movielens_graph인덱스고친진짜원본.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"movielens_graph인덱스고친진짜원본.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dYM0uPdyZArF","outputId":"05176342-9b59-4b60-c896-3ded7caf4f25"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["import os\n","import json\n","from google.colab import drive\n","\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","source":["pip install dgl dglgo -f https://data.dgl.ai/wheels/repo.html"],"metadata":{"id":"ZznqNRK8ZKKs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import dgl\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import pandas as pd\n","from dgl import DGLGraph\n","import re\n","import scipy.sparse as sp"],"metadata":{"id":"x_cGFERVZKIM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"2002aa74-8d79-49b0-e489-389453e792a3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["DGL backend not selected or invalid.  Assuming PyTorch for now.\n"]},{"output_type":"stream","name":"stdout","text":["Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n"]}]},{"cell_type":"code","source":["import numpy as np\n","from numpy import dot\n","from numpy.linalg import norm"],"metadata":{"id":"86aVxA7j5PXo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ratings = []\n","with open(os.path.join('/content/gdrive/MyDrive/Colab Notebooks/인턴/ml-1m/', 'ratings.dat'), encoding='latin1') as f:\n","    for l in f:\n","        user_id, movie_id, rating, timestamp = [int(_) for _ in l.split('::')]\n","        ratings.append({\n","            'user_id': user_id,\n","            'movie_id': movie_id,\n","            'rating': rating,\n","            'timestamp': timestamp,\n","        })\n","ratings = pd.DataFrame(ratings)\n","ratings.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Jfdc7qSiawAN","outputId":"f5eb9a94-6bc2-4994-88a9-3e47190ea071"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   user_id  movie_id  rating  timestamp\n","0        1      1193       5  978300760\n","1        1       661       3  978302109\n","2        1       914       3  978301968\n","3        1      3408       4  978300275\n","4        1      2355       5  978824291"],"text/html":["\n","  <div id=\"df-3ed51c35-709f-4c91-8329-8fa7e22577c0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>movie_id</th>\n","      <th>rating</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1193</td>\n","      <td>5</td>\n","      <td>978300760</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>661</td>\n","      <td>3</td>\n","      <td>978302109</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>914</td>\n","      <td>3</td>\n","      <td>978301968</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>3408</td>\n","      <td>4</td>\n","      <td>978300275</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2355</td>\n","      <td>5</td>\n","      <td>978824291</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ed51c35-709f-4c91-8329-8fa7e22577c0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-3ed51c35-709f-4c91-8329-8fa7e22577c0 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["len(ratings.index)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x3T8lTzE23zu","outputId":"ced5d233-f62d-4052-eb32-33a599ebf09f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1000209"]},"metadata":{},"execution_count":111}]},{"cell_type":"code","source":["len(ratings['user_id'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vJ5d_1xuYZQi","outputId":"d5b045e3-4592-4f66-e0ed-510073964f0f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1000209"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["len(ratings['user_id'].unique()) "],"metadata":{"id":"4p5jBVrHZkGd","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a4f56a62-dd2b-4e90-eede-04eae76e5f0f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6040"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["max(list(ratings['user_id'].unique()))"],"metadata":{"id":"XQcaK15dZkDQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"006b644c-9a70-43e1-e903-5c5d747935ae"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6040"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["user_uni = ratings['user_id'].unique() # 유저 id는 1부터 6040까지 연속된 숫자 \n","for i in range(1, 6041):\n","  if i not in user_uni:\n","    print('d')"],"metadata":{"id":"CEhNq522Ynpx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["min(ratings['movie_id'].values) #movieid의 