['user_id', 'click_article_id', 'pred_score']]\n",
+ "submit(rank_results, topk=5, model_name='ensumble_staking')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 总结\n",
+ "本章主要学习了三个排序模型，包括LGB的Rank， LGB的Classifier还有深度学习的DIN模型， 当然，对于这三个模型的原理部分，我们并没有给出详细的介绍， 请大家课下自己探索原理，也欢迎大家把自己的探索与所学分享出来，我们一块学习和进步。最后，我们进行了简单的模型融合策略，包括简单的加权和Stacking。\n",
+ "\n",
+ "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
+ "\n",
+ ""
+ ]
}
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = finall_tst_ranker_feats[['user_id', 'click_article_id', 'pred_score']]\n",
- "submit(rank_results, topk=5, model_name='ensumble_staking')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 总结\n",
- "本章主要学习了三个排序模型，包括LGB的Rank， LGB的Classifier还有深度学习的DIN模型， 当然，对于这三个模型的原理部分，我们并没有给出详细的介绍， 请大家课下自己探索原理，也欢迎大家把自己的探索与所学分享出来，我们一块学习和进步。最后，我们进行了简单的模型融合策略，包括简单的加权和Stacking。\n",
- "\n",
- "关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autoclose": false,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 1,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
- "toc_cell": false,
- "toc_position": {
- "height": "calc(100% - 180px)",
- "left": "10px",
- "top": "150px",
- "width": "170px"
- },
- "toc_section_display": true,
- "toc_window_display": true
- },
- "varInspector": {
- "cols": {
- "lenName": 16,
- "lenType": 16,
- "lenVar": 40
- },
- "kernels_config": {
- "python": {
- "delete_cmd_postfix": "",
- "delete_cmd_prefix": "del ",
- "library": "var_list.py",
- "varRefreshCmd": "print(var_dic_list())"
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
},
- "r": {
- "delete_cmd_postfix": ") ",
- "delete_cmd_prefix": "rm(",
- "library": "var_list.r",
- "varRefreshCmd": "cat(var_dic_list()) "
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "170px"
+ },
+ "toc_section_display": true,
+ "toc_window_display": true
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
}
- },
- "types_to_exclude": [
- "module",
- "function",
- "builtin_function_or_method",
- "instance",
- "_Feature"
- ],
- "window_display": false
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.1 \350\265\233\351\242\230\347\220\206\350\247\243+Baseline.md" "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.1 \350\265\233\351\242\230\347\220\206\350\247\243+Baseline.md"
index 645152157..5c3930fe0 100644
--- "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.1 \350\265\233\351\242\230\347\220\206\350\247\243+Baseline.md"
+++ "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.1 \350\265\233\351\242\230\347\220\206\350\247\243+Baseline.md"
@@ -377,7 +377,7 @@ submit(tst_recall, topk=5, model_name='itemcf_baseline')
**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：
-
+
diff --git "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.2 \346\225\260\346\215\256\345\210\206\346\236\220.md" "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.2 \346\225\260\346\215\256\345\210\206\346\236\220.md"
index 173d95002..5584973fa 100644
--- "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.2 \346\225\260\346\215\256\345\210\206\346\236\220.md"
+++ "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.2 \346\225\260\346\215\256\345\210\206\346\236\220.md"
@@ -66,7 +66,7 @@ trn_click = trn_click.merge(item_df, how='left', on=['click_article_id'])
trn_click.head()
```
-
+
**train_click_log.csv文件数据中每个字段的含义**
@@ -86,7 +86,7 @@ trn_click.head()
trn_click.info()
```
-
+
@@ -94,7 +94,7 @@ trn_click.info()
trn_click.describe()
```
-
+
```python
@@ -133,7 +133,7 @@ plt.tight_layout()
plt.show()
```
-
+
**从点击时间clik_timestamp来看，分布较为平均，可不做特殊处理。由于时间戳是13位的，后续将时间格式转换成10位方便计算。**
@@ -149,14 +149,14 @@ tst_click = tst_click.merge(item_df, how='left', on=['click_article_id'])
tst_click.head()
```
-
+
```python
tst_click.describe()
```
-
+
**我们可以看出训练集和测试集的用户是完全不一样的**
@@ -187,14 +187,14 @@ tst_click.groupby('user_id')['click_article_id'].count().min() # 注意测试集
item_df.head().append(item_df.tail())
```
-
+
```python
item_df['words_count'].value_counts()
```
-
+
```python
@@ -219,7 +219,7 @@ item_df.shape # 364047篇文章
item_emb_df.head()
```
-
+
```python
item_emb_df.shape
@@ -245,21 +245,21 @@ user_click_count = user_click_merge.groupby(['user_id', 'click_article_id'])['cl
user_click_count[:10]
```
-
+
```python
user_click_count[user_click_count['count']>7]
```
-
+
```python
user_click_count['count'].unique()
```
-
+
```python
@@ -267,7 +267,7 @@ user_click_count['count'].unique()
user_click_count.loc[:,'count'].value_counts()
```
-
+
**可以看出：有1605541（约占99.2%）的用户未重复阅读过文章，仅有极少数用户重复点击过某篇文章。 这个也可以单独制作成特征**
@@ -301,15 +301,15 @@ for _, user_df in sample_users.groupby('user_id'):
plot_envs(user_df, cols, 2, 3)
```
-
+
-
+
-
+
-
+
-
+
**可以看出绝大多数数的用户的点击环境是比较固定的。思路：可以基于这些环境的统计特征来代表该用户本身的属性**
@@ -322,7 +322,7 @@ plt.plot(user_click_item_count)
```
-
+
**可以根据用户的点击文章次数看出用户的活跃度**
@@ -332,7 +332,7 @@ plt.plot(user_click_item_count)
plt.plot(user_click_item_count[:50])
```
-
+
**点击次数排前50的用户的点击次数都在100次以上。思路：我们可以定义点击次数大于等于100次的用户为活跃用户，这是一种简单的处理思路， 判断用户活跃度，更加全面的是再结合上点击时间，后面我们会基于点击次数和点击时间两个方面来判断用户活跃度。**
@@ -342,7 +342,7 @@ plt.plot(user_click_item_count[:50])
plt.plot(user_click_item_count[25000:50000])
```
-
+
**可以看出点击次数小于等于两次的用户非常的多，这些用户可以认为是非活跃用户**
@@ -358,14 +358,14 @@ item_click_count = sorted(user_click_merge.groupby('click_article_id')['user_id'
plt.plot(item_click_count)
```
-
+
```python
plt.plot(item_click_count[:100])
```
-
+
**可以看出点击次数最多的前100篇新闻，点击次数大于1000次**
@@ -374,7 +374,7 @@ plt.plot(item_click_count[:100])
plt.plot(item_click_count[:20])
```
-
+
**点击次数最多的前20篇新闻，点击次数大于2500。思路：可以定义这些新闻为热门新闻， 这个也是简单的处理方式，后面我们也是根据点击次数和时间进行文章热度的一个划分。**
@@ -383,7 +383,7 @@ plt.plot(item_click_count[:20])
plt.plot(item_click_count[3500:])
```
-
+
**可以发现很多新闻只被点击过一两次。思路：可以定义这些新闻是冷门新闻。**
@@ -397,7 +397,7 @@ union_item = tmp.groupby(['click_article_id','next_item'])['click_timestamp'].ag
union_item[['count']].describe()
```
-
+
**由统计数据可以看出，平均共现次数2.88，最高为1687。**
@@ -411,14 +411,14 @@ y = union_item['count']
plt.scatter(x, y)
```
-
+
```python
plt.plot(union_item['count'].values[40000:])
```
-
+
**大概有70000个pair至少共现一次。**
@@ -432,7 +432,7 @@ plt.plot(union_item['count'].values[40000:])
plt.plot(user_click_merge['category_id'].value_counts().values)
```
-
+
```python
@@ -440,7 +440,7 @@ plt.plot(user_click_merge['category_id'].value_counts().values)
plt.plot(user_click_merge['category_id'].value_counts().values[150:])
```
-
+
```python
@@ -455,7 +455,7 @@ user_click_merge['words_count'].describe()
plt.plot(user_click_merge['words_count'].values)
```
-
+
@@ -469,7 +469,7 @@ plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), re
```
-
+
**从上图中可以看出有一小部分用户阅读类型是极其广泛的，大部分人都处在20个新闻类型以下。**
@@ -478,7 +478,7 @@ plt.plot(sorted(user_click_merge.groupby('user_id')['category_id'].nunique(), re
user_click_merge.groupby('user_id')['category_id'].nunique().reset_index().describe()
```
-
+
### 用户查看文章的长度的分布
@@ -490,7 +490,7 @@ plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), rever
```
-
+
@@ -504,7 +504,7 @@ plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), rever
plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), reverse=True)[1000:45000])
```
-
+
**可以发现大多数人都是看250字以下的文章**
@@ -514,7 +514,7 @@ plt.plot(sorted(user_click_merge.groupby('user_id')['words_count'].mean(), rever
user_click_merge.groupby('user_id')['words_count'].mean().reset_index().describe()
```
-
+
@@ -536,7 +536,7 @@ user_click_merge = user_click_merge.sort_values('click_timestamp')
user_click_merge.head()
```
-
+
```python
@@ -558,7 +558,7 @@ mean_diff_click_time = user_click_merge.groupby('user_id')['click_timestamp', 'c
plt.plot(sorted(mean_diff_click_time.values, reverse=True))
```
-
+
**从上图可以发现不同用户点击文章的时间差是有差异的。**
@@ -573,7 +573,7 @@ mean_diff_created_time = user_click_merge.groupby('user_id')['click_timestamp',
plt.plot(sorted(mean_diff_created_time.values, reverse=True))
```
-
+
**从图中可以发现用户先后点击文章，文章的创建时间也是有差异的**
@@ -602,7 +602,7 @@ sub_user_info = user_click_merge[user_click_merge['user_id'].isin(sub_user_ids)]
sub_user_info.head()
```
-
+
```python
@@ -625,7 +625,7 @@ for _, user_df in sub_user_info.groupby('user_id'):
```
-
+
@@ -654,5 +654,5 @@ for _, user_df in sub_user_info.groupby('user_id'):
**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：
-
+
diff --git "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.md" "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.md"
index 323cf46fe..9bf554093 100644
--- "a/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.md"
+++ "b/docs/\347\254\254\344\272\214\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\256\236\346\210\230/2.1\347\253\236\350\265\233\345\256\236\350\267\265/markdown/2.3 \345\244\232\350\267\257\345\217\254\345\233\236.md"
@@ -2,7 +2,7 @@
所谓的“多路召回”策略，就是指采用不同的策略、特征或简单模型，分别召回一部分候选集，然后把候选集混合在一起供后续排序模型使用，可以明显的看出，“多路召回策略”是在“计算速度”和“召回率”之间进行权衡的结果。其中，各种简单策略保证候选集的快速召回，从不同角度设计的策略保证召回率接近理想的状态，不至于损伤排序效果。如下图是多路召回的一个示意图，在多路召回中，每个策略之间毫不相关，所以一般可以写并发多线程同时进行，这样可以更加高效。
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3. 根据皮尔逊相关系数， 可以找到与物品5最相似的2个物品是 item1 和 item4， 下面基于上面的公式计算最终得分：
@@ -196,7 +196,7 @@ $$
比如下面这个例子：
-
+
+ 左边矩阵中，$A, B, C, D$ 表示的是物品。
+ 可以看出，$D $ 是一件热门物品，其与 $A、B、C$ 的相似度比较大。因此，推荐系统更可能将 $D$ 推荐给用过 $A、B、C$ 的用户。
@@ -242,7 +242,7 @@ $$
>
> 举例来说明，如下图（`X,Y,Z` 表示物品，`d,e,f`表示用户）：
>
-> 
+> 
>
> + 如果使用余弦相似度进行计算，用户 d 和 e 之间较为相似。但是实际上，用户 d 和 f 之间应该更加相似。只不过由于 d 倾向于打高分，e 倾向于打低分导致二者之间的余弦相似度更高。
> + 这种情况下，可以考虑使用皮尔逊相关系数计算用户之间的相似性关系。
diff --git a/docs/ch02/ch2.1/ch2.1.1/usercf.md b/docs/ch02/ch2.1/ch2.1.1/usercf.md
index 2a34353e3..72a3c8617 100644
--- a/docs/ch02/ch2.1/ch2.1.1/usercf.md
+++ b/docs/ch02/ch2.1/ch2.1.1/usercf.md
@@ -99,13 +99,13 @@
+ 例如，我们要对用户 $A$ 进行物品推荐，可以先找到和他有相似兴趣的其他用户。
+ 然后，将共同兴趣用户喜欢的，但用户 $A$ 未交互过的物品推荐给 $A$。
-
+
## 计算过程
以下图为例，给用户推荐物品的过程可以形象化为一个猜测用户对物品进行打分的任务，表格里面是5个用户对于5件物品的一个打分情况，就可以理解为用户对物品的喜欢程度。
-
+
UserCF算法的两个步骤：
@@ -164,7 +164,7 @@ UserCF算法的两个步骤：
+ 基于 sklearn 计算所有用户之间的皮尔逊相关系数。可以看出，与 Alice 相似度最高的用户为用户1和用户2。
- 
+ 
2. **根据相似度用户计算 Alice对物品5的最终得分**
用户1对物品5的评分是3， 用户2对物品5的打分是5， 那么根据上面的计算公式， 可以计算出 Alice 对物品5的最终得分是
diff --git a/docs/ch02/ch2.1/ch2.1.2/Airbnb.md b/docs/ch02/ch2.1/ch2.1.2/Airbnb.md
index 65ce7bc37..efa24a285 100644
--- a/docs/ch02/ch2.1/ch2.1.2/Airbnb.md
+++ b/docs/ch02/ch2.1/ch2.1.2/Airbnb.md
@@ -16,11 +16,11 @@
- 当用户在查看某一个房源时，接下来的有两种方式继续搜索：
- 返回搜索结果页，继续查看其他搜索结果。
-
+
- 在当前房源的详情页下，「相似房源」板块（你可能还喜欢）所推荐的房源。
-
+
- Airbnb 平台 99% 的房源预订来自于搜索排序和相似房源推荐。
# Embedding 方法
@@ -59,7 +59,7 @@ $$
- Airbnb 将最终预定的房源，始终作为滑窗的上下文，即全局上下文。如下图：
- 如图，对于当前滑动窗口的 central listing，实线箭头表示context listings，虚线（指向booked listing）表示 global context listing。
-
+
- booked listing 作为全局正样本，故优化的目标函数更新为：
@@ -103,12 +103,12 @@ $$
- 理论上，同一区域的房源相似性应该更高，不同区域房源相似性更低。
- Airbnb 利用 k-means 聚类，将加利福尼亚州的房源聚成100个集群，来验证类似位置的房源是否聚集在一起。
-
+
- 评估不同类型、价格区间的房源之间的相似性。
- 简而言之，我们希望类型相同、价格区间一致的房源它们之间的相似度更高。
-
+
- 评估房源的隐式特征
- Airbnb 在训练房源（listing）的 Embedding时，并没有用到房源的图像信息。
@@ -117,7 +117,7 @@ $$
- 大致原理就是，利用训练好的 Embedding 进行 K 近邻相似度检索。
- 如下，与查询房源在 Embedding 相似性高的其他房源，它们之间的外观风格也很相似。
-
+
## User-type & Listing-type Embedding
@@ -169,7 +169,7 @@ Airbnb 除了挖掘 Listing 的短期兴趣特征表示外，还对 User 和 Lis
- 所有的属性，都基于一定的规则进行了分桶（buckets）。例如21岁，被分桶到 20-30 岁的区间。
- 对于首次预定的用户，他的属性为 buckets 的前5行，因为预定之前没有历史预定相关的信息。
-
+
看到过前面那个简单的例子后，现在可以看一个原文的 Listing-type 的例子：
@@ -233,7 +233,7 @@ Type Embedding 的学习同样是基于 Skip-Gram 模型，但是有两点需要
\end{aligned}
$$
-
+
# 实验部分
@@ -276,13 +276,13 @@ Airbnb 的搜索排名的大致流程为：
- 表中的 Embedding Features 包含了8种类型，前6种类型的特征计算方式相同。
-
+
**① 基于 Listing Embedding Features 的特征构建**
- Airbnb 保留了用户过去两周6种不同类型的历史行为，如下图：
-
+
- 对于每个行为，还要将其按照 market （地域）进行划分。以 $ H_c $ 为例：
@@ -312,7 +312,7 @@ Airbnb 的搜索排名的大致流程为：
为了验证上述特征的构建是否有效，Airbnb 还做了特征重要性排序，如下表：
-
+
**（3)模型**
特征构建完成后，开始对模型进行训练。
diff --git a/docs/ch02/ch2.1/ch2.1.2/word2vec.md b/docs/ch02/ch2.1/ch2.1.2/word2vec.md
index 2ed19ec8b..2883242cf 100644
--- a/docs/ch02/ch2.1/ch2.1.2/word2vec.md
+++ b/docs/ch02/ch2.1/ch2.1.2/word2vec.md
@@ -56,7 +56,7 @@ one-hot向量的维度是词汇表的大小（如：500，000）
如果我们可以使用某种方法为每个单词构建一个合适的dense vector，如下图，那么通过点积等数学计算就可以获得单词之间的某种联系
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![\"image-20201119132726873\"](\"http://ryluo.oss-cn-chengdu.aliyuncs.com/abc/image-20201119132726873.png\")
\n",
- "\n",
- "上图只是一个多路召回的例子，也就是说可以使用多种不同的策略来获取用户排序的候选商品集合，而具体使用哪些召回策略其实是与业务强相关的 ，针对不同的任务就会有对于该业务真实场景下需要考虑的召回规则。例如新闻推荐，召回规则可以是“热门新闻”、“作者召回”、“关键词召回”、“主题召回“、”协同过滤召回“等等。 \n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 导包"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:29.834662Z",
- "start_time": "2020-11-16T11:26:27.811511Z"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd \n",
- "import numpy as np\n",
- "from tqdm import tqdm \n",
- "from collections import defaultdict \n",
- "import os, math, warnings, math, pickle\n",
- "from tqdm import tqdm\n",
- "import faiss\n",
- "import collections\n",
- "import random\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from datetime import datetime\n",
- "from deepctr.feature_column import SparseFeat, VarLenSparseFeat\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from tensorflow.python.keras import backend as K\n",
- "from tensorflow.python.keras.models import Model\n",
- "from tensorflow.python.keras.preprocessing.sequence import pad_sequences\n",
- "\n",
- "from deepmatch.models import *\n",
- "from deepmatch.utils import sampledsoftmaxloss\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:31.831215Z",
- "start_time": "2020-11-16T11:26:31.826939Z"
- }
- },
- "outputs": [],
- "source": [
- "data_path = './data_raw/'\n",
- "save_path = './temp_results/'\n",
- "# 做召回评估的一个标志, 如果不进行评估就是直接使用全量数据进行召回\n",
- "metric_recall = False"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 读取数据\n",
- "在一般的rs比赛中读取数据部分主要分为三种模式， 不同的模式对应的不同的数据集：\n",
- "1. debug模式： 这个的目的是帮助我们基于数据先搭建一个简易的baseline并跑通， 保证写的baseline代码没有什么问题。 由于推荐比赛的数据往往非常巨大， 如果一上来直接采用全部的数据进行分析，搭建baseline框架， 往往会带来时间和设备上的损耗， **所以这时候我们往往需要从海量数据的训练集中随机抽取一部分样本来进行调试(train_click_log_sample)**， 先跑通一个baseline。\n",
- "2. 线下验证模式： 这个的目的是帮助我们在线下基于已有的训练集数据， 来选择好合适的模型和一些超参数。 **所以我们这一块只需要加载整个训练集(train_click_log)**， 然后把整个训练集再分成训练集和验证集。 训练集是模型的训练数据， 验证集部分帮助我们调整模型的参数和其他的一些超参数。\n",
- "3. 线上模式： 我们用debug模式搭建起一个推荐系统比赛的baseline， 用线下验证模式选择好了模型和一些超参数， 这一部分就是真正的对于给定的测试集进行预测， 提交到线上， **所以这一块使用的训练数据集是全量的数据集(train_click_log+test_click_log)**\n",
- "\n",
- "下面就分别对这三种不同的数据读取模式先建立不同的代导入函数， 方便后面针对不同的模式下导入数据。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:34.476240Z",
- "start_time": "2020-11-16T11:26:34.467352Z"
- }
- },
- "outputs": [],
- "source": [
- "# debug模式： 从训练集中划出一部分数据来调试代码\n",
- "def get_all_click_sample(data_path, sample_nums=10000):\n",
- " \"\"\"\n",
- " 训练集中采样一部分数据调试\n",
- " data_path: 原数据的存储路径\n",
- " sample_nums: 采样数目（这里由于机器的内存限制，可以采样用户做）\n",
- " \"\"\"\n",
- " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " all_user_ids = all_click.user_id.unique()\n",
- "\n",
- " sample_user_ids = np.random.choice(all_user_ids, size=sample_nums, replace=False) \n",
- " all_click = all_click[all_click['user_id'].isin(sample_user_ids)]\n",
- " \n",
- " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
- " return all_click\n",
- "\n",
- "# 读取点击数据，这里分成线上和线下，如果是为了获取线上提交结果应该讲测试集中的点击数据合并到总的数据中\n",
- "# 如果是为了线下验证模型的有效性或者特征的有效性，可以只使用训练集\n",
- "def get_all_click_df(data_path='./data_raw/', offline=True):\n",
- " if offline:\n",
- " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " else:\n",
- " trn_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
- " tst_click = pd.read_csv(data_path + 'testA_click_log.csv')\n",
- "\n",
- " all_click = trn_click.append(tst_click)\n",
- " \n",
- " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
- " return all_click"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:35.168738Z",
- "start_time": "2020-11-16T11:26:35.163210Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取文章的基本属性\n",
- "def get_item_info_df(data_path):\n",
- " item_info_df = pd.read_csv(data_path + 'articles.csv')\n",
- " \n",
- " # 为了方便与训练集中的click_article_id拼接，需要把article_id修改成click_article_id\n",
- " item_info_df = item_info_df.rename(columns={'article_id': 'click_article_id'})\n",
- " \n",
- " return item_info_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:36.152958Z",
- "start_time": "2020-11-16T11:26:36.146324Z"
- }
- },
- "outputs": [],
- "source": [
- "# 读取文章的Embedding数据\n",
- "def get_item_emb_dict(data_path):\n",
- " item_emb_df = pd.read_csv(data_path + 'articles_emb.csv')\n",
- " \n",
- " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
- " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols])\n",
- " # 进行归一化\n",
- " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
- "\n",
- " item_emb_dict = dict(zip(item_emb_df['article_id'], item_emb_np))\n",
- " pickle.dump(item_emb_dict, open(save_path + 'item_content_emb.pkl', 'wb'))\n",
- " \n",
- " return item_emb_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:37.333536Z",
- "start_time": "2020-11-16T11:26:37.329545Z"
- }
- },
- "outputs": [],
- "source": [
- "max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:42.163494Z",
- "start_time": "2020-11-16T11:26:38.018094Z"
- }
- },
- "outputs": [],
- "source": [
- "# 采样数据\n",
- "# all_click_df = get_all_click_sample(data_path)\n",
- "\n",
- "# 全量训练集\n",
- "all_click_df = get_all_click_df(offline=False)\n",
- "\n",
- "# 对时间戳进行归一化,用于在关联规则的时候计算权重\n",
- "all_click_df['click_timestamp'] = all_click_df[['click_timestamp']].apply(max_min_scaler)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:26:44.343500Z",
- "start_time": "2020-11-16T11:26:44.113891Z"
- }
- },
- "outputs": [],
- "source": [
- "item_info_df = get_item_info_df(data_path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:24.295343Z",
- "start_time": "2020-11-16T11:26:44.398007Z"
- }
- },
- "outputs": [],
- "source": [
- "item_emb_dict = get_item_emb_dict(data_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 工具函数"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取用户-文章-时间函数\n",
- "这个在基于关联规则的用户协同过滤的时候会用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:33.791656Z",
- "start_time": "2020-11-16T11:27:33.784305Z"
- }
- },
- "outputs": [],
- "source": [
- "# 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- "def get_user_item_time(click_df):\n",
- " \n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " \n",
- " def make_item_time_pair(df):\n",
- " return list(zip(df['click_article_id'], df['click_timestamp']))\n",
- " \n",
- " user_item_time_df = click_df.groupby('user_id')['click_article_id', 'click_timestamp'].apply(lambda x: make_item_time_pair(x))\\\n",
- " .reset_index().rename(columns={0: 'item_time_list'})\n",
- " user_item_time_dict = dict(zip(user_item_time_df['user_id'], user_item_time_df['item_time_list']))\n",
- " \n",
- " return user_item_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取文章-用户-时间函数\n",
- "这个在基于关联规则的文章协同过滤的时候会用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:38.327581Z",
- "start_time": "2020-11-16T11:27:38.321059Z"
- }
- },
- "outputs": [],
- "source": [
- "# 根据时间获取商品被点击的用户序列 {item1: [(user1, time1), (user2, time2)...]...}\n",
- "# 这里的时间是用户点击当前商品的时间，好像没有直接的关系。\n",
- "def get_item_user_time_dict(click_df):\n",
- " def make_user_time_pair(df):\n",
- " return list(zip(df['user_id'], df['click_timestamp']))\n",
- " \n",
- " click_df = click_df.sort_values('click_timestamp')\n",
- " item_user_time_df = click_df.groupby('click_article_id')['user_id', 'click_timestamp'].apply(lambda x: make_user_time_pair(x))\\\n",
- " .reset_index().rename(columns={0: 'user_time_list'})\n",
- " \n",
- " item_user_time_dict = dict(zip(item_user_time_df['click_article_id'], item_user_time_df['user_time_list']))\n",
- " return item_user_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取历史和最后一次点击\n",
- "这个在评估召回结果， 特征工程和制作标签转成监督学习测试集的时候回用到"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:50.894683Z",
- "start_time": "2020-11-16T11:27:50.888002Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取当前数据的历史点击和最后一次点击\n",
- "def get_hist_and_last_click(all_click):\n",
- " \n",
- " all_click = all_click.sort_values(by=['user_id', 'click_timestamp'])\n",
- " click_last_df = all_click.groupby('user_id').tail(1)\n",
- "\n",
- " # 如果用户只有一个点击，hist为空了，会导致训练的时候这个用户不可见，此时默认泄露一下\n",
- " def hist_func(user_df):\n",
- " if len(user_df) == 1:\n",
- " return user_df\n",
- " else:\n",
- " return user_df[:-1]\n",
- "\n",
- " click_hist_df = all_click.groupby('user_id').apply(hist_func).reset_index(drop=True)\n",
- "\n",
- " return click_hist_df, click_last_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取文章属性特征"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:55.893810Z",
- "start_time": "2020-11-16T11:27:55.887623Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取文章id对应的基本属性，保存成字典的形式，方便后面召回阶段，冷启动阶段直接使用\n",
- "def get_item_info_dict(item_info_df):\n",
- " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
- " item_info_df['created_at_ts'] = item_info_df[['created_at_ts']].apply(max_min_scaler)\n",
- " \n",
- " item_type_dict = dict(zip(item_info_df['click_article_id'], item_info_df['category_id']))\n",
- " item_words_dict = dict(zip(item_info_df['click_article_id'], item_info_df['words_count']))\n",
- " item_created_time_dict = dict(zip(item_info_df['click_article_id'], item_info_df['created_at_ts']))\n",
- " \n",
- " return item_type_dict, item_words_dict, item_created_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-13T06:42:38.730939Z",
- "start_time": "2020-11-13T06:42:38.728461Z"
- }
- },
- "source": [
- "### 获取用户历史点击的文章信息"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:27:59.650781Z",
- "start_time": "2020-11-16T11:27:59.640572Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_user_hist_item_info_dict(all_click):\n",
- " \n",
- " # 获取user_id对应的用户历史点击文章类型的集合字典\n",
- " user_hist_item_typs = all_click.groupby('user_id')['category_id'].agg(set).reset_index()\n",
- " user_hist_item_typs_dict = dict(zip(user_hist_item_typs['user_id'], user_hist_item_typs['category_id']))\n",
- " \n",
- " # 获取user_id对应的用户点击文章的集合\n",
- " user_hist_item_ids_dict = all_click.groupby('user_id')['click_article_id'].agg(set).reset_index()\n",
- " user_hist_item_ids_dict = dict(zip(user_hist_item_ids_dict['user_id'], user_hist_item_ids_dict['click_article_id']))\n",
- " \n",
- " # 获取user_id对应的用户历史点击的文章的平均字数字典\n",
- " user_hist_item_words = all_click.groupby('user_id')['words_count'].agg('mean').reset_index()\n",
- " user_hist_item_words_dict = dict(zip(user_hist_item_words['user_id'], user_hist_item_words['words_count']))\n",
- " \n",
- " # 获取user_id对应的用户最后一次点击的文章的创建时间\n",
- " all_click_ = all_click.sort_values('click_timestamp')\n",
- " user_last_item_created_time = all_click_.groupby('user_id')['created_at_ts'].apply(lambda x: x.iloc[-1]).reset_index()\n",
- " \n",
- " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
- " user_last_item_created_time['created_at_ts'] = user_last_item_created_time[['created_at_ts']].apply(max_min_scaler)\n",
- " \n",
- " user_last_item_created_time_dict = dict(zip(user_last_item_created_time['user_id'], \\\n",
- " user_last_item_created_time['created_at_ts']))\n",
- " \n",
- " return user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 获取点击次数最多的topk个文章"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:04.761105Z",
- "start_time": "2020-11-16T11:28:04.756419Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取近期点击最多的文章\n",
- "def get_item_topk_click(click_df, k):\n",
- " topk_click = click_df['click_article_id'].value_counts().index[:k]\n",
- " return topk_click"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 定义多路召回字典"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:08.321506Z",
- "start_time": "2020-11-16T11:28:07.623281Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取文章的属性信息，保存成字典的形式方便查询\n",
- "item_type_dict, item_words_dict, item_created_time_dict = get_item_info_dict(item_info_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:28:13.791569Z",
- "start_time": "2020-11-16T11:28:13.786522Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义一个多路召回的字典，将各路召回的结果都保存在这个字典当中\n",
- "user_multi_recall_dict = {'itemcf_sim_itemcf_recall': {},\n",
- " 'embedding_sim_item_recall': {},\n",
- " 'youtubednn_recall': {},\n",
- " 'youtubednn_usercf_recall': {}, \n",
- " 'cold_start_recall': {}}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T05:41:12.710754Z",
- "start_time": "2020-11-16T05:40:57.842614Z"
- }
- },
- "outputs": [],
- "source": [
- "# 提取最后一次点击作为召回评估，如果不需要做召回评估直接使用全量的训练集进行召回(线下验证模型)\n",
- "# 如果不是召回评估，直接使用全量数据进行召回，不用将最后一次提取出来\n",
- "trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 召回效果评估函数\n",
- "做完了召回有时候也需要对当前的召回方法或者参数进行调整以达到更好的召回效果，因为召回的结果决定了最终排序的上限，下面也会提供一个召回评估的方法"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T05:41:18.579118Z",
- "start_time": "2020-11-16T05:41:18.571887Z"
- }
- },
- "outputs": [],
- "source": [
- "# 依次评估召回的前10, 20, 30, 40, 50个文章中的击中率\n",
- "def metrics_recall(user_recall_items_dict, trn_last_click_df, topk=5):\n",
- " last_click_item_dict = dict(zip(trn_last_click_df['user_id'], trn_last_click_df['click_article_id']))\n",
- " user_num = len(user_recall_items_dict)\n",
- " \n",
- " for k in range(10, topk+1, 10):\n",
- " hit_num = 0\n",
- " for user, item_list in user_recall_items_dict.items():\n",
- " # 获取前k个召回的结果\n",
- " tmp_recall_items = [x[0] for x in user_recall_items_dict[user][:k]]\n",
- " if last_click_item_dict[user] in set(tmp_recall_items):\n",
- " hit_num += 1\n",
- " \n",
- " hit_rate = round(hit_num * 1.0 / user_num, 5)\n",
- " print(' topk: ', k, ' : ', 'hit_num: ', hit_num, 'hit_rate: ', hit_rate, 'user_num : ', user_num)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 计算相似性矩阵\n",
- "\n",
- "这一部分主要是通过协同过滤以及向量检索得到相似性矩阵，相似性矩阵主要分为user2user和item2item，下面依次获取基于itemcf的item2item的相似性矩阵，"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### itemcf i2i_sim\n",
- "\n",
- "借鉴KDD2020的去偏商品推荐，在计算item2item相似性矩阵时，使用关联规则，使得计算的文章的相似性还考虑到了:\n",
- "1. 用户点击的时间权重\n",
- "2. 用户点击的顺序权重\n",
- "3. 文章创建的时间权重"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:30:51.872262Z",
- "start_time": "2020-11-16T11:30:51.860099Z"
- }
- },
- "outputs": [],
- "source": [
- "def itemcf_sim(df, item_created_time_dict):\n",
- " \"\"\"\n",
- " 文章与文章之间的相似性矩阵计算\n",
- " :param df: 数据表\n",
- " :item_created_time_dict: 文章创建时间的字典\n",
- " return : 文章与文章的相似性矩阵\n",
- " \n",
- " 思路: 基于物品的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
- " \"\"\"\n",
- " \n",
- " user_item_time_dict = get_user_item_time(df)\n",
- " \n",
- " # 计算物品相似度\n",
- " i2i_sim = {}\n",
- " item_cnt = defaultdict(int)\n",
- " for user, item_time_list in tqdm(user_item_time_dict.items()):\n",
- " # 在基于商品的协同过滤优化的时候可以考虑时间因素\n",
- " for loc1, (i, i_click_time) in enumerate(item_time_list):\n",
- " item_cnt[i] += 1\n",
- " i2i_sim.setdefault(i, {})\n",
- " for loc2, (j, j_click_time) in enumerate(item_time_list):\n",
- " if(i == j):\n",
- " continue\n",
- " \n",
- " # 考虑文章的正向顺序点击和反向顺序点击 \n",
- " loc_alpha = 1.0 if loc2 > loc1 else 0.7\n",
- " # 位置信息权重，其中的参数可以调节\n",
- " loc_weight = loc_alpha * (0.9 ** (np.abs(loc2 - loc1) - 1))\n",
- " # 点击时间权重，其中的参数可以调节\n",
- " click_time_weight = np.exp(0.7 ** np.abs(i_click_time - j_click_time))\n",
- " # 两篇文章创建时间的权重，其中的参数可以调节\n",
- " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " i2i_sim[i].setdefault(j, 0)\n",
- " # 考虑多种因素的权重计算最终的文章之间的相似度\n",
- " i2i_sim[i][j] += loc_weight * click_time_weight * created_time_weight / math.log(len(item_time_list) + 1)\n",
- " \n",
- " i2i_sim_ = i2i_sim.copy()\n",
- " for i, related_items in i2i_sim.items():\n",
- " for j, wij in related_items.items():\n",
- " i2i_sim_[i][j] = wij / math.sqrt(item_cnt[i] * item_cnt[j])\n",
- " \n",
- " # 将得到的相似性矩阵保存到本地\n",
- " pickle.dump(i2i_sim_, open(save_path + 'itemcf_i2i_sim.pkl', 'wb'))\n",
- " \n",
- " return i2i_sim_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:47:09.937002Z",
- "start_time": "2020-11-16T11:30:57.394334Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [14:20<00:00, 290.38it/s]\n"
- ]
- }
- ],
- "source": [
- "i2i_sim = itemcf_sim(all_click_df, item_created_time_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### usercf u2u_sim\n",
- "\n",
- "在计算用户之间的相似度的时候，也可以使用一些简单的关联规则，比如用户活跃度权重，这里将用户的点击次数作为用户活跃度的指标"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:14.951940Z",
- "start_time": "2020-11-16T09:11:14.945654Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_user_activate_degree_dict(all_click_df):\n",
- " all_click_df_ = all_click_df.groupby('user_id')['click_article_id'].count().reset_index()\n",
- " \n",
- " # 用户活跃度归一化\n",
- " mm = MinMaxScaler()\n",
- " all_click_df_['click_article_id'] = mm.fit_transform(all_click_df_[['click_article_id']])\n",
- " user_activate_degree_dict = dict(zip(all_click_df_['user_id'], all_click_df_['click_article_id']))\n",
- " \n",
- " return user_activate_degree_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:19.879276Z",
- "start_time": "2020-11-16T09:11:19.868808Z"
- }
- },
- "outputs": [],
- "source": [
- "def usercf_sim(all_click_df, user_activate_degree_dict):\n",
- " \"\"\"\n",
- " 用户相似性矩阵计算\n",
- " :param all_click_df: 数据表\n",
- " :param user_activate_degree_dict: 用户活跃度的字典\n",
- " return 用户相似性矩阵\n",
- " \n",
- " 思路: 基于用户的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
- " \"\"\"\n",
- " item_user_time_dict = get_item_user_time_dict(all_click_df)\n",
- " \n",
- " u2u_sim = {}\n",
- " user_cnt = defaultdict(int)\n",
- " for item, user_time_list in tqdm(item_user_time_dict.items()):\n",
- " for u, click_time in user_time_list:\n",
- " user_cnt[u] += 1\n",
- " u2u_sim.setdefault(u, {})\n",
- " for v, click_time in user_time_list:\n",
- " u2u_sim[u].setdefault(v, 0)\n",
- " if u == v:\n",
- " continue\n",
- " # 用户平均活跃度作为活跃度的权重，这里的式子也可以改善\n",
- " activate_weight = 100 * 0.5 * (user_activate_degree_dict[u] + user_activate_degree_dict[v]) \n",
- " u2u_sim[u][v] += activate_weight / math.log(len(user_time_list) + 1)\n",
- " \n",
- " u2u_sim_ = u2u_sim.copy()\n",
- " for u, related_users in u2u_sim.items():\n",
- " for v, wij in related_users.items():\n",
- " u2u_sim_[u][v] = wij / math.sqrt(user_cnt[u] * user_cnt[v])\n",
- " \n",
- " # 将得到的相似性矩阵保存到本地\n",
- " pickle.dump(u2u_sim_, open(save_path + 'usercf_u2u_sim.pkl', 'wb'))\n",
- "\n",
- " return u2u_sim_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T06:59:46.701572Z",
- "start_time": "2020-11-16T06:59:26.852246Z"
- }
- },
- "outputs": [],
- "source": [
- "# 由于usercf计算时候太耗费内存了，这里就不直接运行了\n",
- "# 如果是采样的话，是可以运行的\n",
- "user_activate_degree_dict = get_user_activate_degree_dict(all_click_df)\n",
- "u2u_sim = usercf_sim(all_click_df, user_activate_degree_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### item embedding sim\n",
- "\n",
- "使用Embedding计算item之间的相似度是为了后续冷启动的时候可以获取未出现在点击数据中的文章，后面有对冷启动专门的介绍，这里简单的说一下faiss。\n",
- "\n",
- "aiss是Facebook的AI团队开源的一套用于做聚类或者相似性搜索的软件库，底层是用C++实现。Faiss因为超级优越的性能，被广泛应用于推荐相关的业务当中.\n",
- "\n",
- "faiss工具包一般使用在推荐系统中的向量召回部分。在做向量召回的时候要么是u2u,u2i或者i2i，这里的u和i指的是user和item.我们知道在实际的场景中user和item的数量都是海量的，我们最容易想到的基于向量相似度的召回就是使用两层循环遍历user列表或者item列表计算两个向量的相似度，但是这样做在面对海量数据是不切实际的，faiss就是用来加速计算某个查询向量最相似的topk个索引向量。\n",
- "\n",
- "**faiss查询的原理：**\n",
- "\n",
- "faiss使用了PCA和PQ(Product quantization乘积量化)两种技术进行向量压缩和编码，当然还使用了其他的技术进行优化，但是PCA和PQ是其中最核心部分。\n",
- "\n",
- "1. PCA降维算法细节参考下面这个链接进行学习 \n",
- "[主成分分析（PCA）原理总结](https://www.cnblogs.com/pinard/p/6239403.html) \n",
- "\n",
- "2. PQ编码的细节下面这个链接进行学习 \n",
- "[实例理解product quantization算法](http://www.fabwrite.com/productquantization)\n",
- "\n",
- "**faiss使用**\n",
- "\n",
- "[faiss官方教程](https://github.com/facebookresearch/faiss/wiki/Getting-started)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:11:28.631803Z",
- "start_time": "2020-11-16T09:11:28.619926Z"
- }
- },
- "outputs": [],
- "source": [
- "# 向量检索相似度计算\n",
- "# topk指的是每个item, faiss搜索后返回最相似的topk个item\n",
- "def embdding_sim(click_df, item_emb_df, save_path, topk):\n",
- " \"\"\"\n",
- " 基于内容的文章embedding相似性矩阵计算\n",
- " :param click_df: 数据表\n",
- " :param item_emb_df: 文章的embedding\n",
- " :param save_path: 保存路径\n",
- " :patam topk: 找最相似的topk篇\n",
- " return 文章相似性矩阵\n",
- " \n",
- " 思路: 对于每一篇文章， 基于embedding的相似性返回topk个与其最相似的文章， 只不过由于文章数量太多，这里用了faiss进行加速\n",
- " \"\"\"\n",
- " \n",
- " # 文章索引与文章id的字典映射\n",
- " item_idx_2_rawid_dict = dict(zip(item_emb_df.index, item_emb_df['article_id']))\n",
- " \n",
- " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
- " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols].values, dtype=np.float32)\n",
- " # 向量进行单位化\n",
- " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
- " \n",
- " # 建立faiss索引\n",
- " item_index = faiss.IndexFlatIP(item_emb_np.shape[1])\n",
- " item_index.add(item_emb_np)\n",
- " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
- " sim, idx = item_index.search(item_emb_np, topk) # 返回的是列表\n",
- " \n",
- " # 将向量检索的结果保存成原始id的对应关系\n",
- " item_sim_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(item_emb_np)), sim, idx)):\n",
- " target_raw_id = item_idx_2_rawid_dict[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = item_idx_2_rawid_dict[rele_idx]\n",
- " item_sim_dict[target_raw_id][rele_raw_id] = item_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " # 保存i2i相似度矩阵\n",
- " pickle.dump(item_sim_dict, open(save_path + 'emb_i2i_sim.pkl', 'wb')) \n",
- " \n",
- " return item_sim_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T09:32:35.926116Z",
- "start_time": "2020-11-16T09:11:44.586967Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "364047it [00:23, 15292.14it/s]\n"
- ]
- }
- ],
- "source": [
- "item_emb_df = pd.read_csv(data_path + '/articles_emb.csv')\n",
- "emb_i2i_sim = embdding_sim(all_click_df, item_emb_df, save_path, topk=10) # topk可以自行设置"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 召回\n",
- "这个就是我们开篇提到的那个问题， 面的36万篇文章， 20多万用户的推荐， 我们又有哪些策略来缩减问题的规模？ 我们就可以再召回阶段筛选出用户对于点击文章的候选集合， 从而降低问题的规模。召回常用的策略：\n",
- "* Youtube DNN 召回\n",
- "* 基于文章的召回\n",
- " * 文章的协同过滤\n",
- " * 基于文章embedding的召回\n",
- "* 基于用户的召回\n",
- " * 用户的协同过滤\n",
- " * 用户embedding\n",
- "\n",
- "上面的各种召回方式一部分在基于用户已经看得文章的基础上去召回与这些文章相似的一些文章， 而这个相似性的计算方式不同， 就得到了不同的召回方式， 比如文章的协同过滤， 文章内容的embedding等。还有一部分是根据用户的相似性进行推荐，对于某用户推荐与其相似的其他用户看过的文章，比如用户的协同过滤和用户embedding。 还有一种思路是类似矩阵分解的思路，先计算出用户和文章的embedding之后，就可以直接算用户和文章的相似度， 根据这个相似度进行推荐， 比如YouTube DNN。 我们下面详细来看一下每一个召回方法："
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### YoutubeDNN召回\n",
- "**(这一步是直接获取用户召回的候选文章列表)**\n",
- "\n",
- "[论文下载地址](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)\n",
- "\n",
- "**Youtubednn召回架构**\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "关于YoutubeDNN原理和应用推荐看王喆的两篇博客：\n",
- "\n",
- "1. [重读Youtube深度学习推荐系统论文，字字珠玑，惊为神文](https://zhuanlan.zhihu.com/p/52169807)\n",
