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Autoencoder RecSys Models on ML-1m"],"metadata":{"id":"tT6lcgsxsYzT"}},{"cell_type":"markdown","source":["## Setup"],"metadata":{"id":"MugCaZzrsrM9"}},{"cell_type":"code","metadata":{"id":"FwYoeMVyJvzL"},"source":["import numpy as np\n","import pandas as pd\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","import os,sys,inspect\n","import gc\n","from tqdm.notebook import tqdm\n","import random\n","import heapq\n","\n","from sklearn.preprocessing import LabelEncoder\n","from scipy.sparse import csr_matrix\n","\n","from tensorflow import keras\n","import tensorflow as tf\n","from tensorflow.keras import optimizers, callbacks, layers, losses\n","from tensorflow.keras.layers import Dense, Concatenate, Activation, Add, BatchNormalization, Dropout, Input, Embedding, Flatten, Multiply\n","from tensorflow.keras.models import Model, Sequential, load_model"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sB_OJdPrYZSK"},"source":["SEED = 42\n","np.random.seed(SEED)\n","tf.random.set_seed(SEED)\n","os.environ['PYTHONHASHSEED']=str(SEED)\n","random.seed(SEED)\n","gpus = tf.config.experimental.list_physical_devices('GPU')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0fxfIpXLTe-B"},"source":["if gpus:\n","    try:\n","        tf.config.experimental.set_memory_growth(gpus[0], True)\n","    except RuntimeError as e:\n","        print(e)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EpIyG5zgtaai","executionInfo":{"status":"ok","timestamp":1639716148938,"user_tz":-330,"elapsed":28,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"9d5c6806-8dde-45d4-a715-1639547315ab"},"source":["print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Num GPUs Available:  0\n"]}]},{"cell_type":"code","source":["!wget -q --show-progress https://files.grouplens.org/datasets/movielens/ml-1m.zip\n","!unzip ml-1m.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UlyLzNq1sooD","executionInfo":{"status":"ok","timestamp":1639716176022,"user_tz":-330,"elapsed":1132,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"bdabe7d6-81af-4772-c63e-7b466287e619"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ml-1m.zip           100%[===================>]   5.64M  25.0MB/s    in 0.2s    \n","Archive:  ml-1m.zip\n","   creating: ml-1m/\n","  inflating: ml-1m/movies.dat        \n","  inflating: ml-1m/ratings.dat       \n","  inflating: ml-1m/README            \n","  inflating: ml-1m/users.dat         \n"]}]},{"cell_type":"code","metadata":{"id":"4DbVJCtKPuBF"},"source":["def mish(x):\n","    return x*tf.math.tanh(tf.math.softplus(x))\n","\n","def leakyrelu(x, factor=0.2):\n","    return tf.maximum(x, factor*x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XOfeaOEgLsGG"},"source":["def load_data(filepath, threshold=0):\n","    df = pd.read_csv(filepath,\n","                     sep=\"::\",\n","                     header=None,\n","                     engine='python',\n","                     names=['userId', 'movieId', 'rating', 'time'])\n","    df = df.drop('time', axis=1)\n","    df['userId'] = df['userId'].astype(int)\n","    df['movieId'] = df['movieId'].astype(int)\n","    df['rating'] = df['rating'].astype(float)\n","    \n","    df = df[['userId', 'movieId', 'rating']]\n","    if threshold > 0:\n","        df['rating'] = np.where(df['rating']>threshold, 1, 0)  \n","    else:\n","        df['rating'] = 1.\n","    m_codes = df['movieId'].astype('category').cat.codes\n","    u_codes = df['userId'].astype('category').cat.codes\n","    df['movieId'] = m_codes\n","    df['userId'] = u_codes\n","\n","    return df\n","    \n","\n","def add_negative(df, uiid, times=4):\n","    df_ = df.copy()\n","    user_id = df_['userId'].unique()\n","    item_id = df_['movieId'].unique()\n","    \n","    for i in tqdm(user_id):\n","        cnt = 0\n","        n = len(df_[df_['userId']==i])\n","        n_negative = min(n*times, len(item_id)-n-1)\n","        available_negative = list(set(uiid) - set(df[df['userId']==i]['movieId'].values))\n","        \n","        new = np.random.choice(available_negative, n_negative, replace=False)\n","        new = [[i, j, 0] for j in new]\n","        df_ = df_.append(pd.DataFrame(new, columns=df.columns), ignore_index=True)\n","    \n","    return df_\n","\n","def extract_from_df(df, n_positive, n_negative):\n","    df_ = df.copy()\n","    rtd = []\n","    \n","    user_id = df['userId'].unique()\n","    \n","    for i in tqdm(user_id):\n","        rtd += list(np.random.choice(df[df['userId']==i][df['rating']==1]['movieId'].index, n_positive, replace=False))\n","        rtd += list(np.random.choice(df[df['userId']==i][df['rating']==0]['movieId'].index, n_negative, replace=False))\n","        \n","    return rtd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GFQjwqAPMH8C"},"source":["def