최소값이 1이니 6040을 더하면 겹치지 않을 것 "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aUJlinObbepo","outputId":"166b1eb5-e138-408d-9800-8ea91d360a67"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["np_movieid = ratings['movie_id'].values\n","np_movieid"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4p7GU4TC5Yc9","outputId":"cd165528-84bc-4828-b84b-811ad32e00dd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1193,  661,  914, ...,  562, 1096, 1097])"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["npplus = np.full((1000209,), 6040)\n","npplus"],"metadata":{"id":"cp6KCbFh3RLH","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1370c908-f241-44c4-cd17-07befc58d0ae"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([6040, 6040, 6040, ..., 6040, 6040, 6040])"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["ratings['movie_id_plus'] = list(np_movieid +  npplus)   #ratings['movie_id_plus']는 영화 id + 6040\n","ratings.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"alPitmG_3r-X","outputId":"3a4cda43-3421-4929-f886-39950d7a840f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   user_id  movie_id  rating  timestamp  movie_id_plus\n","0        1      1193       5  978300760           7233\n","1        1       661       3  978302109           6701\n","2        1       914       3  978301968           6954\n","3        1      3408       4  978300275           9448\n","4        1      2355       5  978824291           8395"],"text/html":["\n","  <div id=\"df-240c3b74-ed72-4045-8c3c-bb463cd5313f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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   </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>3408</td>\n","      <td>4</td>\n","      <td>978300275</td>\n","      <td>9448</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2355</td>\n","      <td>5</td>\n","      <td>978824291</td>\n","      <td>8395</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-240c3b74-ed72-4045-8c3c-bb463cd5313f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["user_id = ratings['user_id'].unique()\n","movie_id = ratings['movie_id'].unique()"],"metadata":{"id":"oKDzfZhVHKNq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["user_id_len = len(user_id)\n","movie_id_len = len(movie_id)\n","totalid = user_id_len + movie_id_len                   #total id 개수는 9746 \n","totalid"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QsA0CeKpFceY","outputId":"cdeb1de8-39d2-42f9-a1b9-92f63f82aed6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9746"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["user_id_len, movie_id_len                             # 노드 개수 => 유저: 6040 영화 3706 "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ETBJH0hjcGSg","outputId":"b2ea0e45-5c19-4531-c2f3-df97311fd7e4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(6040, 3706)"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["movie_id = np.sort(movie_id)\n","movie_id"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vpFQ7F6bdlza","outputId":"77000747-f7dc-4377-8236-8aa395a90e47"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([   1,    2,    3, ..., 3950, 3951, 3952])"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["nodeidx = {}   #key는 user_id, movie_id/ value는 node id \n","id = 0         #노드 id는 0부터 9745 (마지막에 id는 테이블에 넣지 않았으니)\n","\n","for i in range(user_id_len):\n","  nodeidx[user_id[i]] = id\n","  id += 1\n","print(id)     #node id 0~6039는 유저거 "],"metadata":{"id":"Qb23z3lS6XUQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6dad72a7-8a51-4e78-b4c8-078a804769d7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["6040\n"]}]},{"cell_type":"code","source":["movie_id[0] + 6040"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ygO-6F8ZfT4x","outputId":"9b5939e5-213a-4842-a101-fdf13fad5146"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6041"]},"metadata":{},"execution_count":42}]},{"cell_type":"code","source":["for j in range(movie_id_len): # 노드 id 6040 ~ 9745는 무비거 \n","  nodeidx[movie_id[j] + 6040]  = id\n","  id += 