- "2. [YouTube深度学习推荐系统的十大工程问题](https://zhuanlan.zhihu.com/p/52504407)\n",
- "\n",
- "\n",
- "**参考文献:**\n",
- "1. https://zhuanlan.zhihu.com/p/52169807 (YouTubeDNN原理)\n",
- "2. https://zhuanlan.zhihu.com/p/26306795 (Word2Vec知乎众赞文章) --- word2vec放到排序中的w2v的介绍部分\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:13:11.058766Z",
- "start_time": "2020-11-16T10:13:11.041084Z"
- }
- },
- "outputs": [],
- "source": [
- "# 获取双塔召回时的训练验证数据\n",
- "# negsample指的是通过滑窗构建样本的时候，负样本的数量\n",
- "def gen_data_set(data, negsample=0):\n",
- " data.sort_values(\"click_timestamp\", inplace=True)\n",
- " item_ids = data['click_article_id'].unique()\n",
- "\n",
- " train_set = []\n",
- " test_set = []\n",
- " for reviewerID, hist in tqdm(data.groupby('user_id')):\n",
- " pos_list = hist['click_article_id'].tolist()\n",
- " \n",
- " if negsample > 0:\n",
- " candidate_set = list(set(item_ids) - set(pos_list)) # 用户没看过的文章里面选择负样本\n",
- " neg_list = np.random.choice(candidate_set,size=len(pos_list)*negsample,replace=True) # 对于每个正样本，选择n个负样本\n",
- " \n",
- " # 长度只有一个的时候，需要把这条数据也放到训练集中，不然的话最终学到的embedding就会有缺失\n",
- " if len(pos_list) == 1:\n",
- " train_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
- " test_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
- " \n",
- " # 滑窗构造正负样本\n",
- " for i in range(1, len(pos_list)):\n",
- " hist = pos_list[:i]\n",
- " \n",
- " if i != len(pos_list) - 1:\n",
- " train_set.append((reviewerID, hist[::-1], pos_list[i], 1, len(hist[::-1]))) # 正样本 [user_id, his_item, pos_item, label, len(his_item)]\n",
- " for negi in range(negsample):\n",
- " train_set.append((reviewerID, hist[::-1], neg_list[i*negsample+negi], 0,len(hist[::-1]))) # 负样本 [user_id, his_item, neg_item, label, len(his_item)]\n",
- " else:\n",
- " # 将最长的那一个序列长度作为测试数据\n",
- " test_set.append((reviewerID, hist[::-1], pos_list[i],1,len(hist[::-1])))\n",
- " \n",
- " random.shuffle(train_set)\n",
- " random.shuffle(test_set)\n",
- " \n",
- " return train_set, test_set\n",
- "\n",
- "# 将输入的数据进行padding，使得序列特征的长度都一致\n",
- "def gen_model_input(train_set,user_profile,seq_max_len):\n",
- "\n",
- " train_uid = np.array([line[0] for line in train_set])\n",
- " train_seq = [line[1] for line in train_set]\n",
- " train_iid = np.array([line[2] for line in train_set])\n",
- " train_label = np.array([line[3] for line in train_set])\n",
- " train_hist_len = np.array([line[4] for line in train_set])\n",
- "\n",
- " train_seq_pad = pad_sequences(train_seq, maxlen=seq_max_len, padding='post', truncating='post', value=0)\n",
- " train_model_input = {\"user_id\": train_uid, \"click_article_id\": train_iid, \"hist_article_id\": train_seq_pad,\n",
- " \"hist_len\": train_hist_len}\n",
- "\n",
- " return train_model_input, train_label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:13:18.124452Z",
- "start_time": "2020-11-16T10:13:18.098284Z"
- }
- },
- "outputs": [],
- "source": [
- "def youtubednn_u2i_dict(data, topk=20): \n",
- " sparse_features = [\"click_article_id\", \"user_id\"]\n",
- " SEQ_LEN = 30 # 用户点击序列的长度，短的填充，长的截断\n",
- " \n",
- " user_profile_ = data[[\"user_id\"]].drop_duplicates('user_id')\n",
- " item_profile_ = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
- " \n",
- " # 类别编码\n",
- " features = [\"click_article_id\", \"user_id\"]\n",
- " feature_max_idx = {}\n",
- " \n",
- " for feature in features:\n",
- " lbe = LabelEncoder()\n",
- " data[feature] = lbe.fit_transform(data[feature])\n",
- " feature_max_idx[feature] = data[feature].max() + 1\n",
- " \n",
- " # 提取user和item的画像，这里具体选择哪些特征还需要进一步的分析和考虑\n",
- " user_profile = data[[\"user_id\"]].drop_duplicates('user_id')\n",
- " item_profile = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
- " \n",
- " user_index_2_rawid = dict(zip(user_profile['user_id'], user_profile_['user_id']))\n",
- " item_index_2_rawid = dict(zip(item_profile['click_article_id'], item_profile_['click_article_id']))\n",
- " \n",
- " # 划分训练和测试集\n",
- " # 由于深度学习需要的数据量通常都是非常大的，所以为了保证召回的效果，往往会通过滑窗的形式扩充训练样本\n",
- " train_set, test_set = gen_data_set(data, 0)\n",
- " # 整理输入数据，具体的操作可以看上面的函数\n",
- " train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)\n",
- " test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)\n",
- " \n",
- " # 确定Embedding的维度\n",
- " embedding_dim = 16\n",
- " \n",
- " # 将数据整理成模型可以直接输入的形式\n",
- " user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),\n",
- " VarLenSparseFeat(SparseFeat('hist_article_id', feature_max_idx['click_article_id'], embedding_dim,\n",
- " embedding_name=\"click_article_id\"), SEQ_LEN, 'mean', 'hist_len'),]\n",
- " item_feature_columns = [SparseFeat('click_article_id', feature_max_idx['click_article_id'], embedding_dim)]\n",
- " \n",
- " # 模型的定义 \n",
- " # num_sampled: 负采样时的样本数量\n",
- " model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))\n",
- " # 模型编译\n",
- " model.compile(optimizer=\"adam\", loss=sampledsoftmaxloss) \n",
- " \n",
- " # 模型训练，这里可以定义验证集的比例，如果设置为0的话就是全量数据直接进行训练\n",
- " history = model.fit(train_model_input, train_label, batch_size=256, epochs=1, verbose=1, validation_split=0.0)\n",
- " \n",
- " # 训练完模型之后,提取训练的Embedding，包括user端和item端\n",
- " test_user_model_input = test_model_input\n",
- " all_item_model_input = {\"click_article_id\": item_profile['click_article_id'].values}\n",
- "\n",
- " user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)\n",
- " item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)\n",
- " \n",
- " # 保存当前的item_embedding 和 user_embedding 排序的时候可能能够用到，但是需要注意保存的时候需要和原始的id对应\n",
- " user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)\n",
- " item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)\n",
- " \n",
- " # embedding保存之前归一化一下\n",
- " user_embs = user_embs / np.linalg.norm(user_embs, axis=1, keepdims=True)\n",
- " item_embs = item_embs / np.linalg.norm(item_embs, axis=1, keepdims=True)\n",
- " \n",
- " # 将Embedding转换成字典的形式方便查询\n",
- " raw_user_id_emb_dict = {user_index_2_rawid[k]: \\\n",
- " v for k, v in zip(user_profile['user_id'], user_embs)}\n",
- " raw_item_id_emb_dict = {item_index_2_rawid[k]: \\\n",
- " v for k, v in zip(item_profile['click_article_id'], item_embs)}\n",
- " # 将Embedding保存到本地\n",
- " pickle.dump(raw_user_id_emb_dict, open(save_path + 'user_youtube_emb.pkl', 'wb'))\n",
- " pickle.dump(raw_item_id_emb_dict, open(save_path + 'item_youtube_emb.pkl', 'wb'))\n",
- " \n",
- " # faiss紧邻搜索，通过user_embedding 搜索与其相似性最高的topk个item\n",
- " index = faiss.IndexFlatIP(embedding_dim)\n",
- " # 上面已经进行了归一化，这里可以不进行归一化了\n",
- "# faiss.normalize_L2(user_embs)\n",
- "# faiss.normalize_L2(item_embs)\n",
- " index.add(item_embs) # 将item向量构建索引\n",
- " sim, idx = index.search(np.ascontiguousarray(user_embs), topk) # 通过user去查询最相似的topk个item\n",
- " \n",
- " user_recall_items_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(test_user_model_input['user_id'], sim, idx)):\n",
- " target_raw_id = user_index_2_rawid[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = item_index_2_rawid[rele_idx]\n",
- " user_recall_items_dict[target_raw_id][rele_raw_id] = user_recall_items_dict.get(target_raw_id, {})\\\n",
- " .get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " user_recall_items_dict = {k: sorted(v.items(), key=lambda x: x[1], reverse=True) for k, v in user_recall_items_dict.items()}\n",
- " # 将召回的结果进行排序\n",
- " \n",
- " # 保存召回的结果\n",
- " # 这里是直接通过向量的方式得到了召回结果，相比于上面的召回方法，上面的只是得到了i2i及u2u的相似性矩阵，还需要进行协同过滤召回才能得到召回结果\n",
- " # 可以直接对这个召回结果进行评估，为了方便可以统一写一个评估函数对所有的召回结果进行评估\n",
- " pickle.dump(user_recall_items_dict, open(save_path + 'youtube_u2i_dict.pkl', 'wb'))\n",
- " return user_recall_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T10:21:46.420014Z",
- "start_time": "2020-11-16T10:13:35.351131Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [02:02<00:00, 2038.57it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "keep_dims is deprecated, use keepdims instead\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Deprecated in favor of operator or tf.math.divide.\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "1149673/1149673 [==============================] - 216s 188us/sample - loss: 0.1326\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "250000it [00:32, 7720.75it/s]\n"
- ]
- }
- ],
- "source": [
- "# 由于这里需要做召回评估，所以讲训练集中的最后一次点击都提取了出来\n",
- "if not metric_recall:\n",
- " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(all_click_df, topk=20)\n",
- "else:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(trn_hist_click_df, topk=20)\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['youtubednn_recall'], trn_last_click_df, topk=20)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### itemcf recall\n",
- "\n",
- "上面已经通过协同过滤，Embedding检索的方式得到了文章的相似度矩阵，下面使用协同过滤的思想，给用户召回与其历史文章相似的文章。\n",
- "这里在召回的时候，也是用了关联规则的方式：\n",
- "1. 考虑相似文章与历史点击文章顺序的权重(细节看代码)\n",
- "2. 考虑文章创建时间的权重，也就是考虑相似文章与历史点击文章创建时间差的权重\n",
- "3. 考虑文章内容相似度权重(使用Embedding计算相似文章相似度，但是这里需要注意，在Embedding的时候并没有计算所有商品两两之间的相似度，所以相似的文章与历史点击文章不存在相似度，需要做特殊处理)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T11:48:40.580553Z",
- "start_time": "2020-11-16T11:48:40.567130Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于商品的召回i2i\n",
- "def item_based_recommend(user_id, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
- " \"\"\"\n",
- " 基于文章协同过滤的召回\n",
- " :param user_id: 用户id\n",
- " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- " :param i2i_sim: 字典，文章相似性矩阵\n",
- " :param sim_item_topk: 整数， 选择与当前文章最相似的前k篇文章\n",
- " :param recall_item_num: 整数， 最后的召回文章数量\n",
- " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
- " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
- " \n",
- " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
- " \"\"\"\n",
- " # 获取用户历史交互的文章\n",
- " user_hist_items = user_item_time_dict[user_id]\n",
- " user_hist_items_ = {user_id for user_id, _ in user_hist_items}\n",
- " \n",
- " item_rank = {}\n",
- " for loc, (i, click_time) in enumerate(user_hist_items):\n",
- " for j, wij in sorted(i2i_sim[i].items(), key=lambda x: x[1], reverse=True)[:sim_item_topk]:\n",
- " if j in user_hist_items_:\n",
- " continue\n",
- " \n",
- " # 文章创建时间差权重\n",
- " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " # 相似文章和历史点击文章序列中历史文章所在的位置权重\n",
- " loc_weight = (0.9 ** (len(user_hist_items) - loc))\n",
- " \n",
- " content_weight = 1.0\n",
- " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
- " content_weight += emb_i2i_sim[i][j]\n",
- " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
- " content_weight += emb_i2i_sim[j][i]\n",
- " \n",
- " item_rank.setdefault(j, 0)\n",
- " item_rank[j] += created_time_weight * loc_weight * content_weight * wij\n",
- " \n",
- " # 不足10个，用热门商品补全\n",
- " if len(item_rank) < recall_item_num:\n",
- " for i, item in enumerate(item_topk_click):\n",
- " if item in item_rank.items(): # 填充的item应该不在原来的列表中\n",
- " continue\n",
- " item_rank[item] = - i - 100 # 随便给个负数就行\n",
- " if len(item_rank) == recall_item_num:\n",
- " break\n",
- " \n",
- " item_rank = sorted(item_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num]\n",
- " \n",
- " return item_rank"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### itemcf sim召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T14:41:23.433038Z",
- "start_time": "2020-11-16T11:48:46.286350Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [2:51:13<00:00, 24.33it/s] \n"
- ]
- }
- ],
- "source": [
- "# 先进行itemcf召回, 为了召回评估，所以提取最后一次点击\n",
- "\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "\n",
- "i2i_sim = pickle.load(open(save_path + 'itemcf_i2i_sim.pkl', 'rb'))\n",
- "emb_i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl', 'rb'))\n",
- "\n",
- "sim_item_topk = 20\n",
- "recall_item_num = 10\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, \\\n",
- " i2i_sim, sim_item_topk, recall_item_num, \\\n",
- " item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- "\n",
- "user_multi_recall_dict['itemcf_sim_itemcf_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['itemcf_sim_itemcf_recall'], open(save_path + 'itemcf_recall_dict.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['itemcf_sim_itemcf_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### embedding sim 召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T15:04:51.527795Z",
- "start_time": "2020-11-16T14:59:03.907519Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [04:35<00:00, 905.85it/s] \n"
- ]
- }
- ],
- "source": [
- "# 这里是为了召回评估，所以提取最后一次点击\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
- "\n",
- "sim_item_topk = 20\n",
- "recall_item_num = 10\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- " \n",
- "user_multi_recall_dict['embedding_sim_item_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['embedding_sim_item_recall'], open(save_path + 'embedding_sim_item_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['embedding_sim_item_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### usercf召回\n",
- "\n",
- "基于用户协同过滤，核心思想是给用户推荐与其相似的用户历史点击文章，因为这里涉及到了相似用户的历史文章，这里仍然可以加上一些关联规则来给用户可能点击的文章进行加权，这里使用的关联规则主要是考虑相似用户的历史点击文章与被推荐用户历史点击商品的关系权重，而这里的关系就可以直接借鉴基于物品的协同过滤相似的做法，只不过这里是对被推荐物品关系的一个累加的过程，下面是使用的一些关系权重，及相关的代码：\n",
- "\n",
- "1. 计算被推荐用户历史点击文章与相似用户历史点击文章的相似度，文章创建时间差，相对位置的总和，作为各自的权重"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:09:32.293990Z",
- "start_time": "2020-11-17T02:09:32.278678Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于用户的召回 u2u2i\n",
- "def user_based_recommend(user_id, user_item_time_dict, u2u_sim, sim_user_topk, recall_item_num, \n",
- " item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
- " \"\"\"\n",
- " 基于文章协同过滤的召回\n",
- " :param user_id: 用户id\n",
- " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
- " :param u2u_sim: 字典，文章相似性矩阵\n",
- " :param sim_user_topk: 整数， 选择与当前用户最相似的前k个用户\n",
- " :param recall_item_num: 整数， 最后的召回文章数量\n",
- " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
- " :param item_created_time_dict: 文章创建时间列表\n",
- " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
- " \n",
- " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
- " \"\"\"\n",
- " # 历史交互\n",
- " user_item_time_list = user_item_time_dict[user_id] # [(item1, time1), (item2, time2)..]\n",
- " user_hist_items = set([i for i, t in user_item_time_list]) # 存在一个用户与某篇文章的多次交互， 这里得去重\n",
- " \n",
- " items_rank = {}\n",
- " for sim_u, wuv in sorted(u2u_sim[user_id].items(), key=lambda x: x[1], reverse=True)[:sim_user_topk]:\n",
- " for i, click_time in user_item_time_dict[sim_u]:\n",
- " if i in user_hist_items:\n",
- " continue\n",
- " items_rank.setdefault(i, 0)\n",
- " \n",
- " loc_weight = 1.0\n",
- " content_weight = 1.0\n",
- " created_time_weight = 1.0\n",
- " \n",
- " # 当前文章与该用户看的历史文章进行一个权重交互\n",
- " for loc, (j, click_time) in enumerate(user_item_time_list):\n",
- " # 点击时的相对位置权重\n",
- " loc_weight += 0.9 ** (len(user_item_time_list) - loc)\n",
- " # 内容相似性权重\n",
- " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
- " content_weight += emb_i2i_sim[i][j]\n",
- " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
- " content_weight += emb_i2i_sim[j][i]\n",
- " \n",
- " # 创建时间差权重\n",
- " created_time_weight += np.exp(0.8 * np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
- " \n",
- " items_rank[i] += loc_weight * content_weight * created_time_weight * wuv\n",
- " \n",
- " # 热度补全\n",
- " if len(items_rank) < recall_item_num:\n",
- " for i, item in enumerate(item_topk_click):\n",
- " if item in items_rank.items(): # 填充的item应该不在原来的列表中\n",
- " continue\n",
- " items_rank[item] = - i - 100 # 随便给个复数就行\n",
- " if len(items_rank) == recall_item_num:\n",
- " break\n",
- " \n",
- " items_rank = sorted(items_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num] \n",
- " \n",
- " return items_rank"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### usercf sim召回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:05:41.652501Z",
- "start_time": "2020-11-16T07:05:40.953871Z"
- }
- },
- "outputs": [],
- "source": [
- "# 这里是为了召回评估，所以提取最后一次点击\n",
- "# 由于usercf中计算user之间的相似度的过程太费内存了，全量数据这里就没有跑，跑了一个采样之后的数据\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- " \n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "\n",
- "u2u_sim = pickle.load(open(save_path + 'usercf_u2u_sim.pkl', 'rb'))\n",
- "\n",
- "sim_user_topk = 20\n",
- "recall_item_num = 10\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim) \n",
- "\n",
- "pickle.dump(user_recall_items_dict, open(save_path + 'usercf_u2u2i_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_recall_items_dict, trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T03:09:35.853516Z",
- "start_time": "2020-11-16T03:09:35.737625Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### user embedding sim召回\n",
- "\n",
- "虽然没有直接跑usercf的计算用户之间的相似度，为了验证上述基于用户的协同过滤的代码，下面使用了YoutubeDNN过程中产生的user embedding来进行向量检索每个user最相似的topk个user，在使用这里得到的u2u的相似性矩阵，使用usercf进行召回，具体代码如下"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:09:46.807811Z",
- "start_time": "2020-11-17T02:09:46.798033Z"
- }
- },
- "outputs": [],
- "source": [
- "# 使用Embedding的方式获取u2u的相似性矩阵\n",
- "# topk指的是每个user, faiss搜索后返回最相似的topk个user\n",
- "def u2u_embdding_sim(click_df, user_emb_dict, save_path, topk):\n",
- " \n",
- " user_list = []\n",
- " user_emb_list = []\n",
- " for user_id, user_emb in user_emb_dict.items():\n",
- " user_list.append(user_id)\n",
- " user_emb_list.append(user_emb)\n",
- " \n",
- " user_index_2_rawid_dict = {k: v for k, v in zip(range(len(user_list)), user_list)} \n",
- " \n",
- " user_emb_np = np.array(user_emb_list, dtype=np.float32)\n",
- " \n",
- " # 建立faiss索引\n",
- " user_index = faiss.IndexFlatIP(user_emb_np.shape[1])\n",
- " user_index.add(user_emb_np)\n",
- " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
- " sim, idx = user_index.search(user_emb_np, topk) # 返回的是列表\n",
- " \n",
- " # 将向量检索的结果保存成原始id的对应关系\n",
- " user_sim_dict = collections.defaultdict(dict)\n",
- " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(user_emb_np)), sim, idx)):\n",
- " target_raw_id = user_index_2_rawid_dict[target_idx]\n",
- " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
- " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
- " rele_raw_id = user_index_2_rawid_dict[rele_idx]\n",
- " user_sim_dict[target_raw_id][rele_raw_id] = user_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
- " \n",
- " # 保存i2i相似度矩阵\n",
- " pickle.dump(user_sim_dict, open(save_path + 'youtube_u2u_sim.pkl', 'wb')) \n",
- " return user_sim_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:14:31.355905Z",
- "start_time": "2020-11-17T02:09:53.236531Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "250000it [00:23, 10507.45it/s]\n"
- ]
- }
- ],
- "source": [
- "# 读取YoutubeDNN过程中产生的user embedding, 然后使用faiss计算用户之间的相似度\n",
- "# 这里需要注意，这里得到的user embedding其实并不是很好，因为YoutubeDNN中使用的是用户点击序列来训练的user embedding,\n",
- "# 如果序列普遍都比较短的话，其实效果并不是很好\n",
- "user_emb_dict = pickle.load(open(save_path + 'user_youtube_emb.pkl', 'rb'))\n",
- "u2u_sim = u2u_embdding_sim(all_click_df, user_emb_dict, save_path, topk=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "通过YoutubeDNN得到的user_embedding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T02:49:40.755431Z",
- "start_time": "2020-11-17T02:28:47.003514Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [19:43<00:00, 211.22it/s]\n"
- ]
- }
- ],
- "source": [
- "# 使用召回评估函数验证当前召回方式的效果\n",
- "if metric_recall:\n",
- " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
- "else:\n",
- " trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "u2u_sim = pickle.load(open(save_path + 'youtube_u2u_sim.pkl', 'rb'))\n",
- "\n",
- "sim_user_topk = 20\n",
- "recall_item_num = 10\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
- " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
- " \n",
- "user_multi_recall_dict['youtubednn_usercf_recall'] = user_recall_items_dict\n",
- "pickle.dump(user_multi_recall_dict['youtubednn_usercf_recall'], open(save_path + 'youtubednn_usercf_recall.pkl', 'wb'))\n",
- "\n",
- "if metric_recall:\n",
- " # 召回效果评估\n",
- " metrics_recall(user_multi_recall_dict['youtubednn_usercf_recall'], trn_last_click_df, topk=recall_item_num)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:07:44.326253Z",
- "start_time": "2020-11-16T07:07:43.798931Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 冷启动问题"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**冷启动问题可以分成三类：文章冷启动，用户冷启动，系统冷启动。**\n",
- "\n",
- "- 文章冷启动：对于一个平台系统新加入的文章，该文章没有任何的交互记录，如何推荐给用户的问题。(对于我们场景可以认为是，日志数据中没有出现过的文章都可以认为是冷启动的文章)\n",
- "- 用户冷启动：对于一个平台系统新来的用户，该用户还没有文章的交互信息，如何给该用户进行推荐。(对于我们场景就是，测试集中的用户是否在测试集对应的log数据中出现过，如果没有出现过，那么可以认为该用户是冷启动用户。但是有时候并没有这么严格，我们也可以自己设定某些指标来判别哪些用户是冷启动用户，比如通过使用时长，点击率，留存率等等)\n",
- "- 系统冷启动：就是对于一个平台刚上线，还没有任何的相关历史数据，此时就是系统冷启动，其实也就是前面两种的一个综合。\n",
- "\n",
- "**当前场景下冷启动问题的分析：**\n",
- "\n",
- "对当前的数据进行分析会发现，日志中所有出现过的点击文章只有3w多个，而整个文章库中却有30多万，那么测试集中的用户最后一次点击是否会点击没有出现在日志中的文章呢？如果存在这种情况，说明用户点击的文章之前没有任何的交互信息，这也就是我们所说的文章冷启动。通过数据分析还可以发现，测试集用户只有一次点击的数据占得比例还不少，其实仅仅通过用户的一次点击就给用户推荐文章使用模型的方式也是比较难的，这里其实也可以考虑用户冷启动的问题，但是这里只给出物品冷启动的一些解决方案及代码，关于用户冷启动的话提一些可行性的做法。\n",
- "\n",
- "1. 文章冷启动(没有冷启动的探索问题) \n",
- " 其实我们这里不是为了做文章的冷启动而做冷启动，而是猜测用户可能会点击一些没有在log数据中出现的文章，我们要做的就是如何从将近27万的文章中选择一些文章作为用户冷启动的文章，这里其实也可以看成是一种召回策略，我们这里就采用简单的比较好理解的基于规则的召回策略来获取用户可能点击的未出现在log数据中的文章。\n",
- " 现在的问题变成了：如何给每个用户考虑从27万个商品中获取一小部分商品？随机选一些可能是一种方案。下面给出一些参考的方案。\n",
- " 1. 首先基于Embedding召回一部分与用户历史相似的文章\n",
- " 2. 从基于Embedding召回的文章中通过一些规则过滤掉一些文章，使得留下的文章用户更可能点击。我们这里的规则，可以是，留下那些与用户历史点击文章主题相同的文章，或者字数相差不大的文章。并且留下的文章尽量是与测试集用户最后一次点击时间更接近的文章，或者是当天的文章也行。\n",
- "2. 用户冷启动 \n",
- " 这里对测试集中的用户点击数据进行分析会发现，测试集中有百分之20的用户只有一次点击，那么这些点击特别少的用户的召回是不是可以单独做一些策略上的补充呢？或者是在排序后直接基于规则加上一些文章呢？这些都可以去尝试，这里没有提供具体的做法。\n",
- " \n",
- "**注意：** \n",
- "\n",
- "这里看似和基于embedding计算的item之间相似度然后做itemcf是一致的，但是现在我们的目的不一样，我们这里的目的是找到相似的向量，并且还没有出现在log日志中的商品，再加上一些其他的冷启动的策略，这里需要找回的数量会偏多一点，不然被筛选完之后可能都没有文章了"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T04:30:23.027164Z",
- "start_time": "2020-11-17T04:23:09.960235Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [05:01<00:00, 828.60it/s] \n"
- ]
- }
- ],
- "source": [
- "# 先进行itemcf召回，这里不需要做召回评估，这里只是一种策略\n",
- "trn_hist_click_df = all_click_df\n",
- "\n",
- "user_recall_items_dict = collections.defaultdict(dict)\n",
- "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
- "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
- "\n",
- "sim_item_topk = 150\n",
- "recall_item_num = 100 # 稍微召回多一点文章，便于后续的规则筛选\n",
- "\n",
- "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
- "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
- " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
- " recall_item_num, item_topk_click,item_created_time_dict, emb_i2i_sim)\n",
- "pickle.dump(user_recall_items_dict, open(save_path + 'cold_start_items_raw_dict.pkl', 'wb'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T06:11:39.267581Z",
- "start_time": "2020-11-17T06:11:39.252563Z"
- }
- },
- "outputs": [],
- "source": [
- "# 基于规则进行文章过滤\n",
- "# 保留文章主题与用户历史浏览主题相似的文章\n",
- "# 保留文章字数与用户历史浏览文章字数相差不大的文章\n",
- "# 保留最后一次点击当天的文章\n",
- "# 按照相似度返回最终的结果\n",
- "\n",
- "def get_click_article_ids_set(all_click_df):\n",
- " return set(all_click_df.click_article_id.values)\n",
- "\n",
- "def cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
- " user_last_item_created_time_dict, item_type_dict, item_words_dict, \n",
- " item_created_time_dict, click_article_ids_set, recall_item_num):\n",
- " \"\"\"\n",
- " 冷启动的情况下召回一些文章\n",
- " :param user_recall_items_dict: 基于内容embedding相似性召回来的很多文章， 字典， {user1: [(item1, item2), ..], }\n",
- " :param user_hist_item_typs_dict: 字典， 用户点击的文章的主题映射\n",
- " :param user_hist_item_words_dict: 字典， 用户点击的历史文章的字数映射\n",
- " :param user_last_item_created_time_idct: 字典，用户点击的历史文章创建时间映射\n",
- " :param item_tpye_idct: 字典，文章主题映射\n",
- " :param item_words_dict: 字典，文章字数映射\n",
- " :param item_created_time_dict: 字典， 文章创建时间映射\n",
- " :param click_article_ids_set: 集合，用户点击过得文章, 也就是日志里面出现过的文章\n",
- " :param recall_item_num: 召回文章的数量， 这个指的是没有出现在日志里面的文章数量\n",
- " \"\"\"\n",
- " \n",
- " cold_start_user_items_dict = {}\n",
- " for user, item_list in tqdm(user_recall_items_dict.items()):\n",
- " cold_start_user_items_dict.setdefault(user, [])\n",
- " for item, score in item_list:\n",
- " # 获取历史文章信息\n",
- " hist_item_type_set = user_hist_item_typs_dict[user]\n",
- " hist_mean_words = user_hist_item_words_dict[user]\n",
- " hist_last_item_created_time = user_last_item_created_time_dict[user]\n",
- " hist_last_item_created_time = datetime.fromtimestamp(hist_last_item_created_time)\n",
- " \n",
- " # 获取当前召回文章的信息\n",
- " curr_item_type = item_type_dict[item]\n",
- " curr_item_words = item_words_dict[item]\n",
- " curr_item_created_time = item_created_time_dict[item]\n",
- " curr_item_created_time = datetime.fromtimestamp(curr_item_created_time)\n",
- "\n",
- " # 首先，文章不能出现在用户的历史点击中， 然后根据文章主题，文章单词数，文章创建时间进行筛选\n",
- " if curr_item_type not in hist_item_type_set or \\\n",
- " item in click_article_ids_set or \\\n",
- " abs(curr_item_words - hist_mean_words) > 200 or \\\n",
- " abs((curr_item_created_time - hist_last_item_created_time).days) > 90: \n",
- " continue\n",
- " \n",
- " cold_start_user_items_dict[user].append((item, score)) # {user1: [(item1, score1), (item2, score2)..]...}\n",
- " \n",
- " # 需要控制一下冷启动召回的数量\n",
- " cold_start_user_items_dict = {k: sorted(v, key=lambda x:x[1], reverse=True)[:recall_item_num] \\\n",
- " for k, v in cold_start_user_items_dict.items()}\n",
- " \n",
- " pickle.dump(cold_start_user_items_dict, open(save_path + 'cold_start_user_items_dict.pkl', 'wb'))\n",
- " \n",
- " return cold_start_user_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T06:35:38.758278Z",
- "start_time": "2020-11-17T06:31:40.164332Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 250000/250000 [01:49<00:00, 2293.37it/s]\n"
- ]
- }
- ],
- "source": [
- "all_click_df_ = all_click_df.copy()\n",
- "all_click_df_ = all_click_df_.merge(item_info_df, how='left', on='click_article_id')\n",
- "user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict = get_user_hist_item_info_dict(all_click_df_)\n",
- "click_article_ids_set = get_click_article_ids_set(all_click_df)\n",
- "# 需要注意的是\n",
- "# 这里使用了很多规则来筛选冷启动的文章，所以前面再召回的阶段就应该尽可能的多召回一些文章，否则很容易被删掉\n",
- "cold_start_user_items_dict = cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
- " user_last_item_created_time_dict, item_type_dict, item_words_dict, \\\n",
- " item_created_time_dict, click_article_ids_set, recall_item_num)\n",
- "\n",
- "user_multi_recall_dict['cold_start_recall'] = cold_start_user_items_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-16T07:13:33.099298Z",
- "start_time": "2020-11-16T07:13:32.655036Z"
- }
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 多路召回合并\n",
- "多路召回合并就是将前面所有的召回策略得到的用户文章列表合并起来，下面是对前面所有召回结果的汇总\n",
- "1. 基于itemcf计算的item之间的相似度sim进行的召回 \n",
- "2. 基于embedding搜索得到的item之间的相似度进行的召回\n",
- "3. YoutubeDNN召回\n",
- "4. YoutubeDNN得到的user之间的相似度进行的召回\n",
- "5. 基于冷启动策略的召回\n",
- "\n",
- "**注意：** \n",
- "在做召回评估的时候就会发现有些召回的效果不错有些召回的效果很差，所以对每一路召回的结果，我们可以认为的定义一些权重，来做最终的相似度融合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:02:16.033971Z",
- "start_time": "2020-11-17T07:02:16.019819Z"
- }
- },
- "outputs": [],
- "source": [
- "def combine_recall_results(user_multi_recall_dict, weight_dict=None, topk=25):\n",
- " final_recall_items_dict = {}\n",
- " \n",
- " # 对每一种召回结果按照用户进行归一化，方便后面多种召回结果，相同用户的物品之间权重相加\n",
- " def norm_user_recall_items_sim(sorted_item_list):\n",
- " # 如果冷启动中没有文章或者只有一篇文章，直接返回，出现这种情况的原因可能是冷启动召回的文章数量太少了，\n",
- " # 基于规则筛选之后就没有文章了, 这里还可以做一些其他的策略性的筛选\n",
- " if len(sorted_item_list) < 2:\n",
- " return sorted_item_list\n",
- " \n",
- " min_sim = sorted_item_list[-1][1]\n",
- " max_sim = sorted_item_list[0][1]\n",
- " \n",
- " norm_sorted_item_list = []\n",
- " for item, score in sorted_item_list:\n",
- " if max_sim > 0:\n",
- " norm_score = 1.0 * (score - min_sim) / (max_sim - min_sim) if max_sim > min_sim else 1.0\n",
- " else:\n",
- " norm_score = 0.0\n",
- " norm_sorted_item_list.append((item, norm_score))\n",
- " \n",
- " return norm_sorted_item_list\n",
- " \n",
- " print('多路召回合并...')\n",
- " for method, user_recall_items in tqdm(user_multi_recall_dict.items()):\n",
- " print(method + '...')\n",
- " # 在计算最终召回结果的时候，也可以为每一种召回结果设置一个权重\n",
- " if weight_dict == None:\n",
- " recall_method_weight = 1\n",
- " else:\n",
- " recall_method_weight = weight_dict[method]\n",
- " \n",
- " for user_id, sorted_item_list in user_recall_items.items(): # 进行归一化\n",
- " user_recall_items[user_id] = norm_user_recall_items_sim(sorted_item_list)\n",
- " \n",
- " for user_id, sorted_item_list in user_recall_items.items():\n",
- " # print('user_id')\n",
- " final_recall_items_dict.setdefault(user_id, {})\n",
- " for item, score in sorted_item_list:\n",
- " final_recall_items_dict[user_id].setdefault(item, 0)\n",
- " final_recall_items_dict[user_id][item] += recall_method_weight * score \n",
- " \n",
- " final_recall_items_dict_rank = {}\n",
- " # 多路召回时也可以控制最终的召回数量\n",
- " for user, recall_item_dict in final_recall_items_dict.items():\n",
- " final_recall_items_dict_rank[user] = sorted(recall_item_dict.items(), key=lambda x: x[1], reverse=True)[:topk]\n",
- "\n",
- " # 将多路召回后的最终结果字典保存到本地\n",
- " pickle.dump(final_recall_items_dict_rank, open(os.path.join(save_path, 'final_recall_items_dict.pkl'),'wb'))\n",
- "\n",
- " return final_recall_items_dict_rank"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:02:21.078455Z",
- "start_time": "2020-11-17T07:02:21.074060Z"
- }
- },
- "outputs": [],
- "source": [
- "# 这里直接对多路召回的权重给了一个相同的值，其实可以根据前面召回的情况来调整参数的值\n",
- "weight_dict = {'itemcf_sim_itemcf_recall': 1.0,\n",
- " 'embedding_sim_item_recall': 1.0,\n",
- " 'youtubednn_recall': 1.0,\n",
- " 'youtubednn_usercf_recall': 1.0, \n",
- " 'cold_start_recall': 1.0}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-17T07:04:35.747924Z",
- "start_time": "2020-11-17T07:02:26.889573Z"
- }
- },
- "outputs": [
+ "cells": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/5 [00:00\n",
+ "\n",
+ "上图只是一个多路召回的例子，也就是说可以使用多种不同的策略来获取用户排序的候选商品集合，而具体使用哪些召回策略其实是与业务强相关的 ，针对不同的任务就会有对于该业务真实场景下需要考虑的召回规则。例如新闻推荐，召回规则可以是“热门新闻”、“作者召回”、“关键词召回”、“主题召回“、”协同过滤召回“等等。 \n",
+ "\n"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "多路召回合并...\n",
- "itemcf_sim_itemcf_recall...\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 导包"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 20%|██ | 1/5 [00:08<00:34, 8.66s/it]"
- ]
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:29.834662Z",
+ "start_time": "2020-11-16T11:26:27.811511Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np\n",
+ "from tqdm import tqdm \n",
+ "from collections import defaultdict \n",
+ "import os, math, warnings, math, pickle\n",
+ "from tqdm import tqdm\n",
+ "import faiss\n",
+ "import collections\n",
+ "import random\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from datetime import datetime\n",
+ "from deepctr.feature_column import SparseFeat, VarLenSparseFeat\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from tensorflow.python.keras import backend as K\n",
+ "from tensorflow.python.keras.models import Model\n",
+ "from tensorflow.python.keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "from deepmatch.models import *\n",
+ "from deepmatch.utils import sampledsoftmaxloss\n",
+ "warnings.filterwarnings('ignore')"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "embedding_sim_item_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:31.831215Z",
+ "start_time": "2020-11-16T11:26:31.826939Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = './data_raw/'\n",
+ "save_path = './temp_results/'\n",
+ "# 做召回评估的一个标志, 如果不进行评估就是直接使用全量数据进行召回\n",
+ "metric_recall = False"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 40%|████ | 2/5 [00:16<00:24, 8.29s/it]"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 读取数据\n",
+ "在一般的rs比赛中读取数据部分主要分为三种模式， 不同的模式对应的不同的数据集：\n",
+ "1. debug模式： 这个的目的是帮助我们基于数据先搭建一个简易的baseline并跑通， 保证写的baseline代码没有什么问题。 由于推荐比赛的数据往往非常巨大， 如果一上来直接采用全部的数据进行分析，搭建baseline框架， 往往会带来时间和设备上的损耗， **所以这时候我们往往需要从海量数据的训练集中随机抽取一部分样本来进行调试(train_click_log_sample)**， 先跑通一个baseline。\n",
+ "2. 线下验证模式： 这个的目的是帮助我们在线下基于已有的训练集数据， 来选择好合适的模型和一些超参数。 **所以我们这一块只需要加载整个训练集(train_click_log)**， 然后把整个训练集再分成训练集和验证集。 训练集是模型的训练数据， 验证集部分帮助我们调整模型的参数和其他的一些超参数。\n",
+ "3. 线上模式： 我们用debug模式搭建起一个推荐系统比赛的baseline， 用线下验证模式选择好了模型和一些超参数， 这一部分就是真正的对于给定的测试集进行预测， 提交到线上， **所以这一块使用的训练数据集是全量的数据集(train_click_log+test_click_log)**\n",
+ "\n",
+ "下面就分别对这三种不同的数据读取模式先建立不同的代导入函数， 方便后面针对不同的模式下导入数据。"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "youtubednn_recall...\n",
- "youtubednn_usercf_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:34.476240Z",
+ "start_time": "2020-11-16T11:26:34.467352Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# debug模式： 从训练集中划出一部分数据来调试代码\n",
+ "def get_all_click_sample(data_path, sample_nums=10000):\n",
+ " \"\"\"\n",
+ " 训练集中采样一部分数据调试\n",
+ " data_path: 原数据的存储路径\n",
+ " sample_nums: 采样数目（这里由于机器的内存限制，可以采样用户做）\n",
+ " \"\"\"\n",
+ " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " all_user_ids = all_click.user_id.unique()\n",
+ "\n",
+ " sample_user_ids = np.random.choice(all_user_ids, size=sample_nums, replace=False) \n",
+ " all_click = all_click[all_click['user_id'].isin(sample_user_ids)]\n",
+ " \n",
+ " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
+ " return all_click\n",
+ "\n",
+ "# 读取点击数据，这里分成线上和线下，如果是为了获取线上提交结果应该讲测试集中的点击数据合并到总的数据中\n",
+ "# 如果是为了线下验证模型的有效性或者特征的有效性，可以只使用训练集\n",
+ "def get_all_click_df(data_path='./data_raw/', offline=True):\n",
+ " if offline:\n",
+ " all_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " else:\n",
+ " trn_click = pd.read_csv(data_path + 'train_click_log.csv')\n",
+ " tst_click = pd.read_csv(data_path + 'testA_click_log.csv')\n",
+ "\n",
+ " all_click = trn_click.append(tst_click)\n",
+ " \n",
+ " all_click = all_click.drop_duplicates((['user_id', 'click_article_id', 'click_timestamp']))\n",
+ " return all_click"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 80%|████████ | 4/5 [00:23<00:06, 6.98s/it]"
- ]
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:35.168738Z",
+ "start_time": "2020-11-16T11:26:35.163210Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取文章的基本属性\n",
+ "def get_item_info_df(data_path):\n",
+ " item_info_df = pd.read_csv(data_path + 'articles.csv')\n",
+ " \n",
+ " # 为了方便与训练集中的click_article_id拼接，需要把article_id修改成click_article_id\n",
+ " item_info_df = item_info_df.rename(columns={'article_id': 'click_article_id'})\n",
+ " \n",
+ " return item_info_df"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "cold_start_recall...\n"
- ]
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:36.152958Z",