eval_NDCG(true, pred):\n","    top_k = pred\n","\n","    for i, item in enumerate(top_k, 1):\n","        if item == true:\n","            return 1 / np.log2(i+1)\n","    return 0"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"S78F5a7AYgQz"},"source":["## CDAE"]},{"cell_type":"markdown","metadata":{"id":"1sAQYk5eYv1j"},"source":["### Load data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"rCg36MGFTue5","executionInfo":{"status":"ok","timestamp":1639716202713,"user_tz":-330,"elapsed":5930,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"d87757a9-77d6-4c1f-e057-5adab8f37e60"},"source":["df = load_data('./ml-1m/ratings.dat', threshold=3)\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-e6c63b7e-8973-4f96-9007-bdc99c903b13\">\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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>639</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6c63b7e-8973-4f96-9007-bdc99c903b13')\"\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: 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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","  "],"text/plain":["   userId  movieId  rating\n","0       0     1104       1\n","1       0      639       0\n","2       0      853       0\n","3       0     3177       1\n","4       0     2162       1"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"uo-H-H5yZjSI"},"source":["### Preprocessing"]},{"cell_type":"code","metadata":{"id":"4_0IEsjWM4nI"},"source":["df = df[df['rating']==1].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","\n","cnt = tdf.sum(1)\n","df = df[df['userId'].isin(np.where(cnt >= 10)[0])].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","tdf.iloc[:,:] = 0\n","\n","test_idx = []\n","for i in tdf.index:\n","    test_idx += list(np.random.choice(df[df['userId']==i].index, 1))\n","    \n","train = df.loc[list(set(df.index)-set(test_idx)),:]\n","test = df.loc[test_idx, :]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"QCEA3JWYNgHT","executionInfo":{"status":"ok","timestamp":1639716243239,"user_tz":-330,"elapsed":49,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"3077ce6b-b907-4e0d-b2b2-94c6e019dc42"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-7c905706-0869-444e-a0c4-2962eb8c80dd\">\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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1195</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2599</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>570512</th>\n","      <td>6037</td>\n","      <td>346</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570513</th>\n","      <td>6037</td>\n","      <td>1120</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570514</th>\n","      <td>6037</td>\n","      <td>1133</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570515</th>\n","      <td>6037</td>\n","      <td>1204</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570516</th>\n","      <td>6037</td>\n","      <td>1007</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>570517 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c905706-0869-444e-a0c4-2962eb8c80dd')\"\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 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-7c905706-0869-444e-a0c4-2962eb8c80dd');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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 1\n","570516    6037     1007       1\n","\n","[570517 rows x 3 columns]"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ov0ZmaZabiVe","executionInfo":{"status":"ok","timestamp":1639716249445,"user_tz":-330,"elapsed":425,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"f4c9d415-cd86-4274-8d0e-dca78697f41d"},"source":["df.shape, train.shape, test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((570517, 3), (564569, 3), (5948, 3))"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"id":"AyFVjZiUNtBV","executionInfo":{"status":"ok","timestamp":1630835048249,"user_tz":-330,"elapsed":51728,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"e9cbcb22-0779-4089-a265-085b72948158"},"source":["for uid, iid in zip(train['userId'].values, train['movieId'].values):\n","    tdf.loc[uid, iid] = 1\n","train =  tdf.copy()\n","train"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>movieId</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      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<th>3680</th>\n","      <th>3681</th>\n","      <th>3682</th>\n","      <th>3683</th>\n","      <th>3684</th>\n","      <th>3685</th>\n","      <th>3686</th>\n","      <th>3687</th>\n","      <th>3688</th>\n","      <th>3689</th>\n","      <th>3690</th>\n","      <th>3691</th>\n","      <th>3692</th>\n","      <th>3693</th>\n","      <th>3694</th>\n","      <th>3695</th>\n","      <th>3696</th>\n","      <th>3697</th>\n","      <th>3698</th>\n","      <th>3699</th>\n","      <th>3700</th>\n","      <th>3701</th>\n","      <th>3702</th>\n","      <th>3703</th>\n","      <th>3704</th>\n","      <th>3705</th>\n","    </tr>\n","    <tr>\n","      <th>userId</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      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<td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>6035</th>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>6036</th>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>6037</th>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      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0.0\n","2         0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","3         0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","4         0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","...       ...   ...   ...   ...   ...   ...  ...   ...   ...   ...   ...   ...   ...\n","6033      0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","6034      1.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","6035      0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","6036      0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","6037      0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n","\n","[5948 rows x 3533 columns]"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"_br-gyTNd40x"},"source":["### Model architecture"]},{"cell_type":"code","metadata":{"id":"UK-_v59OYjHz"},"source":["class CDAE(tf.keras.models.Model):\n","    def __init__(self, input_dim, latent_dim, n_user, lamda=1e-4):\n","        super().__init__()\n","        self.input_dim = input_dim\n","        self.latent_dim = latent_dim\n","        self.lamda = lamda\n","        self.n_user = n_user\n","        self.embedding = Embedding(n_user, latent_dim, )        \n","\n","        self.model = self.build()\n","\n","    def compile(self, optimizer, loss_fn=None):\n","        super().compile()\n","        self.optimizer = optimizer\n","        self.loss_fn = loss_fn\n","\n","        \n","    def build(self):\n","        self.encoder = self.build_encoder()\n","        self.decoder = self.build_decoder()\n","        \n","        rating = Input(shape=(self.input_dim, ), name='rating_input')\n","        user_id = Input(shape=(1, ), name='user_input')\n","        \n","        emb = self.embedding(user_id)\n","        emb = tf.squeeze(emb, 1)\n","        enc = self.encoder(rating) + emb\n","        enc = tf.nn.tanh(enc)\n","        outputs = self.decoder(enc)\n","    \n","        return Model([rating, user_id], outputs)\n","    \n","    def build_encoder(self):\n","        inputs = Input(shape = (self.input_dim, ))\n","        \n","        encoder = Sequential()\n","        encoder.add(Dropout(0.2))\n","        encoder.add(Dense(self.latent_dim, activation='tanh'))\n","        \n","        outputs = encoder(inputs)\n","        \n","        return Model(inputs, outputs)\n","    \n","    def build_decoder(self):\n","        inputs = Input(shape = (self.latent_dim, ))\n","        \n","        encoder = Sequential()\n","        encoder.add(Dense(self.input_dim, activation='sigmoid'))\n","        \n","        outputs = encoder(inputs)\n","        \n","        return Model(inputs, outputs)\n","    \n","    def train_step(self, data):\n","        x = data['rating']\n","        user_ids = data['id']\n","        with tf.GradientTape() as tape:\n","            pred = self.model([x, user_ids])\n","            \n","            rec_loss = tf.losses.binary_crossentropy(x, pred)\n","            loss = rec_loss\n","\n","        grads = tape.gradient(loss, self.model.trainable_weights)\n","        self.optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n","        \n","        return {'loss': loss}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-R-qDHX_dD3T"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"Fjtaq3RkcIO6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630835242291,"user_tz":-330,"elapsed":194051,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"a69c188d-f725-4553-ee6d-d6df3484f19d"},"source":["loader = tf.data.Dataset.from_tensor_slices({'rating': train.values, 'id': np.arange(len(train))})\n","loader = loader.batch(32, drop_remainder=True).shuffle(len(train))\n","model = CDAE(train.shape[1], 200, len(train))\n","model.compile(optimizer=tf.optimizers.Adam())\n","model.fit(loader, epochs=25)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/25\n","185/185 [==============================] - 7s 31ms/step - loss: 0.1558\n","Epoch 2/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.1036\n","Epoch 3/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.1007\n","Epoch 