1\n","print(id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EKma5Ow9ehcY","outputId":"ec571d21-3b81-48e6-ee0e-ccac8f8104f1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["9746\n"]}]},{"cell_type":"code","source":["nodeidx[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nH1tT6AvPGzK","outputId":"929fbbe7-f711-4313-ae94-2869c15182c1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["nodeidx[6040]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uLAfD1Ljgtep","outputId":"ba38b3e6-35f6-4e67-d60c-36bd32231816"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6039"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["nodeidx[6041] "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gxHjTsmKgvnS","outputId":"c98af74a-60a9-4bea-c6af-9390dddf0cf3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6040"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","source":["nodeidx[9992] #movie id는 제일 큰 거 "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FDEhf09Rg0iO","outputId":"70441b2b-7bbe-4982-e623-f2482e6f9f25"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9745"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","source":["len(nodeidx)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ok8jMnvuhJZ7","outputId":"100c2c73-10ca-46bd-f27f-7d2dba466ecd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9746"]},"metadata":{},"execution_count":49}]},{"cell_type":"code","source":["ratings.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"bndnStup70XN","outputId":"502a8a77-aeaa-439c-e907-bf72d41adcc8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   user_id  movie_id  rating  timestamp  movie_id_plus\n","0        1      1193       5  978300760           7233\n","1        1       661       3  978302109           6701\n","2        1       914       3  978301968           6954\n","3        1      3408       4  978300275           9448\n","4        1      2355       5  978824291           8395\n","5        1      1197       3  978302268           7237\n","6        1      1287       5  978302039           7327\n","7        1      2804       5  978300719           8844\n","8        1       594       4  978302268           6634\n","9        1       919       4  978301368           6959"],"text/html":["\n","  <div id=\"df-fa9d61fe-e336-442c-bb91-868fa061c332\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>movie_id</th>\n","      <th>rating</th>\n","      <th>timestamp</th>\n","      <th>movie_id_plus</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1193</td>\n","      <td>5</td>\n","      <td>978300760</td>\n","      <td>7233</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>661</td>\n","      <td>3</td>\n","      <td>978302109</td>\n","      <td>6701</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>914</td>\n","      <td>3</td>\n","      <td>978301968</td>\n","      <td>6954</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>3408</td>\n","      <td>4</td>\n","      <td>978300275</td>\n","      <td>9448</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2355</td>\n","      <td>5</td>\n","      <td>978824291</td>\n","      <td>8395</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1</td>\n","      <td>1197</td>\n","      <td>3</td>\n","      <td>978302268</td>\n","      <td>7237</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1</td>\n","      <td>1287</td>\n","      <td>5</td>\n","      <td>978302039</td>\n","      <td>7327</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2804</td>\n","      <td>5</td>\n","      <td>978300719</td>\n","      <td>8844</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>594</td>\n","      <td>4</td>\n","      <td>978302268</td>\n","      <td>6634</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>919</td>\n","      <td>4</td>\n","      <td>978301368</td>\n","      <td>6959</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-fa9d61fe-e336-442c-bb91-868fa061c332');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":50}]},{"cell_type":"code","source":[" ex = ratings.iloc[0, 4]\n"," ex"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oZY9oM9ohURr","outputId":"8ebbf288-b485-44f9-8367-a15a4e5d2cdd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7233"]},"metadata":{},"execution_count":51}]},{"cell_type":"code","source":["users = []  #user_id에 해당하는 노드 id를 넣음 \n","movies = []\n","\n","for i in range(len(ratings.index)):\n","  user = ratings.iloc[i,0]\n","  users.append(nodeidx[user])\n","\n","  movie = ratings.iloc[i, 4]\n","  movies.append(nodeidx[movie])\n"],"metadata":{"id":"vcmulEuM6XQb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(users), len(movies)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w2BS2xi2iWCj","outputId":"282c1f41-4f3e-4703-db16-650bb1ad5e9e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000209, 