+ "start_time": "2020-11-16T11:26:36.146324Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 读取文章的Embedding数据\n",
+ "def get_item_emb_dict(data_path):\n",
+ " item_emb_df = pd.read_csv(data_path + 'articles_emb.csv')\n",
+ " \n",
+ " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
+ " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols])\n",
+ " # 进行归一化\n",
+ " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
+ "\n",
+ " item_emb_dict = dict(zip(item_emb_df['article_id'], item_emb_np))\n",
+ " pickle.dump(item_emb_dict, open(save_path + 'item_content_emb.pkl', 'wb'))\n",
+ " \n",
+ " return item_emb_dict"
+ ]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 5/5 [00:42<00:00, 8.40s/it]\n"
- ]
- }
- ],
- "source": [
- "# 最终合并之后每个用户召回150个商品进行排序\n",
- "final_recall_items_dict_rank = combine_recall_results(user_multi_recall_dict, weight_dict, topk=150)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 总结\n",
- "\n",
- "上述实现了如下召回策略：\n",
- "\n",
- "1. 基于关联规则的itemcf\n",
- "2. 基于关联规则的usercf\n",
- "3. youtubednn召回\n",
- "4. 冷启动召回\n",
- "\n",
- "对于上述实现的召回策略其实都不是最优的结果，我们只是做了个简单的尝试，其中还有很多地方可以优化，包括已经实现的这些召回策略的参数或者新加一些，修改一些关联规则都可以。当然还可以尝试更多的召回策略，比如对新闻进行热度召回等等。\n",
- "\n",
- "\n",
- "\n",
- "**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：\n",
- "\n",
- ""
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- },
- "latex_envs": {
- "LaTeX_envs_menu_present": true,
- "autoclose": false,
- "autocomplete": true,
- "bibliofile": "biblio.bib",
- "cite_by": "apalike",
- "current_citInitial": 1,
- "eqLabelWithNumbers": true,
- "eqNumInitial": 1,
- "hotkeys": {
- "equation": "Ctrl-E",
- "itemize": "Ctrl-I"
- },
- "labels_anchors": false,
- "latex_user_defs": false,
- "report_style_numbering": false,
- "user_envs_cfg": false
- },
- "nbTranslate": {
- "displayLangs": [
- "*"
- ],
- "hotkey": "alt-t",
- "langInMainMenu": true,
- "sourceLang": "en",
- "targetLang": "fr",
- "useGoogleTranslate": true
- },
- "tianchi_metadata": {
- "competitions": [],
- "datasets": [
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:37.333536Z",
+ "start_time": "2020-11-16T11:26:37.329545Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:42.163494Z",
+ "start_time": "2020-11-16T11:26:38.018094Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 采样数据\n",
+ "# all_click_df = get_all_click_sample(data_path)\n",
+ "\n",
+ "# 全量训练集\n",
+ "all_click_df = get_all_click_df(offline=False)\n",
+ "\n",
+ "# 对时间戳进行归一化,用于在关联规则的时候计算权重\n",
+ "all_click_df['click_timestamp'] = all_click_df[['click_timestamp']].apply(max_min_scaler)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:26:44.343500Z",
+ "start_time": "2020-11-16T11:26:44.113891Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "item_info_df = get_item_info_df(data_path)"
+ ]
+ },
{
- "id": "83580",
- "title": "零基础入门推荐系统 - 新闻推荐"
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:24.295343Z",
+ "start_time": "2020-11-16T11:26:44.398007Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "item_emb_dict = get_item_emb_dict(data_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 工具函数"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取用户-文章-时间函数\n",
+ "这个在基于关联规则的用户协同过滤的时候会用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:33.791656Z",
+ "start_time": "2020-11-16T11:27:33.784305Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ "def get_user_item_time(click_df):\n",
+ " \n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " \n",
+ " def make_item_time_pair(df):\n",
+ " return list(zip(df['click_article_id'], df['click_timestamp']))\n",
+ " \n",
+ " user_item_time_df = click_df.groupby('user_id')['click_article_id', 'click_timestamp'].apply(lambda x: make_item_time_pair(x))\\\n",
+ " .reset_index().rename(columns={0: 'item_time_list'})\n",
+ " user_item_time_dict = dict(zip(user_item_time_df['user_id'], user_item_time_df['item_time_list']))\n",
+ " \n",
+ " return user_item_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取文章-用户-时间函数\n",
+ "这个在基于关联规则的文章协同过滤的时候会用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:38.327581Z",
+ "start_time": "2020-11-16T11:27:38.321059Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 根据时间获取商品被点击的用户序列 {item1: [(user1, time1), (user2, time2)...]...}\n",
+ "# 这里的时间是用户点击当前商品的时间，好像没有直接的关系。\n",
+ "def get_item_user_time_dict(click_df):\n",
+ " def make_user_time_pair(df):\n",
+ " return list(zip(df['user_id'], df['click_timestamp']))\n",
+ " \n",
+ " click_df = click_df.sort_values('click_timestamp')\n",
+ " item_user_time_df = click_df.groupby('click_article_id')['user_id', 'click_timestamp'].apply(lambda x: make_user_time_pair(x))\\\n",
+ " .reset_index().rename(columns={0: 'user_time_list'})\n",
+ " \n",
+ " item_user_time_dict = dict(zip(item_user_time_df['click_article_id'], item_user_time_df['user_time_list']))\n",
+ " return item_user_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取历史和最后一次点击\n",
+ "这个在评估召回结果， 特征工程和制作标签转成监督学习测试集的时候回用到"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:50.894683Z",
+ "start_time": "2020-11-16T11:27:50.888002Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取当前数据的历史点击和最后一次点击\n",
+ "def get_hist_and_last_click(all_click):\n",
+ " \n",
+ " all_click = all_click.sort_values(by=['user_id', 'click_timestamp'])\n",
+ " click_last_df = all_click.groupby('user_id').tail(1)\n",
+ "\n",
+ " # 如果用户只有一个点击，hist为空了，会导致训练的时候这个用户不可见，此时默认泄露一下\n",
+ " def hist_func(user_df):\n",
+ " if len(user_df) == 1:\n",
+ " return user_df\n",
+ " else:\n",
+ " return user_df[:-1]\n",
+ "\n",
+ " click_hist_df = all_click.groupby('user_id').apply(hist_func).reset_index(drop=True)\n",
+ "\n",
+ " return click_hist_df, click_last_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取文章属性特征"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:55.893810Z",
+ "start_time": "2020-11-16T11:27:55.887623Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取文章id对应的基本属性，保存成字典的形式，方便后面召回阶段，冷启动阶段直接使用\n",
+ "def get_item_info_dict(item_info_df):\n",
+ " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
+ " item_info_df['created_at_ts'] = item_info_df[['created_at_ts']].apply(max_min_scaler)\n",
+ " \n",
+ " item_type_dict = dict(zip(item_info_df['click_article_id'], item_info_df['category_id']))\n",
+ " item_words_dict = dict(zip(item_info_df['click_article_id'], item_info_df['words_count']))\n",
+ " item_created_time_dict = dict(zip(item_info_df['click_article_id'], item_info_df['created_at_ts']))\n",
+ " \n",
+ " return item_type_dict, item_words_dict, item_created_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-13T06:42:38.730939Z",
+ "start_time": "2020-11-13T06:42:38.728461Z"
+ }
+ },
+ "source": [
+ "### 获取用户历史点击的文章信息"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:27:59.650781Z",
+ "start_time": "2020-11-16T11:27:59.640572Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_user_hist_item_info_dict(all_click):\n",
+ " \n",
+ " # 获取user_id对应的用户历史点击文章类型的集合字典\n",
+ " user_hist_item_typs = all_click.groupby('user_id')['category_id'].agg(set).reset_index()\n",
+ " user_hist_item_typs_dict = dict(zip(user_hist_item_typs['user_id'], user_hist_item_typs['category_id']))\n",
+ " \n",
+ " # 获取user_id对应的用户点击文章的集合\n",
+ " user_hist_item_ids_dict = all_click.groupby('user_id')['click_article_id'].agg(set).reset_index()\n",
+ " user_hist_item_ids_dict = dict(zip(user_hist_item_ids_dict['user_id'], user_hist_item_ids_dict['click_article_id']))\n",
+ " \n",
+ " # 获取user_id对应的用户历史点击的文章的平均字数字典\n",
+ " user_hist_item_words = all_click.groupby('user_id')['words_count'].agg('mean').reset_index()\n",
+ " user_hist_item_words_dict = dict(zip(user_hist_item_words['user_id'], user_hist_item_words['words_count']))\n",
+ " \n",
+ " # 获取user_id对应的用户最后一次点击的文章的创建时间\n",
+ " all_click_ = all_click.sort_values('click_timestamp')\n",
+ " user_last_item_created_time = all_click_.groupby('user_id')['created_at_ts'].apply(lambda x: x.iloc[-1]).reset_index()\n",
+ " \n",
+ " max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))\n",
+ " user_last_item_created_time['created_at_ts'] = user_last_item_created_time[['created_at_ts']].apply(max_min_scaler)\n",
+ " \n",
+ " user_last_item_created_time_dict = dict(zip(user_last_item_created_time['user_id'], \\\n",
+ " user_last_item_created_time['created_at_ts']))\n",
+ " \n",
+ " return user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 获取点击次数最多的topk个文章"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:04.761105Z",
+ "start_time": "2020-11-16T11:28:04.756419Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取近期点击最多的文章\n",
+ "def get_item_topk_click(click_df, k):\n",
+ " topk_click = click_df['click_article_id'].value_counts().index[:k]\n",
+ " return topk_click"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 定义多路召回字典"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:08.321506Z",
+ "start_time": "2020-11-16T11:28:07.623281Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取文章的属性信息，保存成字典的形式方便查询\n",
+ "item_type_dict, item_words_dict, item_created_time_dict = get_item_info_dict(item_info_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:28:13.791569Z",
+ "start_time": "2020-11-16T11:28:13.786522Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义一个多路召回的字典，将各路召回的结果都保存在这个字典当中\n",
+ "user_multi_recall_dict = {'itemcf_sim_itemcf_recall': {},\n",
+ " 'embedding_sim_item_recall': {},\n",
+ " 'youtubednn_recall': {},\n",
+ " 'youtubednn_usercf_recall': {}, \n",
+ " 'cold_start_recall': {}}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T05:41:12.710754Z",
+ "start_time": "2020-11-16T05:40:57.842614Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 提取最后一次点击作为召回评估，如果不需要做召回评估直接使用全量的训练集进行召回(线下验证模型)\n",
+ "# 如果不是召回评估，直接使用全量数据进行召回，不用将最后一次提取出来\n",
+ "trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 召回效果评估函数\n",
+ "做完了召回有时候也需要对当前的召回方法或者参数进行调整以达到更好的召回效果，因为召回的结果决定了最终排序的上限，下面也会提供一个召回评估的方法"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T05:41:18.579118Z",
+ "start_time": "2020-11-16T05:41:18.571887Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 依次评估召回的前10, 20, 30, 40, 50个文章中的击中率\n",
+ "def metrics_recall(user_recall_items_dict, trn_last_click_df, topk=5):\n",
+ " last_click_item_dict = dict(zip(trn_last_click_df['user_id'], trn_last_click_df['click_article_id']))\n",
+ " user_num = len(user_recall_items_dict)\n",
+ " \n",
+ " for k in range(10, topk+1, 10):\n",
+ " hit_num = 0\n",
+ " for user, item_list in user_recall_items_dict.items():\n",
+ " # 获取前k个召回的结果\n",
+ " tmp_recall_items = [x[0] for x in user_recall_items_dict[user][:k]]\n",
+ " if last_click_item_dict[user] in set(tmp_recall_items):\n",
+ " hit_num += 1\n",
+ " \n",
+ " hit_rate = round(hit_num * 1.0 / user_num, 5)\n",
+ " print(' topk: ', k, ' : ', 'hit_num: ', hit_num, 'hit_rate: ', hit_rate, 'user_num : ', user_num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 计算相似性矩阵\n",
+ "\n",
+ "这一部分主要是通过协同过滤以及向量检索得到相似性矩阵，相似性矩阵主要分为user2user和item2item，下面依次获取基于itemcf的item2item的相似性矩阵，"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### itemcf i2i_sim\n",
+ "\n",
+ "借鉴KDD2020的去偏商品推荐，在计算item2item相似性矩阵时，使用关联规则，使得计算的文章的相似性还考虑到了:\n",
+ "1. 用户点击的时间权重\n",
+ "2. 用户点击的顺序权重\n",
+ "3. 文章创建的时间权重"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:30:51.872262Z",
+ "start_time": "2020-11-16T11:30:51.860099Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def itemcf_sim(df, item_created_time_dict):\n",
+ " \"\"\"\n",
+ " 文章与文章之间的相似性矩阵计算\n",
+ " :param df: 数据表\n",
+ " :item_created_time_dict: 文章创建时间的字典\n",
+ " return : 文章与文章的相似性矩阵\n",
+ " \n",
+ " 思路: 基于物品的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
+ " \"\"\"\n",
+ " \n",
+ " user_item_time_dict = get_user_item_time(df)\n",
+ " \n",
+ " # 计算物品相似度\n",
+ " i2i_sim = {}\n",
+ " item_cnt = defaultdict(int)\n",
+ " for user, item_time_list in tqdm(user_item_time_dict.items()):\n",
+ " # 在基于商品的协同过滤优化的时候可以考虑时间因素\n",
+ " for loc1, (i, i_click_time) in enumerate(item_time_list):\n",
+ " item_cnt[i] += 1\n",
+ " i2i_sim.setdefault(i, {})\n",
+ " for loc2, (j, j_click_time) in enumerate(item_time_list):\n",
+ " if(i == j):\n",
+ " continue\n",
+ " \n",
+ " # 考虑文章的正向顺序点击和反向顺序点击 \n",
+ " loc_alpha = 1.0 if loc2 > loc1 else 0.7\n",
+ " # 位置信息权重，其中的参数可以调节\n",
+ " loc_weight = loc_alpha * (0.9 ** (np.abs(loc2 - loc1) - 1))\n",
+ " # 点击时间权重，其中的参数可以调节\n",
+ " click_time_weight = np.exp(0.7 ** np.abs(i_click_time - j_click_time))\n",
+ " # 两篇文章创建时间的权重，其中的参数可以调节\n",
+ " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " i2i_sim[i].setdefault(j, 0)\n",
+ " # 考虑多种因素的权重计算最终的文章之间的相似度\n",
+ " i2i_sim[i][j] += loc_weight * click_time_weight * created_time_weight / math.log(len(item_time_list) + 1)\n",
+ " \n",
+ " i2i_sim_ = i2i_sim.copy()\n",
+ " for i, related_items in i2i_sim.items():\n",
+ " for j, wij in related_items.items():\n",
+ " i2i_sim_[i][j] = wij / math.sqrt(item_cnt[i] * item_cnt[j])\n",
+ " \n",
+ " # 将得到的相似性矩阵保存到本地\n",
+ " pickle.dump(i2i_sim_, open(save_path + 'itemcf_i2i_sim.pkl', 'wb'))\n",
+ " \n",
+ " return i2i_sim_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:47:09.937002Z",
+ "start_time": "2020-11-16T11:30:57.394334Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [14:20<00:00, 290.38it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "i2i_sim = itemcf_sim(all_click_df, item_created_time_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### usercf u2u_sim\n",
+ "\n",
+ "在计算用户之间的相似度的时候，也可以使用一些简单的关联规则，比如用户活跃度权重，这里将用户的点击次数作为用户活跃度的指标"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:14.951940Z",
+ "start_time": "2020-11-16T09:11:14.945654Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_user_activate_degree_dict(all_click_df):\n",
+ " all_click_df_ = all_click_df.groupby('user_id')['click_article_id'].count().reset_index()\n",
+ " \n",
+ " # 用户活跃度归一化\n",
+ " mm = MinMaxScaler()\n",
+ " all_click_df_['click_article_id'] = mm.fit_transform(all_click_df_[['click_article_id']])\n",
+ " user_activate_degree_dict = dict(zip(all_click_df_['user_id'], all_click_df_['click_article_id']))\n",
+ " \n",
+ " return user_activate_degree_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:19.879276Z",
+ "start_time": "2020-11-16T09:11:19.868808Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def usercf_sim(all_click_df, user_activate_degree_dict):\n",
+ " \"\"\"\n",
+ " 用户相似性矩阵计算\n",
+ " :param all_click_df: 数据表\n",
+ " :param user_activate_degree_dict: 用户活跃度的字典\n",
+ " return 用户相似性矩阵\n",
+ " \n",
+ " 思路: 基于用户的协同过滤(详细请参考上一期推荐系统基础的组队学习) + 关联规则\n",
+ " \"\"\"\n",
+ " item_user_time_dict = get_item_user_time_dict(all_click_df)\n",
+ " \n",
+ " u2u_sim = {}\n",
+ " user_cnt = defaultdict(int)\n",
+ " for item, user_time_list in tqdm(item_user_time_dict.items()):\n",
+ " for u, click_time in user_time_list:\n",
+ " user_cnt[u] += 1\n",
+ " u2u_sim.setdefault(u, {})\n",
+ " for v, click_time in user_time_list:\n",
+ " u2u_sim[u].setdefault(v, 0)\n",
+ " if u == v:\n",
+ " continue\n",
+ " # 用户平均活跃度作为活跃度的权重，这里的式子也可以改善\n",
+ " activate_weight = 100 * 0.5 * (user_activate_degree_dict[u] + user_activate_degree_dict[v]) \n",
+ " u2u_sim[u][v] += activate_weight / math.log(len(user_time_list) + 1)\n",
+ " \n",
+ " u2u_sim_ = u2u_sim.copy()\n",
+ " for u, related_users in u2u_sim.items():\n",
+ " for v, wij in related_users.items():\n",
+ " u2u_sim_[u][v] = wij / math.sqrt(user_cnt[u] * user_cnt[v])\n",
+ " \n",
+ " # 将得到的相似性矩阵保存到本地\n",
+ " pickle.dump(u2u_sim_, open(save_path + 'usercf_u2u_sim.pkl', 'wb'))\n",
+ "\n",
+ " return u2u_sim_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T06:59:46.701572Z",
+ "start_time": "2020-11-16T06:59:26.852246Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 由于usercf计算时候太耗费内存了，这里就不直接运行了\n",
+ "# 如果是采样的话，是可以运行的\n",
+ "user_activate_degree_dict = get_user_activate_degree_dict(all_click_df)\n",
+ "u2u_sim = usercf_sim(all_click_df, user_activate_degree_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### item embedding sim\n",
+ "\n",
+ "使用Embedding计算item之间的相似度是为了后续冷启动的时候可以获取未出现在点击数据中的文章，后面有对冷启动专门的介绍，这里简单的说一下faiss。\n",
+ "\n",
+ "aiss是Facebook的AI团队开源的一套用于做聚类或者相似性搜索的软件库，底层是用C++实现。Faiss因为超级优越的性能，被广泛应用于推荐相关的业务当中.\n",
+ "\n",
+ "faiss工具包一般使用在推荐系统中的向量召回部分。在做向量召回的时候要么是u2u,u2i或者i2i，这里的u和i指的是user和item.我们知道在实际的场景中user和item的数量都是海量的，我们最容易想到的基于向量相似度的召回就是使用两层循环遍历user列表或者item列表计算两个向量的相似度，但是这样做在面对海量数据是不切实际的，faiss就是用来加速计算某个查询向量最相似的topk个索引向量。\n",
+ "\n",
+ "**faiss查询的原理：**\n",
+ "\n",
+ "faiss使用了PCA和PQ(Product quantization乘积量化)两种技术进行向量压缩和编码，当然还使用了其他的技术进行优化，但是PCA和PQ是其中最核心部分。\n",
+ "\n",
+ "1. PCA降维算法细节参考下面这个链接进行学习 \n",
+ "[主成分分析（PCA）原理总结](https://www.cnblogs.com/pinard/p/6239403.html) \n",
+ "\n",
+ "2. PQ编码的细节下面这个链接进行学习 \n",
+ "[实例理解product quantization算法](http://www.fabwrite.com/productquantization)\n",
+ "\n",
+ "**faiss使用**\n",
+ "\n",
+ "[faiss官方教程](https://github.com/facebookresearch/faiss/wiki/Getting-started)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:11:28.631803Z",
+ "start_time": "2020-11-16T09:11:28.619926Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 向量检索相似度计算\n",
+ "# topk指的是每个item, faiss搜索后返回最相似的topk个item\n",
+ "def embdding_sim(click_df, item_emb_df, save_path, topk):\n",
+ " \"\"\"\n",
+ " 基于内容的文章embedding相似性矩阵计算\n",
+ " :param click_df: 数据表\n",
+ " :param item_emb_df: 文章的embedding\n",
+ " :param save_path: 保存路径\n",
+ " :patam topk: 找最相似的topk篇\n",
+ " return 文章相似性矩阵\n",
+ " \n",
+ " 思路: 对于每一篇文章， 基于embedding的相似性返回topk个与其最相似的文章， 只不过由于文章数量太多，这里用了faiss进行加速\n",
+ " \"\"\"\n",
+ " \n",
+ " # 文章索引与文章id的字典映射\n",
+ " item_idx_2_rawid_dict = dict(zip(item_emb_df.index, item_emb_df['article_id']))\n",
+ " \n",
+ " item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]\n",
+ " item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols].values, dtype=np.float32)\n",
+ " # 向量进行单位化\n",
+ " item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)\n",
+ " \n",
+ " # 建立faiss索引\n",
+ " item_index = faiss.IndexFlatIP(item_emb_np.shape[1])\n",
+ " item_index.add(item_emb_np)\n",
+ " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
+ " sim, idx = item_index.search(item_emb_np, topk) # 返回的是列表\n",
+ " \n",
+ " # 将向量检索的结果保存成原始id的对应关系\n",
+ " item_sim_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(item_emb_np)), sim, idx)):\n",
+ " target_raw_id = item_idx_2_rawid_dict[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = item_idx_2_rawid_dict[rele_idx]\n",
+ " item_sim_dict[target_raw_id][rele_raw_id] = item_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " # 保存i2i相似度矩阵\n",
+ " pickle.dump(item_sim_dict, open(save_path + 'emb_i2i_sim.pkl', 'wb')) \n",
+ " \n",
+ " return item_sim_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T09:32:35.926116Z",
+ "start_time": "2020-11-16T09:11:44.586967Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "364047it [00:23, 15292.14it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "item_emb_df = pd.read_csv(data_path + '/articles_emb.csv')\n",
+ "emb_i2i_sim = embdding_sim(all_click_df, item_emb_df, save_path, topk=10) # topk可以自行设置"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 召回\n",
+ "这个就是我们开篇提到的那个问题， 面的36万篇文章， 20多万用户的推荐， 我们又有哪些策略来缩减问题的规模？ 我们就可以再召回阶段筛选出用户对于点击文章的候选集合， 从而降低问题的规模。召回常用的策略：\n",
+ "* Youtube DNN 召回\n",
+ "* 基于文章的召回\n",
+ " * 文章的协同过滤\n",
+ " * 基于文章embedding的召回\n",
+ "* 基于用户的召回\n",
+ " * 用户的协同过滤\n",
+ " * 用户embedding\n",
+ "\n",
+ "上面的各种召回方式一部分在基于用户已经看得文章的基础上去召回与这些文章相似的一些文章， 而这个相似性的计算方式不同， 就得到了不同的召回方式， 比如文章的协同过滤， 文章内容的embedding等。还有一部分是根据用户的相似性进行推荐，对于某用户推荐与其相似的其他用户看过的文章，比如用户的协同过滤和用户embedding。 还有一种思路是类似矩阵分解的思路，先计算出用户和文章的embedding之后，就可以直接算用户和文章的相似度， 根据这个相似度进行推荐， 比如YouTube DNN。 我们下面详细来看一下每一个召回方法："
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### YoutubeDNN召回\n",
+ "**(这一步是直接获取用户召回的候选文章列表)**\n",
+ "\n",
+ "[论文下载地址](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)\n",
+ "\n",
+ "**Youtubednn召回架构**\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "关于YoutubeDNN原理和应用推荐看王喆的两篇博客：\n",
+ "\n",
+ "1. [重读Youtube深度学习推荐系统论文，字字珠玑，惊为神文](https://zhuanlan.zhihu.com/p/52169807)\n",
+ "2. [YouTube深度学习推荐系统的十大工程问题](https://zhuanlan.zhihu.com/p/52504407)\n",
+ "\n",
+ "\n",
+ "**参考文献:**\n",
+ "1. https://zhuanlan.zhihu.com/p/52169807 (YouTubeDNN原理)\n",
+ "2. https://zhuanlan.zhihu.com/p/26306795 (Word2Vec知乎众赞文章) --- word2vec放到排序中的w2v的介绍部分\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:13:11.058766Z",
+ "start_time": "2020-11-16T10:13:11.041084Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 获取双塔召回时的训练验证数据\n",
+ "# negsample指的是通过滑窗构建样本的时候，负样本的数量\n",
+ "def gen_data_set(data, negsample=0):\n",
+ " data.sort_values(\"click_timestamp\", inplace=True)\n",
+ " item_ids = data['click_article_id'].unique()\n",
+ "\n",
+ " train_set = []\n",
+ " test_set = []\n",
+ " for reviewerID, hist in tqdm(data.groupby('user_id')):\n",
+ " pos_list = hist['click_article_id'].tolist()\n",
+ " \n",
+ " if negsample > 0:\n",
+ " candidate_set = list(set(item_ids) - set(pos_list)) # 用户没看过的文章里面选择负样本\n",
+ " neg_list = np.random.choice(candidate_set,size=len(pos_list)*negsample,replace=True) # 对于每个正样本，选择n个负样本\n",
+ " \n",
+ " # 长度只有一个的时候，需要把这条数据也放到训练集中，不然的话最终学到的embedding就会有缺失\n",
+ " if len(pos_list) == 1:\n",
+ " train_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
+ " test_set.append((reviewerID, [pos_list[0]], pos_list[0],1,len(pos_list)))\n",
+ " \n",
+ " # 滑窗构造正负样本\n",
+ " for i in range(1, len(pos_list)):\n",
+ " hist = pos_list[:i]\n",
+ " \n",
+ " if i != len(pos_list) - 1:\n",
+ " train_set.append((reviewerID, hist[::-1], pos_list[i], 1, len(hist[::-1]))) # 正样本 [user_id, his_item, pos_item, label, len(his_item)]\n",
+ " for negi in range(negsample):\n",
+ " train_set.append((reviewerID, hist[::-1], neg_list[i*negsample+negi], 0,len(hist[::-1]))) # 负样本 [user_id, his_item, neg_item, label, len(his_item)]\n",
+ " else:\n",
+ " # 将最长的那一个序列长度作为测试数据\n",
+ " test_set.append((reviewerID, hist[::-1], pos_list[i],1,len(hist[::-1])))\n",
+ " \n",
+ " random.shuffle(train_set)\n",
+ " random.shuffle(test_set)\n",
+ " \n",
+ " return train_set, test_set\n",
+ "\n",
+ "# 将输入的数据进行padding，使得序列特征的长度都一致\n",
+ "def gen_model_input(train_set,user_profile,seq_max_len):\n",
+ "\n",
+ " train_uid = np.array([line[0] for line in train_set])\n",
+ " train_seq = [line[1] for line in train_set]\n",
+ " train_iid = np.array([line[2] for line in train_set])\n",
+ " train_label = np.array([line[3] for line in train_set])\n",
+ " train_hist_len = np.array([line[4] for line in train_set])\n",
+ "\n",
+ " train_seq_pad = pad_sequences(train_seq, maxlen=seq_max_len, padding='post', truncating='post', value=0)\n",
+ " train_model_input = {\"user_id\": train_uid, \"click_article_id\": train_iid, \"hist_article_id\": train_seq_pad,\n",
+ " \"hist_len\": train_hist_len}\n",
+ "\n",
+ " return train_model_input, train_label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:13:18.124452Z",
+ "start_time": "2020-11-16T10:13:18.098284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def youtubednn_u2i_dict(data, topk=20): \n",
+ " sparse_features = [\"click_article_id\", \"user_id\"]\n",
+ " SEQ_LEN = 30 # 用户点击序列的长度，短的填充，长的截断\n",
+ " \n",
+ " user_profile_ = data[[\"user_id\"]].drop_duplicates('user_id')\n",
+ " item_profile_ = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
+ " \n",
+ " # 类别编码\n",
+ " features = [\"click_article_id\", \"user_id\"]\n",
+ " feature_max_idx = {}\n",
+ " \n",
+ " for feature in features:\n",
+ " lbe = LabelEncoder()\n",
+ " data[feature] = lbe.fit_transform(data[feature])\n",
+ " feature_max_idx[feature] = data[feature].max() + 1\n",
+ " \n",
+ " # 提取user和item的画像，这里具体选择哪些特征还需要进一步的分析和考虑\n",
+ " user_profile = data[[\"user_id\"]].drop_duplicates('user_id')\n",
+ " item_profile = data[[\"click_article_id\"]].drop_duplicates('click_article_id') \n",
+ " \n",
+ " user_index_2_rawid = dict(zip(user_profile['user_id'], user_profile_['user_id']))\n",
+ " item_index_2_rawid = dict(zip(item_profile['click_article_id'], item_profile_['click_article_id']))\n",
+ " \n",
+ " # 划分训练和测试集\n",
+ " # 由于深度学习需要的数据量通常都是非常大的，所以为了保证召回的效果，往往会通过滑窗的形式扩充训练样本\n",
+ " train_set, test_set = gen_data_set(data, 0)\n",
+ " # 整理输入数据，具体的操作可以看上面的函数\n",
+ " train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)\n",
+ " test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)\n",
+ " \n",
+ " # 确定Embedding的维度\n",
+ " embedding_dim = 16\n",
+ " \n",
+ " # 将数据整理成模型可以直接输入的形式\n",
+ " user_feature_columns = [SparseFeat('user_id', feature_max_idx['user_id'], embedding_dim),\n",
+ " VarLenSparseFeat(SparseFeat('hist_article_id', feature_max_idx['click_article_id'], embedding_dim,\n",
+ " embedding_name=\"click_article_id\"), SEQ_LEN, 'mean', 'hist_len'),]\n",
+ " item_feature_columns = [SparseFeat('click_article_id', feature_max_idx['click_article_id'], embedding_dim)]\n",
+ " \n",
+ " # 模型的定义 \n",
+ " # num_sampled: 负采样时的样本数量\n",
+ " model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))\n",
+ " # 模型编译\n",
+ " model.compile(optimizer=\"adam\", loss=sampledsoftmaxloss) \n",
+ " \n",
+ " # 模型训练，这里可以定义验证集的比例，如果设置为0的话就是全量数据直接进行训练\n",
+ " history = model.fit(train_model_input, train_label, batch_size=256, epochs=1, verbose=1, validation_split=0.0)\n",
+ " \n",
+ " # 训练完模型之后,提取训练的Embedding，包括user端和item端\n",
+ " test_user_model_input = test_model_input\n",
+ " all_item_model_input = {\"click_article_id\": item_profile['click_article_id'].values}\n",
+ "\n",
+ " user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)\n",
+ " item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)\n",
+ " \n",
+ " # 保存当前的item_embedding 和 user_embedding 排序的时候可能能够用到，但是需要注意保存的时候需要和原始的id对应\n",
+ " user_embs = user_embedding_model.predict(test_user_model_input, batch_size=2 ** 12)\n",
+ " item_embs = item_embedding_model.predict(all_item_model_input, batch_size=2 ** 12)\n",
+ " \n",
+ " # embedding保存之前归一化一下\n",
+ " user_embs = user_embs / np.linalg.norm(user_embs, axis=1, keepdims=True)\n",
+ " item_embs = item_embs / np.linalg.norm(item_embs, axis=1, keepdims=True)\n",
+ " \n",
+ " # 将Embedding转换成字典的形式方便查询\n",
+ " raw_user_id_emb_dict = {user_index_2_rawid[k]: \\\n",
+ " v for k, v in zip(user_profile['user_id'], user_embs)}\n",
+ " raw_item_id_emb_dict = {item_index_2_rawid[k]: \\\n",
+ " v for k, v in zip(item_profile['click_article_id'], item_embs)}\n",
+ " # 将Embedding保存到本地\n",
+ " pickle.dump(raw_user_id_emb_dict, open(save_path + 'user_youtube_emb.pkl', 'wb'))\n",
+ " pickle.dump(raw_item_id_emb_dict, open(save_path + 'item_youtube_emb.pkl', 'wb'))\n",
+ " \n",
+ " # faiss紧邻搜索，通过user_embedding 搜索与其相似性最高的topk个item\n",
+ " index = faiss.IndexFlatIP(embedding_dim)\n",
+ " # 上面已经进行了归一化，这里可以不进行归一化了\n",
+ "# faiss.normalize_L2(user_embs)\n",
+ "# faiss.normalize_L2(item_embs)\n",
+ " index.add(item_embs) # 将item向量构建索引\n",
+ " sim, idx = index.search(np.ascontiguousarray(user_embs), topk) # 通过user去查询最相似的topk个item\n",
+ " \n",
+ " user_recall_items_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(test_user_model_input['user_id'], sim, idx)):\n",
+ " target_raw_id = user_index_2_rawid[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = item_index_2_rawid[rele_idx]\n",
+ " user_recall_items_dict[target_raw_id][rele_raw_id] = user_recall_items_dict.get(target_raw_id, {})\\\n",
+ " .get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " user_recall_items_dict = {k: sorted(v.items(), key=lambda x: x[1], reverse=True) for k, v in user_recall_items_dict.items()}\n",
+ " # 将召回的结果进行排序\n",
+ " \n",
+ " # 保存召回的结果\n",
+ " # 这里是直接通过向量的方式得到了召回结果，相比于上面的召回方法，上面的只是得到了i2i及u2u的相似性矩阵，还需要进行协同过滤召回才能得到召回结果\n",
+ " # 可以直接对这个召回结果进行评估，为了方便可以统一写一个评估函数对所有的召回结果进行评估\n",
+ " pickle.dump(user_recall_items_dict, open(save_path + 'youtube_u2i_dict.pkl', 'wb'))\n",
+ " return user_recall_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T10:21:46.420014Z",
+ "start_time": "2020-11-16T10:13:35.351131Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [02:02<00:00, 2038.57it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "keep_dims is deprecated, use keepdims instead\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:253: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Deprecated in favor of operator or tf.math.divide.\n",
+ "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "1149673/1149673 [==============================] - 216s 188us/sample - loss: 0.1326\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "250000it [00:32, 7720.75it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 由于这里需要做召回评估，所以讲训练集中的最后一次点击都提取了出来\n",
+ "if not metric_recall:\n",
+ " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(all_click_df, topk=20)\n",
+ "else:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ " user_multi_recall_dict['youtubednn_recall'] = youtubednn_u2i_dict(trn_hist_click_df, topk=20)\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['youtubednn_recall'], trn_last_click_df, topk=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### itemcf recall\n",
+ "\n",
+ "上面已经通过协同过滤，Embedding检索的方式得到了文章的相似度矩阵，下面使用协同过滤的思想，给用户召回与其历史文章相似的文章。\n",
+ "这里在召回的时候，也是用了关联规则的方式：\n",
+ "1. 考虑相似文章与历史点击文章顺序的权重(细节看代码)\n",
+ "2. 考虑文章创建时间的权重，也就是考虑相似文章与历史点击文章创建时间差的权重\n",
+ "3. 考虑文章内容相似度权重(使用Embedding计算相似文章相似度，但是这里需要注意，在Embedding的时候并没有计算所有商品两两之间的相似度，所以相似的文章与历史点击文章不存在相似度，需要做特殊处理)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T11:48:40.580553Z",
+ "start_time": "2020-11-16T11:48:40.567130Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于商品的召回i2i\n",
+ "def item_based_recommend(user_id, user_item_time_dict, i2i_sim, sim_item_topk, recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
+ " \"\"\"\n",
+ " 基于文章协同过滤的召回\n",
+ " :param user_id: 用户id\n",
+ " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ " :param i2i_sim: 字典，文章相似性矩阵\n",
+ " :param sim_item_topk: 整数， 选择与当前文章最相似的前k篇文章\n",
+ " :param recall_item_num: 整数， 最后的召回文章数量\n",
+ " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
+ " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
+ " \n",
+ " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
+ " \"\"\"\n",
+ " # 获取用户历史交互的文章\n",
+ " user_hist_items = user_item_time_dict[user_id]\n",
+ " user_hist_items_ = {user_id for user_id, _ in user_hist_items}\n",
+ " \n",
+ " item_rank = {}\n",
+ " for loc, (i, click_time) in enumerate(user_hist_items):\n",
+ " for j, wij in sorted(i2i_sim[i].items(), key=lambda x: x[1], reverse=True)[:sim_item_topk]:\n",
+ " if j in user_hist_items_:\n",
+ " continue\n",
+ " \n",
+ " # 文章创建时间差权重\n",
+ " created_time_weight = np.exp(0.8 ** np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " # 相似文章和历史点击文章序列中历史文章所在的位置权重\n",
+ " loc_weight = (0.9 ** (len(user_hist_items) - loc))\n",
+ " \n",
+ " content_weight = 1.0\n",
+ " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
+ " content_weight += emb_i2i_sim[i][j]\n",
+ " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
+ " content_weight += emb_i2i_sim[j][i]\n",
+ " \n",
+ " item_rank.setdefault(j, 0)\n",
+ " item_rank[j] += created_time_weight * loc_weight * content_weight * wij\n",
+ " \n",
+ " # 不足10个，用热门商品补全\n",
+ " if len(item_rank) < recall_item_num:\n",
+ " for i, item in enumerate(item_topk_click):\n",
+ " if item in item_rank.items(): # 填充的item应该不在原来的列表中\n",
+ " continue\n",
+ " item_rank[item] = - i - 100 # 随便给个负数就行\n",
+ " if len(item_rank) == recall_item_num:\n",
+ " break\n",
+ " \n",
+ " item_rank = sorted(item_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num]\n",
+ " \n",
+ " return item_rank"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### itemcf sim召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T14:41:23.433038Z",
+ "start_time": "2020-11-16T11:48:46.286350Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [2:51:13<00:00, 24.33it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 先进行itemcf召回, 为了召回评估，所以提取最后一次点击\n",
+ "\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "\n",
+ "i2i_sim = pickle.load(open(save_path + 'itemcf_i2i_sim.pkl', 'rb'))\n",
+ "emb_i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_item_topk = 20\n",
+ "recall_item_num = 10\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, \\\n",
+ " i2i_sim, sim_item_topk, recall_item_num, \\\n",
+ " item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ "\n",
+ "user_multi_recall_dict['itemcf_sim_itemcf_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['itemcf_sim_itemcf_recall'], open(save_path + 'itemcf_recall_dict.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['itemcf_sim_itemcf_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### embedding sim 召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T15:04:51.527795Z",
+ "start_time": "2020-11-16T14:59:03.907519Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [04:35<00:00, 905.85it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 这里是为了召回评估，所以提取最后一次点击\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
+ "\n",
+ "sim_item_topk = 20\n",
+ "recall_item_num = 10\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ " \n",
+ "user_multi_recall_dict['embedding_sim_item_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['embedding_sim_item_recall'], open(save_path + 'embedding_sim_item_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['embedding_sim_item_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### usercf召回\n",
+ "\n",
+ "基于用户协同过滤，核心思想是给用户推荐与其相似的用户历史点击文章，因为这里涉及到了相似用户的历史文章，这里仍然可以加上一些关联规则来给用户可能点击的文章进行加权，这里使用的关联规则主要是考虑相似用户的历史点击文章与被推荐用户历史点击商品的关系权重，而这里的关系就可以直接借鉴基于物品的协同过滤相似的做法，只不过这里是对被推荐物品关系的一个累加的过程，下面是使用的一些关系权重，及相关的代码：\n",
+ "\n",
+ "1. 计算被推荐用户历史点击文章与相似用户历史点击文章的相似度，文章创建时间差，相对位置的总和，作为各自的权重"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:09:32.293990Z",
+ "start_time": "2020-11-17T02:09:32.278678Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于用户的召回 u2u2i\n",
+ "def user_based_recommend(user_id, user_item_time_dict, u2u_sim, sim_user_topk, recall_item_num, \n",
+ " item_topk_click, item_created_time_dict, emb_i2i_sim):\n",
+ " \"\"\"\n",
+ " 基于文章协同过滤的召回\n",
+ " :param user_id: 用户id\n",
+ " :param user_item_time_dict: 字典, 根据点击时间获取用户的点击文章序列 {user1: [(item1, time1), (item2, time2)..]...}\n",
+ " :param u2u_sim: 字典，文章相似性矩阵\n",
+ " :param sim_user_topk: 整数， 选择与当前用户最相似的前k个用户\n",
+ " :param recall_item_num: 整数， 最后的召回文章数量\n",
+ " :param item_topk_click: 列表，点击次数最多的文章列表，用户召回补全\n",
+ " :param item_created_time_dict: 文章创建时间列表\n",
+ " :param emb_i2i_sim: 字典基于内容embedding算的文章相似矩阵\n",
+ " \n",
+ " return: 召回的文章列表 [(item1, score1), (item2, score2)...]\n",
+ " \"\"\"\n",
+ " # 历史交互\n",
+ " user_item_time_list = user_item_time_dict[user_id] # [(item1, time1), (item2, time2)..]\n",
+ " user_hist_items = set([i for i, t in user_item_time_list]) # 存在一个用户与某篇文章的多次交互， 这里得去重\n",
