GRU4RECF-Attention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import argparse\n",
    "from torch.utils.data import DataLoader\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "from preprocessing import *\n",
    "from dataset import *\n",
    "from metrics import *\n",
    "from model import *\n",
    "from utils import bert2dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Tue Mar 23 08:39:11 2021\n",
    "\n",
    "@author: lpott\n",
    "\"\"\"\n",
    "import argparse\n",
    "from torch.utils.data import DataLoader\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "from preprocessing import *\n",
    "from dataset import *\n",
    "from metrics import *\n",
    "from model import *\n",
    "from utils import bert2dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "\n",
    "read_filename =\"data/movielens-1m/ratings.dat\"\n",
    "read_bert_filename = \"data/bert_sequence_20m.txt\"\n",
    "read_movie_filename = \"\"#\"movies-1m.csv\"\n",
    "size = \"1m\"\n",
    "\n",
    "num_epochs = 100\n",
    "lr = 1e-3\n",
    "batch_size = 64\n",
    "reg = 1e-4\n",
    "train_method = \"alternate\"\n",
    "loss_type = \"BPR\"\n",
    "num_neg_samples = 10\n",
    "\n",
    "\n",
    "hidden_dim = 256\n",
    "attn_dim = 64\n",
    "embedding_dim = 256\n",
    "bert_dim= 768\n",
    "window = 0\n",
    "\n",
    "freeze_plot = False\n",
    "tied = False\n",
    "dropout= 0\n",
    "\n",
    "k = 10\n",
    "max_length = 200\n",
    "min_len = 10\n",
    "\n",
    "\n",
    "# nextitnet options...\n",
    "hidden_layers = 3\n",
    "dilations = [1,2,2,4]\n",
    "\n",
    "model_type = \"attention\"\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available(): \n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Creating DataFrame ==========\n",
      "user_id        6040\n",
      "item_id        3706\n",
      "rating            5\n",
      "timestamp    458455\n",
      "dtype: int64\n",
      "(1000209, 4)\n",
      "Minimum Session Length: 20\n",
      "Maximum Session Length: 2314\n",
      "Average Session Length: 165.60\n",
      "========== Filtering Sessions <= 10  DataFrame ==========\n",
      "user_id        6040\n",
      "item_id        3706\n",
      "rating            5\n",
      "timestamp    458455\n",
      "dtype: int64\n",
      "(1000209, 4)\n",
      "Minimum Session Length: 20\n",
      "Maximum Session Length: 2314\n",
      "Average Session Length: 165.60\n"
     ]
    }
   ],
   "source": [
    "# ------------------Data Initialization----------------------#\n",
    "\n",
    "# convert .dat file to time-sorted pandas dataframe\n",
    "ml_1m = create_df(read_filename,size=size)\n",
    "\n",
    "# remove users who have sessions lengths less than min_len\n",
    "ml_1m = filter_df(ml_1m,item_min=min_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Initialize Reset DataFrame Object ==========\n",
      "========== Resetting user ids and item ids in DataFrame ==========\n"
     ]
    }
   ],
   "source": [
    "# ------------------Data Initialization----------------------#\n",
    "if read_movie_filename != \"\":\n",
    "    ml_movie_df = create_movie_df(read_movie_filename,size=size)\n",
    "    ml_movie_df = convert_genres(ml_movie_df)\n",
    "    \n",
    "    # initialize reset object\n",
    "    reset_object = reset_df()\n",
    "    \n",
    "    # map all user ids, item ids, and genres to range 0 - number of users/items/genres\n",
    "    ml_1m,ml_movie_df = reset_object.fit_transform(ml_1m,ml_movie_df)\n",
    "    \n",
    "    # value that padded genre tokens shall take\n",
    "    pad_genre_token = reset_object.genre_enc.transform([\"NULL\"]).item()\n",
    "    \n",
    "    genre_dim = len(np.unique(np.concatenate(ml_movie_df.genre))) - 1\n",
    "\n",
    "else:\n",
    "    # initialize reset object\n",
    "    reset_object = reset_df()\n",
    "    \n",
    "    # map all user ids and item ids to range 0 - Number of Users/Items \n",
    "    # i.e. [1,7,5] -> [0,2,1]\n",
    "    ml_1m = reset_object.fit_transform(ml_1m)\n",
    "    \n",
    "    pad_genre_token = None\n",
    "    ml_movie_df = None\n",