1000209)"]},"metadata":{},"execution_count":54}]},{"cell_type":"code","source":["edges_src = torch.from_numpy(np.array(users))\n","edges_dst = torch.from_numpy(np.array(movies))"],"metadata":{"id":"gEXxmb7v7APz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["graph = dgl.graph((edges_src, edges_dst))\n","print(graph)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ERRdqK497AM_","outputId":"3537caaa-a104-435f-ed2a-22b018f2c30f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Graph(num_nodes=9746, num_edges=1000209,\n","      ndata_schemes={}\n","      edata_schemes={})\n"]}]},{"cell_type":"code","source":["edge_features = torch.from_numpy(ratings['rating'].to_numpy())\n","len(edge_features)"],"metadata":{"id":"xWFHWmDT7fC-","colab":{"base_uri":"https://localhost:8080/"},"outputId":"11e2d6b1-40f2-4725-e394-390ff7ca8a0c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1000209"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["graph.edata['w'] = edge_features"],"metadata":{"id":"7H9ToY7K7fAf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(graph)"],"metadata":{"id":"1Bq5eCb17e9Q","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c186b28a-0688-4bdc-e4bf-cc80b895ec83"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Graph(num_nodes=9746, num_edges=1000209,\n","      ndata_schemes={}\n","      edata_schemes={'w': Scheme(shape=(), dtype=torch.int64)})\n"]}]},{"cell_type":"markdown","source":["https://docs.dgl.ai/tutorials/blitz/4_link_predict.html?highlight=negative"],"metadata":{"id":"UmPC2HWMK1Tl"}},{"cell_type":"code","source":["u, v = graph.edges() #엣지 길이 "],"metadata":{"id":"DNaveUvVK2Io"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["eids = np.arange(graph.number_of_edges()) #엣지를 가지고 train, test로 나누어줌\n","\n","eids = np.random.permutation(eids)\n","test_size = int(len(eids) * 0.1)    #엣지 중 10%를 택함\n","train_size = graph.number_of_edges() - test_size\n","\n","test_pos_u, test_pos_v = u[eids[:test_size]], v[eids[:test_size]]\n","train_pos_u, train_pos_v = u[eids[test_size:]], v[eids[test_size:]]"],"metadata":{"id":"qVd3q5rOK3c-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#여기서부터 node pairs with no edges between them as negative examples.\n","adj = sp.coo_matrix((np.ones(len(u)), (u.numpy(), v.numpy())), shape=(9746,9746)      )\n","print(adj.shape)\n","adj_neg = 1 - adj.todense() - np.eye(graph.number_of_nodes())\n","neg_u, neg_v = np.where(adj_neg != 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MGI1dTb-Iz9O","outputId":"c9e142d5-cd7a-429c-e09e-e370ec89a7e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(9746, 9746)\n"]}]},{"cell_type":"code","source":["neg_eids = np.random.choice(len(neg_u), graph.number_of_edges())\n","test_neg_u, test_neg_v = neg_u[neg_eids[:test_size]], neg_v[neg_eids[:test_size]]\n","train_neg_u, train_neg_v = neg_u[neg_eids[test_size:]], neg_v[neg_eids[test_size:]]"],"metadata":{"id":"YyyzelKMIz6F"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_g = dgl.remove_edges(graph, eids[:test_size])"],"metadata":{"id":"7ODoBEaBIz3i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=graph.number_of_nodes())\n","train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=graph.number_of_nodes())\n","\n","test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=graph.number_of_nodes())\n","test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=graph.number_of_nodes())\n"],"metadata":{"id":"AfohbW3iKUBB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"hfqwmbtbKT-a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from dgl.nn import SAGEConv\n","\n","# ----------- 2. create model -------------- #\n","# build a two-layer GraphSAGE model\n","class GraphSAGE(nn.Module):\n","    def __init__(self, in_feats, h_feats):\n","        super(GraphSAGE, self).__init__()\n","        self.conv1 = SAGEConv(in_feats, h_feats, 'mean')\n","        self.conv2 = SAGEConv(h_feats, h_feats, 'mean')\n","    \n","    def forward(self, g, in_feat):\n","        h = self.conv1(g, in_feat)\n","        h = F.relu(h)\n","        h = self.conv2(g, h)\n","        return h"],"metadata":{"id":"DQ4GPiM7G4c3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import dgl.function as fn\n","\n","class DotPredictor(nn.Module):\n","    def forward(self, g, h):\n","        with g.local_scope():\n","            g.ndata['h'] = h\n","            # Compute a new edge feature named 'score' by a dot-product between the\n","            # source node feature 'h' and destination node feature 'h'.