+ " \n",
+ " items_rank = {}\n",
+ " for sim_u, wuv in sorted(u2u_sim[user_id].items(), key=lambda x: x[1], reverse=True)[:sim_user_topk]:\n",
+ " for i, click_time in user_item_time_dict[sim_u]:\n",
+ " if i in user_hist_items:\n",
+ " continue\n",
+ " items_rank.setdefault(i, 0)\n",
+ " \n",
+ " loc_weight = 1.0\n",
+ " content_weight = 1.0\n",
+ " created_time_weight = 1.0\n",
+ " \n",
+ " # 当前文章与该用户看的历史文章进行一个权重交互\n",
+ " for loc, (j, click_time) in enumerate(user_item_time_list):\n",
+ " # 点击时的相对位置权重\n",
+ " loc_weight += 0.9 ** (len(user_item_time_list) - loc)\n",
+ " # 内容相似性权重\n",
+ " if emb_i2i_sim.get(i, {}).get(j, None) is not None:\n",
+ " content_weight += emb_i2i_sim[i][j]\n",
+ " if emb_i2i_sim.get(j, {}).get(i, None) is not None:\n",
+ " content_weight += emb_i2i_sim[j][i]\n",
+ " \n",
+ " # 创建时间差权重\n",
+ " created_time_weight += np.exp(0.8 * np.abs(item_created_time_dict[i] - item_created_time_dict[j]))\n",
+ " \n",
+ " items_rank[i] += loc_weight * content_weight * created_time_weight * wuv\n",
+ " \n",
+ " # 热度补全\n",
+ " if len(items_rank) < recall_item_num:\n",
+ " for i, item in enumerate(item_topk_click):\n",
+ " if item in items_rank.items(): # 填充的item应该不在原来的列表中\n",
+ " continue\n",
+ " items_rank[item] = - i - 100 # 随便给个复数就行\n",
+ " if len(items_rank) == recall_item_num:\n",
+ " break\n",
+ " \n",
+ " items_rank = sorted(items_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num] \n",
+ " \n",
+ " return items_rank"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### usercf sim召回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:05:41.652501Z",
+ "start_time": "2020-11-16T07:05:40.953871Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 这里是为了召回评估，所以提取最后一次点击\n",
+ "# 由于usercf中计算user之间的相似度的过程太费内存了，全量数据这里就没有跑，跑了一个采样之后的数据\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ " \n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "\n",
+ "u2u_sim = pickle.load(open(save_path + 'usercf_u2u_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_user_topk = 20\n",
+ "recall_item_num = 10\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim) \n",
+ "\n",
+ "pickle.dump(user_recall_items_dict, open(save_path + 'usercf_u2u2i_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_recall_items_dict, trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T03:09:35.853516Z",
+ "start_time": "2020-11-16T03:09:35.737625Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### user embedding sim召回\n",
+ "\n",
+ "虽然没有直接跑usercf的计算用户之间的相似度，为了验证上述基于用户的协同过滤的代码，下面使用了YoutubeDNN过程中产生的user embedding来进行向量检索每个user最相似的topk个user，在使用这里得到的u2u的相似性矩阵，使用usercf进行召回，具体代码如下"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:09:46.807811Z",
+ "start_time": "2020-11-17T02:09:46.798033Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 使用Embedding的方式获取u2u的相似性矩阵\n",
+ "# topk指的是每个user, faiss搜索后返回最相似的topk个user\n",
+ "def u2u_embdding_sim(click_df, user_emb_dict, save_path, topk):\n",
+ " \n",
+ " user_list = []\n",
+ " user_emb_list = []\n",
+ " for user_id, user_emb in user_emb_dict.items():\n",
+ " user_list.append(user_id)\n",
+ " user_emb_list.append(user_emb)\n",
+ " \n",
+ " user_index_2_rawid_dict = {k: v for k, v in zip(range(len(user_list)), user_list)} \n",
+ " \n",
+ " user_emb_np = np.array(user_emb_list, dtype=np.float32)\n",
+ " \n",
+ " # 建立faiss索引\n",
+ " user_index = faiss.IndexFlatIP(user_emb_np.shape[1])\n",
+ " user_index.add(user_emb_np)\n",
+ " # 相似度查询，给每个索引位置上的向量返回topk个item以及相似度\n",
+ " sim, idx = user_index.search(user_emb_np, topk) # 返回的是列表\n",
+ " \n",
+ " # 将向量检索的结果保存成原始id的对应关系\n",
+ " user_sim_dict = collections.defaultdict(dict)\n",
+ " for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(user_emb_np)), sim, idx)):\n",
+ " target_raw_id = user_index_2_rawid_dict[target_idx]\n",
+ " # 从1开始是为了去掉商品本身, 所以最终获得的相似商品只有topk-1\n",
+ " for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]): \n",
+ " rele_raw_id = user_index_2_rawid_dict[rele_idx]\n",
+ " user_sim_dict[target_raw_id][rele_raw_id] = user_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value\n",
+ " \n",
+ " # 保存i2i相似度矩阵\n",
+ " pickle.dump(user_sim_dict, open(save_path + 'youtube_u2u_sim.pkl', 'wb')) \n",
+ " return user_sim_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:14:31.355905Z",
+ "start_time": "2020-11-17T02:09:53.236531Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "250000it [00:23, 10507.45it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 读取YoutubeDNN过程中产生的user embedding, 然后使用faiss计算用户之间的相似度\n",
+ "# 这里需要注意，这里得到的user embedding其实并不是很好，因为YoutubeDNN中使用的是用户点击序列来训练的user embedding,\n",
+ "# 如果序列普遍都比较短的话，其实效果并不是很好\n",
+ "user_emb_dict = pickle.load(open(save_path + 'user_youtube_emb.pkl', 'rb'))\n",
+ "u2u_sim = u2u_embdding_sim(all_click_df, user_emb_dict, save_path, topk=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "通过YoutubeDNN得到的user_embedding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T02:49:40.755431Z",
+ "start_time": "2020-11-17T02:28:47.003514Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [19:43<00:00, 211.22it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 使用召回评估函数验证当前召回方式的效果\n",
+ "if metric_recall:\n",
+ " trn_hist_click_df, trn_last_click_df = get_hist_and_last_click(all_click_df)\n",
+ "else:\n",
+ " trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "u2u_sim = pickle.load(open(save_path + 'youtube_u2u_sim.pkl', 'rb'))\n",
+ "\n",
+ "sim_user_topk = 20\n",
+ "recall_item_num = 10\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = user_based_recommend(user, user_item_time_dict, u2u_sim, sim_user_topk, \\\n",
+ " recall_item_num, item_topk_click, item_created_time_dict, emb_i2i_sim)\n",
+ " \n",
+ "user_multi_recall_dict['youtubednn_usercf_recall'] = user_recall_items_dict\n",
+ "pickle.dump(user_multi_recall_dict['youtubednn_usercf_recall'], open(save_path + 'youtubednn_usercf_recall.pkl', 'wb'))\n",
+ "\n",
+ "if metric_recall:\n",
+ " # 召回效果评估\n",
+ " metrics_recall(user_multi_recall_dict['youtubednn_usercf_recall'], trn_last_click_df, topk=recall_item_num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:07:44.326253Z",
+ "start_time": "2020-11-16T07:07:43.798931Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 冷启动问题"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**冷启动问题可以分成三类：文章冷启动，用户冷启动，系统冷启动。**\n",
+ "\n",
+ "- 文章冷启动：对于一个平台系统新加入的文章，该文章没有任何的交互记录，如何推荐给用户的问题。(对于我们场景可以认为是，日志数据中没有出现过的文章都可以认为是冷启动的文章)\n",
+ "- 用户冷启动：对于一个平台系统新来的用户，该用户还没有文章的交互信息，如何给该用户进行推荐。(对于我们场景就是，测试集中的用户是否在测试集对应的log数据中出现过，如果没有出现过，那么可以认为该用户是冷启动用户。但是有时候并没有这么严格，我们也可以自己设定某些指标来判别哪些用户是冷启动用户，比如通过使用时长，点击率，留存率等等)\n",
+ "- 系统冷启动：就是对于一个平台刚上线，还没有任何的相关历史数据，此时就是系统冷启动，其实也就是前面两种的一个综合。\n",
+ "\n",
+ "**当前场景下冷启动问题的分析：**\n",
+ "\n",
+ "对当前的数据进行分析会发现，日志中所有出现过的点击文章只有3w多个，而整个文章库中却有30多万，那么测试集中的用户最后一次点击是否会点击没有出现在日志中的文章呢？如果存在这种情况，说明用户点击的文章之前没有任何的交互信息，这也就是我们所说的文章冷启动。通过数据分析还可以发现，测试集用户只有一次点击的数据占得比例还不少，其实仅仅通过用户的一次点击就给用户推荐文章使用模型的方式也是比较难的，这里其实也可以考虑用户冷启动的问题，但是这里只给出物品冷启动的一些解决方案及代码，关于用户冷启动的话提一些可行性的做法。\n",
+ "\n",
+ "1. 文章冷启动(没有冷启动的探索问题) \n",
+ " 其实我们这里不是为了做文章的冷启动而做冷启动，而是猜测用户可能会点击一些没有在log数据中出现的文章，我们要做的就是如何从将近27万的文章中选择一些文章作为用户冷启动的文章，这里其实也可以看成是一种召回策略，我们这里就采用简单的比较好理解的基于规则的召回策略来获取用户可能点击的未出现在log数据中的文章。\n",
+ " 现在的问题变成了：如何给每个用户考虑从27万个商品中获取一小部分商品？随机选一些可能是一种方案。下面给出一些参考的方案。\n",
+ " 1. 首先基于Embedding召回一部分与用户历史相似的文章\n",
+ " 2. 从基于Embedding召回的文章中通过一些规则过滤掉一些文章，使得留下的文章用户更可能点击。我们这里的规则，可以是，留下那些与用户历史点击文章主题相同的文章，或者字数相差不大的文章。并且留下的文章尽量是与测试集用户最后一次点击时间更接近的文章，或者是当天的文章也行。\n",
+ "2. 用户冷启动 \n",
+ " 这里对测试集中的用户点击数据进行分析会发现，测试集中有百分之20的用户只有一次点击，那么这些点击特别少的用户的召回是不是可以单独做一些策略上的补充呢？或者是在排序后直接基于规则加上一些文章呢？这些都可以去尝试，这里没有提供具体的做法。\n",
+ " \n",
+ "**注意：** \n",
+ "\n",
+ "这里看似和基于embedding计算的item之间相似度然后做itemcf是一致的，但是现在我们的目的不一样，我们这里的目的是找到相似的向量，并且还没有出现在log日志中的商品，再加上一些其他的冷启动的策略，这里需要找回的数量会偏多一点，不然被筛选完之后可能都没有文章了"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T04:30:23.027164Z",
+ "start_time": "2020-11-17T04:23:09.960235Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [05:01<00:00, 828.60it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "# 先进行itemcf召回，这里不需要做召回评估，这里只是一种策略\n",
+ "trn_hist_click_df = all_click_df\n",
+ "\n",
+ "user_recall_items_dict = collections.defaultdict(dict)\n",
+ "user_item_time_dict = get_user_item_time(trn_hist_click_df)\n",
+ "i2i_sim = pickle.load(open(save_path + 'emb_i2i_sim.pkl','rb'))\n",
+ "\n",
+ "sim_item_topk = 150\n",
+ "recall_item_num = 100 # 稍微召回多一点文章，便于后续的规则筛选\n",
+ "\n",
+ "item_topk_click = get_item_topk_click(trn_hist_click_df, k=50)\n",
+ "for user in tqdm(trn_hist_click_df['user_id'].unique()):\n",
+ " user_recall_items_dict[user] = item_based_recommend(user, user_item_time_dict, i2i_sim, sim_item_topk, \n",
+ " recall_item_num, item_topk_click,item_created_time_dict, emb_i2i_sim)\n",
+ "pickle.dump(user_recall_items_dict, open(save_path + 'cold_start_items_raw_dict.pkl', 'wb'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T06:11:39.267581Z",
+ "start_time": "2020-11-17T06:11:39.252563Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 基于规则进行文章过滤\n",
+ "# 保留文章主题与用户历史浏览主题相似的文章\n",
+ "# 保留文章字数与用户历史浏览文章字数相差不大的文章\n",
+ "# 保留最后一次点击当天的文章\n",
+ "# 按照相似度返回最终的结果\n",
+ "\n",
+ "def get_click_article_ids_set(all_click_df):\n",
+ " return set(all_click_df.click_article_id.values)\n",
+ "\n",
+ "def cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
+ " user_last_item_created_time_dict, item_type_dict, item_words_dict, \n",
+ " item_created_time_dict, click_article_ids_set, recall_item_num):\n",
+ " \"\"\"\n",
+ " 冷启动的情况下召回一些文章\n",
+ " :param user_recall_items_dict: 基于内容embedding相似性召回来的很多文章， 字典， {user1: [(item1, item2), ..], }\n",
+ " :param user_hist_item_typs_dict: 字典， 用户点击的文章的主题映射\n",
+ " :param user_hist_item_words_dict: 字典， 用户点击的历史文章的字数映射\n",
+ " :param user_last_item_created_time_idct: 字典，用户点击的历史文章创建时间映射\n",
+ " :param item_tpye_idct: 字典，文章主题映射\n",
+ " :param item_words_dict: 字典，文章字数映射\n",
+ " :param item_created_time_dict: 字典， 文章创建时间映射\n",
+ " :param click_article_ids_set: 集合，用户点击过得文章, 也就是日志里面出现过的文章\n",
+ " :param recall_item_num: 召回文章的数量， 这个指的是没有出现在日志里面的文章数量\n",
+ " \"\"\"\n",
+ " \n",
+ " cold_start_user_items_dict = {}\n",
+ " for user, item_list in tqdm(user_recall_items_dict.items()):\n",
+ " cold_start_user_items_dict.setdefault(user, [])\n",
+ " for item, score in item_list:\n",
+ " # 获取历史文章信息\n",
+ " hist_item_type_set = user_hist_item_typs_dict[user]\n",
+ " hist_mean_words = user_hist_item_words_dict[user]\n",
+ " hist_last_item_created_time = user_last_item_created_time_dict[user]\n",
+ " hist_last_item_created_time = datetime.fromtimestamp(hist_last_item_created_time)\n",
+ " \n",
+ " # 获取当前召回文章的信息\n",
+ " curr_item_type = item_type_dict[item]\n",
+ " curr_item_words = item_words_dict[item]\n",
+ " curr_item_created_time = item_created_time_dict[item]\n",
+ " curr_item_created_time = datetime.fromtimestamp(curr_item_created_time)\n",
+ "\n",
+ " # 首先，文章不能出现在用户的历史点击中， 然后根据文章主题，文章单词数，文章创建时间进行筛选\n",
+ " if curr_item_type not in hist_item_type_set or \\\n",
+ " item in click_article_ids_set or \\\n",
+ " abs(curr_item_words - hist_mean_words) > 200 or \\\n",
+ " abs((curr_item_created_time - hist_last_item_created_time).days) > 90: \n",
+ " continue\n",
+ " \n",
+ " cold_start_user_items_dict[user].append((item, score)) # {user1: [(item1, score1), (item2, score2)..]...}\n",
+ " \n",
+ " # 需要控制一下冷启动召回的数量\n",
+ " cold_start_user_items_dict = {k: sorted(v, key=lambda x:x[1], reverse=True)[:recall_item_num] \\\n",
+ " for k, v in cold_start_user_items_dict.items()}\n",
+ " \n",
+ " pickle.dump(cold_start_user_items_dict, open(save_path + 'cold_start_user_items_dict.pkl', 'wb'))\n",
+ " \n",
+ " return cold_start_user_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T06:35:38.758278Z",
+ "start_time": "2020-11-17T06:31:40.164332Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 250000/250000 [01:49<00:00, 2293.37it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "all_click_df_ = all_click_df.copy()\n",
+ "all_click_df_ = all_click_df_.merge(item_info_df, how='left', on='click_article_id')\n",
+ "user_hist_item_typs_dict, user_hist_item_ids_dict, user_hist_item_words_dict, user_last_item_created_time_dict = get_user_hist_item_info_dict(all_click_df_)\n",
+ "click_article_ids_set = get_click_article_ids_set(all_click_df)\n",
+ "# 需要注意的是\n",
+ "# 这里使用了很多规则来筛选冷启动的文章，所以前面再召回的阶段就应该尽可能的多召回一些文章，否则很容易被删掉\n",
+ "cold_start_user_items_dict = cold_start_items(user_recall_items_dict, user_hist_item_typs_dict, user_hist_item_words_dict, \\\n",
+ " user_last_item_created_time_dict, item_type_dict, item_words_dict, \\\n",
+ " item_created_time_dict, click_article_ids_set, recall_item_num)\n",
+ "\n",
+ "user_multi_recall_dict['cold_start_recall'] = cold_start_user_items_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-16T07:13:33.099298Z",
+ "start_time": "2020-11-16T07:13:32.655036Z"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 多路召回合并\n",
+ "多路召回合并就是将前面所有的召回策略得到的用户文章列表合并起来，下面是对前面所有召回结果的汇总\n",
+ "1. 基于itemcf计算的item之间的相似度sim进行的召回 \n",
+ "2. 基于embedding搜索得到的item之间的相似度进行的召回\n",
+ "3. YoutubeDNN召回\n",
+ "4. YoutubeDNN得到的user之间的相似度进行的召回\n",
+ "5. 基于冷启动策略的召回\n",
+ "\n",
+ "**注意：** \n",
+ "在做召回评估的时候就会发现有些召回的效果不错有些召回的效果很差，所以对每一路召回的结果，我们可以认为的定义一些权重，来做最终的相似度融合"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:02:16.033971Z",
+ "start_time": "2020-11-17T07:02:16.019819Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def combine_recall_results(user_multi_recall_dict, weight_dict=None, topk=25):\n",
+ " final_recall_items_dict = {}\n",
+ " \n",
+ " # 对每一种召回结果按照用户进行归一化，方便后面多种召回结果，相同用户的物品之间权重相加\n",
+ " def norm_user_recall_items_sim(sorted_item_list):\n",
+ " # 如果冷启动中没有文章或者只有一篇文章，直接返回，出现这种情况的原因可能是冷启动召回的文章数量太少了，\n",
+ " # 基于规则筛选之后就没有文章了, 这里还可以做一些其他的策略性的筛选\n",
+ " if len(sorted_item_list) < 2:\n",
+ " return sorted_item_list\n",
+ " \n",
+ " min_sim = sorted_item_list[-1][1]\n",
+ " max_sim = sorted_item_list[0][1]\n",
+ " \n",
+ " norm_sorted_item_list = []\n",
+ " for item, score in sorted_item_list:\n",
+ " if max_sim > 0:\n",
+ " norm_score = 1.0 * (score - min_sim) / (max_sim - min_sim) if max_sim > min_sim else 1.0\n",
+ " else:\n",
+ " norm_score = 0.0\n",
+ " norm_sorted_item_list.append((item, norm_score))\n",
+ " \n",
+ " return norm_sorted_item_list\n",
+ " \n",
+ " print('多路召回合并...')\n",
+ " for method, user_recall_items in tqdm(user_multi_recall_dict.items()):\n",
+ " print(method + '...')\n",
+ " # 在计算最终召回结果的时候，也可以为每一种召回结果设置一个权重\n",
+ " if weight_dict == None:\n",
+ " recall_method_weight = 1\n",
+ " else:\n",
+ " recall_method_weight = weight_dict[method]\n",
+ " \n",
+ " for user_id, sorted_item_list in user_recall_items.items(): # 进行归一化\n",
+ " user_recall_items[user_id] = norm_user_recall_items_sim(sorted_item_list)\n",
+ " \n",
+ " for user_id, sorted_item_list in user_recall_items.items():\n",
+ " # print('user_id')\n",
+ " final_recall_items_dict.setdefault(user_id, {})\n",
+ " for item, score in sorted_item_list:\n",
+ " final_recall_items_dict[user_id].setdefault(item, 0)\n",
+ " final_recall_items_dict[user_id][item] += recall_method_weight * score \n",
+ " \n",
+ " final_recall_items_dict_rank = {}\n",
+ " # 多路召回时也可以控制最终的召回数量\n",
+ " for user, recall_item_dict in final_recall_items_dict.items():\n",
+ " final_recall_items_dict_rank[user] = sorted(recall_item_dict.items(), key=lambda x: x[1], reverse=True)[:topk]\n",
+ "\n",
+ " # 将多路召回后的最终结果字典保存到本地\n",
+ " pickle.dump(final_recall_items_dict_rank, open(os.path.join(save_path, 'final_recall_items_dict.pkl'),'wb'))\n",
+ "\n",
+ " return final_recall_items_dict_rank"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:02:21.078455Z",
+ "start_time": "2020-11-17T07:02:21.074060Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 这里直接对多路召回的权重给了一个相同的值，其实可以根据前面召回的情况来调整参数的值\n",
+ "weight_dict = {'itemcf_sim_itemcf_recall': 1.0,\n",
+ " 'embedding_sim_item_recall': 1.0,\n",
+ " 'youtubednn_recall': 1.0,\n",
+ " 'youtubednn_usercf_recall': 1.0, \n",
+ " 'cold_start_recall': 1.0}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-17T07:04:35.747924Z",
+ "start_time": "2020-11-17T07:02:26.889573Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/5 [00:00= topk\n",
- " \n",
- " del recall_df['pred_score']\n",
- " submit = recall_df[recall_df['rank'] <= topk].set_index(['user_id', 'rank']).unstack(-1).reset_index()\n",
- " \n",
- " submit.columns = [int(col) if isinstance(col, int) else col for col in submit.columns.droplevel(0)]\n",
- " # 按照提交格式定义列名\n",
- " submit = submit.rename(columns={'': 'user_id', 1: 'article_1', 2: 'article_2', \n",
- " 3: 'article_3', 4: 'article_4', 5: 'article_5'})\n",
- " \n",
- " save_name = save_path + model_name + '_' + datetime.today().strftime('%m-%d') + '.csv'\n",
- " submit.to_csv(save_name, index=False, header=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:04.332198Z",
- "start_time": "2020-11-18T04:21:04.325020Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序结果归一化\n",
- "def norm_sim(sim_df, weight=0.0):\n",
- " # print(sim_df.head())\n",
- " min_sim = sim_df.min()\n",
- " max_sim = sim_df.max()\n",
- " if max_sim == min_sim:\n",
- " sim_df = sim_df.apply(lambda sim: 1.0)\n",
- " else:\n",
- " sim_df = sim_df.apply(lambda sim: 1.0 * (sim - min_sim) / (max_sim - min_sim))\n",
- "\n",
- " sim_df = sim_df.apply(lambda sim: sim + weight) # plus one\n",
- " return sim_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## LGB排序模型"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:07.787698Z",
- "start_time": "2020-11-18T04:21:07.536514Z"
- }
- },
- "outputs": [],
- "source": [
- "# 防止中间出错之后重新读取数据\n",
- "trn_user_item_feats_df_rank_model = trn_user_item_feats_df.copy()\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_rank_model = val_user_item_feats_df.copy()\n",
- " \n",
- "tst_user_item_feats_df_rank_model = tst_user_item_feats_df.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:10.839656Z",
- "start_time": "2020-11-18T04:21:10.833109Z"
- }
- },
- "outputs": [],
- "source": [
- "# 定义特征列\n",
- "lgb_cols = ['sim0', 'time_diff0', 'word_diff0','sim_max', 'sim_min', 'sim_sum', \n",
- " 'sim_mean', 'score','click_size', 'time_diff_mean', 'active_level',\n",
- " 'click_environment','click_deviceGroup', 'click_os', 'click_country', \n",
- " 'click_region','click_referrer_type', 'user_time_hob1', 'user_time_hob2',\n",
- " 'words_hbo', 'category_id', 'created_at_ts','words_count']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:14.126608Z",
- "start_time": "2020-11-18T04:21:13.493653Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型分组\n",
- "trn_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
- "g_train = trn_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
- " g_val = val_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:16.136151Z",
- "start_time": "2020-11-18T04:21:16.124444Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型定义\n",
- "lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:22.965433Z",
- "start_time": "2020-11-18T04:21:17.799127Z"
- }
- },
- "outputs": [],
- "source": [
- "# 排序模型训练\n",
- "if offline:\n",
- " lgb_ranker.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'], group=g_train,\n",
- " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
- " eval_group= [g_val], eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
- "else:\n",
- " lgb_ranker.fit(trn_user_item_feats_df[lgb_cols], trn_user_item_feats_df['label'], group=g_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:28.616665Z",
- "start_time": "2020-11-18T04:21:24.672280Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df['pred_score'] = lgb_ranker.predict(tst_user_item_feats_df[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
- "\n",
- "# 将这里的排序结果保存一份，用户后面的模型融合\n",
- "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_ranker_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:21:40.253692Z",
- "start_time": "2020-11-18T04:21:30.546587Z"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 排序模型\n",
+ "通过召回的操作， 我们已经进行了问题规模的缩减， 对于每个用户， 选择出了N篇文章作为了候选集，并基于召回的候选集构建了与用户历史相关的特征，以及用户本身的属性特征，文章本省的属性特征，以及用户与文章之间的特征，下面就是使用机器学习模型来对构造好的特征进行学习，然后对测试集进行预测，得到测试集中的每个候选集用户点击的概率，返回点击概率最大的topk个文章，作为最终的结果。\n",
+ "\n",
+ "排序阶段选择了三个比较有代表性的排序模型，它们分别是：\n",
+ "\n",
+ "1. LGB的排序模型\n",
+ "2. LGB的分类模型\n",
+ "3. 深度学习的分类模型DIN\n",
+ "\n",
+ "得到了最终的排序模型输出的结果之后，还选择了两种比较经典的模型集成的方法：\n",
+ "\n",
+ "1. 输出结果加权融合\n",
+ "2. Staking（将模型的输出结果再使用一个简单模型进行预测）"
+ ]
},
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_ranker')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:26.195838Z",
- "start_time": "2020-11-18T04:21:46.115002Z"
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:39.770642Z",
+ "start_time": "2020-11-18T04:20:38.500875Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pickle\n",
+ "from tqdm import tqdm\n",
+ "import gc, os\n",
+ "import time\n",
+ "from datetime import datetime\n",
+ "import lightgbm as lgb\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1]\tvalid_0's ndcg@1: 0.909975\tvalid_0's ndcg@2: 0.963068\tvalid_0's ndcg@3: 0.96533\tvalid_0's ndcg@4: 0.965729\tvalid_0's ndcg@5: 0.965864\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9143\tvalid_0's ndcg@2: 0.964711\tvalid_0's ndcg@3: 0.966961\tvalid_0's ndcg@4: 0.967338\tvalid_0's ndcg@5: 0.967483\n",
- "[3]\tvalid_0's ndcg@1: 0.9181\tvalid_0's ndcg@2: 0.966114\tvalid_0's ndcg@3: 0.968289\tvalid_0's ndcg@4: 0.968773\tvalid_0's ndcg@5: 0.96887\n",
- "[4]\tvalid_0's ndcg@1: 0.925575\tvalid_0's ndcg@2: 0.969093\tvalid_0's ndcg@3: 0.971193\tvalid_0's ndcg@4: 0.971603\tvalid_0's ndcg@5: 0.97169\n",
- "[5]\tvalid_0's ndcg@1: 0.9267\tvalid_0's ndcg@2: 0.969635\tvalid_0's ndcg@3: 0.97166\tvalid_0's ndcg@4: 0.972037\tvalid_0's ndcg@5: 0.972133\n",
- "[6]\tvalid_0's ndcg@1: 0.927\tvalid_0's ndcg@2: 0.969682\tvalid_0's ndcg@3: 0.971757\tvalid_0's ndcg@4: 0.972134\tvalid_0's ndcg@5: 0.972231\n",
- "[7]\tvalid_0's ndcg@1: 0.928825\tvalid_0's ndcg@2: 0.970451\tvalid_0's ndcg@3: 0.972476\tvalid_0's ndcg@4: 0.97282\tvalid_0's ndcg@5: 0.972927\n",
- "[8]\tvalid_0's ndcg@1: 0.930025\tvalid_0's ndcg@2: 0.970988\tvalid_0's ndcg@3: 0.972951\tvalid_0's ndcg@4: 0.973295\tvalid_0's ndcg@5: 0.973402\n",
- "[9]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971347\tvalid_0's ndcg@3: 0.973384\tvalid_0's ndcg@4: 0.973707\tvalid_0's ndcg@5: 0.973794\n",
- "[10]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.971385\tvalid_0's ndcg@3: 0.973372\tvalid_0's ndcg@4: 0.973717\tvalid_0's ndcg@5: 0.973794\n",
- "[11]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.971433\tvalid_0's ndcg@3: 0.973333\tvalid_0's ndcg@4: 0.973699\tvalid_0's ndcg@5: 0.973767\n",
- "[12]\tvalid_0's ndcg@1: 0.93145\tvalid_0's ndcg@2: 0.971656\tvalid_0's ndcg@3: 0.973493\tvalid_0's ndcg@4: 0.973881\tvalid_0's ndcg@5: 0.973949\n",
- "[13]\tvalid_0's ndcg@1: 0.932525\tvalid_0's ndcg@2: 0.971927\tvalid_0's ndcg@3: 0.973839\tvalid_0's ndcg@4: 0.974227\tvalid_0's ndcg@5: 0.974304\n",
- "[14]\tvalid_0's ndcg@1: 0.932575\tvalid_0's ndcg@2: 0.971898\tvalid_0's ndcg@3: 0.973823\tvalid_0's ndcg@4: 0.974243\tvalid_0's ndcg@5: 0.97432\n",
- "[15]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972239\tvalid_0's ndcg@3: 0.974189\tvalid_0's ndcg@4: 0.974587\tvalid_0's ndcg@5: 0.974665\n",
- "[16]\tvalid_0's ndcg@1: 0.933475\tvalid_0's ndcg@2: 0.972309\tvalid_0's ndcg@3: 0.974209\tvalid_0's ndcg@4: 0.974596\tvalid_0's ndcg@5: 0.974674\n",
- "[17]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972369\tvalid_0's ndcg@3: 0.974307\tvalid_0's ndcg@4: 0.974684\tvalid_0's ndcg@5: 0.974761\n",
- "[18]\tvalid_0's ndcg@1: 0.9339\tvalid_0's ndcg@2: 0.972497\tvalid_0's ndcg@3: 0.974372\tvalid_0's ndcg@4: 0.974749\tvalid_0's ndcg@5: 0.974836\n",
- "[19]\tvalid_0's ndcg@1: 0.9345\tvalid_0's ndcg@2: 0.972845\tvalid_0's ndcg@3: 0.974645\tvalid_0's ndcg@4: 0.974979\tvalid_0's ndcg@5: 0.975085\n",
- "[20]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.97484\tvalid_0's ndcg@4: 0.975174\tvalid_0's ndcg@5: 0.975271\n",
- "[21]\tvalid_0's ndcg@1: 0.935\tvalid_0's ndcg@2: 0.973092\tvalid_0's ndcg@3: 0.97488\tvalid_0's ndcg@4: 0.975192\tvalid_0's ndcg@5: 0.975289\n",
- "[22]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.974988\tvalid_0's ndcg@4: 0.975289\tvalid_0's ndcg@5: 0.975386\n",
- "[23]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974824\tvalid_0's ndcg@4: 0.975136\tvalid_0's ndcg@5: 0.975223\n",
- "[24]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973274\tvalid_0's ndcg@3: 0.975087\tvalid_0's ndcg@4: 0.975388\tvalid_0's ndcg@5: 0.975475\n",
- "[25]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973345\tvalid_0's ndcg@3: 0.97512\tvalid_0's ndcg@4: 0.975443\tvalid_0's ndcg@5: 0.97553\n",
- "[26]\tvalid_0's ndcg@1: 0.93525\tvalid_0's ndcg@2: 0.9732\tvalid_0's ndcg@3: 0.975\tvalid_0's ndcg@4: 0.975313\tvalid_0's ndcg@5: 0.9754\n",
- "[27]\tvalid_0's ndcg@1: 0.935175\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.974983\tvalid_0's ndcg@4: 0.975295\tvalid_0's ndcg@5: 0.975382\n",
- "[28]\tvalid_0's ndcg@1: 0.935425\tvalid_0's ndcg@2: 0.973328\tvalid_0's ndcg@3: 0.975041\tvalid_0's ndcg@4: 0.975374\tvalid_0's ndcg@5: 0.975471\n",
- "[29]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973225\tvalid_0's ndcg@3: 0.974963\tvalid_0's ndcg@4: 0.975297\tvalid_0's ndcg@5: 0.975403\n",
- "[30]\tvalid_0's ndcg@1: 0.9353\tvalid_0's ndcg@2: 0.973235\tvalid_0's ndcg@3: 0.97501\tvalid_0's ndcg@4: 0.975311\tvalid_0's ndcg@5: 0.975418\n",
- "[31]\tvalid_0's ndcg@1: 0.9356\tvalid_0's ndcg@2: 0.973361\tvalid_0's ndcg@3: 0.975099\tvalid_0's ndcg@4: 0.975422\tvalid_0's ndcg@5: 0.975528\n",
- "[32]\tvalid_0's ndcg@1: 0.9364\tvalid_0's ndcg@2: 0.973641\tvalid_0's ndcg@3: 0.975391\tvalid_0's ndcg@4: 0.975714\tvalid_0's ndcg@5: 0.97582\n",
- "[33]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973751\tvalid_0's ndcg@3: 0.975501\tvalid_0's ndcg@4: 0.975824\tvalid_0's ndcg@5: 0.975931\n",
- "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.973902\tvalid_0's ndcg@3: 0.975677\tvalid_0's ndcg@4: 0.975989\tvalid_0's ndcg@5: 0.976095\n",
- "[35]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974105\tvalid_0's ndcg@3: 0.975892\tvalid_0's ndcg@4: 0.976194\tvalid_0's ndcg@5: 0.9763\n",
- "[36]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974184\tvalid_0's ndcg@3: 0.975984\tvalid_0's ndcg@4: 0.976296\tvalid_0's ndcg@5: 0.976402\n",
- "[37]\tvalid_0's ndcg@1: 0.93845\tvalid_0's ndcg@2: 0.974366\tvalid_0's ndcg@3: 0.976166\tvalid_0's ndcg@4: 0.976467\tvalid_0's ndcg@5: 0.976574\n",
- "[38]\tvalid_0's ndcg@1: 0.938925\tvalid_0's ndcg@2: 0.974557\tvalid_0's ndcg@3: 0.976332\tvalid_0's ndcg@4: 0.976655\tvalid_0's ndcg@5: 0.976751\n",
- "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974471\tvalid_0's ndcg@3: 0.976234\tvalid_0's ndcg@4: 0.976557\tvalid_0's ndcg@5: 0.976653\n",
- "[40]\tvalid_0's ndcg@1: 0.938325\tvalid_0's ndcg@2: 0.974335\tvalid_0's ndcg@3: 0.97611\tvalid_0's ndcg@4: 0.976433\tvalid_0's ndcg@5: 0.97653\n",
- "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.974669\tvalid_0's ndcg@3: 0.976431\tvalid_0's ndcg@4: 0.976743\tvalid_0's ndcg@5: 0.97683\n",
- "[42]\tvalid_0's ndcg@1: 0.939375\tvalid_0's ndcg@2: 0.974833\tvalid_0's ndcg@3: 0.976546\tvalid_0's ndcg@4: 0.976858\tvalid_0's ndcg@5: 0.976945\n",
- "[43]\tvalid_0's ndcg@1: 0.939625\tvalid_0's ndcg@2: 0.974878\tvalid_0's ndcg@3: 0.976628\tvalid_0's ndcg@4: 0.97694\tvalid_0's ndcg@5: 0.977027\n",
- "[44]\tvalid_0's ndcg@1: 0.9395\tvalid_0's ndcg@2: 0.974832\tvalid_0's ndcg@3: 0.97657\tvalid_0's ndcg@4: 0.976893\tvalid_0's ndcg@5: 0.97698\n",
- "[45]\tvalid_0's ndcg@1: 0.939775\tvalid_0's ndcg@2: 0.974949\tvalid_0's ndcg@3: 0.976674\tvalid_0's ndcg@4: 0.976997\tvalid_0's ndcg@5: 0.977084\n",
- "[46]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.974945\tvalid_0's ndcg@3: 0.976708\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977107\n",
- "[47]\tvalid_0's ndcg@1: 0.94005\tvalid_0's ndcg@2: 0.975004\tvalid_0's ndcg@3: 0.976766\tvalid_0's ndcg@4: 0.977078\tvalid_0's ndcg@5: 0.977175\n",
- "[48]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
- "[49]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975189\tvalid_0's ndcg@3: 0.976939\tvalid_0's ndcg@4: 0.97723\tvalid_0's ndcg@5: 0.977327\n",
- "[50]\tvalid_0's ndcg@1: 0.9405\tvalid_0's ndcg@2: 0.975264\tvalid_0's ndcg@3: 0.976989\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
- "[51]\tvalid_0's ndcg@1: 0.941125\tvalid_0's ndcg@2: 0.975526\tvalid_0's ndcg@3: 0.977226\tvalid_0's ndcg@4: 0.977528\tvalid_0's ndcg@5: 0.977605\n",
- "[52]\tvalid_0's ndcg@1: 0.941\tvalid_0's ndcg@2: 0.97548\tvalid_0's ndcg@3: 0.977193\tvalid_0's ndcg@4: 0.977484\tvalid_0's ndcg@5: 0.977561\n",
- "[53]\tvalid_0's ndcg@1: 0.9411\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.977259\tvalid_0's ndcg@4: 0.977539\tvalid_0's ndcg@5: 0.977616\n",
- "[54]\tvalid_0's ndcg@1: 0.9412\tvalid_0's ndcg@2: 0.975712\tvalid_0's ndcg@3: 0.977299\tvalid_0's ndcg@4: 0.97759\tvalid_0's ndcg@5: 0.977667\n",
- "[55]\tvalid_0's ndcg@1: 0.94155\tvalid_0's ndcg@2: 0.975841\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977719\tvalid_0's ndcg@5: 0.977797\n",
- "[56]\tvalid_0's ndcg@1: 0.941825\tvalid_0's ndcg@2: 0.975943\tvalid_0's ndcg@3: 0.97753\tvalid_0's ndcg@4: 0.977821\tvalid_0's ndcg@5: 0.977898\n",
- "[57]\tvalid_0's ndcg@1: 0.9416\tvalid_0's ndcg@2: 0.975891\tvalid_0's ndcg@3: 0.977429\tvalid_0's ndcg@4: 0.977741\tvalid_0's ndcg@5: 0.977818\n",
- "[58]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977494\tvalid_0's ndcg@4: 0.977795\tvalid_0's ndcg@5: 0.977873\n",
- "[59]\tvalid_0's ndcg@1: 0.942025\tvalid_0's ndcg@2: 0.975985\tvalid_0's ndcg@3: 0.977547\tvalid_0's ndcg@4: 0.977881\tvalid_0's ndcg@5: 0.977958\n",
- "[60]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975994\tvalid_0's ndcg@3: 0.977569\tvalid_0's ndcg@4: 0.977892\tvalid_0's ndcg@5: 0.977969\n",
- "[61]\tvalid_0's ndcg@1: 0.94205\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977559\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.97796\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 读取排序特征"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[62]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976027\tvalid_0's ndcg@3: 0.97764\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.978028\n",
- "[63]\tvalid_0's ndcg@1: 0.942125\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977622\tvalid_0's ndcg@4: 0.977912\tvalid_0's ndcg@5: 0.977999\n",
- "[64]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977793\tvalid_0's ndcg@4: 0.978105\tvalid_0's ndcg@5: 0.978192\n",
- "[65]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976227\tvalid_0's ndcg@3: 0.977802\tvalid_0's ndcg@4: 0.978125\tvalid_0's ndcg@5: 0.978212\n",
- "[66]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976132\tvalid_0's ndcg@3: 0.977695\tvalid_0's ndcg@4: 0.978018\tvalid_0's ndcg@5: 0.978105\n",
- "[67]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976092\tvalid_0's ndcg@3: 0.977679\tvalid_0's ndcg@4: 0.978002\tvalid_0's ndcg@5: 0.978089\n",
- "[68]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976148\tvalid_0's ndcg@3: 0.977698\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.978108\n",
- "[69]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976123\tvalid_0's ndcg@3: 0.977686\tvalid_0's ndcg@4: 0.978009\tvalid_0's ndcg@5: 0.978096\n",
- "[70]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976222\tvalid_0's ndcg@3: 0.977785\tvalid_0's ndcg@4: 0.978097\tvalid_0's ndcg@5: 0.978184\n",
- "[71]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976188\tvalid_0's ndcg@3: 0.977763\tvalid_0's ndcg@4: 0.978075\tvalid_0's ndcg@5: 0.978162\n",
- "[72]\tvalid_0's ndcg@1: 0.9427\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977809\tvalid_0's ndcg@4: 0.978121\tvalid_0's ndcg@5: 0.978208\n",
- "[73]\tvalid_0's ndcg@1: 0.9428\tvalid_0's ndcg@2: 0.976255\tvalid_0's ndcg@3: 0.977843\tvalid_0's ndcg@4: 0.978155\tvalid_0's ndcg@5: 0.978242\n",
- "[74]\tvalid_0's ndcg@1: 0.94295\tvalid_0's ndcg@2: 0.97631\tvalid_0's ndcg@3: 0.977898\tvalid_0's ndcg@4: 0.97821\tvalid_0's ndcg@5: 0.978297\n",
- "[75]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976329\tvalid_0's ndcg@3: 0.977941\tvalid_0's ndcg@4: 0.978232\tvalid_0's ndcg@5: 0.978319\n",
- "[76]\tvalid_0's ndcg@1: 0.9433\tvalid_0's ndcg@2: 0.976471\tvalid_0's ndcg@3: 0.978059\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978437\n",
- "[77]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976416\tvalid_0's ndcg@3: 0.977991\tvalid_0's ndcg@4: 0.978314\tvalid_0's ndcg@5: 0.978381\n",
- "[78]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976657\tvalid_0's ndcg@3: 0.978194\tvalid_0's ndcg@4: 0.978517\tvalid_0's ndcg@5: 0.978585\n",
- "[79]\tvalid_0's ndcg@1: 0.94365\tvalid_0's ndcg@2: 0.976663\tvalid_0's ndcg@3: 0.978188\tvalid_0's ndcg@4: 0.978501\tvalid_0's ndcg@5: 0.978578\n",
- "[80]\tvalid_0's ndcg@1: 0.943725\tvalid_0's ndcg@2: 0.976628\tvalid_0's ndcg@3: 0.978203\tvalid_0's ndcg@4: 0.978515\tvalid_0's ndcg@5: 0.978593\n",
- "[81]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97672\tvalid_0's ndcg@3: 0.978295\tvalid_0's ndcg@4: 0.978607\tvalid_0's ndcg@5: 0.978685\n",
- "[82]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978397\tvalid_0's ndcg@4: 0.97872\tvalid_0's ndcg@5: 0.978787\n",
- "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976788\tvalid_0's ndcg@3: 0.978375\tvalid_0's ndcg@4: 0.978698\tvalid_0's ndcg@5: 0.978766\n",
- "[84]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.97679\tvalid_0's ndcg@3: 0.97839\tvalid_0's ndcg@4: 0.978702\tvalid_0's ndcg@5: 0.97878\n",
- "[85]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.976809\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978723\tvalid_0's ndcg@5: 0.9788\n",
- "[86]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976939\tvalid_0's ndcg@3: 0.978502\tvalid_0's ndcg@4: 0.978814\tvalid_0's ndcg@5: 0.978891\n",
- "[87]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.976976\tvalid_0's ndcg@3: 0.978551\tvalid_0's ndcg@4: 0.978852\tvalid_0's ndcg@5: 0.97893\n",
- "[88]\tvalid_0's ndcg@1: 0.944925\tvalid_0's ndcg@2: 0.977102\tvalid_0's ndcg@3: 0.978677\tvalid_0's ndcg@4: 0.978968\tvalid_0's ndcg@5: 0.979045\n",
- "[89]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978758\tvalid_0's ndcg@4: 0.979048\tvalid_0's ndcg@5: 0.979126\n",
- "[90]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.978735\tvalid_0's ndcg@4: 0.979026\tvalid_0's ndcg@5: 0.979104\n",