    "    genre_dim = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Reading .txt file with all item id and embeddings ==========\n"
     ]
    }
   ],
   "source": [
    "# ------------------Data Initialization----------------------#\n",
    "# how many unique users, items, ratings and timestamps are there\n",
    "n_users,n_items,n_ratings,n_timestamp = ml_1m.nunique()\n",
    "\n",
    "# value that padded tokens shall take\n",
    "pad_token = n_items\n",
    "\n",
    "# the output dimension for softmax layer\n",
    "output_dim = n_items\n",
    "\n",
    "# get the item id : bert plot embedding dictionary\n",
    "if bert_dim != 0:\n",
    "    feature_embed = bert2dict(bert_filename=read_bert_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 46/6040 [00:00<00:13, 448.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Creating User Histories ==========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6040/6040 [00:11<00:00, 515.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary of every user's session (history)\n",
    "# i.e. {user: [user clicks]}\n",
    "if size == \"1m\":\n",
    "    user_history = create_user_history(ml_1m)\n",
    "\n",
    "elif size == \"20m\":\n",
    "    import pickle\n",
    "    with open('userhistory.pickle', 'rb') as handle:\n",
    "        user_history = pickle.load(handle)\n",
    "# create a dictionary of all items a user has not clicked\n",
    "# i.e. {user: [items not clicked by user]}\n",
    "# user_noclicks = create_user_noclick(user_history,ml_1m,n_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6040/6040 [00:00<00:00, 111126.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Splitting User Histories into Train, Validation, and Test Splits ==========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# split data by leave-one-out strategy\n",
    "# have train dictionary {user: [last 41 items prior to last 2 items in user session]}\n",
    "# have val dictionary {user: [last 41 items prior to last item in user session]}\n",
    "# have test dictionary {user: [last 41 items]}\n",
    "# i.e. if max_length = 4, [1,2,3,4,5,6] -> [1,2,3,4] , [2,3,4,5] , [3,4,5,6]\n",
    "train_history,val_history,test_history = train_val_test_split(user_history,max_length=max_length)\n",
    "\n",
    "# initialize the train,validation, and test pytorch dataset objects\n",
    "# eval pads all items except last token to predict\n",
    "train_dataset = GRUDataset(train_history,genre_df=ml_movie_df,mode='train',max_length=max_length,pad_token=pad_token,pad_genre_token=pad_genre_token)\n",
    "val_dataset = GRUDataset(val_history,genre_df=ml_movie_df,mode='eval',max_length=max_length,pad_token=pad_token,pad_genre_token=pad_genre_token)\n",
    "test_dataset = GRUDataset(test_history,genre_df=ml_movie_df,mode='eval',max_length=max_length,pad_token=pad_token,pad_genre_token=pad_genre_token)\n",
    "\n",
    "# create the train,validation, and test pytorch dataloader objects\n",
    "train_dl = DataLoader(train_dataset,batch_size = batch_size,shuffle=True)\n",
    "val_dl = DataLoader(val_dataset,batch_size=64)\n",
    "test_dl = DataLoader(test_dataset,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bert dim: 768\n",
      "Genre dim: 0\n",
      "Pad Token: 3706\n",
      "Pad Genre Token: None\n"
     ]
    }
   ],
   "source": [
    "print(\"Bert dim: {:d}\".format(bert_dim))\n",
    "print(\"Genre dim: {:d}\".format(genre_dim))\n",
    "print(\"Pad Token: {}\".format(pad_token))\n",
    "print(\"Pad Genre Token: {}\".format(pad_genre_token))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------Model Initialization----------------------#\n",
    "\n",
    "# initialize gru4rec model with arguments specified earlier\n",
    "if model_type == \"feature_add\":\n",
    "    model = gru4recF(embedding_dim=embedding_dim,\n",
    "             hidden_dim=hidden_dim,\n",
    "             output_dim=output_dim,\n",
    "             genre_dim=genre_dim,\n",
    "             batch_first=True,\n",
    "             max_length=max_length,\n",
    "             pad_token=pad_token,\n",
    "             pad_genre_token=pad_genre_token,\n",