\n","            g.apply_edges(fn.u_dot_v('h', 'h', 'score'))\n","            # u_dot_v returns a 1-element vector for each edge so you need to squeeze it.\n","            return g.edata['score'][:, 0]"],"metadata":{"id":"Kfi7DZFaG4ab"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class MLPPredictor(nn.Module):\n","    def __init__(self, h_feats):\n","        super().__init__()\n","        self.W1 = nn.Linear(h_feats * 2, h_feats)\n","        self.W2 = nn.Linear(h_feats, 1)\n","\n","    def apply_edges(self, edges):\n","        \"\"\"\n","        Computes a scalar score for each edge of the given graph.\n","        Parameters\n","        ----------\n","        edges :\n","            Has three members ``src``, ``dst`` and ``data``, each of\n","            which is a dictionary representing the features of the\n","            source nodes, the destination nodes, and the edges\n","            themselves.\n","        Returns\n","        -------\n","        dict\n","            A dictionary of new edge features.\n","        \"\"\"\n","        h = torch.cat([edges.src['h'], edges.dst['h']], 1)\n","        return {'score': self.W2(F.relu(self.W1(h))).squeeze(1)}\n","\n","    def forward(self, g, h):\n","        with g.local_scope():\n","            g.ndata['h'] = h\n","            g.apply_edges(self.apply_edges)\n","            return g.edata['score']"],"metadata":{"id":"aHWhXPE4luWN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = GraphSAGE(9746, 16)\n","# You can replace DotPredictor with MLPPredictor.\n","#pred = MLPPredictor(16)\n","pred = DotPredictor()\n","\n","def compute_loss(pos_score, neg_score):\n","    scores = torch.cat([pos_score, neg_score])\n","    labels = torch.cat([torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])])\n","    return F.binary_cross_entropy_with_logits(scores, labels)\n","\n","def compute_auc(pos_score, neg_score):\n","    scores = torch.cat([pos_score, neg_score]).numpy()\n","    labels = torch.cat(\n","        [torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]).numpy()\n","    return roc_auc_score(labels, scores)"],"metadata":{"id":"JwiH5V9sluT5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import itertools\n","optimizer = torch.optim.Adam(itertools.chain(model.parameters(), pred.parameters()), lr=0.01)"],"metadata":{"id":"u7YlERSQnLIg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["feat = torch.zeros(9746, 9746) + torch.eye(9746)\n","feat"],"metadata":{"id":"GimGWtlbipx1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"72351e92-f46b-4c82-b872-2bade4b99beb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[1., 0., 0.,  ..., 0., 0., 0.],\n","        [0., 1., 0.,  ..., 0., 0., 0.],\n","        [0., 0., 1.,  ..., 0., 0., 0.],\n","        ...,\n","        [0., 0., 0.,  ..., 1., 0., 0.],\n","        [0., 0., 0.,  ..., 0., 1., 0.],\n","        [0., 0., 0.,  ..., 0., 0., 1.]])"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["feat.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"edgDh4iiDv0v","outputId":"99a15ff5-f92b-476d-aa37-0265cf1ef51a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([9746, 9746])"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["all_logits = []\n","for e in range(100):\n","    # forward\n","    h = model(train_g, feat)\n","    pos_score = pred(train_pos_g, h)\n","    neg_score = pred(train_neg_g, h)\n","    loss = compute_loss(pos_score, neg_score)\n","    \n","    # backward\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","    \n","    if e % 5 == 0:\n","        print('In epoch {}, loss: {}'.format(e, loss))"],"metadata":{"id":"Hww1vfh7luRX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"d74cd269-1f0d-4449-bef2-9d4edd39ffd6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["In epoch 0, loss: 0.6932427287101746\n","In epoch 5, loss: 0.6619951725006104\n","In epoch 10, loss: 0.5943281650543213\n","In epoch 15, loss: 0.5724420547485352\n","In epoch 20, loss: 0.5647295117378235\n","In epoch 25, loss: 0.5594874620437622\n","In epoch 30, loss: 0.5558831095695496\n","In epoch 35, loss: 0.5498736500740051\n","In epoch 40, loss: 0.5414936542510986\n","In epoch 45, loss: 0.5335385203361511\n","In epoch 50, loss: 