- "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977208\tvalid_0's ndcg@3: 0.978858\tvalid_0's ndcg@4: 0.979138\tvalid_0's ndcg@5: 0.979215\n",
- "[92]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.977267\tvalid_0's ndcg@3: 0.978905\tvalid_0's ndcg@4: 0.979174\tvalid_0's ndcg@5: 0.979251\n",
- "[93]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977193\tvalid_0's ndcg@3: 0.978818\tvalid_0's ndcg@4: 0.979098\tvalid_0's ndcg@5: 0.979176\n",
- "[94]\tvalid_0's ndcg@1: 0.94545\tvalid_0's ndcg@2: 0.97728\tvalid_0's ndcg@3: 0.97888\tvalid_0's ndcg@4: 0.97916\tvalid_0's ndcg@5: 0.979238\n",
- "[95]\tvalid_0's ndcg@1: 0.9458\tvalid_0's ndcg@2: 0.977394\tvalid_0's ndcg@3: 0.979006\tvalid_0's ndcg@4: 0.979286\tvalid_0's ndcg@5: 0.979364\n",
- "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979114\tvalid_0's ndcg@4: 0.979394\tvalid_0's ndcg@5: 0.979472\n",
- "[97]\tvalid_0's ndcg@1: 0.946475\tvalid_0's ndcg@2: 0.977659\tvalid_0's ndcg@3: 0.979259\tvalid_0's ndcg@4: 0.979539\tvalid_0's ndcg@5: 0.979616\n",
- "[98]\tvalid_0's ndcg@1: 0.94675\tvalid_0's ndcg@2: 0.97776\tvalid_0's ndcg@3: 0.97936\tvalid_0's ndcg@4: 0.979651\tvalid_0's ndcg@5: 0.979719\n",
- "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
- "[100]\tvalid_0's ndcg@1: 0.9468\tvalid_0's ndcg@2: 0.977794\tvalid_0's ndcg@3: 0.979369\tvalid_0's ndcg@4: 0.979671\tvalid_0's ndcg@5: 0.979739\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[99]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.977831\tvalid_0's ndcg@3: 0.979419\tvalid_0's ndcg@4: 0.97971\tvalid_0's ndcg@5: 0.979777\n",
- "[1]\tvalid_0's ndcg@1: 0.909075\tvalid_0's ndcg@2: 0.963019\tvalid_0's ndcg@3: 0.965069\tvalid_0's ndcg@4: 0.965543\tvalid_0's ndcg@5: 0.965601\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9123\tvalid_0's ndcg@2: 0.964273\tvalid_0's ndcg@3: 0.966248\tvalid_0's ndcg@4: 0.966722\tvalid_0's ndcg@5: 0.966789\n",
- "[3]\tvalid_0's ndcg@1: 0.915075\tvalid_0's ndcg@2: 0.965691\tvalid_0's ndcg@3: 0.967466\tvalid_0's ndcg@4: 0.967854\tvalid_0's ndcg@5: 0.967922\n",
- "[4]\tvalid_0's ndcg@1: 0.91845\tvalid_0's ndcg@2: 0.967047\tvalid_0's ndcg@3: 0.968735\tvalid_0's ndcg@4: 0.969133\tvalid_0's ndcg@5: 0.969201\n",
- "[5]\tvalid_0's ndcg@1: 0.92355\tvalid_0's ndcg@2: 0.968961\tvalid_0's ndcg@3: 0.970674\tvalid_0's ndcg@4: 0.97104\tvalid_0's ndcg@5: 0.971098\n",
- "[6]\tvalid_0's ndcg@1: 0.9253\tvalid_0's ndcg@2: 0.969607\tvalid_0's ndcg@3: 0.971345\tvalid_0's ndcg@4: 0.971689\tvalid_0's ndcg@5: 0.971747\n",
- "[7]\tvalid_0's ndcg@1: 0.926225\tvalid_0's ndcg@2: 0.969933\tvalid_0's ndcg@3: 0.971708\tvalid_0's ndcg@4: 0.972031\tvalid_0's ndcg@5: 0.972079\n",
- "[8]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.970104\tvalid_0's ndcg@3: 0.971804\tvalid_0's ndcg@4: 0.972116\tvalid_0's ndcg@5: 0.972184\n",
- "[9]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970682\tvalid_0's ndcg@3: 0.972307\tvalid_0's ndcg@4: 0.972598\tvalid_0's ndcg@5: 0.972675\n",
- "[10]\tvalid_0's ndcg@1: 0.92775\tvalid_0's ndcg@2: 0.970653\tvalid_0's ndcg@3: 0.972316\tvalid_0's ndcg@4: 0.972617\tvalid_0's ndcg@5: 0.972685\n",
- "[11]\tvalid_0's ndcg@1: 0.9283\tvalid_0's ndcg@2: 0.97084\tvalid_0's ndcg@3: 0.97254\tvalid_0's ndcg@4: 0.97281\tvalid_0's ndcg@5: 0.972887\n",
- "[12]\tvalid_0's ndcg@1: 0.9287\tvalid_0's ndcg@2: 0.971051\tvalid_0's ndcg@3: 0.972701\tvalid_0's ndcg@4: 0.97297\tvalid_0's ndcg@5: 0.973048\n",
- "[13]\tvalid_0's ndcg@1: 0.9297\tvalid_0's ndcg@2: 0.971389\tvalid_0's ndcg@3: 0.973001\tvalid_0's ndcg@4: 0.973313\tvalid_0's ndcg@5: 0.9734\n",
- "[14]\tvalid_0's ndcg@1: 0.92955\tvalid_0's ndcg@2: 0.971444\tvalid_0's ndcg@3: 0.972994\tvalid_0's ndcg@4: 0.973284\tvalid_0's ndcg@5: 0.973371\n",
- "[15]\tvalid_0's ndcg@1: 0.930225\tvalid_0's ndcg@2: 0.97174\tvalid_0's ndcg@3: 0.973253\tvalid_0's ndcg@4: 0.973543\tvalid_0's ndcg@5: 0.97363\n",
- "[16]\tvalid_0's ndcg@1: 0.930425\tvalid_0's ndcg@2: 0.971798\tvalid_0's ndcg@3: 0.973298\tvalid_0's ndcg@4: 0.97361\tvalid_0's ndcg@5: 0.973698\n",
- "[17]\tvalid_0's ndcg@1: 0.93125\tvalid_0's ndcg@2: 0.971992\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973903\tvalid_0's ndcg@5: 0.97398\n",
- "[18]\tvalid_0's ndcg@1: 0.931925\tvalid_0's ndcg@2: 0.972257\tvalid_0's ndcg@3: 0.973845\tvalid_0's ndcg@4: 0.974146\tvalid_0's ndcg@5: 0.974224\n",
- "[19]\tvalid_0's ndcg@1: 0.932375\tvalid_0's ndcg@2: 0.972376\tvalid_0's ndcg@3: 0.974038\tvalid_0's ndcg@4: 0.974318\tvalid_0's ndcg@5: 0.974376\n",
- "[20]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.972269\tvalid_0's ndcg@3: 0.973907\tvalid_0's ndcg@4: 0.974187\tvalid_0's ndcg@5: 0.974245\n",
- "[21]\tvalid_0's ndcg@1: 0.932725\tvalid_0's ndcg@2: 0.972568\tvalid_0's ndcg@3: 0.974181\tvalid_0's ndcg@4: 0.974471\tvalid_0's ndcg@5: 0.974529\n",
- "[22]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972735\tvalid_0's ndcg@3: 0.974298\tvalid_0's ndcg@4: 0.974599\tvalid_0's ndcg@5: 0.974657\n",
- "[23]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972642\tvalid_0's ndcg@3: 0.974255\tvalid_0's ndcg@4: 0.974545\tvalid_0's ndcg@5: 0.974594\n",
- "[24]\tvalid_0's ndcg@1: 0.933175\tvalid_0's ndcg@2: 0.972734\tvalid_0's ndcg@3: 0.974347\tvalid_0's ndcg@4: 0.974638\tvalid_0's ndcg@5: 0.974686\n",
- "[25]\tvalid_0's ndcg@1: 0.9331\tvalid_0's ndcg@2: 0.972754\tvalid_0's ndcg@3: 0.974366\tvalid_0's ndcg@4: 0.974636\tvalid_0's ndcg@5: 0.974674\n"
- ]
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:41.843180Z",
+ "start_time": "2020-11-18T04:20:41.837287Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = './data_raw/'\n",
+ "save_path = './temp_results/'\n",
+ "offline = False"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[26]\tvalid_0's ndcg@1: 0.933275\tvalid_0's ndcg@2: 0.972787\tvalid_0's ndcg@3: 0.974424\tvalid_0's ndcg@4: 0.974694\tvalid_0's ndcg@5: 0.974732\n",
- "[27]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972809\tvalid_0's ndcg@3: 0.974434\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.974732\n",
- "[28]\tvalid_0's ndcg@1: 0.933625\tvalid_0's ndcg@2: 0.972932\tvalid_0's ndcg@3: 0.974557\tvalid_0's ndcg@4: 0.974826\tvalid_0's ndcg@5: 0.974855\n",
- "[29]\tvalid_0's ndcg@1: 0.933725\tvalid_0's ndcg@2: 0.972937\tvalid_0's ndcg@3: 0.974587\tvalid_0's ndcg@4: 0.974856\tvalid_0's ndcg@5: 0.974885\n",
- "[30]\tvalid_0's ndcg@1: 0.93355\tvalid_0's ndcg@2: 0.972873\tvalid_0's ndcg@3: 0.974523\tvalid_0's ndcg@4: 0.974792\tvalid_0's ndcg@5: 0.974821\n",
- "[31]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973065\tvalid_0's ndcg@3: 0.974753\tvalid_0's ndcg@4: 0.975022\tvalid_0's ndcg@5: 0.975051\n",
- "[32]\tvalid_0's ndcg@1: 0.93435\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974815\tvalid_0's ndcg@4: 0.975084\tvalid_0's ndcg@5: 0.975113\n",
- "[33]\tvalid_0's ndcg@1: 0.934475\tvalid_0's ndcg@2: 0.97323\tvalid_0's ndcg@3: 0.974855\tvalid_0's ndcg@4: 0.975135\tvalid_0's ndcg@5: 0.975164\n",
- "[34]\tvalid_0's ndcg@1: 0.9342\tvalid_0's ndcg@2: 0.973113\tvalid_0's ndcg@3: 0.974738\tvalid_0's ndcg@4: 0.975028\tvalid_0's ndcg@5: 0.975057\n",
- "[35]\tvalid_0's ndcg@1: 0.93455\tvalid_0's ndcg@2: 0.973258\tvalid_0's ndcg@3: 0.97487\tvalid_0's ndcg@4: 0.975172\tvalid_0's ndcg@5: 0.975201\n",
- "[36]\tvalid_0's ndcg@1: 0.9344\tvalid_0's ndcg@2: 0.973265\tvalid_0's ndcg@3: 0.974828\tvalid_0's ndcg@4: 0.975129\tvalid_0's ndcg@5: 0.975158\n",
- "[37]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973438\tvalid_0's ndcg@3: 0.975013\tvalid_0's ndcg@4: 0.975304\tvalid_0's ndcg@5: 0.975323\n",
- "[38]\tvalid_0's ndcg@1: 0.934975\tvalid_0's ndcg@2: 0.973541\tvalid_0's ndcg@3: 0.975066\tvalid_0's ndcg@4: 0.975367\tvalid_0's ndcg@5: 0.975386\n",
- "[39]\tvalid_0's ndcg@1: 0.935275\tvalid_0's ndcg@2: 0.973667\tvalid_0's ndcg@3: 0.975192\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975502\n",
- "[40]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973624\tvalid_0's ndcg@3: 0.975174\tvalid_0's ndcg@4: 0.975454\tvalid_0's ndcg@5: 0.975473\n",
- "[41]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973686\tvalid_0's ndcg@3: 0.975223\tvalid_0's ndcg@4: 0.975503\tvalid_0's ndcg@5: 0.975522\n",
- "[42]\tvalid_0's ndcg@1: 0.93545\tvalid_0's ndcg@2: 0.973716\tvalid_0's ndcg@3: 0.975266\tvalid_0's ndcg@4: 0.975546\tvalid_0's ndcg@5: 0.975565\n",
- "[43]\tvalid_0's ndcg@1: 0.93615\tvalid_0's ndcg@2: 0.974022\tvalid_0's ndcg@3: 0.975534\tvalid_0's ndcg@4: 0.975814\tvalid_0's ndcg@5: 0.975843\n",
- "[44]\tvalid_0's ndcg@1: 0.936225\tvalid_0's ndcg@2: 0.974112\tvalid_0's ndcg@3: 0.975562\tvalid_0's ndcg@4: 0.975853\tvalid_0's ndcg@5: 0.975882\n",
- "[45]\tvalid_0's ndcg@1: 0.9365\tvalid_0's ndcg@2: 0.974167\tvalid_0's ndcg@3: 0.975654\tvalid_0's ndcg@4: 0.975945\tvalid_0's ndcg@5: 0.975974\n",
- "[46]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974206\tvalid_0's ndcg@3: 0.975694\tvalid_0's ndcg@4: 0.975995\tvalid_0's ndcg@5: 0.976024\n",
- "[47]\tvalid_0's ndcg@1: 0.93685\tvalid_0's ndcg@2: 0.974311\tvalid_0's ndcg@3: 0.975786\tvalid_0's ndcg@4: 0.976077\tvalid_0's ndcg@5: 0.976106\n",
- "[48]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974408\tvalid_0's ndcg@3: 0.975845\tvalid_0's ndcg@4: 0.976147\tvalid_0's ndcg@5: 0.976185\n",
- "[49]\tvalid_0's ndcg@1: 0.936975\tvalid_0's ndcg@2: 0.974342\tvalid_0's ndcg@3: 0.975829\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.976159\n",
- "[50]\tvalid_0's ndcg@1: 0.9371\tvalid_0's ndcg@2: 0.974388\tvalid_0's ndcg@3: 0.97585\tvalid_0's ndcg@4: 0.976152\tvalid_0's ndcg@5: 0.976191\n",
- "[51]\tvalid_0's ndcg@1: 0.937025\tvalid_0's ndcg@2: 0.974329\tvalid_0's ndcg@3: 0.975841\tvalid_0's ndcg@4: 0.976121\tvalid_0's ndcg@5: 0.97616\n",
- "[52]\tvalid_0's ndcg@1: 0.9377\tvalid_0's ndcg@2: 0.974578\tvalid_0's ndcg@3: 0.976078\tvalid_0's ndcg@4: 0.976369\tvalid_0's ndcg@5: 0.976407\n",
- "[53]\tvalid_0's ndcg@1: 0.9378\tvalid_0's ndcg@2: 0.974615\tvalid_0's ndcg@3: 0.976115\tvalid_0's ndcg@4: 0.976405\tvalid_0's ndcg@5: 0.976444\n",
- "[54]\tvalid_0's ndcg@1: 0.938\tvalid_0's ndcg@2: 0.974689\tvalid_0's ndcg@3: 0.976214\tvalid_0's ndcg@4: 0.976483\tvalid_0's ndcg@5: 0.976521\n",
- "[55]\tvalid_0's ndcg@1: 0.938225\tvalid_0's ndcg@2: 0.974803\tvalid_0's ndcg@3: 0.976303\tvalid_0's ndcg@4: 0.976572\tvalid_0's ndcg@5: 0.976611\n",
- "[56]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.9748\tvalid_0's ndcg@3: 0.976275\tvalid_0's ndcg@4: 0.976555\tvalid_0's ndcg@5: 0.976594\n",
- "[57]\tvalid_0's ndcg@1: 0.938525\tvalid_0's ndcg@2: 0.974914\tvalid_0's ndcg@3: 0.976414\tvalid_0's ndcg@4: 0.976683\tvalid_0's ndcg@5: 0.976722\n",
- "[58]\tvalid_0's ndcg@1: 0.93875\tvalid_0's ndcg@2: 0.975028\tvalid_0's ndcg@3: 0.976503\tvalid_0's ndcg@4: 0.976773\tvalid_0's ndcg@5: 0.976811\n",
- "[59]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975198\tvalid_0's ndcg@3: 0.976648\tvalid_0's ndcg@4: 0.976918\tvalid_0's ndcg@5: 0.976956\n",
- "[60]\tvalid_0's ndcg@1: 0.939025\tvalid_0's ndcg@2: 0.975177\tvalid_0's ndcg@3: 0.976615\tvalid_0's ndcg@4: 0.976884\tvalid_0's ndcg@5: 0.976923\n",
- "[61]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975205\tvalid_0's ndcg@3: 0.976642\tvalid_0's ndcg@4: 0.976912\tvalid_0's ndcg@5: 0.97695\n",
- "[62]\tvalid_0's ndcg@1: 0.93965\tvalid_0's ndcg@2: 0.975424\tvalid_0's ndcg@3: 0.976836\tvalid_0's ndcg@4: 0.977116\tvalid_0's ndcg@5: 0.977155\n",
- "[63]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.975596\tvalid_0's ndcg@3: 0.976996\tvalid_0's ndcg@4: 0.977276\tvalid_0's ndcg@5: 0.977315\n",
- "[64]\tvalid_0's ndcg@1: 0.940375\tvalid_0's ndcg@2: 0.975723\tvalid_0's ndcg@3: 0.977123\tvalid_0's ndcg@4: 0.977392\tvalid_0's ndcg@5: 0.977431\n",
- "[65]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977154\tvalid_0's ndcg@4: 0.977423\tvalid_0's ndcg@5: 0.977462\n",
- "[66]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975744\tvalid_0's ndcg@3: 0.977156\tvalid_0's ndcg@4: 0.977426\tvalid_0's ndcg@5: 0.977464\n",
- "[67]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.97576\tvalid_0's ndcg@3: 0.977172\tvalid_0's ndcg@4: 0.977431\tvalid_0's ndcg@5: 0.977469\n",
- "[68]\tvalid_0's ndcg@1: 0.940675\tvalid_0's ndcg@2: 0.975849\tvalid_0's ndcg@3: 0.977249\tvalid_0's ndcg@4: 0.977508\tvalid_0's ndcg@5: 0.977546\n",
- "[69]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.976017\tvalid_0's ndcg@3: 0.977454\tvalid_0's ndcg@4: 0.977724\tvalid_0's ndcg@5: 0.977762\n",
- "[70]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.977362\tvalid_0's ndcg@4: 0.977631\tvalid_0's ndcg@5: 0.97767\n",
- "[71]\tvalid_0's ndcg@1: 0.94105\tvalid_0's ndcg@2: 0.975925\tvalid_0's ndcg@3: 0.97735\tvalid_0's ndcg@4: 0.97763\tvalid_0's ndcg@5: 0.977668\n",
- "[72]\tvalid_0's ndcg@1: 0.941325\tvalid_0's ndcg@2: 0.976058\tvalid_0's ndcg@3: 0.97747\tvalid_0's ndcg@4: 0.977739\tvalid_0's ndcg@5: 0.977778\n",
- "[73]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977476\tvalid_0's ndcg@4: 0.977756\tvalid_0's ndcg@5: 0.977795\n",
- "[74]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.97759\tvalid_0's ndcg@4: 0.97788\tvalid_0's ndcg@5: 0.977919\n",
- "[75]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.97619\tvalid_0's ndcg@3: 0.977602\tvalid_0's ndcg@4: 0.977882\tvalid_0's ndcg@5: 0.977921\n",
- "[76]\tvalid_0's ndcg@1: 0.94195\tvalid_0's ndcg@2: 0.976273\tvalid_0's ndcg@3: 0.977685\tvalid_0's ndcg@4: 0.977965\tvalid_0's ndcg@5: 0.978004\n",
- "[77]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.97627\tvalid_0's ndcg@3: 0.97767\tvalid_0's ndcg@4: 0.97795\tvalid_0's ndcg@5: 0.977989\n",
- "[78]\tvalid_0's ndcg@1: 0.94235\tvalid_0's ndcg@2: 0.976452\tvalid_0's ndcg@3: 0.977839\tvalid_0's ndcg@4: 0.978119\tvalid_0's ndcg@5: 0.978158\n",
- "[79]\tvalid_0's ndcg@1: 0.94265\tvalid_0's ndcg@2: 0.976562\tvalid_0's ndcg@3: 0.977937\tvalid_0's ndcg@4: 0.978228\tvalid_0's ndcg@5: 0.978267\n",
- "[80]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976667\tvalid_0's ndcg@3: 0.978067\tvalid_0's ndcg@4: 0.978347\tvalid_0's ndcg@5: 0.978385\n",
- "[81]\tvalid_0's ndcg@1: 0.94305\tvalid_0's ndcg@2: 0.97671\tvalid_0's ndcg@3: 0.978098\tvalid_0's ndcg@4: 0.978378\tvalid_0's ndcg@5: 0.978416\n",
- "[82]\tvalid_0's ndcg@1: 0.943175\tvalid_0's ndcg@2: 0.97674\tvalid_0's ndcg@3: 0.978115\tvalid_0's ndcg@4: 0.978417\tvalid_0's ndcg@5: 0.978456\n",
- "[83]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976752\tvalid_0's ndcg@3: 0.97814\tvalid_0's ndcg@4: 0.978441\tvalid_0's ndcg@5: 0.97848\n",
- "[84]\tvalid_0's ndcg@1: 0.943375\tvalid_0's ndcg@2: 0.976767\tvalid_0's ndcg@3: 0.978179\tvalid_0's ndcg@4: 0.978481\tvalid_0's ndcg@5: 0.97852\n",
- "[85]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976721\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978437\tvalid_0's ndcg@5: 0.978475\n",
- "[86]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976792\tvalid_0's ndcg@3: 0.978204\tvalid_0's ndcg@4: 0.978506\tvalid_0's ndcg@5: 0.978535\n",
- "[87]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.976851\tvalid_0's ndcg@3: 0.978239\tvalid_0's ndcg@4: 0.97854\tvalid_0's ndcg@5: 0.978569\n",
- "[88]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976882\tvalid_0's ndcg@3: 0.978282\tvalid_0's ndcg@4: 0.978572\tvalid_0's ndcg@5: 0.978611\n",
- "[89]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.976915\tvalid_0's ndcg@3: 0.97834\tvalid_0's ndcg@4: 0.97863\tvalid_0's ndcg@5: 0.978669\n"
- ]
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:20:53.358138Z",
+ "start_time": "2020-11-18T04:20:44.232944Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 重新读取数据的时候，发现click_article_id是一个浮点数，所以将其转换成int类型\n",
+ "trn_user_item_feats_df = pd.read_csv(save_path + 'trn_user_item_feats_df.csv')\n",
+ "trn_user_item_feats_df['click_article_id'] = trn_user_item_feats_df['click_article_id'].astype(int)\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df = pd.read_csv(save_path + 'val_user_item_feats_df.csv')\n",
+ " val_user_item_feats_df['click_article_id'] = val_user_item_feats_df['click_article_id'].astype(int)\n",
+ "else:\n",
+ " val_user_item_feats_df = None\n",
+ " \n",
+ "tst_user_item_feats_df = pd.read_csv(save_path + 'tst_user_item_feats_df.csv')\n",
+ "tst_user_item_feats_df['click_article_id'] = tst_user_item_feats_df['click_article_id'].astype(int)\n",
+ "\n",
+ "# 做特征的时候为了方便，给测试集也打上了一个无效的标签，这里直接删掉就行\n",
+ "del tst_user_item_feats_df['label']"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[90]\tvalid_0's ndcg@1: 0.943925\tvalid_0's ndcg@2: 0.976986\tvalid_0's ndcg@3: 0.978398\tvalid_0's ndcg@4: 0.978689\tvalid_0's ndcg@5: 0.978728\n",
- "[91]\tvalid_0's ndcg@1: 0.943875\tvalid_0's ndcg@2: 0.976999\tvalid_0's ndcg@3: 0.978399\tvalid_0's ndcg@4: 0.978679\tvalid_0's ndcg@5: 0.978717\n",
- "[92]\tvalid_0's ndcg@1: 0.94395\tvalid_0's ndcg@2: 0.977058\tvalid_0's ndcg@3: 0.978421\tvalid_0's ndcg@4: 0.978711\tvalid_0's ndcg@5: 0.97876\n",
- "[93]\tvalid_0's ndcg@1: 0.944075\tvalid_0's ndcg@2: 0.977104\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978759\tvalid_0's ndcg@5: 0.978807\n",
- "[94]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977125\tvalid_0's ndcg@3: 0.978513\tvalid_0's ndcg@4: 0.978793\tvalid_0's ndcg@5: 0.978841\n",
- "[95]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977153\tvalid_0's ndcg@3: 0.97854\tvalid_0's ndcg@4: 0.97882\tvalid_0's ndcg@5: 0.978869\n",
- "[96]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977144\tvalid_0's ndcg@3: 0.978531\tvalid_0's ndcg@4: 0.978811\tvalid_0's ndcg@5: 0.97886\n",
- "[97]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977221\tvalid_0's ndcg@3: 0.978584\tvalid_0's ndcg@4: 0.978864\tvalid_0's ndcg@5: 0.978912\n",
- "[98]\tvalid_0's ndcg@1: 0.944575\tvalid_0's ndcg@2: 0.977289\tvalid_0's ndcg@3: 0.978651\tvalid_0's ndcg@4: 0.978942\tvalid_0's ndcg@5: 0.97899\n",
- "[99]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977341\tvalid_0's ndcg@3: 0.978691\tvalid_0's ndcg@4: 0.978993\tvalid_0's ndcg@5: 0.979032\n",
- "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.9451\tvalid_0's ndcg@2: 0.977482\tvalid_0's ndcg@3: 0.978857\tvalid_0's ndcg@4: 0.979148\tvalid_0's ndcg@5: 0.979187\n",
- "[1]\tvalid_0's ndcg@1: 0.911575\tvalid_0's ndcg@2: 0.964384\tvalid_0's ndcg@3: 0.966321\tvalid_0's ndcg@4: 0.966623\tvalid_0's ndcg@5: 0.966671\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9136\tvalid_0's ndcg@2: 0.965257\tvalid_0's ndcg@3: 0.967107\tvalid_0's ndcg@4: 0.967398\tvalid_0's ndcg@5: 0.967456\n",
- "[3]\tvalid_0's ndcg@1: 0.917425\tvalid_0's ndcg@2: 0.966732\tvalid_0's ndcg@3: 0.968545\tvalid_0's ndcg@4: 0.968814\tvalid_0's ndcg@5: 0.968882\n",
- "[4]\tvalid_0's ndcg@1: 0.9222\tvalid_0's ndcg@2: 0.968558\tvalid_0's ndcg@3: 0.970383\tvalid_0's ndcg@4: 0.970619\tvalid_0's ndcg@5: 0.970668\n",
- "[5]\tvalid_0's ndcg@1: 0.925875\tvalid_0's ndcg@2: 0.969914\tvalid_0's ndcg@3: 0.971714\tvalid_0's ndcg@4: 0.971972\tvalid_0's ndcg@5: 0.972021\n",
- "[6]\tvalid_0's ndcg@1: 0.926875\tvalid_0's ndcg@2: 0.970425\tvalid_0's ndcg@3: 0.972112\tvalid_0's ndcg@4: 0.972371\tvalid_0's ndcg@5: 0.972419\n",
- "[7]\tvalid_0's ndcg@1: 0.927475\tvalid_0's ndcg@2: 0.970631\tvalid_0's ndcg@3: 0.972306\tvalid_0's ndcg@4: 0.972586\tvalid_0's ndcg@5: 0.972634\n",
- "[8]\tvalid_0's ndcg@1: 0.93015\tvalid_0's ndcg@2: 0.971649\tvalid_0's ndcg@3: 0.973287\tvalid_0's ndcg@4: 0.973567\tvalid_0's ndcg@5: 0.973625\n",
- "[9]\tvalid_0's ndcg@1: 0.9312\tvalid_0's ndcg@2: 0.972084\tvalid_0's ndcg@3: 0.973684\tvalid_0's ndcg@4: 0.973964\tvalid_0's ndcg@5: 0.974022\n",
- "[10]\tvalid_0's ndcg@1: 0.93225\tvalid_0's ndcg@2: 0.972456\tvalid_0's ndcg@3: 0.974081\tvalid_0's ndcg@4: 0.974361\tvalid_0's ndcg@5: 0.974409\n",
- "[11]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.972704\tvalid_0's ndcg@3: 0.974379\tvalid_0's ndcg@4: 0.974648\tvalid_0's ndcg@5: 0.974696\n",
- "[12]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972949\tvalid_0's ndcg@3: 0.974574\tvalid_0's ndcg@4: 0.974832\tvalid_0's ndcg@5: 0.974881\n",
- "[13]\tvalid_0's ndcg@1: 0.93415\tvalid_0's ndcg@2: 0.97322\tvalid_0's ndcg@3: 0.97482\tvalid_0's ndcg@4: 0.975079\tvalid_0's ndcg@5: 0.975127\n",
- "[14]\tvalid_0's ndcg@1: 0.9352\tvalid_0's ndcg@2: 0.973671\tvalid_0's ndcg@3: 0.975246\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975531\n",
- "[15]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.973877\tvalid_0's ndcg@3: 0.975452\tvalid_0's ndcg@4: 0.975699\tvalid_0's ndcg@5: 0.975748\n",
- "[16]\tvalid_0's ndcg@1: 0.935825\tvalid_0's ndcg@2: 0.973917\tvalid_0's ndcg@3: 0.975442\tvalid_0's ndcg@4: 0.975712\tvalid_0's ndcg@5: 0.97576\n",
- "[17]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.97411\tvalid_0's ndcg@3: 0.975697\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975995\n",
- "[18]\tvalid_0's ndcg@1: 0.936925\tvalid_0's ndcg@2: 0.974292\tvalid_0's ndcg@3: 0.975867\tvalid_0's ndcg@4: 0.976114\tvalid_0's ndcg@5: 0.976163\n",
- "[19]\tvalid_0's ndcg@1: 0.937525\tvalid_0's ndcg@2: 0.974545\tvalid_0's ndcg@3: 0.976095\tvalid_0's ndcg@4: 0.976342\tvalid_0's ndcg@5: 0.976391\n",
- "[20]\tvalid_0's ndcg@1: 0.937775\tvalid_0's ndcg@2: 0.974653\tvalid_0's ndcg@3: 0.976203\tvalid_0's ndcg@4: 0.976429\tvalid_0's ndcg@5: 0.976487\n",
- "[21]\tvalid_0's ndcg@1: 0.938825\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976597\tvalid_0's ndcg@4: 0.976823\tvalid_0's ndcg@5: 0.976881\n",
- "[22]\tvalid_0's ndcg@1: 0.93885\tvalid_0's ndcg@2: 0.975097\tvalid_0's ndcg@3: 0.976609\tvalid_0's ndcg@4: 0.976846\tvalid_0's ndcg@5: 0.976895\n",
- "[23]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976733\tvalid_0's ndcg@4: 0.976959\tvalid_0's ndcg@5: 0.977008\n",
- "[24]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.975246\tvalid_0's ndcg@3: 0.976721\tvalid_0's ndcg@4: 0.976947\tvalid_0's ndcg@5: 0.977005\n",
- "[25]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975421\tvalid_0's ndcg@3: 0.976909\tvalid_0's ndcg@4: 0.977124\tvalid_0's ndcg@5: 0.977182\n",
- "[26]\tvalid_0's ndcg@1: 0.9393\tvalid_0's ndcg@2: 0.975342\tvalid_0's ndcg@3: 0.976804\tvalid_0's ndcg@4: 0.97702\tvalid_0's ndcg@5: 0.977078\n",
- "[27]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975323\tvalid_0's ndcg@3: 0.976798\tvalid_0's ndcg@4: 0.977014\tvalid_0's ndcg@5: 0.977062\n",
- "[28]\tvalid_0's ndcg@1: 0.93925\tvalid_0's ndcg@2: 0.975308\tvalid_0's ndcg@3: 0.976783\tvalid_0's ndcg@4: 0.977009\tvalid_0's ndcg@5: 0.977057\n",
- "[29]\tvalid_0's ndcg@1: 0.94\tvalid_0's ndcg@2: 0.975569\tvalid_0's ndcg@3: 0.977056\tvalid_0's ndcg@4: 0.977282\tvalid_0's ndcg@5: 0.977331\n",
- "[30]\tvalid_0's ndcg@1: 0.940325\tvalid_0's ndcg@2: 0.975673\tvalid_0's ndcg@3: 0.977173\tvalid_0's ndcg@4: 0.977399\tvalid_0's ndcg@5: 0.977447\n",
- "[31]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975731\tvalid_0's ndcg@3: 0.977243\tvalid_0's ndcg@4: 0.977469\tvalid_0's ndcg@5: 0.977518\n",
- "[32]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
- "[33]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975766\tvalid_0's ndcg@3: 0.977241\tvalid_0's ndcg@4: 0.977457\tvalid_0's ndcg@5: 0.977505\n",
- "[34]\tvalid_0's ndcg@1: 0.940625\tvalid_0's ndcg@2: 0.975831\tvalid_0's ndcg@3: 0.977306\tvalid_0's ndcg@4: 0.977521\tvalid_0's ndcg@5: 0.97757\n",
- "[35]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975868\tvalid_0's ndcg@3: 0.977343\tvalid_0's ndcg@4: 0.977558\tvalid_0's ndcg@5: 0.977606\n",
- "[36]\tvalid_0's ndcg@1: 0.94115\tvalid_0's ndcg@2: 0.976056\tvalid_0's ndcg@3: 0.977506\tvalid_0's ndcg@4: 0.977722\tvalid_0's ndcg@5: 0.97777\n",
- "[37]\tvalid_0's ndcg@1: 0.9414\tvalid_0's ndcg@2: 0.976133\tvalid_0's ndcg@3: 0.977595\tvalid_0's ndcg@4: 0.977811\tvalid_0's ndcg@5: 0.977859\n",
- "[38]\tvalid_0's ndcg@1: 0.94175\tvalid_0's ndcg@2: 0.976278\tvalid_0's ndcg@3: 0.977715\tvalid_0's ndcg@4: 0.977941\tvalid_0's ndcg@5: 0.97799\n",
- "[39]\tvalid_0's ndcg@1: 0.942075\tvalid_0's ndcg@2: 0.976366\tvalid_0's ndcg@3: 0.977841\tvalid_0's ndcg@4: 0.978056\tvalid_0's ndcg@5: 0.978105\n",
- "[40]\tvalid_0's ndcg@1: 0.94215\tvalid_0's ndcg@2: 0.976409\tvalid_0's ndcg@3: 0.977872\tvalid_0's ndcg@4: 0.978087\tvalid_0's ndcg@5: 0.978136\n",
- "[41]\tvalid_0's ndcg@1: 0.94245\tvalid_0's ndcg@2: 0.97652\tvalid_0's ndcg@3: 0.977983\tvalid_0's ndcg@4: 0.978198\tvalid_0's ndcg@5: 0.978246\n",
- "[42]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
- "[43]\tvalid_0's ndcg@1: 0.942975\tvalid_0's ndcg@2: 0.976682\tvalid_0's ndcg@3: 0.97817\tvalid_0's ndcg@4: 0.978385\tvalid_0's ndcg@5: 0.978434\n",
- "[44]\tvalid_0's ndcg@1: 0.94285\tvalid_0's ndcg@2: 0.976636\tvalid_0's ndcg@3: 0.978111\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978386\n",
- "[45]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.9768\tvalid_0's ndcg@3: 0.978262\tvalid_0's ndcg@4: 0.978488\tvalid_0's ndcg@5: 0.978537\n",
- "[46]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
- "[47]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97836\tvalid_0's ndcg@4: 0.978576\tvalid_0's ndcg@5: 0.978634\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 返回排序后的结果"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[48]\tvalid_0's ndcg@1: 0.943525\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.978373\tvalid_0's ndcg@4: 0.978577\tvalid_0's ndcg@5: 0.978636\n",
- "[49]\tvalid_0's ndcg@1: 0.9436\tvalid_0's ndcg@2: 0.976913\tvalid_0's ndcg@3: 0.978388\tvalid_0's ndcg@4: 0.978614\tvalid_0's ndcg@5: 0.978663\n",
- "[50]\tvalid_0's ndcg@1: 0.943975\tvalid_0's ndcg@2: 0.97702\tvalid_0's ndcg@3: 0.97852\tvalid_0's ndcg@4: 0.978746\tvalid_0's ndcg@5: 0.978794\n",
- "[51]\tvalid_0's ndcg@1: 0.9441\tvalid_0's ndcg@2: 0.97705\tvalid_0's ndcg@3: 0.97855\tvalid_0's ndcg@4: 0.978787\tvalid_0's ndcg@5: 0.978836\n",
- "[52]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.977121\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978846\tvalid_0's ndcg@5: 0.978894\n",
- "[53]\tvalid_0's ndcg@1: 0.944225\tvalid_0's ndcg@2: 0.977081\tvalid_0's ndcg@3: 0.978618\tvalid_0's ndcg@4: 0.978834\tvalid_0's ndcg@5: 0.978882\n",
- "[54]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977071\tvalid_0's ndcg@3: 0.978609\tvalid_0's ndcg@4: 0.978824\tvalid_0's ndcg@5: 0.978873\n",
- "[55]\tvalid_0's ndcg@1: 0.94435\tvalid_0's ndcg@2: 0.977143\tvalid_0's ndcg@3: 0.978668\tvalid_0's ndcg@4: 0.978883\tvalid_0's ndcg@5: 0.978931\n",
- "[56]\tvalid_0's ndcg@1: 0.9444\tvalid_0's ndcg@2: 0.977177\tvalid_0's ndcg@3: 0.978702\tvalid_0's ndcg@4: 0.978906\tvalid_0's ndcg@5: 0.978955\n",
- "[57]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.977263\tvalid_0's ndcg@3: 0.978788\tvalid_0's ndcg@4: 0.979003\tvalid_0's ndcg@5: 0.979051\n",
- "[58]\tvalid_0's ndcg@1: 0.9448\tvalid_0's ndcg@2: 0.977293\tvalid_0's ndcg@3: 0.978843\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979096\n",
- "[59]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977472\tvalid_0's ndcg@3: 0.978997\tvalid_0's ndcg@4: 0.979202\tvalid_0's ndcg@5: 0.97925\n",
- "[60]\tvalid_0's ndcg@1: 0.9455\tvalid_0's ndcg@2: 0.97763\tvalid_0's ndcg@3: 0.979118\tvalid_0's ndcg@4: 0.979322\tvalid_0's ndcg@5: 0.979371\n",
- "[61]\tvalid_0's ndcg@1: 0.945725\tvalid_0's ndcg@2: 0.977682\tvalid_0's ndcg@3: 0.979194\tvalid_0's ndcg@4: 0.979399\tvalid_0's ndcg@5: 0.979447\n",
- "[62]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977812\tvalid_0's ndcg@3: 0.979312\tvalid_0's ndcg@4: 0.979495\tvalid_0's ndcg@5: 0.979543\n",
- "[63]\tvalid_0's ndcg@1: 0.946\tvalid_0's ndcg@2: 0.977878\tvalid_0's ndcg@3: 0.97934\tvalid_0's ndcg@4: 0.979523\tvalid_0's ndcg@5: 0.979572\n",
- "[64]\tvalid_0's ndcg@1: 0.946525\tvalid_0's ndcg@2: 0.978056\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979714\tvalid_0's ndcg@5: 0.979762\n",
- "[65]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.978105\tvalid_0's ndcg@3: 0.979592\tvalid_0's ndcg@4: 0.979775\tvalid_0's ndcg@5: 0.979823\n",
- "[66]\tvalid_0's ndcg@1: 0.9465\tvalid_0's ndcg@2: 0.978046\tvalid_0's ndcg@3: 0.979534\tvalid_0's ndcg@4: 0.979706\tvalid_0's ndcg@5: 0.979755\n",
- "[67]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.978127\tvalid_0's ndcg@3: 0.979614\tvalid_0's ndcg@4: 0.979776\tvalid_0's ndcg@5: 0.979824\n",
- "[68]\tvalid_0's ndcg@1: 0.9467\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.979608\tvalid_0's ndcg@4: 0.97978\tvalid_0's ndcg@5: 0.979828\n",
- "[69]\tvalid_0's ndcg@1: 0.946875\tvalid_0's ndcg@2: 0.978216\tvalid_0's ndcg@3: 0.979679\tvalid_0's ndcg@4: 0.979851\tvalid_0's ndcg@5: 0.9799\n",
- "[70]\tvalid_0's ndcg@1: 0.9469\tvalid_0's ndcg@2: 0.978194\tvalid_0's ndcg@3: 0.979682\tvalid_0's ndcg@4: 0.979854\tvalid_0's ndcg@5: 0.979902\n",
- "[71]\tvalid_0's ndcg@1: 0.947025\tvalid_0's ndcg@2: 0.978209\tvalid_0's ndcg@3: 0.979721\tvalid_0's ndcg@4: 0.979893\tvalid_0's ndcg@5: 0.979942\n",
- "[72]\tvalid_0's ndcg@1: 0.9472\tvalid_0's ndcg@2: 0.978273\tvalid_0's ndcg@3: 0.979773\tvalid_0's ndcg@4: 0.979956\tvalid_0's ndcg@5: 0.980005\n",
- "[73]\tvalid_0's ndcg@1: 0.947475\tvalid_0's ndcg@2: 0.978391\tvalid_0's ndcg@3: 0.979878\tvalid_0's ndcg@4: 0.980061\tvalid_0's ndcg@5: 0.980109\n",
- "[74]\tvalid_0's ndcg@1: 0.94715\tvalid_0's ndcg@2: 0.978271\tvalid_0's ndcg@3: 0.979758\tvalid_0's ndcg@4: 0.979941\tvalid_0's ndcg@5: 0.97999\n",
- "[75]\tvalid_0's ndcg@1: 0.947275\tvalid_0's ndcg@2: 0.978333\tvalid_0's ndcg@3: 0.979808\tvalid_0's ndcg@4: 0.979991\tvalid_0's ndcg@5: 0.980039\n",
- "[76]\tvalid_0's ndcg@1: 0.9474\tvalid_0's ndcg@2: 0.97841\tvalid_0's ndcg@3: 0.979873\tvalid_0's ndcg@4: 0.980045\tvalid_0's ndcg@5: 0.980093\n",
- "[77]\tvalid_0's ndcg@1: 0.94745\tvalid_0's ndcg@2: 0.97846\tvalid_0's ndcg@3: 0.979898\tvalid_0's ndcg@4: 0.98007\tvalid_0's ndcg@5: 0.980118\n",
- "[78]\tvalid_0's ndcg@1: 0.94775\tvalid_0's ndcg@2: 0.978555\tvalid_0's ndcg@3: 0.980005\tvalid_0's ndcg@4: 0.980177\tvalid_0's ndcg@5: 0.980226\n",
- "[79]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
- "[80]\tvalid_0's ndcg@1: 0.947875\tvalid_0's ndcg@2: 0.978617\tvalid_0's ndcg@3: 0.980055\tvalid_0's ndcg@4: 0.980238\tvalid_0's ndcg@5: 0.980276\n",
- "[81]\tvalid_0's ndcg@1: 0.948175\tvalid_0's ndcg@2: 0.978744\tvalid_0's ndcg@3: 0.980169\tvalid_0's ndcg@4: 0.980352\tvalid_0's ndcg@5: 0.98039\n",
- "[82]\tvalid_0's ndcg@1: 0.948375\tvalid_0's ndcg@2: 0.97888\tvalid_0's ndcg@3: 0.980255\tvalid_0's ndcg@4: 0.980438\tvalid_0's ndcg@5: 0.980477\n",
- "[83]\tvalid_0's ndcg@1: 0.94825\tvalid_0's ndcg@2: 0.978834\tvalid_0's ndcg@3: 0.980209\tvalid_0's ndcg@4: 0.980392\tvalid_0's ndcg@5: 0.980431\n",
- "[84]\tvalid_0's ndcg@1: 0.948275\tvalid_0's ndcg@2: 0.978844\tvalid_0's ndcg@3: 0.980219\tvalid_0's ndcg@4: 0.980402\tvalid_0's ndcg@5: 0.98044\n",
- "[85]\tvalid_0's ndcg@1: 0.948475\tvalid_0's ndcg@2: 0.978917\tvalid_0's ndcg@3: 0.980292\tvalid_0's ndcg@4: 0.980475\tvalid_0's ndcg@5: 0.980514\n",
- "[86]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979102\tvalid_0's ndcg@3: 0.980477\tvalid_0's ndcg@4: 0.98066\tvalid_0's ndcg@5: 0.980699\n",
- "[87]\tvalid_0's ndcg@1: 0.948975\tvalid_0's ndcg@2: 0.979086\tvalid_0's ndcg@3: 0.980474\tvalid_0's ndcg@4: 0.980657\tvalid_0's ndcg@5: 0.980695\n",
- "[88]\tvalid_0's ndcg@1: 0.949025\tvalid_0's ndcg@2: 0.979136\tvalid_0's ndcg@3: 0.980499\tvalid_0's ndcg@4: 0.980682\tvalid_0's ndcg@5: 0.98072\n",
- "[89]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979285\tvalid_0's ndcg@3: 0.98061\tvalid_0's ndcg@4: 0.980793\tvalid_0's ndcg@5: 0.980832\n",
- "[90]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
- "[91]\tvalid_0's ndcg@1: 0.9493\tvalid_0's ndcg@2: 0.979269\tvalid_0's ndcg@3: 0.980607\tvalid_0's ndcg@4: 0.98079\tvalid_0's ndcg@5: 0.980828\n",
- "[92]\tvalid_0's ndcg@1: 0.9494\tvalid_0's ndcg@2: 0.97929\tvalid_0's ndcg@3: 0.98064\tvalid_0's ndcg@4: 0.980823\tvalid_0's ndcg@5: 0.980862\n",
- "[93]\tvalid_0's ndcg@1: 0.949375\tvalid_0's ndcg@2: 0.979297\tvalid_0's ndcg@3: 0.980634\tvalid_0's ndcg@4: 0.980817\tvalid_0's ndcg@5: 0.980856\n",
- "[94]\tvalid_0's ndcg@1: 0.949525\tvalid_0's ndcg@2: 0.979336\tvalid_0's ndcg@3: 0.980686\tvalid_0's ndcg@4: 0.980869\tvalid_0's ndcg@5: 0.980908\n",
- "[95]\tvalid_0's ndcg@1: 0.949825\tvalid_0's ndcg@2: 0.979416\tvalid_0's ndcg@3: 0.980791\tvalid_0's ndcg@4: 0.980974\tvalid_0's ndcg@5: 0.981012\n",
- "[96]\tvalid_0's ndcg@1: 0.94975\tvalid_0's ndcg@2: 0.979404\tvalid_0's ndcg@3: 0.980779\tvalid_0's ndcg@4: 0.980951\tvalid_0's ndcg@5: 0.98099\n",
- "[97]\tvalid_0's ndcg@1: 0.950025\tvalid_0's ndcg@2: 0.979537\tvalid_0's ndcg@3: 0.980874\tvalid_0's ndcg@4: 0.981057\tvalid_0's ndcg@5: 0.981096\n",
- "[98]\tvalid_0's ndcg@1: 0.9501\tvalid_0's ndcg@2: 0.979564\tvalid_0's ndcg@3: 0.980889\tvalid_0's ndcg@4: 0.981083\tvalid_0's ndcg@5: 0.981122\n",
- "[99]\tvalid_0's ndcg@1: 0.950275\tvalid_0's ndcg@2: 0.979629\tvalid_0's ndcg@3: 0.980967\tvalid_0's ndcg@4: 0.98115\tvalid_0's ndcg@5: 0.981188\n",
- "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.950325\tvalid_0's ndcg@2: 0.979647\tvalid_0's ndcg@3: 0.980985\tvalid_0's ndcg@4: 0.981168\tvalid_0's ndcg@5: 0.981207\n",
- "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.96382\tvalid_0's ndcg@3: 0.965707\tvalid_0's ndcg@4: 0.966009\tvalid_0's ndcg@5: 0.966086\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.91415\tvalid_0's ndcg@2: 0.965492\tvalid_0's ndcg@3: 0.967254\tvalid_0's ndcg@4: 0.967556\tvalid_0's ndcg@5: 0.967604\n",
- "[3]\tvalid_0's ndcg@1: 0.916025\tvalid_0's ndcg@2: 0.966389\tvalid_0's ndcg@3: 0.967976\tvalid_0's ndcg@4: 0.968278\tvalid_0's ndcg@5: 0.968355\n",
- "[4]\tvalid_0's ndcg@1: 0.919\tvalid_0's ndcg@2: 0.967392\tvalid_0's ndcg@3: 0.96903\tvalid_0's ndcg@4: 0.969364\tvalid_0's ndcg@5: 0.969431\n",
- "[5]\tvalid_0's ndcg@1: 0.921125\tvalid_0's ndcg@2: 0.968192\tvalid_0's ndcg@3: 0.969855\tvalid_0's ndcg@4: 0.970156\tvalid_0's ndcg@5: 0.970224\n",
- "[6]\tvalid_0's ndcg@1: 0.921675\tvalid_0's ndcg@2: 0.968411\tvalid_0's ndcg@3: 0.970111\tvalid_0's ndcg@4: 0.97037\tvalid_0's ndcg@5: 0.970437\n",
- "[7]\tvalid_0's ndcg@1: 0.9237\tvalid_0's ndcg@2: 0.969332\tvalid_0's ndcg@3: 0.970882\tvalid_0's ndcg@4: 0.97113\tvalid_0's ndcg@5: 0.971217\n",
- "[8]\tvalid_0's ndcg@1: 0.925775\tvalid_0's ndcg@2: 0.970129\tvalid_0's ndcg@3: 0.971642\tvalid_0's ndcg@4: 0.971922\tvalid_0's ndcg@5: 0.97199\n",
- "[9]\tvalid_0's ndcg@1: 0.926775\tvalid_0's ndcg@2: 0.970435\tvalid_0's ndcg@3: 0.971985\tvalid_0's ndcg@4: 0.972276\tvalid_0's ndcg@5: 0.972334\n"
- ]
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:01.809368Z",