    "             bert_dim=bert_dim,\n",
    "             tied = tied,\n",
    "             dropout=dropout)\n",
    "\n",
    "\n",
    "if model_type == \"feature_concat\":\n",
    "    model = gru4recFC(embedding_dim=embedding_dim,\n",
    "             hidden_dim=hidden_dim,\n",
    "             output_dim=output_dim,\n",
    "             genre_dim=genre_dim,\n",
    "             batch_first=True,\n",
    "             max_length=max_length,\n",
    "             pad_token=pad_token,\n",
    "             pad_genre_token=pad_genre_token,\n",
    "             bert_dim=bert_dim,\n",
    "             tied = tied,\n",
    "             dropout=dropout)\n",
    "\n",
    "if model_type == \"vanilla\":\n",
    "    model = gru4rec_vanilla(hidden_dim=hidden_dim,\n",
    "                            output_dim=output_dim,\n",
    "                            batch_first=True,\n",
    "                            max_length=max_length,\n",
    "                            pad_token=pad_token,\n",
    "                            tied=tied,\n",
    "                            embedding_dim=embedding_dim)\n",
    "\n",
    "if model_type ==\"feature_only\":\n",
    "    model = gru4rec_feature(hidden_dim=hidden_dim,\n",
    "                            output_dim=output_dim,\n",
    "                            batch_first=True,\n",
    "                            max_length=max_length,\n",
    "                            pad_token=pad_token,\n",
    "                            bert_dim=bert_dim)\n",
    "\n",
    "if model_type == \"conv\":\n",
    "    model = gru4rec_conv(embedding_dim,\n",
    "                 hidden_dim,\n",
    "                 output_dim,\n",
    "                 batch_first=True,\n",
    "                 max_length=200,\n",
    "                 pad_token=0,\n",
    "                 dropout=0,\n",
    "                 window=3,\n",
    "                 tied=tied)\n",
    "    \n",
    "if model_type == \"nextitnet\":\n",
    "    model = NextItNet(embedding_dim=embedding_dim,\n",
    "                      output_dim=output_dim,\n",
    "                      hidden_layers=hidden_layers,\n",
    "                      dilations=dilations,\n",
    "                      pad_token=n_items,\n",
    "                      max_len=max_length)\n",
    "\n",
    "if model_type == \"attention\":\n",
    "    model = gru4recF_attention(embedding_dim=embedding_dim,\n",
    "             hidden_dim=hidden_dim,\n",
    "             output_dim=output_dim,\n",
    "             genre_dim=genre_dim,\n",
    "             attn_dim=attn_dim,\n",
    "             batch_first=True,\n",
    "             max_length=max_length,\n",
    "             pad_token=pad_token,\n",
    "             pad_genre_token=pad_genre_token,\n",
    "             bert_dim=bert_dim,\n",
    "             tied = tied,\n",
    "             dropout=dropout, cat=True)\n",
    "#     modelD = gru4recF_decoder(hidden_dim=hidden_dim, output_dim=output_dim, dropout=0, max_length=max_length)\n",
    "#     modelD = modelD.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if bert_dim != 0:\n",
    "    model.init_weight(reset_object,feature_embed)\n",
    "    \n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['plot_embedding.weight',\n",
       " 'plot_projection.weight',\n",
       " 'plot_projection.bias',\n",
       " 'encoder_layer.weight_ih_l0',\n",
       " 'encoder_layer.weight_hh_l0',\n",
       " 'encoder_layer.bias_ih_l0',\n",
       " 'encoder_layer.bias_hh_l0',\n",
       " 'attention_layer.weight',\n",
       " 'attention_layer.bias',\n",
       " 'score_layer.weight',\n",
       " 'score_layer.bias',\n",
       " 'output_layer.weight',\n",
       " 'output_layer.bias']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[name for name,param in model.named_parameters() if ((\"movie\" not in name) or (\"plot_embedding\" in name) or (\"genre\" in name))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['movie_embedding.weight',\n",
       " 'encoder_layer.weight_ih_l0',\n",
       " 'encoder_layer.weight_hh_l0',\n",
       " 'encoder_layer.bias_ih_l0',\n",