0.5265455842018127\n","In epoch 55, loss: 0.5219001770019531\n","In epoch 60, loss: 0.5189764499664307\n","In epoch 65, loss: 0.5170321464538574\n","In epoch 70, loss: 0.5154984593391418\n","In epoch 75, loss: 0.5141327977180481\n","In epoch 80, loss: 0.512814462184906\n","In epoch 85, loss: 0.5115363597869873\n","In epoch 90, loss: 0.5102699995040894\n","In epoch 95, loss: 0.5088672637939453\n"]}]},{"cell_type":"code","source":["h.shape"],"metadata":{"id":"xtO6KMeOluKN","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f06fe38e-e642-4821-9447-951b3b3c4f0f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([9746, 16])"]},"metadata":{},"execution_count":79}]},{"cell_type":"code","source":["# 벡터 유사도 측정 함수\n","def cos_sim(A, B):\n","  return dot(A, B)/(norm(A)*norm(B))"],"metadata":{"id":"ar4-hwoNG4W8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nodeidx[9742], nodeidx[9743], nodeidx[9744], nodeidx[7648], nodeidx[7761], nodeidx[6151], nodeidx[6255]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"koTK3V1MkCvN","outputId":"631030e9-1d69-4e11-f279-989d5c961c37"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(9500, 9501, 9502, 7516, 7614, 6147, 6249)"]},"metadata":{},"execution_count":90}]},{"cell_type":"code","source":["#에어포스원: 764, 타이타닉: 7761, 택시드라이버: 6151, 비포선라이즈: 6255"],"metadata":{"id":"-kEbpd9omzha"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(),h[9500].detach().numpy() )"],"metadata":{"id":"q0m6xD4CF71L","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3a376aa0-708a-41e3-b8dc-c8f1e2499424"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.98707634"]},"metadata":{},"execution_count":94}]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(),h[9502].detach().numpy() )"],"metadata":{"id":"BCoaKK6cF7ya","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c962fee4-4068-4972-f80d-8cb346a55539"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8221346"]},"metadata":{},"execution_count":95}]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(), h[7516].detach().numpy() )"],"metadata":{"id":"WHgKo26jF7wE","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e3821825-8eea-4a78-8e4f-d1323fc0ffe7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.85226387"]},"metadata":{},"execution_count":96}]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(),h[7614].detach().numpy() )"],"metadata":{"id":"gjwIa789F7tS","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9fac1d7b-3e07-462e-8efd-2724ae2e5b95"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.97317624"]},"metadata":{},"execution_count":97}]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(), h[6147].detach().numpy() )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nljqS2VYqeIO","outputId":"dfe82fe4-3643-4931-ee51-9fccebb8f481"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.83457416"]},"metadata":{},"execution_count":98}]},{"cell_type":"code","source":["cos_sim(h[9501].detach().numpy(),h[6249].detach().numpy() )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y7PvDDVdqx9b","outputId":"e656d8de-dd48-4e5b-a2ca-5fd83af7f448"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.3955274"]},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["nodeidx[7890] #예전에 선택했던 무비노드 "],"metadata":{"id":"n9VZC87RsD1I","colab":{"base_uri":"https://localhost:8080/"},"outputId":"16b772dc-6529-41c6-a793-319f5a8efce4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7710"]},"metadata":{},"execution_count":105}]},{"cell_type":"code","source":["cos_sim(h[7710].detach().numpy(), h[9].detach().numpy() )   #예전에 선택했던만큼 나온다. "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"szlwUQ90sE0M","outputId":"881514a7-b89b-439b-97b2-c70a07f674e6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.9019539"]},"metadata":{},"execution_count":106}]},{"cell_type":"code","source":["cos_sim(h[7710].detach().numpy(), h[0].detach().numpy() )"],"metadata":{"id":"6IS_bpHUskAC","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6ce4d20d-102f-44b6-9248-e5e61113e74d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.30725974"]},"metadata":{},"execution_count":107}]},{"cell_type":"code","source":["cos_sim(h[6041].detach().numpy(), h[0].detach().numpy() )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9-hDpFXwn4nH","outputId":"1d7cfcfd-52c3-4c5c-85cc-280786b885fe"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.16116253"]},"metadata":{},"execution_count":109}]}]}