+ "start_time": "2020-11-18T04:21:01.799641Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def submit(recall_df, topk=5, model_name=None):\n",
+ " recall_df = recall_df.sort_values(by=['user_id', 'pred_score'])\n",
+ " recall_df['rank'] = recall_df.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
+ " \n",
+ " # 判断是不是每个用户都有5篇文章及以上\n",
+ " tmp = recall_df.groupby('user_id').apply(lambda x: x['rank'].max())\n",
+ " assert tmp.min() >= topk\n",
+ " \n",
+ " del recall_df['pred_score']\n",
+ " submit = recall_df[recall_df['rank'] <= topk].set_index(['user_id', 'rank']).unstack(-1).reset_index()\n",
+ " \n",
+ " submit.columns = [int(col) if isinstance(col, int) else col for col in submit.columns.droplevel(0)]\n",
+ " # 按照提交格式定义列名\n",
+ " submit = submit.rename(columns={'': 'user_id', 1: 'article_1', 2: 'article_2', \n",
+ " 3: 'article_3', 4: 'article_4', 5: 'article_5'})\n",
+ " \n",
+ " save_name = save_path + model_name + '_' + datetime.today().strftime('%m-%d') + '.csv'\n",
+ " submit.to_csv(save_name, index=False, header=True)"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[10]\tvalid_0's ndcg@1: 0.9277\tvalid_0's ndcg@2: 0.970761\tvalid_0's ndcg@3: 0.972311\tvalid_0's ndcg@4: 0.972612\tvalid_0's ndcg@5: 0.97267\n",
- "[11]\tvalid_0's ndcg@1: 0.928975\tvalid_0's ndcg@2: 0.97131\tvalid_0's ndcg@3: 0.972798\tvalid_0's ndcg@4: 0.973089\tvalid_0's ndcg@5: 0.973166\n",
- "[12]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971505\tvalid_0's ndcg@3: 0.972968\tvalid_0's ndcg@4: 0.973259\tvalid_0's ndcg@5: 0.973326\n",
- "[13]\tvalid_0's ndcg@1: 0.929375\tvalid_0's ndcg@2: 0.971426\tvalid_0's ndcg@3: 0.972939\tvalid_0's ndcg@4: 0.97324\tvalid_0's ndcg@5: 0.973318\n",
- "[14]\tvalid_0's ndcg@1: 0.929775\tvalid_0's ndcg@2: 0.971621\tvalid_0's ndcg@3: 0.973121\tvalid_0's ndcg@4: 0.973412\tvalid_0's ndcg@5: 0.97348\n",
- "[15]\tvalid_0's ndcg@1: 0.9304\tvalid_0's ndcg@2: 0.971868\tvalid_0's ndcg@3: 0.97338\tvalid_0's ndcg@4: 0.97365\tvalid_0's ndcg@5: 0.973717\n",
- "[16]\tvalid_0's ndcg@1: 0.930975\tvalid_0's ndcg@2: 0.972096\tvalid_0's ndcg@3: 0.973558\tvalid_0's ndcg@4: 0.973849\tvalid_0's ndcg@5: 0.973926\n",
- "[17]\tvalid_0's ndcg@1: 0.93105\tvalid_0's ndcg@2: 0.972108\tvalid_0's ndcg@3: 0.973583\tvalid_0's ndcg@4: 0.973884\tvalid_0's ndcg@5: 0.973952\n",
- "[18]\tvalid_0's ndcg@1: 0.931725\tvalid_0's ndcg@2: 0.972373\tvalid_0's ndcg@3: 0.97386\tvalid_0's ndcg@4: 0.974129\tvalid_0's ndcg@5: 0.974207\n",
- "[19]\tvalid_0's ndcg@1: 0.932175\tvalid_0's ndcg@2: 0.972681\tvalid_0's ndcg@3: 0.974068\tvalid_0's ndcg@4: 0.974348\tvalid_0's ndcg@5: 0.974406\n",
- "[20]\tvalid_0's ndcg@1: 0.93305\tvalid_0's ndcg@2: 0.973019\tvalid_0's ndcg@3: 0.974382\tvalid_0's ndcg@4: 0.974673\tvalid_0's ndcg@5: 0.974731\n",
- "[21]\tvalid_0's ndcg@1: 0.933075\tvalid_0's ndcg@2: 0.97306\tvalid_0's ndcg@3: 0.974423\tvalid_0's ndcg@4: 0.974703\tvalid_0's ndcg@5: 0.97477\n",
- "[22]\tvalid_0's ndcg@1: 0.93375\tvalid_0's ndcg@2: 0.973262\tvalid_0's ndcg@3: 0.974649\tvalid_0's ndcg@4: 0.974929\tvalid_0's ndcg@5: 0.975007\n",
- "[23]\tvalid_0's ndcg@1: 0.933675\tvalid_0's ndcg@2: 0.973219\tvalid_0's ndcg@3: 0.974606\tvalid_0's ndcg@4: 0.974886\tvalid_0's ndcg@5: 0.974973\n",
- "[24]\tvalid_0's ndcg@1: 0.934\tvalid_0's ndcg@2: 0.97337\tvalid_0's ndcg@3: 0.974745\tvalid_0's ndcg@4: 0.975014\tvalid_0's ndcg@5: 0.975101\n",
- "[25]\tvalid_0's ndcg@1: 0.934825\tvalid_0's ndcg@2: 0.973674\tvalid_0's ndcg@3: 0.975062\tvalid_0's ndcg@4: 0.975342\tvalid_0's ndcg@5: 0.97541\n",
- "[26]\tvalid_0's ndcg@1: 0.93495\tvalid_0's ndcg@2: 0.973721\tvalid_0's ndcg@3: 0.975096\tvalid_0's ndcg@4: 0.975365\tvalid_0's ndcg@5: 0.975452\n",
- "[27]\tvalid_0's ndcg@1: 0.9358\tvalid_0's ndcg@2: 0.974082\tvalid_0's ndcg@3: 0.975444\tvalid_0's ndcg@4: 0.975713\tvalid_0's ndcg@5: 0.975781\n",
- "[28]\tvalid_0's ndcg@1: 0.935325\tvalid_0's ndcg@2: 0.973875\tvalid_0's ndcg@3: 0.975275\tvalid_0's ndcg@4: 0.975512\tvalid_0's ndcg@5: 0.975599\n",
- "[29]\tvalid_0's ndcg@1: 0.935925\tvalid_0's ndcg@2: 0.974159\tvalid_0's ndcg@3: 0.975522\tvalid_0's ndcg@4: 0.975759\tvalid_0's ndcg@5: 0.975836\n",
- "[30]\tvalid_0's ndcg@1: 0.9362\tvalid_0's ndcg@2: 0.974214\tvalid_0's ndcg@3: 0.975589\tvalid_0's ndcg@4: 0.975847\tvalid_0's ndcg@5: 0.975924\n",
- "[31]\tvalid_0's ndcg@1: 0.93625\tvalid_0's ndcg@2: 0.974216\tvalid_0's ndcg@3: 0.975629\tvalid_0's ndcg@4: 0.975876\tvalid_0's ndcg@5: 0.975944\n",
- "[32]\tvalid_0's ndcg@1: 0.93665\tvalid_0's ndcg@2: 0.974427\tvalid_0's ndcg@3: 0.975814\tvalid_0's ndcg@4: 0.97603\tvalid_0's ndcg@5: 0.976107\n",
- "[33]\tvalid_0's ndcg@1: 0.936775\tvalid_0's ndcg@2: 0.974505\tvalid_0's ndcg@3: 0.975855\tvalid_0's ndcg@4: 0.976081\tvalid_0's ndcg@5: 0.976158\n",
- "[34]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974643\tvalid_0's ndcg@3: 0.975993\tvalid_0's ndcg@4: 0.976219\tvalid_0's ndcg@5: 0.976296\n",
- "[35]\tvalid_0's ndcg@1: 0.937675\tvalid_0's ndcg@2: 0.974805\tvalid_0's ndcg@3: 0.97618\tvalid_0's ndcg@4: 0.976406\tvalid_0's ndcg@5: 0.976484\n",
- "[36]\tvalid_0's ndcg@1: 0.9382\tvalid_0's ndcg@2: 0.974983\tvalid_0's ndcg@3: 0.976371\tvalid_0's ndcg@4: 0.976597\tvalid_0's ndcg@5: 0.976674\n",
- "[37]\tvalid_0's ndcg@1: 0.938175\tvalid_0's ndcg@2: 0.974974\tvalid_0's ndcg@3: 0.976349\tvalid_0's ndcg@4: 0.976586\tvalid_0's ndcg@5: 0.976663\n",
- "[38]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.975143\tvalid_0's ndcg@3: 0.976518\tvalid_0's ndcg@4: 0.976776\tvalid_0's ndcg@5: 0.976844\n",
- "[39]\tvalid_0's ndcg@1: 0.938575\tvalid_0's ndcg@2: 0.975106\tvalid_0's ndcg@3: 0.976481\tvalid_0's ndcg@4: 0.976739\tvalid_0's ndcg@5: 0.976807\n",
- "[40]\tvalid_0's ndcg@1: 0.938675\tvalid_0's ndcg@2: 0.97519\tvalid_0's ndcg@3: 0.976528\tvalid_0's ndcg@4: 0.976775\tvalid_0's ndcg@5: 0.976853\n",
- "[41]\tvalid_0's ndcg@1: 0.9391\tvalid_0's ndcg@2: 0.975347\tvalid_0's ndcg@3: 0.976697\tvalid_0's ndcg@4: 0.976934\tvalid_0's ndcg@5: 0.977001\n",
- "[42]\tvalid_0's ndcg@1: 0.939825\tvalid_0's ndcg@2: 0.975599\tvalid_0's ndcg@3: 0.976961\tvalid_0's ndcg@4: 0.977198\tvalid_0's ndcg@5: 0.977266\n",
- "[43]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975639\tvalid_0's ndcg@3: 0.976977\tvalid_0's ndcg@4: 0.977214\tvalid_0's ndcg@5: 0.977282\n",
- "[44]\tvalid_0's ndcg@1: 0.9398\tvalid_0's ndcg@2: 0.975605\tvalid_0's ndcg@3: 0.976955\tvalid_0's ndcg@4: 0.977192\tvalid_0's ndcg@5: 0.97726\n",
- "[45]\tvalid_0's ndcg@1: 0.9401\tvalid_0's ndcg@2: 0.9757\tvalid_0's ndcg@3: 0.977075\tvalid_0's ndcg@4: 0.977291\tvalid_0's ndcg@5: 0.977368\n",
- "[46]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975845\tvalid_0's ndcg@3: 0.977183\tvalid_0's ndcg@4: 0.97742\tvalid_0's ndcg@5: 0.977497\n",
- "[47]\tvalid_0's ndcg@1: 0.940475\tvalid_0's ndcg@2: 0.975854\tvalid_0's ndcg@3: 0.977204\tvalid_0's ndcg@4: 0.97743\tvalid_0's ndcg@5: 0.977508\n",
- "[48]\tvalid_0's ndcg@1: 0.940575\tvalid_0's ndcg@2: 0.975923\tvalid_0's ndcg@3: 0.977273\tvalid_0's ndcg@4: 0.977488\tvalid_0's ndcg@5: 0.977556\n",
- "[49]\tvalid_0's ndcg@1: 0.9407\tvalid_0's ndcg@2: 0.975922\tvalid_0's ndcg@3: 0.977297\tvalid_0's ndcg@4: 0.977501\tvalid_0's ndcg@5: 0.977588\n",
- "[50]\tvalid_0's ndcg@1: 0.940725\tvalid_0's ndcg@2: 0.975947\tvalid_0's ndcg@3: 0.977322\tvalid_0's ndcg@4: 0.977505\tvalid_0's ndcg@5: 0.977592\n",
- "[51]\tvalid_0's ndcg@1: 0.9406\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.97725\tvalid_0's ndcg@4: 0.977422\tvalid_0's ndcg@5: 0.977509\n",
- "[52]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975997\tvalid_0's ndcg@3: 0.977422\tvalid_0's ndcg@4: 0.977594\tvalid_0's ndcg@5: 0.977691\n",
- "[53]\tvalid_0's ndcg@1: 0.940925\tvalid_0's ndcg@2: 0.975989\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977538\tvalid_0's ndcg@5: 0.977644\n",
- "[54]\tvalid_0's ndcg@1: 0.94125\tvalid_0's ndcg@2: 0.976062\tvalid_0's ndcg@3: 0.977487\tvalid_0's ndcg@4: 0.977659\tvalid_0's ndcg@5: 0.977756\n",
- "[55]\tvalid_0's ndcg@1: 0.94145\tvalid_0's ndcg@2: 0.976183\tvalid_0's ndcg@3: 0.97757\tvalid_0's ndcg@4: 0.977742\tvalid_0's ndcg@5: 0.977839\n",
- "[56]\tvalid_0's ndcg@1: 0.941475\tvalid_0's ndcg@2: 0.976176\tvalid_0's ndcg@3: 0.977576\tvalid_0's ndcg@4: 0.977748\tvalid_0's ndcg@5: 0.977845\n",
- "[57]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.976139\tvalid_0's ndcg@3: 0.977539\tvalid_0's ndcg@4: 0.977712\tvalid_0's ndcg@5: 0.977808\n",
- "[58]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.97625\tvalid_0's ndcg@3: 0.97765\tvalid_0's ndcg@4: 0.977822\tvalid_0's ndcg@5: 0.977919\n",
- "[59]\tvalid_0's ndcg@1: 0.941725\tvalid_0's ndcg@2: 0.976253\tvalid_0's ndcg@3: 0.977653\tvalid_0's ndcg@4: 0.977836\tvalid_0's ndcg@5: 0.977932\n",
- "[60]\tvalid_0's ndcg@1: 0.941675\tvalid_0's ndcg@2: 0.976234\tvalid_0's ndcg@3: 0.977634\tvalid_0's ndcg@4: 0.977817\tvalid_0's ndcg@5: 0.977914\n",
- "[61]\tvalid_0's ndcg@1: 0.9419\tvalid_0's ndcg@2: 0.976333\tvalid_0's ndcg@3: 0.977745\tvalid_0's ndcg@4: 0.977918\tvalid_0's ndcg@5: 0.978005\n",
- "[62]\tvalid_0's ndcg@1: 0.941975\tvalid_0's ndcg@2: 0.976345\tvalid_0's ndcg@3: 0.977757\tvalid_0's ndcg@4: 0.97794\tvalid_0's ndcg@5: 0.978027\n",
- "[63]\tvalid_0's ndcg@1: 0.9423\tvalid_0's ndcg@2: 0.976496\tvalid_0's ndcg@3: 0.977871\tvalid_0's ndcg@4: 0.978065\tvalid_0's ndcg@5: 0.978152\n",
- "[64]\tvalid_0's ndcg@1: 0.942625\tvalid_0's ndcg@2: 0.976632\tvalid_0's ndcg@3: 0.977995\tvalid_0's ndcg@4: 0.978188\tvalid_0's ndcg@5: 0.978275\n",
- "[65]\tvalid_0's ndcg@1: 0.942575\tvalid_0's ndcg@2: 0.976629\tvalid_0's ndcg@3: 0.977979\tvalid_0's ndcg@4: 0.978173\tvalid_0's ndcg@5: 0.97826\n",
- "[66]\tvalid_0's ndcg@1: 0.942725\tvalid_0's ndcg@2: 0.976685\tvalid_0's ndcg@3: 0.978035\tvalid_0's ndcg@4: 0.978229\tvalid_0's ndcg@5: 0.978316\n",
- "[67]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976678\tvalid_0's ndcg@3: 0.978041\tvalid_0's ndcg@4: 0.978224\tvalid_0's ndcg@5: 0.97832\n",
- "[68]\tvalid_0's ndcg@1: 0.94275\tvalid_0's ndcg@2: 0.976694\tvalid_0's ndcg@3: 0.978044\tvalid_0's ndcg@4: 0.978227\tvalid_0's ndcg@5: 0.978324\n",
- "[69]\tvalid_0's ndcg@1: 0.943\tvalid_0's ndcg@2: 0.976834\tvalid_0's ndcg@3: 0.978146\tvalid_0's ndcg@4: 0.978329\tvalid_0's ndcg@5: 0.978426\n",
- "[70]\tvalid_0's ndcg@1: 0.943025\tvalid_0's ndcg@2: 0.976827\tvalid_0's ndcg@3: 0.978152\tvalid_0's ndcg@4: 0.978324\tvalid_0's ndcg@5: 0.978431\n",
- "[71]\tvalid_0's ndcg@1: 0.9432\tvalid_0's ndcg@2: 0.976923\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978504\n",
- "[72]\tvalid_0's ndcg@1: 0.943225\tvalid_0's ndcg@2: 0.976917\tvalid_0's ndcg@3: 0.978254\tvalid_0's ndcg@4: 0.978405\tvalid_0's ndcg@5: 0.978511\n",
- "[73]\tvalid_0's ndcg@1: 0.94315\tvalid_0's ndcg@2: 0.976936\tvalid_0's ndcg@3: 0.978236\tvalid_0's ndcg@4: 0.978409\tvalid_0's ndcg@5: 0.978496\n"
- ]
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:04.332198Z",
+ "start_time": "2020-11-18T04:21:04.325020Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序结果归一化\n",
+ "def norm_sim(sim_df, weight=0.0):\n",
+ " # print(sim_df.head())\n",
+ " min_sim = sim_df.min()\n",
+ " max_sim = sim_df.max()\n",
+ " if max_sim == min_sim:\n",
+ " sim_df = sim_df.apply(lambda sim: 1.0)\n",
+ " else:\n",
+ " sim_df = sim_df.apply(lambda sim: 1.0 * (sim - min_sim) / (max_sim - min_sim))\n",
+ "\n",
+ " sim_df = sim_df.apply(lambda sim: sim + weight) # plus one\n",
+ " return sim_df"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[74]\tvalid_0's ndcg@1: 0.94325\tvalid_0's ndcg@2: 0.976957\tvalid_0's ndcg@3: 0.97827\tvalid_0's ndcg@4: 0.978431\tvalid_0's ndcg@5: 0.978528\n",
- "[75]\tvalid_0's ndcg@1: 0.943075\tvalid_0's ndcg@2: 0.976861\tvalid_0's ndcg@3: 0.978199\tvalid_0's ndcg@4: 0.97836\tvalid_0's ndcg@5: 0.978457\n",
- "[76]\tvalid_0's ndcg@1: 0.94335\tvalid_0's ndcg@2: 0.976963\tvalid_0's ndcg@3: 0.978288\tvalid_0's ndcg@4: 0.978471\tvalid_0's ndcg@5: 0.978568\n",
- "[77]\tvalid_0's ndcg@1: 0.94345\tvalid_0's ndcg@2: 0.977031\tvalid_0's ndcg@3: 0.978331\tvalid_0's ndcg@4: 0.978514\tvalid_0's ndcg@5: 0.978611\n",
- "[78]\tvalid_0's ndcg@1: 0.943475\tvalid_0's ndcg@2: 0.977088\tvalid_0's ndcg@3: 0.97835\tvalid_0's ndcg@4: 0.978533\tvalid_0's ndcg@5: 0.97863\n",
- "[79]\tvalid_0's ndcg@1: 0.943625\tvalid_0's ndcg@2: 0.977096\tvalid_0's ndcg@3: 0.978396\tvalid_0's ndcg@4: 0.978579\tvalid_0's ndcg@5: 0.978676\n",
- "[80]\tvalid_0's ndcg@1: 0.943825\tvalid_0's ndcg@2: 0.977154\tvalid_0's ndcg@3: 0.978479\tvalid_0's ndcg@4: 0.978651\tvalid_0's ndcg@5: 0.978748\n",
- "[81]\tvalid_0's ndcg@1: 0.943775\tvalid_0's ndcg@2: 0.977135\tvalid_0's ndcg@3: 0.97846\tvalid_0's ndcg@4: 0.978633\tvalid_0's ndcg@5: 0.978729\n",
- "[82]\tvalid_0's ndcg@1: 0.9443\tvalid_0's ndcg@2: 0.977361\tvalid_0's ndcg@3: 0.978673\tvalid_0's ndcg@4: 0.978845\tvalid_0's ndcg@5: 0.978933\n",
- "[83]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.977324\tvalid_0's ndcg@3: 0.978624\tvalid_0's ndcg@4: 0.978796\tvalid_0's ndcg@5: 0.978893\n",
- "[84]\tvalid_0's ndcg@1: 0.94405\tvalid_0's ndcg@2: 0.977253\tvalid_0's ndcg@3: 0.978565\tvalid_0's ndcg@4: 0.978737\tvalid_0's ndcg@5: 0.978834\n",
- "[85]\tvalid_0's ndcg@1: 0.944175\tvalid_0's ndcg@2: 0.977283\tvalid_0's ndcg@3: 0.978633\tvalid_0's ndcg@4: 0.978795\tvalid_0's ndcg@5: 0.978882\n",
- "[86]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.97745\tvalid_0's ndcg@3: 0.978763\tvalid_0's ndcg@4: 0.978924\tvalid_0's ndcg@5: 0.979011\n",
- "[87]\tvalid_0's ndcg@1: 0.9445\tvalid_0's ndcg@2: 0.977419\tvalid_0's ndcg@3: 0.978756\tvalid_0's ndcg@4: 0.978918\tvalid_0's ndcg@5: 0.979005\n",
- "[88]\tvalid_0's ndcg@1: 0.944825\tvalid_0's ndcg@2: 0.977554\tvalid_0's ndcg@3: 0.978867\tvalid_0's ndcg@4: 0.979039\tvalid_0's ndcg@5: 0.979126\n",
- "[89]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977767\tvalid_0's ndcg@3: 0.979079\tvalid_0's ndcg@4: 0.979262\tvalid_0's ndcg@5: 0.97934\n",
- "[90]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977773\tvalid_0's ndcg@3: 0.979073\tvalid_0's ndcg@4: 0.979256\tvalid_0's ndcg@5: 0.979334\n",
- "[91]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977792\tvalid_0's ndcg@3: 0.979092\tvalid_0's ndcg@4: 0.979275\tvalid_0's ndcg@5: 0.979352\n",
- "[92]\tvalid_0's ndcg@1: 0.945425\tvalid_0's ndcg@2: 0.977776\tvalid_0's ndcg@3: 0.979088\tvalid_0's ndcg@4: 0.979261\tvalid_0's ndcg@5: 0.979348\n",
- "[93]\tvalid_0's ndcg@1: 0.945375\tvalid_0's ndcg@2: 0.977757\tvalid_0's ndcg@3: 0.979082\tvalid_0's ndcg@4: 0.979244\tvalid_0's ndcg@5: 0.979331\n",
- "[94]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977761\tvalid_0's ndcg@3: 0.979061\tvalid_0's ndcg@4: 0.979223\tvalid_0's ndcg@5: 0.97931\n",
- "[95]\tvalid_0's ndcg@1: 0.9454\tvalid_0's ndcg@2: 0.977798\tvalid_0's ndcg@3: 0.979086\tvalid_0's ndcg@4: 0.979258\tvalid_0's ndcg@5: 0.979345\n",
- "[96]\tvalid_0's ndcg@1: 0.945825\tvalid_0's ndcg@2: 0.977955\tvalid_0's ndcg@3: 0.97923\tvalid_0's ndcg@4: 0.979413\tvalid_0's ndcg@5: 0.9795\n",
- "[97]\tvalid_0's ndcg@1: 0.945925\tvalid_0's ndcg@2: 0.97796\tvalid_0's ndcg@3: 0.97926\tvalid_0's ndcg@4: 0.979443\tvalid_0's ndcg@5: 0.979531\n",
- "[98]\tvalid_0's ndcg@1: 0.9464\tvalid_0's ndcg@2: 0.97812\tvalid_0's ndcg@3: 0.97942\tvalid_0's ndcg@4: 0.979625\tvalid_0's ndcg@5: 0.979702\n",
- "[99]\tvalid_0's ndcg@1: 0.94655\tvalid_0's ndcg@2: 0.978191\tvalid_0's ndcg@3: 0.979479\tvalid_0's ndcg@4: 0.979683\tvalid_0's ndcg@5: 0.97977\n",
- "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.978244\tvalid_0's ndcg@3: 0.979531\tvalid_0's ndcg@4: 0.979725\tvalid_0's ndcg@5: 0.979812\n",
- "[1]\tvalid_0's ndcg@1: 0.910175\tvalid_0's ndcg@2: 0.963031\tvalid_0's ndcg@3: 0.965281\tvalid_0's ndcg@4: 0.965819\tvalid_0's ndcg@5: 0.965887\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's ndcg@1: 0.9141\tvalid_0's ndcg@2: 0.964748\tvalid_0's ndcg@3: 0.96681\tvalid_0's ndcg@4: 0.967316\tvalid_0's ndcg@5: 0.967394\n",
- "[3]\tvalid_0's ndcg@1: 0.915925\tvalid_0's ndcg@2: 0.9655\tvalid_0's ndcg@3: 0.967575\tvalid_0's ndcg@4: 0.968028\tvalid_0's ndcg@5: 0.968105\n",
- "[4]\tvalid_0's ndcg@1: 0.91915\tvalid_0's ndcg@2: 0.966943\tvalid_0's ndcg@3: 0.968968\tvalid_0's ndcg@4: 0.969334\tvalid_0's ndcg@5: 0.969373\n",
- "[5]\tvalid_0's ndcg@1: 0.920625\tvalid_0's ndcg@2: 0.967598\tvalid_0's ndcg@3: 0.969498\tvalid_0's ndcg@4: 0.969896\tvalid_0's ndcg@5: 0.969944\n",
- "[6]\tvalid_0's ndcg@1: 0.922625\tvalid_0's ndcg@2: 0.968336\tvalid_0's ndcg@3: 0.970261\tvalid_0's ndcg@4: 0.970659\tvalid_0's ndcg@5: 0.970688\n",
- "[7]\tvalid_0's ndcg@1: 0.923625\tvalid_0's ndcg@2: 0.968768\tvalid_0's ndcg@3: 0.970656\tvalid_0's ndcg@4: 0.971043\tvalid_0's ndcg@5: 0.971072\n",
- "[8]\tvalid_0's ndcg@1: 0.925825\tvalid_0's ndcg@2: 0.969612\tvalid_0's ndcg@3: 0.971462\tvalid_0's ndcg@4: 0.97186\tvalid_0's ndcg@5: 0.971879\n",
- "[9]\tvalid_0's ndcg@1: 0.926475\tvalid_0's ndcg@2: 0.969899\tvalid_0's ndcg@3: 0.971711\tvalid_0's ndcg@4: 0.97211\tvalid_0's ndcg@5: 0.972129\n",
- "[10]\tvalid_0's ndcg@1: 0.927775\tvalid_0's ndcg@2: 0.97041\tvalid_0's ndcg@3: 0.972185\tvalid_0's ndcg@4: 0.972594\tvalid_0's ndcg@5: 0.972614\n",
- "[11]\tvalid_0's ndcg@1: 0.92885\tvalid_0's ndcg@2: 0.970838\tvalid_0's ndcg@3: 0.972588\tvalid_0's ndcg@4: 0.973008\tvalid_0's ndcg@5: 0.973028\n",
- "[12]\tvalid_0's ndcg@1: 0.930325\tvalid_0's ndcg@2: 0.971367\tvalid_0's ndcg@3: 0.973129\tvalid_0's ndcg@4: 0.973549\tvalid_0's ndcg@5: 0.973569\n",
- "[13]\tvalid_0's ndcg@1: 0.931125\tvalid_0's ndcg@2: 0.971631\tvalid_0's ndcg@3: 0.973443\tvalid_0's ndcg@4: 0.973842\tvalid_0's ndcg@5: 0.973871\n",
- "[14]\tvalid_0's ndcg@1: 0.931525\tvalid_0's ndcg@2: 0.971778\tvalid_0's ndcg@3: 0.973616\tvalid_0's ndcg@4: 0.973993\tvalid_0's ndcg@5: 0.974022\n",
- "[15]\tvalid_0's ndcg@1: 0.9311\tvalid_0's ndcg@2: 0.9717\tvalid_0's ndcg@3: 0.973475\tvalid_0's ndcg@4: 0.973852\tvalid_0's ndcg@5: 0.973872\n",
- "[16]\tvalid_0's ndcg@1: 0.931775\tvalid_0's ndcg@2: 0.971902\tvalid_0's ndcg@3: 0.973702\tvalid_0's ndcg@4: 0.97409\tvalid_0's ndcg@5: 0.974109\n",
- "[17]\tvalid_0's ndcg@1: 0.931425\tvalid_0's ndcg@2: 0.971805\tvalid_0's ndcg@3: 0.97358\tvalid_0's ndcg@4: 0.973967\tvalid_0's ndcg@5: 0.973986\n",
- "[18]\tvalid_0's ndcg@1: 0.931575\tvalid_0's ndcg@2: 0.971876\tvalid_0's ndcg@3: 0.973651\tvalid_0's ndcg@4: 0.974027\tvalid_0's ndcg@5: 0.974047\n",
- "[19]\tvalid_0's ndcg@1: 0.932\tvalid_0's ndcg@2: 0.97208\tvalid_0's ndcg@3: 0.973805\tvalid_0's ndcg@4: 0.974192\tvalid_0's ndcg@5: 0.974212\n",
- "[20]\tvalid_0's ndcg@1: 0.932075\tvalid_0's ndcg@2: 0.972092\tvalid_0's ndcg@3: 0.973829\tvalid_0's ndcg@4: 0.974217\tvalid_0's ndcg@5: 0.974236\n",
- "[21]\tvalid_0's ndcg@1: 0.932675\tvalid_0's ndcg@2: 0.972282\tvalid_0's ndcg@3: 0.974057\tvalid_0's ndcg@4: 0.974444\tvalid_0's ndcg@5: 0.974454\n",
- "[22]\tvalid_0's ndcg@1: 0.932925\tvalid_0's ndcg@2: 0.972358\tvalid_0's ndcg@3: 0.974146\tvalid_0's ndcg@4: 0.974533\tvalid_0's ndcg@5: 0.974543\n",
- "[23]\tvalid_0's ndcg@1: 0.93325\tvalid_0's ndcg@2: 0.972478\tvalid_0's ndcg@3: 0.974253\tvalid_0's ndcg@4: 0.974651\tvalid_0's ndcg@5: 0.974661\n",
- "[24]\tvalid_0's ndcg@1: 0.9335\tvalid_0's ndcg@2: 0.972539\tvalid_0's ndcg@3: 0.974351\tvalid_0's ndcg@4: 0.974739\tvalid_0's ndcg@5: 0.974749\n",
- "[25]\tvalid_0's ndcg@1: 0.93475\tvalid_0's ndcg@2: 0.973\tvalid_0's ndcg@3: 0.974788\tvalid_0's ndcg@4: 0.975197\tvalid_0's ndcg@5: 0.975206\n",
- "[26]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.97312\tvalid_0's ndcg@3: 0.974895\tvalid_0's ndcg@4: 0.975315\tvalid_0's ndcg@5: 0.975325\n",
- "[27]\tvalid_0's ndcg@1: 0.9349\tvalid_0's ndcg@2: 0.973103\tvalid_0's ndcg@3: 0.974865\tvalid_0's ndcg@4: 0.975264\tvalid_0's ndcg@5: 0.975273\n",
- "[28]\tvalid_0's ndcg@1: 0.935075\tvalid_0's ndcg@2: 0.973152\tvalid_0's ndcg@3: 0.974939\tvalid_0's ndcg@4: 0.975327\tvalid_0's ndcg@5: 0.975336\n",
- "[29]\tvalid_0's ndcg@1: 0.935475\tvalid_0's ndcg@2: 0.973315\tvalid_0's ndcg@3: 0.975128\tvalid_0's ndcg@4: 0.975483\tvalid_0's ndcg@5: 0.975492\n",
- "[30]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973522\tvalid_0's ndcg@3: 0.975297\tvalid_0's ndcg@4: 0.975663\tvalid_0's ndcg@5: 0.975673\n",
- "[31]\tvalid_0's ndcg@1: 0.93595\tvalid_0's ndcg@2: 0.973506\tvalid_0's ndcg@3: 0.975281\tvalid_0's ndcg@4: 0.975658\tvalid_0's ndcg@5: 0.975668\n",
- "[32]\tvalid_0's ndcg@1: 0.93675\tvalid_0's ndcg@2: 0.973833\tvalid_0's ndcg@3: 0.975595\tvalid_0's ndcg@4: 0.975961\tvalid_0's ndcg@5: 0.975971\n",
- "[33]\tvalid_0's ndcg@1: 0.936475\tvalid_0's ndcg@2: 0.973763\tvalid_0's ndcg@3: 0.975488\tvalid_0's ndcg@4: 0.975865\tvalid_0's ndcg@5: 0.975874\n",
- "[34]\tvalid_0's ndcg@1: 0.9367\tvalid_0's ndcg@2: 0.973893\tvalid_0's ndcg@3: 0.975568\tvalid_0's ndcg@4: 0.975956\tvalid_0's ndcg@5: 0.975966\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LGB排序模型"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[35]\tvalid_0's ndcg@1: 0.93715\tvalid_0's ndcg@2: 0.974059\tvalid_0's ndcg@3: 0.975722\tvalid_0's ndcg@4: 0.97612\tvalid_0's ndcg@5: 0.97613\n",
- "[36]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
- "[37]\tvalid_0's ndcg@1: 0.9374\tvalid_0's ndcg@2: 0.974183\tvalid_0's ndcg@3: 0.975846\tvalid_0's ndcg@4: 0.976223\tvalid_0's ndcg@5: 0.976232\n",
- "[38]\tvalid_0's ndcg@1: 0.938725\tvalid_0's ndcg@2: 0.974672\tvalid_0's ndcg@3: 0.97636\tvalid_0's ndcg@4: 0.976715\tvalid_0's ndcg@5: 0.976725\n",
- "[39]\tvalid_0's ndcg@1: 0.93865\tvalid_0's ndcg@2: 0.974676\tvalid_0's ndcg@3: 0.976364\tvalid_0's ndcg@4: 0.976697\tvalid_0's ndcg@5: 0.976707\n",
- "[40]\tvalid_0's ndcg@1: 0.939125\tvalid_0's ndcg@2: 0.974867\tvalid_0's ndcg@3: 0.97653\tvalid_0's ndcg@4: 0.976874\tvalid_0's ndcg@5: 0.976884\n",
- "[41]\tvalid_0's ndcg@1: 0.9396\tvalid_0's ndcg@2: 0.975042\tvalid_0's ndcg@3: 0.976705\tvalid_0's ndcg@4: 0.97705\tvalid_0's ndcg@5: 0.977059\n",
- "[42]\tvalid_0's ndcg@1: 0.93985\tvalid_0's ndcg@2: 0.975072\tvalid_0's ndcg@3: 0.976784\tvalid_0's ndcg@4: 0.977129\tvalid_0's ndcg@5: 0.977138\n",
- "[43]\tvalid_0's ndcg@1: 0.940075\tvalid_0's ndcg@2: 0.97517\tvalid_0's ndcg@3: 0.97687\tvalid_0's ndcg@4: 0.977215\tvalid_0's ndcg@5: 0.977225\n",
- "[44]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.97534\tvalid_0's ndcg@3: 0.977015\tvalid_0's ndcg@4: 0.97736\tvalid_0's ndcg@5: 0.97737\n",
- "[45]\tvalid_0's ndcg@1: 0.94055\tvalid_0's ndcg@2: 0.975409\tvalid_0's ndcg@3: 0.977059\tvalid_0's ndcg@4: 0.977403\tvalid_0's ndcg@5: 0.977413\n",
- "[46]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975415\tvalid_0's ndcg@3: 0.97704\tvalid_0's ndcg@4: 0.977396\tvalid_0's ndcg@5: 0.977405\n",
- "[47]\tvalid_0's ndcg@1: 0.940425\tvalid_0's ndcg@2: 0.975363\tvalid_0's ndcg@3: 0.977013\tvalid_0's ndcg@4: 0.977357\tvalid_0's ndcg@5: 0.977367\n",
- "[48]\tvalid_0's ndcg@1: 0.94045\tvalid_0's ndcg@2: 0.975388\tvalid_0's ndcg@3: 0.977025\tvalid_0's ndcg@4: 0.97737\tvalid_0's ndcg@5: 0.977379\n",
- "[49]\tvalid_0's ndcg@1: 0.940525\tvalid_0's ndcg@2: 0.975447\tvalid_0's ndcg@3: 0.977097\tvalid_0's ndcg@4: 0.977409\tvalid_0's ndcg@5: 0.977419\n",
- "[50]\tvalid_0's ndcg@1: 0.941075\tvalid_0's ndcg@2: 0.975666\tvalid_0's ndcg@3: 0.977303\tvalid_0's ndcg@4: 0.977615\tvalid_0's ndcg@5: 0.977625\n",
- "[51]\tvalid_0's ndcg@1: 0.94135\tvalid_0's ndcg@2: 0.975751\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.97771\tvalid_0's ndcg@5: 0.97772\n",
- "[52]\tvalid_0's ndcg@1: 0.9413\tvalid_0's ndcg@2: 0.975717\tvalid_0's ndcg@3: 0.977355\tvalid_0's ndcg@4: 0.977688\tvalid_0's ndcg@5: 0.977698\n",
- "[53]\tvalid_0's ndcg@1: 0.941375\tvalid_0's ndcg@2: 0.975713\tvalid_0's ndcg@3: 0.977376\tvalid_0's ndcg@4: 0.977699\tvalid_0's ndcg@5: 0.977718\n",
- "[54]\tvalid_0's ndcg@1: 0.94185\tvalid_0's ndcg@2: 0.975857\tvalid_0's ndcg@3: 0.977557\tvalid_0's ndcg@4: 0.977869\tvalid_0's ndcg@5: 0.977889\n",
- "[55]\tvalid_0's ndcg@1: 0.941925\tvalid_0's ndcg@2: 0.975837\tvalid_0's ndcg@3: 0.9776\tvalid_0's ndcg@4: 0.977891\tvalid_0's ndcg@5: 0.97791\n",
- "[56]\tvalid_0's ndcg@1: 0.942325\tvalid_0's ndcg@2: 0.975969\tvalid_0's ndcg@3: 0.977719\tvalid_0's ndcg@4: 0.978032\tvalid_0's ndcg@5: 0.978051\n",
- "[57]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976022\tvalid_0's ndcg@3: 0.977772\tvalid_0's ndcg@4: 0.978073\tvalid_0's ndcg@5: 0.978093\n",
- "[58]\tvalid_0's ndcg@1: 0.9425\tvalid_0's ndcg@2: 0.976081\tvalid_0's ndcg@3: 0.977806\tvalid_0's ndcg@4: 0.978108\tvalid_0's ndcg@5: 0.978127\n",
- "[59]\tvalid_0's ndcg@1: 0.9424\tvalid_0's ndcg@2: 0.976076\tvalid_0's ndcg@3: 0.977788\tvalid_0's ndcg@4: 0.978079\tvalid_0's ndcg@5: 0.978098\n",
- "[60]\tvalid_0's ndcg@1: 0.942375\tvalid_0's ndcg@2: 0.976067\tvalid_0's ndcg@3: 0.977779\tvalid_0's ndcg@4: 0.97807\tvalid_0's ndcg@5: 0.978089\n",
- "[61]\tvalid_0's ndcg@1: 0.942225\tvalid_0's ndcg@2: 0.976043\tvalid_0's ndcg@3: 0.97773\tvalid_0's ndcg@4: 0.978021\tvalid_0's ndcg@5: 0.97804\n",
- "[62]\tvalid_0's ndcg@1: 0.942425\tvalid_0's ndcg@2: 0.976117\tvalid_0's ndcg@3: 0.977792\tvalid_0's ndcg@4: 0.978093\tvalid_0's ndcg@5: 0.978112\n",
- "[63]\tvalid_0's ndcg@1: 0.942675\tvalid_0's ndcg@2: 0.976193\tvalid_0's ndcg@3: 0.977881\tvalid_0's ndcg@4: 0.978182\tvalid_0's ndcg@5: 0.978201\n",
- "[64]\tvalid_0's ndcg@1: 0.942925\tvalid_0's ndcg@2: 0.976254\tvalid_0's ndcg@3: 0.977966\tvalid_0's ndcg@4: 0.978268\tvalid_0's ndcg@5: 0.978287\n",
- "[65]\tvalid_0's ndcg@1: 0.9431\tvalid_0's ndcg@2: 0.97635\tvalid_0's ndcg@3: 0.978025\tvalid_0's ndcg@4: 0.978337\tvalid_0's ndcg@5: 0.978357\n",
- "[66]\tvalid_0's ndcg@1: 0.9434\tvalid_0's ndcg@2: 0.976445\tvalid_0's ndcg@3: 0.978132\tvalid_0's ndcg@4: 0.978445\tvalid_0's ndcg@5: 0.978464\n",
- "[67]\tvalid_0's ndcg@1: 0.943275\tvalid_0's ndcg@2: 0.976399\tvalid_0's ndcg@3: 0.978074\tvalid_0's ndcg@4: 0.978397\tvalid_0's ndcg@5: 0.978416\n",
- "[68]\tvalid_0's ndcg@1: 0.943325\tvalid_0's ndcg@2: 0.976401\tvalid_0's ndcg@3: 0.978089\tvalid_0's ndcg@4: 0.978412\tvalid_0's ndcg@5: 0.978431\n",
- "[69]\tvalid_0's ndcg@1: 0.943675\tvalid_0's ndcg@2: 0.976578\tvalid_0's ndcg@3: 0.97819\tvalid_0's ndcg@4: 0.978546\tvalid_0's ndcg@5: 0.978565\n",
- "[70]\tvalid_0's ndcg@1: 0.944025\tvalid_0's ndcg@2: 0.976707\tvalid_0's ndcg@3: 0.97832\tvalid_0's ndcg@4: 0.978675\tvalid_0's ndcg@5: 0.978694\n",
- "[71]\tvalid_0's ndcg@1: 0.9442\tvalid_0's ndcg@2: 0.976772\tvalid_0's ndcg@3: 0.978384\tvalid_0's ndcg@4: 0.97874\tvalid_0's ndcg@5: 0.978759\n",
- "[72]\tvalid_0's ndcg@1: 0.94425\tvalid_0's ndcg@2: 0.976822\tvalid_0's ndcg@3: 0.978409\tvalid_0's ndcg@4: 0.978765\tvalid_0's ndcg@5: 0.978784\n",
- "[73]\tvalid_0's ndcg@1: 0.94445\tvalid_0's ndcg@2: 0.976864\tvalid_0's ndcg@3: 0.978464\tvalid_0's ndcg@4: 0.97883\tvalid_0's ndcg@5: 0.978849\n",
- "[74]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
- "[75]\tvalid_0's ndcg@1: 0.9446\tvalid_0's ndcg@2: 0.976919\tvalid_0's ndcg@3: 0.978519\tvalid_0's ndcg@4: 0.978885\tvalid_0's ndcg@5: 0.978905\n",
- "[76]\tvalid_0's ndcg@1: 0.944625\tvalid_0's ndcg@2: 0.97696\tvalid_0's ndcg@3: 0.978535\tvalid_0's ndcg@4: 0.978901\tvalid_0's ndcg@5: 0.978921\n",
- "[77]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
- "[78]\tvalid_0's ndcg@1: 0.944675\tvalid_0's ndcg@2: 0.976979\tvalid_0's ndcg@3: 0.978554\tvalid_0's ndcg@4: 0.97892\tvalid_0's ndcg@5: 0.978939\n",
- "[79]\tvalid_0's ndcg@1: 0.944525\tvalid_0's ndcg@2: 0.976907\tvalid_0's ndcg@3: 0.978507\tvalid_0's ndcg@4: 0.978863\tvalid_0's ndcg@5: 0.978882\n",
- "[80]\tvalid_0's ndcg@1: 0.94455\tvalid_0's ndcg@2: 0.976885\tvalid_0's ndcg@3: 0.97851\tvalid_0's ndcg@4: 0.978865\tvalid_0's ndcg@5: 0.978885\n",
- "[81]\tvalid_0's ndcg@1: 0.944725\tvalid_0's ndcg@2: 0.97695\tvalid_0's ndcg@3: 0.978575\tvalid_0's ndcg@4: 0.978919\tvalid_0's ndcg@5: 0.978948\n",
- "[82]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.977103\tvalid_0's ndcg@3: 0.978765\tvalid_0's ndcg@4: 0.97911\tvalid_0's ndcg@5: 0.979129\n",
- "[83]\tvalid_0's ndcg@1: 0.945125\tvalid_0's ndcg@2: 0.977066\tvalid_0's ndcg@3: 0.978716\tvalid_0's ndcg@4: 0.979071\tvalid_0's ndcg@5: 0.97909\n",
- "[84]\tvalid_0's ndcg@1: 0.945225\tvalid_0's ndcg@2: 0.97715\tvalid_0's ndcg@3: 0.978775\tvalid_0's ndcg@4: 0.97912\tvalid_0's ndcg@5: 0.979139\n",
- "[85]\tvalid_0's ndcg@1: 0.945025\tvalid_0's ndcg@2: 0.977092\tvalid_0's ndcg@3: 0.978692\tvalid_0's ndcg@4: 0.979047\tvalid_0's ndcg@5: 0.979067\n",
- "[86]\tvalid_0's ndcg@1: 0.9452\tvalid_0's ndcg@2: 0.977172\tvalid_0's ndcg@3: 0.97876\tvalid_0's ndcg@4: 0.979115\tvalid_0's ndcg@5: 0.979135\n",
- "[87]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.97879\tvalid_0's ndcg@4: 0.979156\tvalid_0's ndcg@5: 0.979166\n",
- "[88]\tvalid_0's ndcg@1: 0.9453\tvalid_0's ndcg@2: 0.977178\tvalid_0's ndcg@3: 0.978815\tvalid_0's ndcg@4: 0.979149\tvalid_0's ndcg@5: 0.979168\n",
- "[89]\tvalid_0's ndcg@1: 0.94555\tvalid_0's ndcg@2: 0.977333\tvalid_0's ndcg@3: 0.978933\tvalid_0's ndcg@4: 0.979267\tvalid_0's ndcg@5: 0.979277\n",
- "[90]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977462\tvalid_0's ndcg@3: 0.979062\tvalid_0's ndcg@4: 0.979396\tvalid_0's ndcg@5: 0.979406\n",
- "[91]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977481\tvalid_0's ndcg@3: 0.979081\tvalid_0's ndcg@4: 0.979414\tvalid_0's ndcg@5: 0.979424\n",
- "[92]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977437\tvalid_0's ndcg@3: 0.97905\tvalid_0's ndcg@4: 0.979384\tvalid_0's ndcg@5: 0.979393\n",
- "[93]\tvalid_0's ndcg@1: 0.945875\tvalid_0's ndcg@2: 0.977421\tvalid_0's ndcg@3: 0.979046\tvalid_0's ndcg@4: 0.97938\tvalid_0's ndcg@5: 0.97939\n",
- "[94]\tvalid_0's ndcg@1: 0.9459\tvalid_0's ndcg@2: 0.977431\tvalid_0's ndcg@3: 0.979068\tvalid_0's ndcg@4: 0.979391\tvalid_0's ndcg@5: 0.979401\n",
- "[95]\tvalid_0's ndcg@1: 0.94595\tvalid_0's ndcg@2: 0.977449\tvalid_0's ndcg@3: 0.979074\tvalid_0's ndcg@4: 0.979408\tvalid_0's ndcg@5: 0.979418\n",
- "[96]\tvalid_0's ndcg@1: 0.946075\tvalid_0's ndcg@2: 0.977527\tvalid_0's ndcg@3: 0.979127\tvalid_0's ndcg@4: 0.979461\tvalid_0's ndcg@5: 0.97947\n"
- ]
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:07.787698Z",
+ "start_time": "2020-11-18T04:21:07.536514Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 防止中间出错之后重新读取数据\n",
+ "trn_user_item_feats_df_rank_model = trn_user_item_feats_df.copy()\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_rank_model = val_user_item_feats_df.copy()\n",
+ " \n",
+ "tst_user_item_feats_df_rank_model = tst_user_item_feats_df.copy()"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[97]\tvalid_0's ndcg@1: 0.946375\tvalid_0's ndcg@2: 0.977622\tvalid_0's ndcg@3: 0.979222\tvalid_0's ndcg@4: 0.979577\tvalid_0's ndcg@5: 0.979577\n",
- "[98]\tvalid_0's ndcg@1: 0.946625\tvalid_0's ndcg@2: 0.977714\tvalid_0's ndcg@3: 0.979339\tvalid_0's ndcg@4: 0.979673\tvalid_0's ndcg@5: 0.979673\n",
- "[99]\tvalid_0's ndcg@1: 0.94665\tvalid_0's ndcg@2: 0.977739\tvalid_0's ndcg@3: 0.979352\tvalid_0's ndcg@4: 0.979685\tvalid_0's ndcg@5: 0.979685\n",
- "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's ndcg@1: 0.946675\tvalid_0's ndcg@2: 0.97778\tvalid_0's ndcg@3: 0.97938\tvalid_0's ndcg@4: 0.979703\tvalid_0's ndcg@5: 0.979703\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_rank_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id','label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 训练集与验证集的用户分组\n",
- " train_idx.sort_values(by=['user_id'], inplace=True)\n",
- " g_train = train_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- " \n",
- " valid_idx.sort_values(by=['user_id'], inplace=True)\n",
- " g_val = valid_idx.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
- " \n",
- " # 定义模型\n",
- " lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) \n",
- " # 训练模型\n",
- " lgb_ranker.fit(train_idx[lgb_cols], train_idx['label'], group=g_train,\n",
- " eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], eval_group= [g_val], \n",
- " eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = lgb_ranker.predict(valid_idx[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
- " \n",
- " # 对输出结果进行归一化\n",
- " valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += lgb_ranker.predict(tst_user_item_feats_df_rank_model[lgb_cols], lgb_ranker.best_iteration_)\n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_ranker_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_ranker_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:52.604397Z",
- "start_time": "2020-11-18T04:22:43.253034Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "# 单模型生成提交结果\n",
- "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_ranker')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## LGB分类模型"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:22:58.259730Z",
- "start_time": "2020-11-18T04:22:58.254297Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型及参数的定义\n",
- "lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=500, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:11.258774Z",
- "start_time": "2020-11-18T04:23:00.861936Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型训练\n",
- "if offline:\n",
- " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'],\n",
- " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
- " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
- "else:\n",
- " lgb_Classfication.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:19.591396Z",
- "start_time": "2020-11-18T04:23:13.813850Z"
- }
- },
- "outputs": [],
- "source": [
- "# 模型预测\n",
- "tst_user_item_feats_df['pred_score'] = lgb_Classfication.predict_proba(tst_user_item_feats_df[lgb_cols])[:,1]\n",
- "\n",
- "# 将这里的排序结果保存一份，用户后面的模型融合\n",
- "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_cls_score.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:23:32.352931Z",
- "start_time": "2020-11-18T04:23:22.346609Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_cls')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:11.241196Z",
- "start_time": "2020-11-18T04:23:41.377394Z"
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:10.839656Z",
+ "start_time": "2020-11-18T04:21:10.833109Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 定义特征列\n",
+ "lgb_cols = ['sim0', 'time_diff0', 'word_diff0','sim_max', 'sim_min', 'sim_sum', \n",
+ " 'sim_mean', 'score','click_size', 'time_diff_mean', 'active_level',\n",
+ " 'click_environment','click_deviceGroup', 'click_os', 'click_country', \n",
+ " 'click_region','click_referrer_type', 'user_time_hob1', 'user_time_hob2',\n",
+ " 'words_hbo', 'category_id', 'created_at_ts','words_count']"
+ ]
},
- "scrolled": true
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1]\tvalid_0's auc: 0.764896\tvalid_0's binary_logloss: 0.522153\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.767857\tvalid_0's binary_logloss: 0.52057\n",