       " 'encoder_layer.bias_hh_l0',\n",
       " 'attention_layer.weight',\n",
       " 'attention_layer.bias',\n",
       " 'score_layer.weight',\n",
       " 'score_layer.bias',\n",
       " 'output_layer.weight',\n",
       " 'output_layer.bias']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[name for name,param in model.named_parameters() if (\"plot\" not in name) and (\"genre\" not in name)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize Adam optimizer with gru4rec model parameters\n",
    "if train_method != \"normal\":\n",
    "    optimizer_features = torch.optim.Adam([param for name,param in model.named_parameters() if ((\"movie\" not in name) or (\"plot_embedding\" in name) or (\"genre\" in name)) ],\n",
    "                                          lr=lr,weight_decay=reg) # can change\n",
    "    \n",
    "    optimizer_ids = torch.optim.Adam([param for name,param in model.named_parameters() if (\"plot\" not in name) and (\"genre\" not in name)],\n",
    "                                     lr=lr,weight_decay=reg)\n",
    "\n",
    "elif train_method == \"normal\":\n",
    "    optimizer = torch.optim.Adam(model.parameters(),lr=lr,weight_decay=reg)\n",
    "    gru4recF_decoder_optimizer = torch.optim.Adam(model.parameters(), lr=lr,weight_decay=reg)\n",
    "if freeze_plot and bert_dim !=0:\n",
    "    model.plot_embedding.weight.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3706\n"
     ]
    }
   ],
   "source": [
    "if loss_type == \"XE\":\n",
    "    loss_fn = nn.CrossEntropyLoss(ignore_index=n_items)\n",
    "    \n",
    "elif loss_type == \"BPR\":\n",
    "    loss_fn = BPRLoss(user_history = user_history,\n",
    "                      n_items = n_items, \n",
    "                      df = ml_1m,\n",
    "                      device = device, \n",
    "                      samples=num_neg_samples)\n",
    "\n",
    "elif loss_type == \"BPR_MAX\":\n",
    "    loss_fn = BPRMaxLoss(user_history = user_history,\n",
    "                      n_items = n_items, \n",
    "                      df = ml_1m,\n",
    "                      device = device,\n",
    "                      reg = 2,\n",
    "                      samples=num_neg_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== Creating Hit@10 Metric Object ==========\n"
     ]
    }
   ],
   "source": [
    "Recall_Object = Recall_E_prob(ml_1m,user_history,n_users,n_items,k=k,device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/95 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Epoch 1 ====================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 2/95 [00:33<25:56, 16.73s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-5b96863eccbf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mloss_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"BPR\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mloss_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"BPR_MAX\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_lens\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0muid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/share/miniconda/envs/cs-249/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[0;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/code/ucla/cs-249/project/shared-repo/metrics.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, output, labels, x_lens, uids)\u001b[0m\n\u001b[0;32m    258\u001b[0m                 \u001b[1;31m# check if we match the sample length\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m                 \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minner_neg_ids\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m                     \u001b[0msampled_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    261\u001b[0m                     \u001b[0msampled_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msampled_ids\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0muid_history\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minner_neg_ids\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m                     \u001b[0msampled_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampled_ids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minner_neg_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~/.local/share/miniconda/envs/cs-249/lib/python3.8/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[1;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[0;32m    259\u001b[0m     \u001b[0mar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.local/share/miniconda/envs/cs-249/lib/python3.8/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[1;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m    322\u001b[0m         \u001b[0mar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[0maux\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m     \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maux\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m     \u001b[0mmask\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m     \u001b[0mmask\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maux\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0maux\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# ------------------Training Initialization----------------------#\n",
    "max_train_hit = (0,0,0)\n",
    "max_val_hit = (0,0,0)\n",
    "max_test_hit = (0,0,0)\n",
    "\n",
    "max_train_ndcg = (0,0,0)\n",
    "max_val_ndcg = (0,0,0)\n",
    "max_test_ndcg = (0,0,0)\n",
    "\n",
    "max_train_mrr = 0\n",
    "max_val_mrr = 0\n",
    "max_test_mrr = 0\n",
    "\n",
    "i = 0;\n",
    "for epoch in range(num_epochs):\n",
    "    print(\"=\"*20,\"Epoch {}\".format(epoch+1),\"=\"*20)\n",
    "    \n",
    "    model.train()  \n",
    "    \n",
    "    running_loss = 0\n",
    "\n",
    "    for j,data in enumerate(tqdm(train_dl,position=0,leave=True)):\n",
    "        \n",
    "        if train_method != \"normal\":\n",
    "            optimizer_features.zero_grad()\n",
    "            optimizer_ids.zero_grad()\n",
    "            \n",
    "        elif train_method == \"normal\": \n",
    "            optimizer.zero_grad()\n",
    "        \n",
    "        if genre_dim != 0:            \n",
    "            inputs,genre_inputs,labels,x_lens,uid = data\n",
    "            outputs = model(x=inputs.to(device),x_lens=x_lens.squeeze().tolist(),x_genre=genre_inputs.to(device))\n",
    "        \n",
    "        elif genre_dim == 0:\n",
    "            inputs,labels,x_lens,uid = data \n",
    "            outputs = model(x=inputs.to(device),x_lens=x_lens.squeeze().tolist())\n",
    "       \n",
    "        if tied:\n",
    "            outputs_ignore_pad = outputs[:,:,:-1]\n",
    "            if loss_type == \"XE\":\n",
    "                loss = loss_fn(outputs_ignore_pad.view(-1,outputs_ignore_pad.size(-1)),labels.view(-1).to(device))\n",
    "            elif loss_type == \"BPR\" or loss_type == \"BPR_MAX\":\n",
    "                loss = loss_fn(outputs,labels.to(device),x_lens,uid)\n",
    "\n",
    "            \n",
    "        else:\n",
    "            if loss_type == \"XE\":\n",
    "                loss = loss_fn(outputs.view(-1,outputs.size(-1)),labels.view(-1).to(device))\n",
    "            elif loss_type == \"BPR\" or loss_type == \"BPR_MAX\":   \n",
    "                loss = loss_fn(outputs,labels.to(device),x_lens,uid)\n",
    "\n",
    "        loss.backward()\n",
    "        \n",
    "        \n",
    "        if train_method != \"normal\":\n",
    "            if train_method == \"interleave\":\n",
    "                # interleave on the epochs\n",
    "                if (j+1) % 2 == 0:\n",
    "                    optimizer_features.step()\n",
    "                else:\n",
    "                    optimizer_ids.step()\n",
    "\n",
    "            elif train_method == \"alternate\":\n",
    "                if (epoch+1) % 2 == 0:\n",
    "                    optimizer_features.step()\n",
    "                else:\n",
    "                    optimizer_ids.step()\n",
    "                    \n",
    "        elif train_method == \"normal\":\n",
    "            optimizer.step()\n",
    "\n",
    "        running_loss += loss.detach().cpu().item()\n",
    "\n",