- "[3]\tvalid_0's auc: 0.783096\tvalid_0's binary_logloss: 0.519584\n",
- "[4]\tvalid_0's auc: 0.784354\tvalid_0's binary_logloss: 0.518485\n",
- "[5]\tvalid_0's auc: 0.790554\tvalid_0's binary_logloss: 0.516886\n",
- "[6]\tvalid_0's auc: 0.791954\tvalid_0's binary_logloss: 0.515334\n",
- "[7]\tvalid_0's auc: 0.794257\tvalid_0's binary_logloss: 0.514032\n",
- "[8]\tvalid_0's auc: 0.795222\tvalid_0's binary_logloss: 0.512516\n",
- "[9]\tvalid_0's auc: 0.795417\tvalid_0's binary_logloss: 0.511671\n",
- "[10]\tvalid_0's auc: 0.795913\tvalid_0's binary_logloss: 0.510226\n",
- "[11]\tvalid_0's auc: 0.798222\tvalid_0's binary_logloss: 0.508858\n",
- "[12]\tvalid_0's auc: 0.79825\tvalid_0's binary_logloss: 0.507928\n",
- "[13]\tvalid_0's auc: 0.798842\tvalid_0's binary_logloss: 0.50708\n",
- "[14]\tvalid_0's auc: 0.798935\tvalid_0's binary_logloss: 0.505752\n",
- "[15]\tvalid_0's auc: 0.799543\tvalid_0's binary_logloss: 0.504388\n",
- "[16]\tvalid_0's auc: 0.800844\tvalid_0's binary_logloss: 0.503126\n",
- "[17]\tvalid_0's auc: 0.800855\tvalid_0's binary_logloss: 0.501809\n",
- "[18]\tvalid_0's auc: 0.801653\tvalid_0's binary_logloss: 0.500676\n",
- "[19]\tvalid_0's auc: 0.801518\tvalid_0's binary_logloss: 0.49987\n",
- "[20]\tvalid_0's auc: 0.801662\tvalid_0's binary_logloss: 0.498625\n",
- "[21]\tvalid_0's auc: 0.802093\tvalid_0's binary_logloss: 0.498113\n",
- "[22]\tvalid_0's auc: 0.803071\tvalid_0's binary_logloss: 0.496933\n",
- "[23]\tvalid_0's auc: 0.803222\tvalid_0's binary_logloss: 0.495864\n",
- "[24]\tvalid_0's auc: 0.802927\tvalid_0's binary_logloss: 0.494691\n",
- "[25]\tvalid_0's auc: 0.802581\tvalid_0's binary_logloss: 0.493543\n",
- "[26]\tvalid_0's auc: 0.802965\tvalid_0's binary_logloss: 0.492444\n",
- "[27]\tvalid_0's auc: 0.80298\tvalid_0's binary_logloss: 0.491336\n",
- "[28]\tvalid_0's auc: 0.803226\tvalid_0's binary_logloss: 0.490275\n",
- "[29]\tvalid_0's auc: 0.803436\tvalid_0's binary_logloss: 0.489126\n",
- "[30]\tvalid_0's auc: 0.803796\tvalid_0's binary_logloss: 0.48802\n",
- "[31]\tvalid_0's auc: 0.803601\tvalid_0's binary_logloss: 0.486988\n",
- "[32]\tvalid_0's auc: 0.804416\tvalid_0's binary_logloss: 0.485972\n",
- "[33]\tvalid_0's auc: 0.804529\tvalid_0's binary_logloss: 0.484939\n",
- "[34]\tvalid_0's auc: 0.804534\tvalid_0's binary_logloss: 0.483927\n",
- "[35]\tvalid_0's auc: 0.804819\tvalid_0's binary_logloss: 0.483271\n",
- "[36]\tvalid_0's auc: 0.804774\tvalid_0's binary_logloss: 0.482273\n",
- "[37]\tvalid_0's auc: 0.805237\tvalid_0's binary_logloss: 0.481639\n",
- "[38]\tvalid_0's auc: 0.805546\tvalid_0's binary_logloss: 0.480959\n",
- "[39]\tvalid_0's auc: 0.805598\tvalid_0's binary_logloss: 0.479955\n",
- "[40]\tvalid_0's auc: 0.806011\tvalid_0's binary_logloss: 0.47903\n",
- "[41]\tvalid_0's auc: 0.806664\tvalid_0's binary_logloss: 0.478439\n",
- "[42]\tvalid_0's auc: 0.807021\tvalid_0's binary_logloss: 0.477798\n",
- "[43]\tvalid_0's auc: 0.80726\tvalid_0's binary_logloss: 0.476829\n",
- "[44]\tvalid_0's auc: 0.807157\tvalid_0's binary_logloss: 0.475976\n",
- "[45]\tvalid_0's auc: 0.807788\tvalid_0's binary_logloss: 0.475056\n",
- "[46]\tvalid_0's auc: 0.80805\tvalid_0's binary_logloss: 0.474446\n",
- "[47]\tvalid_0's auc: 0.808097\tvalid_0's binary_logloss: 0.473576\n",
- "[48]\tvalid_0's auc: 0.80815\tvalid_0's binary_logloss: 0.472676\n",
- "[49]\tvalid_0's auc: 0.808304\tvalid_0's binary_logloss: 0.471918\n",
- "[50]\tvalid_0's auc: 0.808749\tvalid_0's binary_logloss: 0.471481\n",
- "[51]\tvalid_0's auc: 0.808972\tvalid_0's binary_logloss: 0.471104\n",
- "[52]\tvalid_0's auc: 0.809326\tvalid_0's binary_logloss: 0.470289\n",
- "[53]\tvalid_0's auc: 0.809472\tvalid_0's binary_logloss: 0.469508\n",
- "[54]\tvalid_0's auc: 0.809505\tvalid_0's binary_logloss: 0.46869\n",
- "[55]\tvalid_0's auc: 0.809594\tvalid_0's binary_logloss: 0.467885\n",
- "[56]\tvalid_0's auc: 0.809847\tvalid_0's binary_logloss: 0.467356\n",
- "[57]\tvalid_0's auc: 0.810262\tvalid_0's binary_logloss: 0.466531\n",
- "[58]\tvalid_0's auc: 0.810407\tvalid_0's binary_logloss: 0.46573\n",
- "[59]\tvalid_0's auc: 0.810618\tvalid_0's binary_logloss: 0.465205\n",
- "[60]\tvalid_0's auc: 0.81066\tvalid_0's binary_logloss: 0.464435\n",
- "[61]\tvalid_0's auc: 0.810638\tvalid_0's binary_logloss: 0.463721\n",
- "[62]\tvalid_0's auc: 0.810658\tvalid_0's binary_logloss: 0.462982\n",
- "[63]\tvalid_0's auc: 0.811106\tvalid_0's binary_logloss: 0.462246\n",
- "[64]\tvalid_0's auc: 0.811313\tvalid_0's binary_logloss: 0.461748\n",
- "[65]\tvalid_0's auc: 0.811351\tvalid_0's binary_logloss: 0.461038\n",
- "[66]\tvalid_0's auc: 0.811433\tvalid_0's binary_logloss: 0.460323\n",
- "[67]\tvalid_0's auc: 0.81158\tvalid_0's binary_logloss: 0.459662\n",
- "[68]\tvalid_0's auc: 0.811561\tvalid_0's binary_logloss: 0.458988\n",
- "[69]\tvalid_0's auc: 0.811748\tvalid_0's binary_logloss: 0.458592\n",
- "[70]\tvalid_0's auc: 0.811919\tvalid_0's binary_logloss: 0.457934\n",
- "[71]\tvalid_0's auc: 0.812073\tvalid_0's binary_logloss: 0.457508\n",
- "[72]\tvalid_0's auc: 0.812273\tvalid_0's binary_logloss: 0.457038\n",
- "[73]\tvalid_0's auc: 0.812561\tvalid_0's binary_logloss: 0.456439\n",
- "[74]\tvalid_0's auc: 0.812633\tvalid_0's binary_logloss: 0.455789\n",
- "[75]\tvalid_0's auc: 0.812757\tvalid_0's binary_logloss: 0.455173\n",
- "[76]\tvalid_0's auc: 0.812923\tvalid_0's binary_logloss: 0.454533\n",
- "[77]\tvalid_0's auc: 0.81295\tvalid_0's binary_logloss: 0.45392\n",
- "[78]\tvalid_0's auc: 0.813073\tvalid_0's binary_logloss: 0.453517\n",
- "[79]\tvalid_0's auc: 0.813202\tvalid_0's binary_logloss: 0.452932\n",
- "[80]\tvalid_0's auc: 0.813611\tvalid_0's binary_logloss: 0.452285\n",
- "[81]\tvalid_0's auc: 0.813769\tvalid_0's binary_logloss: 0.45191\n",
- "[82]\tvalid_0's auc: 0.814468\tvalid_0's binary_logloss: 0.451455\n",
- "[83]\tvalid_0's auc: 0.814656\tvalid_0's binary_logloss: 0.450885\n",
- "[84]\tvalid_0's auc: 0.814755\tvalid_0's binary_logloss: 0.450308\n",
- "[85]\tvalid_0's auc: 0.814824\tvalid_0's binary_logloss: 0.449739\n",
- "[86]\tvalid_0's auc: 0.81499\tvalid_0's binary_logloss: 0.449348\n",
- "[87]\tvalid_0's auc: 0.815232\tvalid_0's binary_logloss: 0.448759\n",
- "[88]\tvalid_0's auc: 0.815452\tvalid_0's binary_logloss: 0.44823\n",
- "[89]\tvalid_0's auc: 0.815593\tvalid_0's binary_logloss: 0.447861\n",
- "[90]\tvalid_0's auc: 0.815591\tvalid_0's binary_logloss: 0.447323\n",
- "[91]\tvalid_0's auc: 0.815672\tvalid_0's binary_logloss: 0.446796\n",
- "[92]\tvalid_0's auc: 0.815875\tvalid_0's binary_logloss: 0.446472\n",
- "[93]\tvalid_0's auc: 0.815984\tvalid_0's binary_logloss: 0.445961\n",
- "[94]\tvalid_0's auc: 0.816026\tvalid_0's binary_logloss: 0.445439\n",
- "[95]\tvalid_0's auc: 0.816172\tvalid_0's binary_logloss: 0.444909\n",
- "[96]\tvalid_0's auc: 0.816321\tvalid_0's binary_logloss: 0.444413\n",
- "[97]\tvalid_0's auc: 0.816751\tvalid_0's binary_logloss: 0.44405\n",
- "[98]\tvalid_0's auc: 0.817226\tvalid_0's binary_logloss: 0.443626\n",
- "[99]\tvalid_0's auc: 0.817286\tvalid_0's binary_logloss: 0.443136\n",
- "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.817391\tvalid_0's binary_logloss: 0.442854\n",
- "[1]\tvalid_0's auc: 0.771584\tvalid_0's binary_logloss: 0.527139\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.775446\tvalid_0's binary_logloss: 0.525462\n",
- "[3]\tvalid_0's auc: 0.790092\tvalid_0's binary_logloss: 0.524461\n",
- "[4]\tvalid_0's auc: 0.791432\tvalid_0's binary_logloss: 0.523322\n",
- "[5]\tvalid_0's auc: 0.797482\tvalid_0's binary_logloss: 0.521614\n",
- "[6]\tvalid_0's auc: 0.79893\tvalid_0's binary_logloss: 0.520007\n",
- "[7]\tvalid_0's auc: 0.800753\tvalid_0's binary_logloss: 0.5187\n",
- "[8]\tvalid_0's auc: 0.802197\tvalid_0's binary_logloss: 0.517125\n",
- "[9]\tvalid_0's auc: 0.802828\tvalid_0's binary_logloss: 0.516269\n",
- "[10]\tvalid_0's auc: 0.803496\tvalid_0's binary_logloss: 0.51474\n",
- "[11]\tvalid_0's auc: 0.804972\tvalid_0's binary_logloss: 0.513321\n",
- "[12]\tvalid_0's auc: 0.804995\tvalid_0's binary_logloss: 0.512334\n",
- "[13]\tvalid_0's auc: 0.80525\tvalid_0's binary_logloss: 0.51151\n",
- "[14]\tvalid_0's auc: 0.805026\tvalid_0's binary_logloss: 0.510149\n",
- "[15]\tvalid_0's auc: 0.805622\tvalid_0's binary_logloss: 0.508708\n",
- "[16]\tvalid_0's auc: 0.806974\tvalid_0's binary_logloss: 0.507384\n",
- "[17]\tvalid_0's auc: 0.807045\tvalid_0's binary_logloss: 0.506017\n",
- "[18]\tvalid_0's auc: 0.807265\tvalid_0's binary_logloss: 0.504853\n",
- "[19]\tvalid_0's auc: 0.807126\tvalid_0's binary_logloss: 0.503972\n",
- "[20]\tvalid_0's auc: 0.806948\tvalid_0's binary_logloss: 0.502693\n",
- "[21]\tvalid_0's auc: 0.807315\tvalid_0's binary_logloss: 0.502166\n",
- "[22]\tvalid_0's auc: 0.808067\tvalid_0's binary_logloss: 0.500948\n",
- "[23]\tvalid_0's auc: 0.808226\tvalid_0's binary_logloss: 0.49987\n",
- "[24]\tvalid_0's auc: 0.808268\tvalid_0's binary_logloss: 0.498623\n",
- "[25]\tvalid_0's auc: 0.808569\tvalid_0's binary_logloss: 0.497389\n",
- "[26]\tvalid_0's auc: 0.809069\tvalid_0's binary_logloss: 0.49624\n",
- "[27]\tvalid_0's auc: 0.809312\tvalid_0's binary_logloss: 0.495095\n",
- "[28]\tvalid_0's auc: 0.809549\tvalid_0's binary_logloss: 0.494012\n",
- "[29]\tvalid_0's auc: 0.809944\tvalid_0's binary_logloss: 0.492834\n",
- "[30]\tvalid_0's auc: 0.810047\tvalid_0's binary_logloss: 0.491735\n",
- "[31]\tvalid_0's auc: 0.810086\tvalid_0's binary_logloss: 0.490633\n"
- ]
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:14.126608Z",
+ "start_time": "2020-11-18T04:21:13.493653Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型分组\n",
+ "trn_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
+ "g_train = trn_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values\n",
+ "\n",
+ "if offline:\n",
+ " val_user_item_feats_df_rank_model.sort_values(by=['user_id'], inplace=True)\n",
+ " g_val = val_user_item_feats_df_rank_model.groupby(['user_id'], as_index=False).count()[\"label\"].values"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[32]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.489595\n",
- "[33]\tvalid_0's auc: 0.810539\tvalid_0's binary_logloss: 0.488536\n",
- "[34]\tvalid_0's auc: 0.810529\tvalid_0's binary_logloss: 0.487489\n",
- "[35]\tvalid_0's auc: 0.810932\tvalid_0's binary_logloss: 0.486775\n",
- "[36]\tvalid_0's auc: 0.810769\tvalid_0's binary_logloss: 0.48577\n",
- "[37]\tvalid_0's auc: 0.811363\tvalid_0's binary_logloss: 0.485123\n",
- "[38]\tvalid_0's auc: 0.811801\tvalid_0's binary_logloss: 0.484413\n",
- "[39]\tvalid_0's auc: 0.811987\tvalid_0's binary_logloss: 0.483371\n",
- "[40]\tvalid_0's auc: 0.812268\tvalid_0's binary_logloss: 0.482407\n",
- "[41]\tvalid_0's auc: 0.813297\tvalid_0's binary_logloss: 0.481742\n",
- "[42]\tvalid_0's auc: 0.813453\tvalid_0's binary_logloss: 0.481108\n",
- "[43]\tvalid_0's auc: 0.813603\tvalid_0's binary_logloss: 0.480163\n",
- "[44]\tvalid_0's auc: 0.813654\tvalid_0's binary_logloss: 0.479239\n",
- "[45]\tvalid_0's auc: 0.814267\tvalid_0's binary_logloss: 0.478299\n",
- "[46]\tvalid_0's auc: 0.81455\tvalid_0's binary_logloss: 0.477678\n",
- "[47]\tvalid_0's auc: 0.81452\tvalid_0's binary_logloss: 0.476766\n",
- "[48]\tvalid_0's auc: 0.814925\tvalid_0's binary_logloss: 0.475815\n",
- "[49]\tvalid_0's auc: 0.814907\tvalid_0's binary_logloss: 0.47503\n",
- "[50]\tvalid_0's auc: 0.815278\tvalid_0's binary_logloss: 0.474588\n",
- "[51]\tvalid_0's auc: 0.815535\tvalid_0's binary_logloss: 0.474171\n",
- "[52]\tvalid_0's auc: 0.815685\tvalid_0's binary_logloss: 0.473335\n",
- "[53]\tvalid_0's auc: 0.815787\tvalid_0's binary_logloss: 0.472509\n",
- "[54]\tvalid_0's auc: 0.815827\tvalid_0's binary_logloss: 0.471686\n",
- "[55]\tvalid_0's auc: 0.815871\tvalid_0's binary_logloss: 0.470838\n",
- "[56]\tvalid_0's auc: 0.816238\tvalid_0's binary_logloss: 0.470285\n",
- "[57]\tvalid_0's auc: 0.816269\tvalid_0's binary_logloss: 0.469495\n",
- "[58]\tvalid_0's auc: 0.816528\tvalid_0's binary_logloss: 0.468654\n",
- "[59]\tvalid_0's auc: 0.816706\tvalid_0's binary_logloss: 0.468122\n",
- "[60]\tvalid_0's auc: 0.816821\tvalid_0's binary_logloss: 0.467352\n",
- "[61]\tvalid_0's auc: 0.816759\tvalid_0's binary_logloss: 0.466622\n",
- "[62]\tvalid_0's auc: 0.81682\tvalid_0's binary_logloss: 0.465867\n",
- "[63]\tvalid_0's auc: 0.817251\tvalid_0's binary_logloss: 0.465112\n",
- "[64]\tvalid_0's auc: 0.817476\tvalid_0's binary_logloss: 0.464589\n",
- "[65]\tvalid_0's auc: 0.817613\tvalid_0's binary_logloss: 0.463831\n",
- "[66]\tvalid_0's auc: 0.817648\tvalid_0's binary_logloss: 0.463098\n",
- "[67]\tvalid_0's auc: 0.817719\tvalid_0's binary_logloss: 0.462414\n",
- "[68]\tvalid_0's auc: 0.817814\tvalid_0's binary_logloss: 0.461727\n",
- "[69]\tvalid_0's auc: 0.817973\tvalid_0's binary_logloss: 0.461329\n",
- "[70]\tvalid_0's auc: 0.818108\tvalid_0's binary_logloss: 0.460674\n",
- "[71]\tvalid_0's auc: 0.818347\tvalid_0's binary_logloss: 0.460222\n",
- "[72]\tvalid_0's auc: 0.818456\tvalid_0's binary_logloss: 0.45977\n",
- "[73]\tvalid_0's auc: 0.818727\tvalid_0's binary_logloss: 0.459157\n",
- "[74]\tvalid_0's auc: 0.818988\tvalid_0's binary_logloss: 0.458437\n",
- "[75]\tvalid_0's auc: 0.819144\tvalid_0's binary_logloss: 0.457808\n",
- "[76]\tvalid_0's auc: 0.819259\tvalid_0's binary_logloss: 0.457159\n",
- "[77]\tvalid_0's auc: 0.819343\tvalid_0's binary_logloss: 0.456512\n",
- "[78]\tvalid_0's auc: 0.81954\tvalid_0's binary_logloss: 0.456045\n",
- "[79]\tvalid_0's auc: 0.819687\tvalid_0's binary_logloss: 0.455416\n",
- "[80]\tvalid_0's auc: 0.819958\tvalid_0's binary_logloss: 0.454765\n",
- "[81]\tvalid_0's auc: 0.820115\tvalid_0's binary_logloss: 0.45436\n",
- "[82]\tvalid_0's auc: 0.820536\tvalid_0's binary_logloss: 0.453965\n",
- "[83]\tvalid_0's auc: 0.820649\tvalid_0's binary_logloss: 0.453383\n",
- "[84]\tvalid_0's auc: 0.820663\tvalid_0's binary_logloss: 0.452804\n",
- "[85]\tvalid_0's auc: 0.820809\tvalid_0's binary_logloss: 0.452167\n",
- "[86]\tvalid_0's auc: 0.821024\tvalid_0's binary_logloss: 0.451735\n",
- "[87]\tvalid_0's auc: 0.821124\tvalid_0's binary_logloss: 0.451167\n",
- "[88]\tvalid_0's auc: 0.821243\tvalid_0's binary_logloss: 0.45061\n",
- "[89]\tvalid_0's auc: 0.821404\tvalid_0's binary_logloss: 0.450215\n",
- "[90]\tvalid_0's auc: 0.821488\tvalid_0's binary_logloss: 0.449656\n",
- "[91]\tvalid_0's auc: 0.821538\tvalid_0's binary_logloss: 0.449107\n",
- "[92]\tvalid_0's auc: 0.82172\tvalid_0's binary_logloss: 0.448752\n",
- "[93]\tvalid_0's auc: 0.821809\tvalid_0's binary_logloss: 0.448188\n",
- "[94]\tvalid_0's auc: 0.82184\tvalid_0's binary_logloss: 0.447659\n",
- "[95]\tvalid_0's auc: 0.821971\tvalid_0's binary_logloss: 0.447108\n",
- "[96]\tvalid_0's auc: 0.822086\tvalid_0's binary_logloss: 0.446596\n",
- "[97]\tvalid_0's auc: 0.82247\tvalid_0's binary_logloss: 0.446244\n",
- "[98]\tvalid_0's auc: 0.822951\tvalid_0's binary_logloss: 0.445812\n",
- "[99]\tvalid_0's auc: 0.822991\tvalid_0's binary_logloss: 0.445329\n",
- "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.823174\tvalid_0's binary_logloss: 0.445037\n",
- "[1]\tvalid_0's auc: 0.769525\tvalid_0's binary_logloss: 0.526256\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.775857\tvalid_0's binary_logloss: 0.524594\n",
- "[3]\tvalid_0's auc: 0.785307\tvalid_0's binary_logloss: 0.523606\n",
- "[4]\tvalid_0's auc: 0.786356\tvalid_0's binary_logloss: 0.522495\n",
- "[5]\tvalid_0's auc: 0.793385\tvalid_0's binary_logloss: 0.520812\n",
- "[6]\tvalid_0's auc: 0.794014\tvalid_0's binary_logloss: 0.519253\n",
- "[7]\tvalid_0's auc: 0.795454\tvalid_0's binary_logloss: 0.517961\n",
- "[8]\tvalid_0's auc: 0.79807\tvalid_0's binary_logloss: 0.516363\n",
- "[9]\tvalid_0's auc: 0.798756\tvalid_0's binary_logloss: 0.51548\n",
- "[10]\tvalid_0's auc: 0.798314\tvalid_0's binary_logloss: 0.514021\n",
- "[11]\tvalid_0's auc: 0.799343\tvalid_0's binary_logloss: 0.512678\n",
- "[12]\tvalid_0's auc: 0.799573\tvalid_0's binary_logloss: 0.511708\n",
- "[13]\tvalid_0's auc: 0.799563\tvalid_0's binary_logloss: 0.510892\n",
- "[14]\tvalid_0's auc: 0.800333\tvalid_0's binary_logloss: 0.509532\n",
- "[15]\tvalid_0's auc: 0.800672\tvalid_0's binary_logloss: 0.508117\n",
- "[16]\tvalid_0's auc: 0.801953\tvalid_0's binary_logloss: 0.506866\n",
- "[17]\tvalid_0's auc: 0.802078\tvalid_0's binary_logloss: 0.5055\n",
- "[18]\tvalid_0's auc: 0.802449\tvalid_0's binary_logloss: 0.504358\n",
- "[19]\tvalid_0's auc: 0.802329\tvalid_0's binary_logloss: 0.503503\n",
- "[20]\tvalid_0's auc: 0.802437\tvalid_0's binary_logloss: 0.502233\n",
- "[21]\tvalid_0's auc: 0.802653\tvalid_0's binary_logloss: 0.50174\n",
- "[22]\tvalid_0's auc: 0.803753\tvalid_0's binary_logloss: 0.50056\n",
- "[23]\tvalid_0's auc: 0.803956\tvalid_0's binary_logloss: 0.499496\n",
- "[24]\tvalid_0's auc: 0.804231\tvalid_0's binary_logloss: 0.498283\n",
- "[25]\tvalid_0's auc: 0.804554\tvalid_0's binary_logloss: 0.497059\n",
- "[26]\tvalid_0's auc: 0.805133\tvalid_0's binary_logloss: 0.495963\n",
- "[27]\tvalid_0's auc: 0.805333\tvalid_0's binary_logloss: 0.494842\n",
- "[28]\tvalid_0's auc: 0.805644\tvalid_0's binary_logloss: 0.493771\n",
- "[29]\tvalid_0's auc: 0.806029\tvalid_0's binary_logloss: 0.492598\n",
- "[30]\tvalid_0's auc: 0.806321\tvalid_0's binary_logloss: 0.491474\n",
- "[31]\tvalid_0's auc: 0.806201\tvalid_0's binary_logloss: 0.490419\n",
- "[32]\tvalid_0's auc: 0.806671\tvalid_0's binary_logloss: 0.489393\n",
- "[33]\tvalid_0's auc: 0.806899\tvalid_0's binary_logloss: 0.488331\n",
- "[34]\tvalid_0's auc: 0.807105\tvalid_0's binary_logloss: 0.487277\n",
- "[35]\tvalid_0's auc: 0.807257\tvalid_0's binary_logloss: 0.486592\n",
- "[36]\tvalid_0's auc: 0.80729\tvalid_0's binary_logloss: 0.485607\n",
- "[37]\tvalid_0's auc: 0.807752\tvalid_0's binary_logloss: 0.484951\n",
- "[38]\tvalid_0's auc: 0.808191\tvalid_0's binary_logloss: 0.484269\n",
- "[39]\tvalid_0's auc: 0.808417\tvalid_0's binary_logloss: 0.483242\n",
- "[40]\tvalid_0's auc: 0.808761\tvalid_0's binary_logloss: 0.482291\n",
- "[41]\tvalid_0's auc: 0.80965\tvalid_0's binary_logloss: 0.48164\n",
- "[42]\tvalid_0's auc: 0.810065\tvalid_0's binary_logloss: 0.480962\n",
- "[43]\tvalid_0's auc: 0.810209\tvalid_0's binary_logloss: 0.479995\n",
- "[44]\tvalid_0's auc: 0.810091\tvalid_0's binary_logloss: 0.479077\n",
- "[45]\tvalid_0's auc: 0.810573\tvalid_0's binary_logloss: 0.478185\n",
- "[46]\tvalid_0's auc: 0.810924\tvalid_0's binary_logloss: 0.477558\n",
- "[47]\tvalid_0's auc: 0.810951\tvalid_0's binary_logloss: 0.476662\n",
- "[48]\tvalid_0's auc: 0.811101\tvalid_0's binary_logloss: 0.475745\n",
- "[49]\tvalid_0's auc: 0.811269\tvalid_0's binary_logloss: 0.474951\n",
- "[50]\tvalid_0's auc: 0.81173\tvalid_0's binary_logloss: 0.474514\n",
- "[51]\tvalid_0's auc: 0.811937\tvalid_0's binary_logloss: 0.474114\n",
- "[52]\tvalid_0's auc: 0.812136\tvalid_0's binary_logloss: 0.473297\n",
- "[53]\tvalid_0's auc: 0.812249\tvalid_0's binary_logloss: 0.472497\n",
- "[54]\tvalid_0's auc: 0.812121\tvalid_0's binary_logloss: 0.471696\n",
- "[55]\tvalid_0's auc: 0.812164\tvalid_0's binary_logloss: 0.470905\n",
- "[56]\tvalid_0's auc: 0.812462\tvalid_0's binary_logloss: 0.470384\n",
- "[57]\tvalid_0's auc: 0.812613\tvalid_0's binary_logloss: 0.4696\n",
- "[58]\tvalid_0's auc: 0.812615\tvalid_0's binary_logloss: 0.468778\n",
- "[59]\tvalid_0's auc: 0.812842\tvalid_0's binary_logloss: 0.468211\n",
- "[60]\tvalid_0's auc: 0.81312\tvalid_0's binary_logloss: 0.467385\n",
- "[61]\tvalid_0's auc: 0.813039\tvalid_0's binary_logloss: 0.466632\n",
- "[62]\tvalid_0's auc: 0.812942\tvalid_0's binary_logloss: 0.465933\n",
- "[63]\tvalid_0's auc: 0.813274\tvalid_0's binary_logloss: 0.465214\n",
- "[64]\tvalid_0's auc: 0.813572\tvalid_0's binary_logloss: 0.464692\n",
- "[65]\tvalid_0's auc: 0.813594\tvalid_0's binary_logloss: 0.463925\n",
- "[66]\tvalid_0's auc: 0.813719\tvalid_0's binary_logloss: 0.463177\n",
- "[67]\tvalid_0's auc: 0.814011\tvalid_0's binary_logloss: 0.462513\n",
- "[68]\tvalid_0's auc: 0.813989\tvalid_0's binary_logloss: 0.461843\n"
- ]
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:16.136151Z",
+ "start_time": "2020-11-18T04:21:16.124444Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型定义\n",
+ "lgb_ranker = lgb.LGBMRanker(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
+ " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
+ " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16) "
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[69]\tvalid_0's auc: 0.814218\tvalid_0's binary_logloss: 0.461443\n",
- "[70]\tvalid_0's auc: 0.814334\tvalid_0's binary_logloss: 0.460775\n",
- "[71]\tvalid_0's auc: 0.814493\tvalid_0's binary_logloss: 0.460332\n",
- "[72]\tvalid_0's auc: 0.814663\tvalid_0's binary_logloss: 0.459867\n",
- "[73]\tvalid_0's auc: 0.814856\tvalid_0's binary_logloss: 0.459266\n",
- "[74]\tvalid_0's auc: 0.815017\tvalid_0's binary_logloss: 0.458585\n",
- "[75]\tvalid_0's auc: 0.815186\tvalid_0's binary_logloss: 0.457958\n",
- "[76]\tvalid_0's auc: 0.815374\tvalid_0's binary_logloss: 0.457316\n",
- "[77]\tvalid_0's auc: 0.81554\tvalid_0's binary_logloss: 0.45665\n",
- "[78]\tvalid_0's auc: 0.81569\tvalid_0's binary_logloss: 0.456217\n",
- "[79]\tvalid_0's auc: 0.815861\tvalid_0's binary_logloss: 0.455615\n",
- "[80]\tvalid_0's auc: 0.816443\tvalid_0's binary_logloss: 0.454895\n",
- "[81]\tvalid_0's auc: 0.816659\tvalid_0's binary_logloss: 0.454503\n",
- "[82]\tvalid_0's auc: 0.817017\tvalid_0's binary_logloss: 0.454149\n",
- "[83]\tvalid_0's auc: 0.817162\tvalid_0's binary_logloss: 0.453578\n",
- "[84]\tvalid_0's auc: 0.817274\tvalid_0's binary_logloss: 0.452984\n",
- "[85]\tvalid_0's auc: 0.817283\tvalid_0's binary_logloss: 0.452416\n",
- "[86]\tvalid_0's auc: 0.817339\tvalid_0's binary_logloss: 0.452022\n",
- "[87]\tvalid_0's auc: 0.817494\tvalid_0's binary_logloss: 0.45146\n",
- "[88]\tvalid_0's auc: 0.817594\tvalid_0's binary_logloss: 0.450926\n",
- "[89]\tvalid_0's auc: 0.817771\tvalid_0's binary_logloss: 0.450553\n",
- "[90]\tvalid_0's auc: 0.81789\tvalid_0's binary_logloss: 0.449985\n",
- "[91]\tvalid_0's auc: 0.817931\tvalid_0's binary_logloss: 0.449439\n",
- "[92]\tvalid_0's auc: 0.818138\tvalid_0's binary_logloss: 0.449094\n",
- "[93]\tvalid_0's auc: 0.818334\tvalid_0's binary_logloss: 0.448527\n",
- "[94]\tvalid_0's auc: 0.818426\tvalid_0's binary_logloss: 0.447989\n",
- "[95]\tvalid_0's auc: 0.818676\tvalid_0's binary_logloss: 0.447407\n",
- "[96]\tvalid_0's auc: 0.818852\tvalid_0's binary_logloss: 0.446884\n",
- "[97]\tvalid_0's auc: 0.81945\tvalid_0's binary_logloss: 0.446455\n",
- "[98]\tvalid_0's auc: 0.819861\tvalid_0's binary_logloss: 0.446045\n",
- "[99]\tvalid_0's auc: 0.819943\tvalid_0's binary_logloss: 0.445543\n",
- "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.820076\tvalid_0's binary_logloss: 0.445258\n",
- "[1]\tvalid_0's auc: 0.770032\tvalid_0's binary_logloss: 0.527241\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.779881\tvalid_0's binary_logloss: 0.525545\n",
- "[3]\tvalid_0's auc: 0.791308\tvalid_0's binary_logloss: 0.524508\n",
- "[4]\tvalid_0's auc: 0.790788\tvalid_0's binary_logloss: 0.52341\n",
- "[5]\tvalid_0's auc: 0.795645\tvalid_0's binary_logloss: 0.521753\n",
- "[6]\tvalid_0's auc: 0.797745\tvalid_0's binary_logloss: 0.520131\n",
- "[7]\tvalid_0's auc: 0.79931\tvalid_0's binary_logloss: 0.518872\n",
- "[8]\tvalid_0's auc: 0.800014\tvalid_0's binary_logloss: 0.517353\n",
- "[9]\tvalid_0's auc: 0.800549\tvalid_0's binary_logloss: 0.516487\n",
- "[10]\tvalid_0's auc: 0.800261\tvalid_0's binary_logloss: 0.515039\n",
- "[11]\tvalid_0's auc: 0.801261\tvalid_0's binary_logloss: 0.513695\n",
- "[12]\tvalid_0's auc: 0.801062\tvalid_0's binary_logloss: 0.512735\n",
- "[13]\tvalid_0's auc: 0.801155\tvalid_0's binary_logloss: 0.51192\n",
- "[14]\tvalid_0's auc: 0.801315\tvalid_0's binary_logloss: 0.510559\n",
- "[15]\tvalid_0's auc: 0.80185\tvalid_0's binary_logloss: 0.509147\n",
- "[16]\tvalid_0's auc: 0.803029\tvalid_0's binary_logloss: 0.507914\n",
- "[17]\tvalid_0's auc: 0.803035\tvalid_0's binary_logloss: 0.506583\n",
- "[18]\tvalid_0's auc: 0.803433\tvalid_0's binary_logloss: 0.505441\n",
- "[19]\tvalid_0's auc: 0.803717\tvalid_0's binary_logloss: 0.504599\n",
- "[20]\tvalid_0's auc: 0.803819\tvalid_0's binary_logloss: 0.503327\n",
- "[21]\tvalid_0's auc: 0.803923\tvalid_0's binary_logloss: 0.502782\n",
- "[22]\tvalid_0's auc: 0.804939\tvalid_0's binary_logloss: 0.501596\n",
- "[23]\tvalid_0's auc: 0.804707\tvalid_0's binary_logloss: 0.500572\n",
- "[24]\tvalid_0's auc: 0.804632\tvalid_0's binary_logloss: 0.499367\n",
- "[25]\tvalid_0's auc: 0.804756\tvalid_0's binary_logloss: 0.498161\n",
- "[26]\tvalid_0's auc: 0.805067\tvalid_0's binary_logloss: 0.497061\n",
- "[27]\tvalid_0's auc: 0.805119\tvalid_0's binary_logloss: 0.495933\n",
- "[28]\tvalid_0's auc: 0.805304\tvalid_0's binary_logloss: 0.494849\n",
- "[29]\tvalid_0's auc: 0.805688\tvalid_0's binary_logloss: 0.493677\n",
- "[30]\tvalid_0's auc: 0.805822\tvalid_0's binary_logloss: 0.492594\n",
- "[31]\tvalid_0's auc: 0.805869\tvalid_0's binary_logloss: 0.49152\n",
- "[32]\tvalid_0's auc: 0.807267\tvalid_0's binary_logloss: 0.490435\n",
- "[33]\tvalid_0's auc: 0.807301\tvalid_0's binary_logloss: 0.489392\n",
- "[34]\tvalid_0's auc: 0.80736\tvalid_0's binary_logloss: 0.488325\n",
- "[35]\tvalid_0's auc: 0.807706\tvalid_0's binary_logloss: 0.487654\n",
- "[36]\tvalid_0's auc: 0.807758\tvalid_0's binary_logloss: 0.486651\n",
- "[37]\tvalid_0's auc: 0.808051\tvalid_0's binary_logloss: 0.486012\n",
- "[38]\tvalid_0's auc: 0.808429\tvalid_0's binary_logloss: 0.485355\n",
- "[39]\tvalid_0's auc: 0.808663\tvalid_0's binary_logloss: 0.484327\n",
- "[40]\tvalid_0's auc: 0.809007\tvalid_0's binary_logloss: 0.483386\n",
- "[41]\tvalid_0's auc: 0.809781\tvalid_0's binary_logloss: 0.482745\n",
- "[42]\tvalid_0's auc: 0.810071\tvalid_0's binary_logloss: 0.482124\n",
- "[43]\tvalid_0's auc: 0.810383\tvalid_0's binary_logloss: 0.481154\n",
- "[44]\tvalid_0's auc: 0.810446\tvalid_0's binary_logloss: 0.480243\n",
- "[45]\tvalid_0's auc: 0.811148\tvalid_0's binary_logloss: 0.479261\n",
- "[46]\tvalid_0's auc: 0.811245\tvalid_0's binary_logloss: 0.478687\n",
- "[47]\tvalid_0's auc: 0.811214\tvalid_0's binary_logloss: 0.477812\n",
- "[48]\tvalid_0's auc: 0.811408\tvalid_0's binary_logloss: 0.47689\n",
- "[49]\tvalid_0's auc: 0.811486\tvalid_0's binary_logloss: 0.476132\n",
- "[50]\tvalid_0's auc: 0.811806\tvalid_0's binary_logloss: 0.475718\n",
- "[51]\tvalid_0's auc: 0.812017\tvalid_0's binary_logloss: 0.475342\n",
- "[52]\tvalid_0's auc: 0.812255\tvalid_0's binary_logloss: 0.474505\n",
- "[53]\tvalid_0's auc: 0.812249\tvalid_0's binary_logloss: 0.473707\n",
- "[54]\tvalid_0's auc: 0.812235\tvalid_0's binary_logloss: 0.47289\n",
- "[55]\tvalid_0's auc: 0.812233\tvalid_0's binary_logloss: 0.472091\n",
- "[56]\tvalid_0's auc: 0.812492\tvalid_0's binary_logloss: 0.471563\n",
- "[57]\tvalid_0's auc: 0.812579\tvalid_0's binary_logloss: 0.47077\n",
- "[58]\tvalid_0's auc: 0.812598\tvalid_0's binary_logloss: 0.469992\n",
- "[59]\tvalid_0's auc: 0.812885\tvalid_0's binary_logloss: 0.469458\n",
- "[60]\tvalid_0's auc: 0.812995\tvalid_0's binary_logloss: 0.468676\n",
- "[61]\tvalid_0's auc: 0.812961\tvalid_0's binary_logloss: 0.467939\n",
- "[62]\tvalid_0's auc: 0.812919\tvalid_0's binary_logloss: 0.467232\n",
- "[63]\tvalid_0's auc: 0.813291\tvalid_0's binary_logloss: 0.466491\n",
- "[64]\tvalid_0's auc: 0.813702\tvalid_0's binary_logloss: 0.465945\n",
- "[65]\tvalid_0's auc: 0.813803\tvalid_0's binary_logloss: 0.465197\n",
- "[66]\tvalid_0's auc: 0.813851\tvalid_0's binary_logloss: 0.4645\n",
- "[67]\tvalid_0's auc: 0.814011\tvalid_0's binary_logloss: 0.463814\n",
- "[68]\tvalid_0's auc: 0.814027\tvalid_0's binary_logloss: 0.463113\n",
- "[69]\tvalid_0's auc: 0.814138\tvalid_0's binary_logloss: 0.462727\n",
- "[70]\tvalid_0's auc: 0.814365\tvalid_0's binary_logloss: 0.462077\n",
- "[71]\tvalid_0's auc: 0.814432\tvalid_0's binary_logloss: 0.461655\n",
- "[72]\tvalid_0's auc: 0.8146\tvalid_0's binary_logloss: 0.461194\n",
- "[73]\tvalid_0's auc: 0.815324\tvalid_0's binary_logloss: 0.460477\n",
- "[74]\tvalid_0's auc: 0.815411\tvalid_0's binary_logloss: 0.459805\n",
- "[75]\tvalid_0's auc: 0.815548\tvalid_0's binary_logloss: 0.459189\n",
- "[76]\tvalid_0's auc: 0.815625\tvalid_0's binary_logloss: 0.458525\n",
- "[77]\tvalid_0's auc: 0.81562\tvalid_0's binary_logloss: 0.457905\n",
- "[78]\tvalid_0's auc: 0.815786\tvalid_0's binary_logloss: 0.45747\n",
- "[79]\tvalid_0's auc: 0.815834\tvalid_0's binary_logloss: 0.456884\n",
- "[80]\tvalid_0's auc: 0.816475\tvalid_0's binary_logloss: 0.45617\n",
- "[81]\tvalid_0's auc: 0.816677\tvalid_0's binary_logloss: 0.455787\n",
- "[82]\tvalid_0's auc: 0.817255\tvalid_0's binary_logloss: 0.455358\n",
- "[83]\tvalid_0's auc: 0.817383\tvalid_0's binary_logloss: 0.454775\n",
- "[84]\tvalid_0's auc: 0.817509\tvalid_0's binary_logloss: 0.454176\n",
- "[85]\tvalid_0's auc: 0.817572\tvalid_0's binary_logloss: 0.453609\n",
- "[86]\tvalid_0's auc: 0.817721\tvalid_0's binary_logloss: 0.453213\n",
- "[87]\tvalid_0's auc: 0.817992\tvalid_0's binary_logloss: 0.452586\n",
- "[88]\tvalid_0's auc: 0.81808\tvalid_0's binary_logloss: 0.45204\n",
- "[89]\tvalid_0's auc: 0.818202\tvalid_0's binary_logloss: 0.451643\n",
- "[90]\tvalid_0's auc: 0.818336\tvalid_0's binary_logloss: 0.451081\n",
- "[91]\tvalid_0's auc: 0.818347\tvalid_0's binary_logloss: 0.450531\n",
- "[92]\tvalid_0's auc: 0.818558\tvalid_0's binary_logloss: 0.450179\n",
- "[93]\tvalid_0's auc: 0.818743\tvalid_0's binary_logloss: 0.449647\n",
- "[94]\tvalid_0's auc: 0.818789\tvalid_0's binary_logloss: 0.449133\n",
- "[95]\tvalid_0's auc: 0.818849\tvalid_0's binary_logloss: 0.44862\n",
- "[96]\tvalid_0's auc: 0.81913\tvalid_0's binary_logloss: 0.448072\n",
- "[97]\tvalid_0's auc: 0.819526\tvalid_0's binary_logloss: 0.447713\n",
- "[98]\tvalid_0's auc: 0.819971\tvalid_0's binary_logloss: 0.447296\n",
- "[99]\tvalid_0's auc: 0.819972\tvalid_0's binary_logloss: 0.446814\n"
- ]
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:22.965433Z",
+ "start_time": "2020-11-18T04:21:17.799127Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 排序模型训练\n",
+ "if offline:\n",
+ " lgb_ranker.fit(trn_user_item_feats_df_rank_model[lgb_cols], trn_user_item_feats_df_rank_model['label'], group=g_train,\n",
+ " eval_set=[(val_user_item_feats_df_rank_model[lgb_cols], val_user_item_feats_df_rank_model['label'])], \n",
+ " eval_group= [g_val], eval_at=[1, 2, 3, 4, 5], eval_metric=['ndcg', ], early_stopping_rounds=50, )\n",
+ "else:\n",
+ " lgb_ranker.fit(trn_user_item_feats_df[lgb_cols], trn_user_item_feats_df['label'], group=g_train)"
+ ]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.820086\tvalid_0's binary_logloss: 0.446533\n",
- "[1]\tvalid_0's auc: 0.768646\tvalid_0's binary_logloss: 0.527167\n",
- "Training until validation scores don't improve for 50 rounds\n",
- "[2]\tvalid_0's auc: 0.779902\tvalid_0's binary_logloss: 0.525481\n",
- "[3]\tvalid_0's auc: 0.789868\tvalid_0's binary_logloss: 0.524485\n",
- "[4]\tvalid_0's auc: 0.791895\tvalid_0's binary_logloss: 0.523382\n",
- "[5]\tvalid_0's auc: 0.795453\tvalid_0's binary_logloss: 0.521759\n",
- "[6]\tvalid_0's auc: 0.796672\tvalid_0's binary_logloss: 0.520166\n",
- "[7]\tvalid_0's auc: 0.798023\tvalid_0's binary_logloss: 0.518857\n",
- "[8]\tvalid_0's auc: 0.799331\tvalid_0's binary_logloss: 0.517297\n",
- "[9]\tvalid_0's auc: 0.800181\tvalid_0's binary_logloss: 0.516416\n",
- "[10]\tvalid_0's auc: 0.800373\tvalid_0's binary_logloss: 0.514967\n",
- "[11]\tvalid_0's auc: 0.801087\tvalid_0's binary_logloss: 0.513631\n",
- "[12]\tvalid_0's auc: 0.801122\tvalid_0's binary_logloss: 0.512658\n",
- "[13]\tvalid_0's auc: 0.801043\tvalid_0's binary_logloss: 0.511833\n",
- "[14]\tvalid_0's auc: 0.801238\tvalid_0's binary_logloss: 0.510461\n",
- "[15]\tvalid_0's auc: 0.801847\tvalid_0's binary_logloss: 0.509034\n",
- "[16]\tvalid_0's auc: 0.803139\tvalid_0's binary_logloss: 0.507759\n",
- "[17]\tvalid_0's auc: 0.803577\tvalid_0's binary_logloss: 0.506361\n",
- "[18]\tvalid_0's auc: 0.803834\tvalid_0's binary_logloss: 0.505229\n",
- "[19]\tvalid_0's auc: 0.803943\tvalid_0's binary_logloss: 0.504371\n",
- "[20]\tvalid_0's auc: 0.80415\tvalid_0's binary_logloss: 0.503102\n",
- "[21]\tvalid_0's auc: 0.804446\tvalid_0's binary_logloss: 0.502564\n",
- "[22]\tvalid_0's auc: 0.805163\tvalid_0's binary_logloss: 0.501396\n",
- "[23]\tvalid_0's auc: 0.805323\tvalid_0's binary_logloss: 0.500327\n",
- "[24]\tvalid_0's auc: 0.805314\tvalid_0's binary_logloss: 0.499123\n",
- "[25]\tvalid_0's auc: 0.80535\tvalid_0's binary_logloss: 0.497927\n",
- "[26]\tvalid_0's auc: 0.805864\tvalid_0's binary_logloss: 0.496834\n",
- "[27]\tvalid_0's auc: 0.805919\tvalid_0's binary_logloss: 0.495667\n",
- "[28]\tvalid_0's auc: 0.806272\tvalid_0's binary_logloss: 0.494606\n",
- "[29]\tvalid_0's auc: 0.806599\tvalid_0's binary_logloss: 0.49343\n",
- "[30]\tvalid_0's auc: 0.806932\tvalid_0's binary_logloss: 0.492303\n",
- "[31]\tvalid_0's auc: 0.806656\tvalid_0's binary_logloss: 0.491249\n",
- "[32]\tvalid_0's auc: 0.807436\tvalid_0's binary_logloss: 0.490188\n",
- "[33]\tvalid_0's auc: 0.807629\tvalid_0's binary_logloss: 0.489117\n",
- "[34]\tvalid_0's auc: 0.807501\tvalid_0's binary_logloss: 0.48808\n",
- "[35]\tvalid_0's auc: 0.807885\tvalid_0's binary_logloss: 0.487383\n",
- "[36]\tvalid_0's auc: 0.807921\tvalid_0's binary_logloss: 0.48636\n",
- "[37]\tvalid_0's auc: 0.808267\tvalid_0's binary_logloss: 0.485724\n",
- "[38]\tvalid_0's auc: 0.808563\tvalid_0's binary_logloss: 0.485076\n",
- "[39]\tvalid_0's auc: 0.808813\tvalid_0's binary_logloss: 0.484039\n",
- "[40]\tvalid_0's auc: 0.809023\tvalid_0's binary_logloss: 0.483091\n",
- "[41]\tvalid_0's auc: 0.809782\tvalid_0's binary_logloss: 0.482441\n",
- "[42]\tvalid_0's auc: 0.810135\tvalid_0's binary_logloss: 0.48179\n",
- "[43]\tvalid_0's auc: 0.810219\tvalid_0's binary_logloss: 0.48082\n",
- "[44]\tvalid_0's auc: 0.81031\tvalid_0's binary_logloss: 0.479906\n",
- "[45]\tvalid_0's auc: 0.810514\tvalid_0's binary_logloss: 0.479024\n",
- "[46]\tvalid_0's auc: 0.810566\tvalid_0's binary_logloss: 0.478437\n",
- "[47]\tvalid_0's auc: 0.810611\tvalid_0's binary_logloss: 0.477529\n",
- "[48]\tvalid_0's auc: 0.810781\tvalid_0's binary_logloss: 0.476637\n",
- "[49]\tvalid_0's auc: 0.81089\tvalid_0's binary_logloss: 0.475883\n",
- "[50]\tvalid_0's auc: 0.811266\tvalid_0's binary_logloss: 0.475459\n",
- "[51]\tvalid_0's auc: 0.811402\tvalid_0's binary_logloss: 0.475078\n",
- "[52]\tvalid_0's auc: 0.811765\tvalid_0's binary_logloss: 0.474246\n",
- "[53]\tvalid_0's auc: 0.811891\tvalid_0's binary_logloss: 0.473452\n",
- "[54]\tvalid_0's auc: 0.811868\tvalid_0's binary_logloss: 0.47263\n",
- "[55]\tvalid_0's auc: 0.81192\tvalid_0's binary_logloss: 0.471804\n",
- "[56]\tvalid_0's auc: 0.812272\tvalid_0's binary_logloss: 0.471275\n",
- "[57]\tvalid_0's auc: 0.812639\tvalid_0's binary_logloss: 0.470396\n",
- "[58]\tvalid_0's auc: 0.812764\tvalid_0's binary_logloss: 0.469597\n",
- "[59]\tvalid_0's auc: 0.813084\tvalid_0's binary_logloss: 0.469049\n",
- "[60]\tvalid_0's auc: 0.813342\tvalid_0's binary_logloss: 0.468244\n",
- "[61]\tvalid_0's auc: 0.813302\tvalid_0's binary_logloss: 0.467499\n",
- "[62]\tvalid_0's auc: 0.813221\tvalid_0's binary_logloss: 0.466758\n",
- "[63]\tvalid_0's auc: 0.813697\tvalid_0's binary_logloss: 0.466017\n",
- "[64]\tvalid_0's auc: 0.813985\tvalid_0's binary_logloss: 0.465501\n",
- "[65]\tvalid_0's auc: 0.81416\tvalid_0's binary_logloss: 0.464725\n",
- "[66]\tvalid_0's auc: 0.814227\tvalid_0's binary_logloss: 0.46398\n",
- "[67]\tvalid_0's auc: 0.814397\tvalid_0's binary_logloss: 0.463309\n",
- "[68]\tvalid_0's auc: 0.814426\tvalid_0's binary_logloss: 0.462627\n",
- "[69]\tvalid_0's auc: 0.814593\tvalid_0's binary_logloss: 0.462244\n",
- "[70]\tvalid_0's auc: 0.814789\tvalid_0's binary_logloss: 0.461571\n",
- "[71]\tvalid_0's auc: 0.814889\tvalid_0's binary_logloss: 0.461144\n",
- "[72]\tvalid_0's auc: 0.815078\tvalid_0's binary_logloss: 0.460684\n",
- "[73]\tvalid_0's auc: 0.815439\tvalid_0's binary_logloss: 0.460063\n",
- "[74]\tvalid_0's auc: 0.815511\tvalid_0's binary_logloss: 0.459386\n",
- "[75]\tvalid_0's auc: 0.815574\tvalid_0's binary_logloss: 0.45877\n",
- "[76]\tvalid_0's auc: 0.815634\tvalid_0's binary_logloss: 0.458128\n",
- "[77]\tvalid_0's auc: 0.815618\tvalid_0's binary_logloss: 0.457495\n",
- "[78]\tvalid_0's auc: 0.81582\tvalid_0's binary_logloss: 0.457057\n",
- "[79]\tvalid_0's auc: 0.81594\tvalid_0's binary_logloss: 0.456475\n",
- "[80]\tvalid_0's auc: 0.815961\tvalid_0's binary_logloss: 0.455885\n",
- "[81]\tvalid_0's auc: 0.816153\tvalid_0's binary_logloss: 0.455511\n",
- "[82]\tvalid_0's auc: 0.816433\tvalid_0's binary_logloss: 0.455186\n",
- "[83]\tvalid_0's auc: 0.816546\tvalid_0's binary_logloss: 0.454625\n",
- "[84]\tvalid_0's auc: 0.816586\tvalid_0's binary_logloss: 0.454039\n",
- "[85]\tvalid_0's auc: 0.816584\tvalid_0's binary_logloss: 0.453482\n",
- "[86]\tvalid_0's auc: 0.816881\tvalid_0's binary_logloss: 0.453048\n",
- "[87]\tvalid_0's auc: 0.817029\tvalid_0's binary_logloss: 0.452485\n",
- "[88]\tvalid_0's auc: 0.81707\tvalid_0's binary_logloss: 0.451941\n",
- "[89]\tvalid_0's auc: 0.817298\tvalid_0's binary_logloss: 0.451544\n",
- "[90]\tvalid_0's auc: 0.817343\tvalid_0's binary_logloss: 0.450975\n",
- "[91]\tvalid_0's auc: 0.817357\tvalid_0's binary_logloss: 0.450422\n",
- "[92]\tvalid_0's auc: 0.817592\tvalid_0's binary_logloss: 0.450109\n",
- "[93]\tvalid_0's auc: 0.817729\tvalid_0's binary_logloss: 0.449542\n",
- "[94]\tvalid_0's auc: 0.817834\tvalid_0's binary_logloss: 0.448982\n",
- "[95]\tvalid_0's auc: 0.81809\tvalid_0's binary_logloss: 0.448398\n",
- "[96]\tvalid_0's auc: 0.818269\tvalid_0's binary_logloss: 0.447908\n",
- "[97]\tvalid_0's auc: 0.818682\tvalid_0's binary_logloss: 0.447547\n",
- "[98]\tvalid_0's auc: 0.819015\tvalid_0's binary_logloss: 0.447165\n",
- "[99]\tvalid_0's auc: 0.819016\tvalid_0's binary_logloss: 0.446669\n",
- "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n",
- "Did not meet early stopping. Best iteration is:\n",
- "[100]\tvalid_0's auc: 0.819127\tvalid_0's binary_logloss: 0.446397\n"
- ]
- }
- ],
- "source": [
- "# 五折交叉验证，这里的五折交叉是以用户为目标进行五折划分\n",
- "# 这一部分与前面的单独训练和验证是分开的\n",
- "def get_kfold_users(trn_df, n=5):\n",
- " user_ids = trn_df['user_id'].unique()\n",
- " user_set = [user_ids[i::n] for i in range(n)]\n",
- " return user_set\n",
- "\n",
- "k_fold = 5\n",
- "trn_df = trn_user_item_feats_df_rank_model\n",
- "user_set = get_kfold_users(trn_df, n=k_fold)\n",
- "\n",
- "score_list = []\n",
- "score_df = trn_df[['user_id', 'click_article_id', 'label']]\n",
- "sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0])\n",
- "\n",
- "# 五折交叉验证，并将中间结果保存用于staking\n",
- "for n_fold, valid_user in enumerate(user_set):\n",
- " train_idx = trn_df[~trn_df['user_id'].isin(valid_user)] # add slide user\n",
- " valid_idx = trn_df[trn_df['user_id'].isin(valid_user)]\n",
- " \n",
- " # 模型及参数的定义\n",
- " lgb_Classfication = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
- " max_depth=-1, n_estimators=100, subsample=0.7, colsample_bytree=0.7, subsample_freq=1,\n",
- " learning_rate=0.01, min_child_weight=50, random_state=2018, n_jobs= 16, verbose=10) \n",
- " # 训练模型\n",
- " lgb_Classfication.fit(train_idx[lgb_cols], train_idx['label'],eval_set=[(valid_idx[lgb_cols], valid_idx['label'])], \n",
- " eval_metric=['auc', ],early_stopping_rounds=50, )\n",
- " \n",
- " # 预测验证集结果\n",
- " valid_idx['pred_score'] = lgb_Classfication.predict_proba(valid_idx[lgb_cols], \n",
- " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
- " \n",
- " # 对输出结果进行归一化 分类模型输出的值本身就是一个概率值不需要进行归一化\n",
- " # valid_idx['pred_score'] = valid_idx[['pred_score']].transform(lambda x: norm_sim(x))\n",
- " \n",
- " valid_idx.sort_values(by=['user_id', 'pred_score'])\n",
- " valid_idx['pred_rank'] = valid_idx.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- " \n",
- " # 将验证集的预测结果放到一个列表中，后面进行拼接\n",
- " score_list.append(valid_idx[['user_id', 'click_article_id', 'pred_score', 'pred_rank']])\n",
- " \n",
- " # 如果是线上测试，需要计算每次交叉验证的结果相加，最后求平均\n",
- " if not offline:\n",
- " sub_preds += lgb_Classfication.predict_proba(tst_user_item_feats_df_rank_model[lgb_cols], \n",
- " num_iteration=lgb_Classfication.best_iteration_)[:,1]\n",
- " \n",
- "score_df_ = pd.concat(score_list, axis=0)\n",
- "score_df = score_df.merge(score_df_, how='left', on=['user_id', 'click_article_id'])\n",
- "# 保存训练集交叉验证产生的新特征\n",
- "score_df[['user_id', 'click_article_id', 'pred_score', 'pred_rank', 'label']].to_csv(save_path + 'trn_lgb_cls_feats.csv', index=False)\n",
- " \n",
- "# 测试集的预测结果，多次交叉验证求平均,将预测的score和对应的rank特征保存，可以用于后面的staking，这里还可以构造其他更多的特征\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = sub_preds / k_fold\n",
- "tst_user_item_feats_df_rank_model['pred_score'] = tst_user_item_feats_df_rank_model['pred_score'].transform(lambda x: norm_sim(x))\n",
- "tst_user_item_feats_df_rank_model.sort_values(by=['user_id', 'pred_score'])\n",
- "tst_user_item_feats_df_rank_model['pred_rank'] = tst_user_item_feats_df_rank_model.groupby(['user_id'])['pred_score'].rank(ascending=False, method='first')\n",
- "\n",
- "# 保存测试集交叉验证的新特征\n",
- "tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score', 'pred_rank']].to_csv(save_path + 'tst_lgb_cls_feats.csv', index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:23.074237Z",
- "start_time": "2020-11-18T04:24:13.812284Z"
- }
- },
- "outputs": [],
- "source": [
- "# 预测结果重新排序, 及生成提交结果\n",
- "rank_results = tst_user_item_feats_df_rank_model[['user_id', 'click_article_id', 'pred_score']]\n",
- "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
- "submit(rank_results, topk=5, model_name='lgb_cls')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## DIN模型"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 用户的历史点击行为列表\n",
- "这个是为后面的DIN模型服务的"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:24:30.508213Z",
- "start_time": "2020-11-18T04:24:27.426372Z"
- }
- },
- "outputs": [],
- "source": [
- "if offline:\n",
- " all_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
- "else:\n",
- " trn_data = pd.read_csv('./data_raw/train_click_log.csv')\n",
- " tst_data = pd.read_csv('./data_raw/testA_click_log.csv')\n",
- " all_data = trn_data.append(tst_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:25:28.082071Z",
- "start_time": "2020-11-18T04:24:33.649524Z"
- }
- },
- "outputs": [],
- "source": [
- "hist_click =all_data[['user_id', 'click_article_id']].groupby('user_id').agg({list}).reset_index()\n",
- "his_behavior_df = pd.DataFrame()\n",
- "his_behavior_df['user_id'] = hist_click['user_id']\n",
- "his_behavior_df['hist_click_article_id'] = hist_click['click_article_id']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:25:52.925866Z",
- "start_time": "2020-11-18T04:25:52.863922Z"
- }
- },
- "outputs": [],
- "source": [
- "trn_user_item_feats_df_din_model = trn_user_item_feats_df.copy()\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_din_model = val_user_item_feats_df.copy()\n",
- "else: \n",
- " val_user_item_feats_df_din_model = None\n",
- " \n",
- "tst_user_item_feats_df_din_model = tst_user_item_feats_df.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:00.070681Z",
- "start_time": "2020-11-18T04:25:56.417197Z"
- }
- },
- "outputs": [],
- "source": [
- "trn_user_item_feats_df_din_model = trn_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
- "\n",
- "if offline:\n",
- " val_user_item_feats_df_din_model = val_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')\n",
- "else:\n",
- " val_user_item_feats_df_din_model = None\n",
- "\n",
- "tst_user_item_feats_df_din_model = tst_user_item_feats_df_din_model.merge(his_behavior_df, on='user_id')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### DIN模型简介\n",
- "我们下面尝试使用DIN模型， DIN的全称是Deep Interest Network， 这是阿里2018年基于前面的深度学习模型无法表达用户多样化的兴趣而提出的一个模型， 它可以通过考虑【给定的候选广告】和【用户的历史行为】的相关性，来计算用户兴趣的表示向量。具体来说就是通过引入局部激活单元，通过软搜索历史行为的相关部分来关注相关的用户兴趣，并采用加权和来获得有关候选广告的用户兴趣的表示。与候选广告相关性较高的行为会获得较高的激活权重，并支配着用户兴趣。该表示向量在不同广告上有所不同，大大提高了模型的表达能力。所以该模型对于此次新闻推荐的任务也比较适合， 我们在这里通过当前的候选文章与用户历史点击文章的相关性来计算用户对于文章的兴趣。 该模型的结构如下：\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "我们这里直接调包来使用这个模型， 关于这个模型的详细细节部分我们会在下一期的推荐系统组队学习中给出。下面说一下该模型如何具体使用：deepctr的函数原型如下：\n",
- "> def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,\n",
- "> dnn_hidden_units=(200, 80), dnn_activation='relu', att_hidden_size=(80, 40), att_activation=\"dice\",\n",
- "> att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024,\n",
- "> task='binary'):\n",
- "> \n",
- "> * dnn_feature_columns: 特征列， 包含数据所有特征的列表\n",
- "> * history_feature_list: 用户历史行为列， 反应用户历史行为的特征的列表\n",
- "> * dnn_use_bn: 是否使用BatchNormalization\n",
- "> * dnn_hidden_units: 全连接层网络的层数和每一层神经元的个数， 一个列表或者元组\n",
- "> * dnn_activation_relu: 全连接网络的激活单元类型\n",
- "> * att_hidden_size: 注意力层的全连接网络的层数和每一层神经元的个数\n",
- "> * att_activation: 注意力层的激活单元类型\n",
- "> * att_weight_normalization: 是否归一化注意力得分\n",
- "> * l2_reg_dnn: 全连接网络的正则化系数\n",
- "> * l2_reg_embedding: embedding向量的正则化稀疏\n",
- "> * dnn_dropout: 全连接网络的神经元的失活概率\n",
- "> * task: 任务， 可以是分类， 也可是是回归\n",
- "\n",
- "在具体使用的时候， 我们必须要传入特征列和历史行为列， 但是再传入之前， 我们需要进行一下特征列的预处理。具体如下：\n",
- "\n",
- "1. 首先，我们要处理数据集， 得到数据， 由于我们是基于用户过去的行为去预测用户是否点击当前文章， 所以我们需要把数据的特征列划分成数值型特征， 离散型特征和历史行为特征列三部分， 对于每一部分， DIN模型的处理会有不同\n",
- " 1. 对于离散型特征， 在我们的数据集中就是那些类别型的特征， 比如user_id这种， 这种类别型特征， 我们首先要经过embedding处理得到每个特征的低维稠密型表示， 既然要经过embedding， 那么我们就需要为每一列的类别特征的取值建立一个字典，并指明embedding维度， 所以在使用deepctr的DIN模型准备数据的时候， 我们需要通过SparseFeat函数指明这些类别型特征, 这个函数的传入参数就是列名， 列的唯一取值(建立字典用)和embedding维度。\n",
- " 2. 对于用户历史行为特征列， 比如文章id， 文章的类别等这种， 同样的我们需要先经过embedding处理， 只不过和上面不一样的地方是，对于这种特征， 我们在得到每个特征的embedding表示之后， 还需要通过一个Attention_layer计算用户的历史行为和当前候选文章的相关性以此得到当前用户的embedding向量， 这个向量就可以基于当前的候选文章与用户过去点击过得历史文章的相似性的程度来反应用户的兴趣， 并且随着用户的不同的历史点击来变化，去动态的模拟用户兴趣的变化过程。这类特征对于每个用户都是一个历史行为序列， 对于每个用户， 历史行为序列长度会不一样， 可能有的用户点击的历史文章多，有的点击的历史文章少， 所以我们还需要把这个长度统一起来， 在为DIN模型准备数据的时候， 我们首先要通过SparseFeat函数指明这些类别型特征， 然后还需要通过VarLenSparseFeat函数再进行序列填充， 使得每个用户的历史序列一样长， 所以这个函数参数中会有个maxlen，来指明序列的最大长度是多少。\n",
- " 3. 对于连续型特征列， 我们只需要用DenseFeat函数来指明列名和维度即可。\n",
- "2. 处理完特征列之后， 我们把相应的数据与列进行对应，就得到了最后的数据。\n",
- "\n",
- "下面根据具体的代码感受一下， 逻辑是这样， 首先我们需要写一个数据准备函数， 在这里面就是根据上面的具体步骤准备数据， 得到数据和特征列， 然后就是建立DIN模型并训练， 最后基于模型进行测试。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:08.405211Z",
- "start_time": "2020-11-18T04:26:04.887013Z"
- }
- },
- "outputs": [],
- "source": [
- "# 导入deepctr\n",
- "from deepctr.models import DIN\n",
- "from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, get_feature_names\n",
- "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
- "\n",
- "from tensorflow.keras import backend as K\n",
- "from tensorflow.keras.layers import *\n",
- "from tensorflow.keras.models import *\n",
- "from tensorflow.keras.callbacks import * \n",
- "import tensorflow as tf\n",
- "\n",
- "import os\n",
- "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
- "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:13.485712Z",
- "start_time": "2020-11-18T04:26:13.476042Z"
- }
- },
- "outputs": [],
- "source": [
- "# 数据准备函数\n",
- "def get_din_feats_columns(df, dense_fea, sparse_fea, behavior_fea, his_behavior_fea, emb_dim=32, max_len=100):\n",
- " \"\"\"\n",
- " 数据准备函数:\n",
- " df: 数据集\n",
- " dense_fea: 数值型特征列\n",
- " sparse_fea: 离散型特征列\n",
- " behavior_fea: 用户的候选行为特征列\n",
- " his_behavior_fea: 用户的历史行为特征列\n",
- " embedding_dim: embedding的维度， 这里为了简单， 统一把离散型特征列采用一样的隐向量维度\n",
- " max_len: 用户序列的最大长度\n",
- " \"\"\"\n",
- " \n",
- " sparse_feature_columns = [SparseFeat(feat, vocabulary_size=df[feat].nunique() + 1, embedding_dim=emb_dim) for feat in sparse_fea]\n",
- " \n",
- " dense_feature_columns = [DenseFeat(feat, 1, ) for feat in dense_fea]\n",
- " \n",
- " var_feature_columns = [VarLenSparseFeat(SparseFeat(feat, vocabulary_size=df['click_article_id'].nunique() + 1,\n",
- " embedding_dim=emb_dim, embedding_name='click_article_id'), maxlen=max_len) for feat in hist_behavior_fea]\n",
- " \n",
- " dnn_feature_columns = sparse_feature_columns + dense_feature_columns + var_feature_columns\n",
- " \n",
- " # 建立x, x是一个字典的形式\n",
- " x = {}\n",
- " for name in get_feature_names(dnn_feature_columns):\n",
- " if name in his_behavior_fea:\n",
- " # 这是历史行为序列\n",
- " his_list = [l for l in df[name]]\n",
- " x[name] = pad_sequences(his_list, maxlen=max_len, padding='post') # 二维数组\n",
- " else:\n",
- " x[name] = df[name].values\n",
- " \n",
- " return x, dnn_feature_columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:18.783217Z",
- "start_time": "2020-11-18T04:26:18.776795Z"
- }
- },
- "outputs": [],
- "source": [
- "# 把特征分开\n",
- "sparse_fea = ['user_id', 'click_article_id', 'category_id', 'click_environment', 'click_deviceGroup', \n",
- " 'click_os', 'click_country', 'click_region', 'click_referrer_type', 'is_cat_hab']\n",
- "\n",
- "behavior_fea = ['click_article_id']\n",
- "\n",
- "hist_behavior_fea = ['hist_click_article_id']\n",
- "\n",
- "dense_fea = ['sim0', 'time_diff0', 'word_diff0', 'sim_max', 'sim_min', 'sim_sum', 'sim_mean', 'score',\n",
- " 'rank','click_size','time_diff_mean','active_level','user_time_hob1','user_time_hob2',\n",
- " 'words_hbo','words_count']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:25.469810Z",
- "start_time": "2020-11-18T04:26:24.779347Z"
- }
- },
- "outputs": [],
- "source": [
- "# dense特征进行归一化, 神经网络训练都需要将数值进行归一化处理\n",
- "mm = MinMaxScaler()\n",
- "\n",
- "# 下面是做一些特殊处理，当在其他的地方出现无效值的时候，不处理无法进行归一化，刚开始可以先把他注释掉，在运行了下面的代码\n",
- "# 之后如果发现报错，应该先去想办法处理如何不出现inf之类的值\n",
- "# trn_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
- "# tst_user_item_feats_df_din_model.replace([np.inf, -np.inf], 0, inplace=True)\n",
- "\n",
- "for feat in dense_fea:\n",
- " trn_user_item_feats_df_din_model[feat] = mm.fit_transform(trn_user_item_feats_df_din_model[[feat]])\n",
- " \n",
- " if val_user_item_feats_df_din_model is not None:\n",
- " val_user_item_feats_df_din_model[feat] = mm.fit_transform(val_user_item_feats_df_din_model[[feat]])\n",
- " \n",
- " tst_user_item_feats_df_din_model[feat] = mm.fit_transform(tst_user_item_feats_df_din_model[[feat]])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:36.727753Z",
- "start_time": "2020-11-18T04:26:28.854705Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:28.616665Z",
+ "start_time": "2020-11-18T04:21:24.672280Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# 模型预测\n",
+ "tst_user_item_feats_df['pred_score'] = lgb_ranker.predict(tst_user_item_feats_df[lgb_cols], num_iteration=lgb_ranker.best_iteration_)\n",
+ "\n",
+ "# 将这里的排序结果保存一份，用户后面的模型融合\n",
+ "tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']].to_csv(save_path + 'lgb_ranker_score.csv', index=False)"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
- ]
- }
- ],
- "source": [
- "# 准备训练数据\n",
- "x_trn, dnn_feature_columns = get_din_feats_columns(trn_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- "y_trn = trn_user_item_feats_df_din_model['label'].values\n",
- "\n",
- "if offline:\n",
- " # 准备验证数据\n",
- " x_val, dnn_feature_columns = get_din_feats_columns(val_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)\n",
- " y_val = val_user_item_feats_df_din_model['label'].values\n",
- " \n",
- "dense_fea = [x for x in dense_fea if x != 'label']\n",
- "x_tst, dnn_feature_columns = get_din_feats_columns(tst_user_item_feats_df_din_model, dense_fea, \n",
- " sparse_fea, behavior_fea, hist_behavior_fea, max_len=50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-11-18T04:26:45.146318Z",
- "start_time": "2020-11-18T04:26:40.423914Z"
- }
- },
- "outputs": [
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2020-11-18T04:21:40.253692Z",
+ "start_time": "2020-11-18T04:21:30.546587Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 预测结果重新排序, 及生成提交结果\n",
+ "rank_results = tst_user_item_feats_df[['user_id', 'click_article_id', 'pred_score']]\n",
+ "rank_results['click_article_id'] = rank_results['click_article_id'].astype(int)\n",
+ "submit(rank_results, topk=5, model_name='lgb_ranker')"
+ ]
+ },
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1288: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
- "Instructions for updating:\n",
- "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
- "WARNING:tensorflow:From /home/ryluo/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:255: add_dispatch_support.
+
上图只是一个多路召回的例子，也就是说可以使用多种不同的策略来获取用户排序的候选商品集合，而具体使用哪些召回策略其实是与业务强相关的 ，针对不同的任务就会有对于该业务真实场景下需要考虑的召回规则。例如新闻推荐，召回规则可以是“热门视频”、“导演召回”、“演员召回”、“最近上映“、”流行趋势“、”类型召回“等等。
@@ -1344,4 +1344,4 @@ final_recall_items_dict_rank = combine_recall_results(user_multi_recall_dict, we
**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：
-
+
diff --git a/docs/ch03/ch3.1/markdown/ch3.1.4.md b/docs/ch03/ch3.1/markdown/ch3.1.4.md
index 197765e8b..e5e267f0e 100644
--- a/docs/ch03/ch3.1/markdown/ch3.1.4.md
+++ b/docs/ch03/ch3.1/markdown/ch3.1.4.md
@@ -193,7 +193,7 @@ Word2Vec主要思想是：一个词的上下文可以很好的表达出词的语
- skip-gram：已知中心词预测周围词。
- cbow：已知周围词预测中心词。
-
+
在使用gensim训练word2vec的时候，有几个比较重要的参数
- size: 表示词向量的维度。
@@ -985,5 +985,5 @@ tst_user_item_feats_df.to_csv(save_path + 'tst_user_item_feats_df.csv', index=Fa
**关于Datawhale：** Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：
-
+
diff --git a/docs/ch03/ch3.1/markdown/ch3.1.5.md b/docs/ch03/ch3.1/markdown/ch3.1.5.md
index 9fef3fda5..0e8f45abe 100644
--- a/docs/ch03/ch3.1/markdown/ch3.1.5.md
+++ b/docs/ch03/ch3.1/markdown/ch3.1.5.md
@@ -407,7 +407,7 @@ tst_user_item_feats_df_din_model = tst_user_item_feats_df_din_model.merge(his_be
我们下面尝试使用DIN模型， DIN的全称是Deep Interest Network， 这是阿里2018年基于前面的深度学习模型无法表达用户多样化的兴趣而提出的一个模型， 它可以通过考虑【给定的候选广告】和【用户的历史行为】的相关性，来计算用户兴趣的表示向量。具体来说就是通过引入局部激活单元，通过软搜索历史行为的相关部分来关注相关的用户兴趣，并采用加权和来获得有关候选广告的用户兴趣的表示。与候选广告相关性较高的行为会获得较高的激活权重，并支配着用户兴趣。该表示向量在不同广告上有所不同，大大提高了模型的表达能力。所以该模型对于此次新闻推荐的任务也比较适合， 我们在这里通过当前的候选文章与用户历史点击文章的相关性来计算用户对于文章的兴趣。 该模型的结构如下：
-
+
我们这里直接调包来使用这个模型， 关于这个模型的详细细节部分我们会在下一期的推荐系统组队学习中给出。下面说一下该模型如何具体使用：deepctr的函数原型如下：
@@ -949,4 +949,4 @@ submit(rank_results, topk=5, model_name='ensumble_staking')
关于Datawhale： Datawhale是一个专注于数据科学与AI领域的开源组织，汇集了众多领域院校和知名企业的优秀学习者，聚合了一群有开源精神和探索精神的团队成员。Datawhale 以“for the learner，和学习者一起成长”为愿景，鼓励真实地展现自我、开放包容、互信互助、敢于试错和勇于担当。同时 Datawhale 用开源的理念去探索开源内容、开源学习和开源方案，赋能人才培养，助力人才成长，建立起人与人，人与知识，人与企业和人与未来的联结。 本次数据挖掘路径学习，专题知识将在天池分享，详情可关注Datawhale：
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/docs/ch03/ch3.2/3.2.1.3.md b/docs/ch03/ch3.2/3.2.1.3.md
index 2d79c1fbf..9153d9f15 100644
--- a/docs/ch03/ch3.2/3.2.1.3.md
+++ b/docs/ch03/ch3.2/3.2.1.3.md
@@ -20,7 +20,7 @@ sudo apt-get install redis-server
下载完成的结果
-
+
**启动Redis服务：**
@@ -30,7 +30,7 @@ sudo apt-get install redis-server
service redis-server status
```
-
+
检查当前进程，查看redis是否启动。（ps: 可以看到redis服务正在监听6379端口）
@@ -38,7 +38,7 @@ service redis-server status
ps -aux|grep redis-server
```
-
+
或者进入redis客户端，与服务器进行通信，当输入ping命令，如果返回 PONG 表示Redis已成功安装。
@@ -46,7 +46,7 @@ ps -aux|grep redis-server
redis-cli
```
-
+
上面的127.0.0.1 是redis服务器的 IP 地址，6379 是 Redis 服务器运行的端口。
diff --git a/docs/ch03/ch3.2/3.2.1.4.md b/docs/ch03/ch3.2/3.2.1.4.md
index 8a74c546e..dc29a96f1 100644
--- a/docs/ch03/ch3.2/3.2.1.4.md
+++ b/docs/ch03/ch3.2/3.2.1.4.md
@@ -129,7 +129,7 @@ class QuotesSpider(scrapy.Spider):
因为新闻爬取项目和新闻推荐系统是放在一起的，为了方便提前学习，下面直接给出项目的目录结构以及重要文件中的代码实现，最终的项目将会和新闻推荐系统一起开源出来
-
+
1. **创建一个scrapy项目：**
@@ -164,7 +164,7 @@ class SinanewsItem(scrapy.Item):
这里需要注意的一点，这里在爬取新闻的时候选择的是一个比较简洁的展示网站进行爬取的，相比直接去最新的新浪新闻观光爬取新闻简单很多，简洁的网站大概的链接：https://news.sina.com.cn/roll/#pageid=153&lid=2509&k=&num=50&page=1
-
+
```python
# -*- coding: utf-8 -*-
@@ -497,7 +497,7 @@ sh run_scrapy_sina.sh
最终查看数据库中的数据：
-
+
### 参考资料
diff --git a/docs/ch03/ch3.2/3.2.1.5.md b/docs/ch03/ch3.2/3.2.1.5.md
index 4cc60eda6..bd9d70acc 100644
--- a/docs/ch03/ch3.2/3.2.1.5.md
+++ b/docs/ch03/ch3.2/3.2.1.5.md
@@ -1,4 +1,4 @@
-
+
# 自动化构建用户及物料画像
@@ -19,13 +19,13 @@
首先说一下新物料添加到物料库的逻辑是什么，新物料添加到物料库这件事情肯定是发生在新闻爬取之后的，然后要将新物料添加到物料库还需要对新物料做一些简单的画像处理，目前我们定义的画像字段如下（处理后的画像存储在Mongodb）：
-
+
具体的逻辑就是遍历今天爬取的所有文章，然后通过文章的title来判断这篇文章是否已经在物料库中(新闻网站有可能有些相同的文章会出现在多天)来去重。然后再根据我们定义的一些字段，给画像相应的字段初始化，最后就是存入画像物料池中。
关于旧物料画像的更新，这里就需要先了解一下旧物料哪些字段会被用户的行为更新。下面是新闻列表展示页，我们会发现前端会展示新闻的阅读、喜欢及收藏次数。而用户的交互（阅读、点赞和收藏）会改变这些值。
-
+
为了能够实时的在前端显示新闻的这些动态行为信息，我们提前将新闻的动态信息存储到了redis中，线上获取的时候是直接从redis中获取新闻的数据，并且如果用户对新闻产生了交互，那么这些动态信息就会被更新，我们也是直接更新redis中的值，这样做主要是为了能够让前端可以实时的获取的新闻最新的动态画像信息。
@@ -175,9 +175,9 @@ if __name__ == "__main__":
上面的内容说完了物料的更新，接下来介绍一下对于更新完的物料是如何添加到redis数据库中去的。关于新闻内容在redis中的存储，我们将新闻的信息拆成了两部分，一部分是新闻不会发生变化的属性（例如，创建时间、标题、新闻内容等），还有一部分是物料的动态属性，在redis中存储的key的标识分别为：static_news_detail:news_id和dynamic_news_detail:news_id 下面是redis中存储的真实内容
-
+
-
+
这么做的目的是为了线上实时更改物料动态信息的时候更加高效一点。当需要获取某篇新闻的详细信息的时候需要查这两份数据并将数据这两部分数据拼起来最终才发送给前端展示。这部分的代码逻辑如下：
@@ -306,11 +306,11 @@ if __name__ == "__main__":
由于我们系统中将所有注册过的用户都放到了一个表里面（新、老用户），所以每次更新画像的话只需要遍历一遍注册表中的所有用户。再说具体的画像构建逻辑之前，得先了解一下用户画像中包含哪些字段，下面是直接从mongo中查出来的
-
+
从上面可以看出，主要是用户的基本信息和用户历史信息相关的一些标签，对于用户的基本属性特征这个可以直接从注册表中获取，那么对于跟用户历史阅读相关的信息，需要统计用户历史的所有阅读、喜欢和收藏的新闻详细信息。为了得到跟用户历史兴趣相关的信息，我们需要对用户的历史阅读、喜欢和收藏这几个历史记录给存起来，其实这些信息都可以从日志信息中获取得到，但是这里有个工程上的事情得先说明一下，先看下面这个图，对于每个用户点进一篇新闻的详情页
-
+
最底部有个喜欢和收藏，这个前端展示的结果是从后端获取的数据，那就意味着后端需要维护一个用户历史点击及收藏过的文章列表，这里我们使用了mysql来存储，主要是怕redis不够用。其实这两个表不仅仅可以用来前端展示用的，还可以用来分析用户的画像，这都给我们整理好了用户历史喜欢和收藏了。
@@ -622,7 +622,7 @@ echo " "
**crontab定时任务：**
-
+
将定时任务拆解一下：
diff --git a/docs/ch03/ch3.2/3.2.2.3.md b/docs/ch03/ch3.2/3.2.2.3.md
index e251e6515..e736c0fa1 100644
--- a/docs/ch03/ch3.2/3.2.2.3.md
+++ b/docs/ch03/ch3.2/3.2.2.3.md
@@ -6,7 +6,7 @@
下面主要展现的是项目的整体部分，主要分为推荐页，热门页以及新闻详情页。
-
+
diff --git a/docs/ch03/ch3.2/3.2.3.md b/docs/ch03/ch3.2/3.2.3.md
index 3beac83f1..b2cce14f1 100644
--- a/docs/ch03/ch3.2/3.2.3.md
+++ b/docs/ch03/ch3.2/3.2.3.md
@@ -1,6 +1,6 @@
-
+
本篇文章主要是讲解推荐系统流程构建，主要包括Offline和Online两个部分。
diff --git a/docs/ch03/ch3.2/3.2.4.3.md b/docs/ch03/ch3.2/3.2.4.3.md
index 957e4719c..8da93411b 100644
--- a/docs/ch03/ch3.2/3.2.4.3.md
+++ b/docs/ch03/ch3.2/3.2.4.3.md
@@ -12,7 +12,7 @@ DSSM(Deep Structured Semantic Model)是由微软研究院于CIKM在2013年提出
### **DSSM 模型结构**
-
+
上图是DSSM模型的结构，该网络结构比较简单，是一个由几层DNN组成网络，我们将要搜索文本(Query)和要匹配的文本(Document)的 embedding 输入到网络，网络输出为 128 维的向量，然后通过向量之间计算余弦相似度来计算向量之间距离，可以看作每一个 query 和 document 之间相似分数，然后在做 softmax。
@@ -28,7 +28,7 @@ DSSM(Deep Structured Semantic Model)是由微软研究院于CIKM在2013年提出
该模型主要是将上述模型中的两个“塔”改为独立的 user 和 item 两个子网络，大概结构如下：
-
+
其结构非常简单，如上图所示，左侧是用户塔，右侧是Item塔。在用户侧结构中，其输入为用户侧特征（用户画像信息、统计属性以及历史行为序列等）；在用户侧结构中，其输入为Item相关特征（Item基本信息、属性信息等）。对于这两个塔本身，则是经典的DNN模型，在训练过程中，其输入由特征OneHot到特征Embedding，再经过几层DNN隐层，两个塔分别输出user embedding和item embedding，最后这两个embedding做内积或者Cosine相似度计算，使得user和item在embedding映射到共同维度的语义空间中。
@@ -38,7 +38,7 @@ DSSM(Deep Structured Semantic Model)是由微软研究院于CIKM在2013年提出
该模型主要的改进是在user塔和Item塔的特征Embedding层上，各自加入一个SENet模块，借助SENet网络用来动态地学习特征的重要性，根据得到的特征权重与对应特征的embedding相乘，进而达到放大重要特征或抑制无效特征的目的，模型大致结构如下所示：
-
+
其模型和朴素DSSM模型的区别在于多加了一个SENet网络，该网络主要是将特征的 embedding 通过 Squeeze 和Excitation 两个阶段得到一个权重向量，在用该向量与特征的embeding对应为相乘，挑选出最要特征之后在进入到朴素的DSSM网络中。 而 SENet 之所以起作用的原因，张俊林老师的解释是 SENet 可以突出那些对高层 User embedding 和 Item embedding 的特征交叉起重要作用的特征，更有利于表达两侧的特征交互，避免单侧无效特征经过DNN双塔非线性融合时带来的噪声，同时又带有非线性的作用。关于SENet网络详细内容可以查看[原文](https://arxiv.org/abs/1709.01507)
@@ -48,7 +48,7 @@ DSSM(Deep Structured Semantic Model)是由微软研究院于CIKM在2013年提出
该模型是Youtube于2019年在RecSys发表的一篇工作，这个模型从结构上来看是最普通的双塔。左边是user塔，输入包括两部分，第一部分是user当前正在观看的视频的特征，第二部分user的特征是用户历史行为的统计量，例如用户最近观看的N条视频的id embedding均值，这两部分融合起来一起输入user侧的输入。右边是item塔，将候选视频的特征作为输入，计算item的 embedding。之后也是再计算两侧embedding的相似度，进行学习。 模型的大致结构如下所示：
-
+
对于该模型，重点并不在于结构上的改变，而是对于负采样问题。因为召回的过程可以被视为是一个多分类问题，模型的输出层选择softmax计算后再计算交叉熵损失。但问题是当候选item特别多的时候，无法对所有的item进行softmax，因此通常的做法是随机从全量item中采样出一个batch的item进行softmax。但是使用batch内的样本作为彼此负样本会带来非常大的偏置问题，即对于热门的样本，被当作负样本的概率更高，因此该模型的贡献在于如何减小batch内负采样所带来的偏置问题？ 关于paper的详细内容可以查看[原文](https://dl.acm.org/doi/10.1145/3298689.3346996)
diff --git a/docs/ch03/ch3.2/3.2.8.1.md b/docs/ch03/ch3.2/3.2.8.1.md
index 15c96e8c0..58553a9a9 100644
--- a/docs/ch03/ch3.2/3.2.8.1.md
+++ b/docs/ch03/ch3.2/3.2.8.1.md
@@ -45,30 +45,30 @@
- 问：在执行`Scrapy`进行新闻爬取实战的时候，写不进去`mongdb`数据库
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
神经元可以接收其他神经元的信息，也可以发送信息给其他神经元。神经元之间没有物理连接，两个“连接”的神经元之间留有 20 纳米左右的缝隙，并靠突触进行互联来传递信息，形成一个神经网络，即神经系统。
@@ -30,7 +30,7 @@
1943 年，心理学家 McCulloch 和数学家 Pitts 根据生物神经元的结构，提出了一种非常简单的神经元模型，MP神经元。现代神经网络中的神经元和 MP 神经元的结构并无太多变化。不同的是，MP 神经元中的激活函数 $f$ 为 $0$ 或 $1$ 的阶跃函数，而现代神经元中的激活函数通常要求是连续可导的函数。
-
+
假设一个神经元接收到了 $n$ 个输入 $x_1, ... ,x_n$，令向量 $x=[x_1;x_2;...;x_n]$ 来表示这组输入，并用净输出 $z \in \mathbb{R}$ 表示一个神经元所获得的输入信号 $x$ 的加权和：
@@ -54,7 +54,7 @@ $$
理想中的激活函数为阶跃函数，它可以将输入值映射为 $0$ 或 $1$ ，这里 $1$ 对应神经元兴奋， $0$ 对应神经元抑制。但是阶跃函数具有不连续，不光滑等不太好的性质。
-
+
为了增强网络的表示能力和学习能力，激活函数需要具备以下几点性质：
@@ -72,7 +72,7 @@ $$
常用的 Sigmoid 函数有 Logistic 函数和 Tanh 函数，它们的形状都呈 S 型，均为两端饱和函数。所谓两端饱和，指的是当变量 $x$ 趋无穷时，函数 $f(x)$ 的导数 $f'(x)$ 趋向于 $0$ 。
-
+
+ Logistic 函数定义：
$$
@@ -155,7 +155,7 @@ $$
$$
其中， $\boldsymbol{W}^{(l)} \in \mathbb{R}^{N_{l-1} \times N_{l}}$ 为第 $l$ 层的参数矩阵，$\boldsymbol{b}^{(l)}\in \mathbb{R}^{N_{l}}$ 为第 $l$ 层的偏置向量，$f_l$ 为第 $l$ 层的激活函数，$\boldsymbol{a}^{(l)}\in \mathbb{R}^{N_{l}}$为第 $l$ 层的输出。示例如下：
-
+
这样，前馈神经网络可以通过逐层进行信息传递，得到网络最后的输出 $a^{L}$：
$$
@@ -172,7 +172,7 @@ $$
假设存在 $y=f(x)$ 的计算，则该计算的反向传播如下：
-
+
反正传播的计算顺序是，将信号 $E$ 乘以节点的局部导数 $\frac{\partial y}{\partial x}$， 然后将结果传递给下一个节点。
@@ -197,9 +197,9 @@ $$
$$
将链式法则用计算图表示，如下：
-
+
-
+
### 2.4.3 反向传播
@@ -231,13 +231,13 @@ y=\frac{1}{1+exp(-x)}
$$
用计算图可以表示为：
-
+
可以看到，复杂的公式经过拆解，已经没那么复杂了。现在，按照前面总结的反向传播规则，可以得到加上反向传播后的计算图：
-
+
+ 以最后一个运算 $/$ （除法）为例，令输入 $t=1+exp(-x)$，则输出 $y=\frac{1}{t}$，有：
$$
@@ -262,7 +262,7 @@ $$
对于一些层数较深的神经网络模型，在训练时可能会出现一些问题，其中就包括梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。梯度消失问题和梯度爆炸问题一般随着网络层数的增加会变得越来越明显。
-
+
我们知道前馈神经网络的传播公式如下：
$$
@@ -304,7 +304,7 @@ $$
根据Sigmoid 函数的表达式，其函数图像（左）及其导数图像（右）如下：
-
+
可以看出，Sigmoid 函数的导数取值范围在 $(0, 0.25]$ 之间，而权重矩阵在初始化时通常 $|| \boldsymbol{W}||<1$，则有 $|| f'(\cdot) \times \boldsymbol{W}||\le 0.25 $ 。由链式法则可得，由于连乘效应，梯度 $\large \frac{\partial \boldsymbol{L}}{\partial \boldsymbol{W}^{(0)}}$ 会越来越小，从而引发梯度消失的问题。
@@ -326,7 +326,7 @@ Youtube 作为全球最大的 UGC 的视频网站，需要在百万量级的视
-
+
从上图可以看出，YoutubeDNN 包含了两个阶段分别为：
@@ -341,7 +341,7 @@ Youtube 作为全球最大的 UGC 的视频网站，需要在百万量级的视
-
+
从模型的结构来看，召回阶段使用的模型并不复杂，为包含多层神经网络的 DNN 模型。下面简单分析模型的流程：
@@ -375,7 +375,7 @@ Youtube 作为全球最大的 UGC 的视频网站，需要在百万量级的视
-
+
可以看出，该阶段使用的模型与前面召回阶段相同，均为 DNN 模型。不同的是：
@@ -406,7 +406,7 @@ Wide＆Deep是谷歌发表在 DLRS 2016 上的文章《Wide & Deep Learning for
Wide & Deep 已成功应用到了 Google Play 的 app 推荐业务，具体的模型结构如下：
-
+
从结构图上看，Wide&Deep 由两部分组成，分别为 Wide 部分和 Deep 部分。简单来说，Wide 部分就是一个线性层，Deep 部分为多层前馈神经网络层，下面先对原理进行介绍：
@@ -465,7 +465,7 @@ $$
下图，是谷歌在应用商店中的推荐模型架构：
-
+
+ Deep 部分的输入是全量的特征向量，包括用户年龄（Age）、已安装应用数量（＃App Installs）、设备类型（Device Class）、已安装应用（User Installed App）、曝光应用（ Impression App）等特征。已安装应用、曝光应用等类别型特征，需要经过Embedding层输入连接层（Concatenated Embedding），拼接成1200维的Embedding向量，再依次经过3层ReLU全连接层，最终输入LogLoss输出层。
diff --git "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.4 \345\270\270\347\224\250\344\274\230\345\214\226\347\256\227\346\263\225.md" "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.4 \345\270\270\347\224\250\344\274\230\345\214\226\347\256\227\346\263\225.md"
index 3abc29527..72199db46 100644
--- "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.4 \345\270\270\347\224\250\344\274\230\345\214\226\347\256\227\346\263\225.md"
+++ "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.4 \345\270\270\347\224\250\344\274\230\345\214\226\347\256\227\346\263\225.md"
@@ -12,7 +12,7 @@
$$
\theta x+(1-\theta) y \in S
$$
-
+
### 1.1.2 凸函数
@@ -22,7 +22,7 @@ f(\lambda x+(1-\lambda)y) \le \lambda f(x)+(1-\lambda)f(y)
$$
直观上来看，对于 $z \in (x, y)$，对应的坐标点 $\left (z,f(z)\right)$ 的位置，均处于点 $(x, f(x))$ 和点 $(y,f(y))$ 连接成的线段的下方。
-
+
凸函数具有一个重要的性质： **局部极小值点为全局极小值点**。
@@ -169,7 +169,7 @@ $$
$$
一旦达到收敛条件的话，迭代就结束。从梯度下降法的迭代公式来看，下一个点的选择与当前点的位置和它的梯度相关。
-
+
不同的优化算法，由于优化目标函数时有着不同的出发点，所以函数在寻找局部极小值点的时对应的轨迹也有所不同。
diff --git "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.5 \346\267\261\345\272\246\345\255\246\344\271\240\346\250\241\345\236\213\346\220\255\345\273\272\345\237\272\347\241\200.md" "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.5 \346\267\261\345\272\246\345\255\246\344\271\240\346\250\241\345\236\213\346\220\255\345\273\272\345\237\272\347\241\200.md"
index b3b5025c7..453b5aee1 100644
--- "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.5 \346\267\261\345\272\246\345\255\246\344\271\240\346\250\241\345\236\213\346\220\255\345\273\272\345\237\272\347\241\200.md"
+++ "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.5 \346\267\261\345\272\246\345\255\246\344\271\240\346\250\241\345\236\213\346\220\255\345\273\272\345\237\272\347\241\200.md"
@@ -122,7 +122,7 @@ model = keras.Model(
keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)
```
-
+
从上面这个图就可以看出，模型多输入，多输出，共享层的结构，并且也会发现搭建的过程也是非常的简单。
@@ -134,7 +134,7 @@ keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=Tr
先说答案：将输入的数据转换成字典的形式，定义输入层的时候让输入层的name和字典中特征的key一致，就可以使得输入的数据和对应的Input层对应，后面搭建模型就是和上面介绍的一样的了。
-
+
直接看个例子吧：
@@ -176,7 +176,7 @@ model.fit(x, y, batch_size=1, epochs=2, validation_split=0.2)
keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)
```
-
+
上面就是举了个简单的例子说明，当多输入特别多的时候，构建模型我们可以将数据转换成字典的形式，然后字典中特征的名称与其对应的Input层的名称一致就行，这里是为了后面搭建复杂模型打基础。
@@ -184,7 +184,7 @@ keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=Tr
相信大家对DeepCTR开源项目应该是有点了解，DeepCTR通过对现有的基于深度学习的点击率预测模型的结构进行抽象总结，在设计过程中采用模块化的思路，各个模块自身具有高复用性，各个模块之间互相独立。 基于深度学习的点击率预测模型按模型内部组件的功能可以划分成以下4个模块：输入模块，嵌入模块，特征提取模块，预测输出模块。关于DeepCTR的介绍可以参考这个文章[DeepCTR：易用可扩展的深度学习点击率预测算法包](https://zhuanlan.zhihu.com/p/53231955)
-
+
这个开源项目做的非常好反而不是特别适合初学者学习，但是又非常适合推荐系统领域的小白去学习，所以本次内容设计我们借鉴了DeepCTR的设计思想，复现课程中的代码，复现的代码中包含了大量的注释，使得学习者在了解了上述所说的函数式API构建模型的基础上，快速看懂源码的设计，以及模型的原理。下面主要说一下我们代码参考DeepCTR项目实现需要注意的几个点。
@@ -232,7 +232,7 @@ keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=Tr
上面在说了类别特征和可变长的序列特征，在这两个Input层之后都需要将其转化成Embedding向量或者Embedding矩阵，在keras中转化成Embedding向量和Embedding矩阵只是相差一个参数的问题
-
+
diff --git "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.6 Word2vec.md" "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.6 Word2vec.md"
index 2ed19ec8b..2883242cf 100644
--- "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.6 Word2vec.md"
+++ "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.0 \346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200/1.0.6 Word2vec.md"
@@ -56,7 +56,7 @@ one-hot向量的维度是词汇表的大小（如：500，000）
如果我们可以使用某种方法为每个单词构建一个合适的dense vector，如下图，那么通过点积等数学计算就可以获得单词之间的某种联系
-
+
+
+ 
TP：真的真了（真实值是真的，预测也是真）
@@ -136,7 +136,7 @@
$$
ROC(**Receiver Operating Characteristic Curve**)曲线：
- 
+ 
ROC曲线的横坐标为假阳性率（False Positive Rate, FPR），N是真实负样本的个数， FP是N个负样本中被分类器预测为正样本的个数。
diff --git "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.1 \345\237\272\347\241\200\346\216\250\350\215\220\347\256\227\346\263\225/1.1.2 \345\215\217\345\220\214\350\277\207\346\273\244-UserCF.md" "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.1 \345\237\272\347\241\200\346\216\250\350\215\220\347\256\227\346\263\225/1.1.2 \345\215\217\345\220\214\350\277\207\346\273\244-UserCF.md"
index 2a34353e3..72a3c8617 100644
--- "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.1 \345\237\272\347\241\200\346\216\250\350\215\220\347\256\227\346\263\225/1.1.2 \345\215\217\345\220\214\350\277\207\346\273\244-UserCF.md"
+++ "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.1 \345\237\272\347\241\200\346\216\250\350\215\220\347\256\227\346\263\225/1.1.2 \345\215\217\345\220\214\350\277\207\346\273\244-UserCF.md"
@@ -99,13 +99,13 @@
+ 例如，我们要对用户 $A$ 进行物品推荐，可以先找到和他有相似兴趣的其他用户。
+ 然后，将共同兴趣用户喜欢的，但用户 $A$ 未交互过的物品推荐给 $A$。
-
+
下面分别介绍一下各层的作用：
@@ -48,7 +48,7 @@ dnn_inputs = Concatenate(axis=1)([dense_dnn_inputs, sparse_dnn_inputs]) # B x (n
该层的主要结构是MLP， 但DeepCrossing采用了残差网络进行的连接。通过多层残差网络对特征向量各个维度充分的交叉组合， 使得模型能够抓取更多的非线性特征和组合特征信息， 增加模型的表达能力。残差网络结构如下图所示：
-
+
Deep Crossing模型使用稍微修改过的残差单元，它不使用卷积内核，改为了两层神经网络。我们可以看到，残差单元是通过两层ReLU变换再将原输入特征相加回来实现的。具体代码实现如下：
@@ -136,11 +136,11 @@ def DeepCrossing(dnn_feature_columns):
为了方便大家的阅读，我们这里还给大家画了一个整体的模型架构图，帮助大家更好的了解每一块以及前向传播。（画的图不是很规范，先将就看一下，后面我们会统一在优化一下这个手工图）。
-
+
下面是一个通过keras画的模型结构图，为了更好的显示，数值特征和类别特征都只是选择了一小部分，画图的代码也在github中。
-
+
## 5. 参考资料
diff --git "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.2 \346\267\261\345\272\246\346\216\250\350\215\220\346\250\241\345\236\213/1.2.3 PNN.md" "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.2 \346\267\261\345\272\246\346\216\250\350\215\220\346\250\241\345\236\213/1.2.3 PNN.md"
index 7b9bb4ccb..c513ee8d0 100644
--- "a/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.2 \346\267\261\345\272\246\346\216\250\350\215\220\346\250\241\345\236\213/1.2.3 PNN.md"
+++ "b/docs/\347\254\254\344\270\200\347\253\240 \346\216\250\350\215\220\347\263\273\347\273\237\345\237\272\347\241\200/1.2 \346\267\261\345\272\246\346\216\250\350\215\220\346\250\241\345\236\213/1.2.3 PNN.md"
@@ -12,13 +12,13 @@ PNN模型其实是对IPNN和OPNN的总称，两者分别对应的是不同的Pro
PNN模型的整体架构如下图所示：
-