    "    del outputs\n",
    "    \n",
    "    if torch.cuda.is_available(): \n",
    "        torch.cuda.empty_cache()\n",
    "\n",
    "    training_hit,training_ndcg,training_mrr = Recall_Object(model,train_dl,\"train\")\n",
    "    validation_hit,validation_ndcg,validation_mrr = Recall_Object(model,val_dl,\"validation\")\n",
    "    testing_hit,testing_ndcg,testing_mrr = Recall_Object(model,test_dl,\"test\")\n",
    "    \n",
    "    if max_val_ndcg[0] < validation_ndcg[0]:\n",
    "        max_val_hit = validation_hit\n",
    "        max_test_hit = testing_hit\n",
    "        max_train_hit = training_hit\n",
    "        \n",
    "        max_train_ndcg = training_ndcg\n",
    "        max_val_ndcg = validation_ndcg\n",
    "        max_test_ndcg = testing_ndcg\n",
    "        \n",
    "        max_train_mrr = training_mrr\n",
    "        max_val_mrr = validation_mrr\n",
    "        max_test_mrr = testing_mrr\n",
    "    \n",
    "    torch.cuda.empty_cache()\n",
    "    print(\"Training Loss: {:.5f}\".format(running_loss/len(train_dl)))\n",
    "    \n",
    "    print(\"Train Hits \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*training_hit))\n",
    "    print(\"Train ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*training_ndcg))\n",
    "    print(\"Train mrr: {}\".format(max_train_mrr))\n",
    "\n",
    "    print(\"Valid Hits \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*validation_hit))\n",
    "    print(\"Valid ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*validation_ndcg))\n",
    "    print(\"Valid mrr: {}\".format(max_val_mrr))\n",
    "\n",
    "    print(\"Test Hits \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*testing_hit))\n",
    "    print(\"Test ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*testing_ndcg))\n",
    "    print(\"Test mrr: {}\".format(max_test_mrr))\n",
    "\n",
    "print(\"=\"*100)\n",
    "print(\"Maximum Training Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_train_hit))\n",
    "print(\"Maximum Validation Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_val_hit))\n",
    "print(\"Maximum Testing Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_test_hit))\n",
    "\n",
    "print(\"Maximum Training ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_train_ndcg))\n",
    "print(\"Maximum Validation ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_val_ndcg))\n",
    "print(\"Maximum Testing ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_test_ndcg))\n",
    "\n",
    "print(\"Maximum Training mrr: {}\".format(max_train_mrr))\n",
    "print(\"Maximum Validation mrr: {}\".format(max_val_mrr))\n",
    "print(\"Maximum Testing mrr: {}\".format(max_test_mrr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=\"*100)\n",
    "print(\"Maximum Training Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_train_hit))\n",
    "print(\"Maximum Validation Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_val_hit))\n",
    "print(\"Maximum Testing Hit \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_test_hit))\n",
    "\n",
    "print(\"Maximum Training ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_train_ndcg))\n",
    "print(\"Maximum Validation ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_val_ndcg))\n",
    "print(\"Maximum Testing ndcg \\t @10: {:.5f} \\t @5 : {:.5f} \\t @1 : {:.5f}\".format(*max_test_ndcg))\n",
    "\n",
    "print(\"Maximum Training mrr: {}\".format(max_train_mrr))\n",
    "print(\"Maximum Validation mrr: {}\".format(max_val_mrr))\n",
    "print(\"Maximum Testing mrr: {}\".format(max_test_mrr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input and label\n",
    "x = [5,3,10,11]\n",
    "y = [3,10,11,13]\n",
    "print(torch.cuda.is_available())\n",
    "print(y[0:5])\n",
    "# notes: gru layer stores hidden layer when using sequence input\n",
    "# use final hidden state from packed output\n",
    "# cross entropy loss used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INTRO:\n",
    "add proposal of what we want to do\n",
    "\n",
    "EVAL:\n"
   ]
  }
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