4/25\n","185/185 [==============================] - 6s 29ms/step - loss: 0.0972\n","Epoch 5/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0922\n","Epoch 6/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0884\n","Epoch 7/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0857\n","Epoch 8/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0833\n","Epoch 9/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0810\n","Epoch 10/25\n","185/185 [==============================] - 5s 29ms/step - loss: 0.0793\n","Epoch 11/25\n","185/185 [==============================] - 6s 29ms/step - loss: 0.0776\n","Epoch 12/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0763\n","Epoch 13/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0747\n","Epoch 14/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0735\n","Epoch 15/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0722\n","Epoch 16/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0709\n","Epoch 17/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0699\n","Epoch 18/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0687\n","Epoch 19/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0678\n","Epoch 20/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0668\n","Epoch 21/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0660\n","Epoch 22/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0649\n","Epoch 23/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0640\n","Epoch 24/25\n","185/185 [==============================] - 6s 30ms/step - loss: 0.0633\n","Epoch 25/25\n","185/185 [==============================] - 6s 31ms/step - loss: 0.0624\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f12cdede850>"]},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"NfDBp4XndBDM"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["0bc4610585dc4b6e89d3bc4598293dc4","0e1c65f3d0a04bbfa4d53d8287faf7b0","707a58e866a9416eaf94f9aea50bb9f5","35c412197fb6462b9d03d9fe8f65e3cb","a89f0935d9c846ae92716177f9a3da31","ae8bf7ee34674a0e9fe0b9aef3b8063d","eea7588021c24de29cd5018918139907","b997a6a3013f4f649bbba23c10d1bbe3","c04be49d501e4f70a3ed182c6253a336","ee609560d3104683b742c9ed66e782d7","4e3a404483954fa3916ac4bbf4bd4929"]},"id":"4vR5WPgGOwbn","executionInfo":{"status":"ok","timestamp":1630835457454,"user_tz":-330,"elapsed":128721,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"6ad93a00-03d5-4158-bb59-0babb6fac1b5"},"source":["top_k = 10\n","np.random.seed(42)\n","\n","scores = []\n","for idx, i in tqdm(enumerate(np.random.choice(train.index, 100))):\n","    item_to_pred = {item: pred for item, pred in zip(train.columns, model.model.predict([train.values, np.arange(len(train))])[idx])}\n","    test_ = test[(test['userId']==i) & (test['rating']==1)]['movieId'].values\n","    items = list(np.random.choice(list(filter(lambda x: x not in np.argwhere(train.values[idx]).flatten(), item_to_pred.keys())), 100)) + list(test_)\n","    top_k_items = heapq.nlargest(top_k, items, key=item_to_pred.get)\n","    \n","    score = eval_NDCG(test_, top_k_items)\n","    scores.append(score)\n","    \n","np.mean(scores)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0bc4610585dc4b6e89d3bc4598293dc4","version_minor":0,"version_major":2},"text/plain":["0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["0.2810483955912976"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"cqBW2VetZvne"},"source":["## EASE"]},{"cell_type":"markdown","metadata":{"id":"R-XpG8cMZvnf"},"source":["### Load data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"D_oXGFDhZvnf","executionInfo":{"status":"ok","timestamp":1630834671821,"user_tz":-330,"elapsed":5997,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"66c7d829-c8f3-4e7b-f03c-1c832040c3f5"},"source":["df = load_data('./ml-1m/ratings.dat', threshold=3)\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>639</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   userId  movieId  rating\n","0       0     1104       1\n","1       0      639       0\n","2       0      853       0\n","3       0     3177       1\n","4       0     2162       1"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"OfHD5JjfZvnh"},"source":["### Preprocessing"]},{"cell_type":"code","metadata":{"id":"KRzrwrHpZvnh"},"source":["test_idx = []\n","user_id = df\n","for i in df['userId'].unique():\n","    test_idx += list(np.random.choice(df[df['userId']==i].index, 1))\n","    \n","train = df.iloc[list(set(df.index)-set(test_idx)),:]\n","test = df.iloc[test_idx, :]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5hbeGQeyZvni","executionInfo":{"status":"ok","timestamp":1630834902542,"user_tz":-330,"elapsed":16,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"19ad5720-8a92-47bc-b018-b360fe911342"},"source":["df.shape, train.shape, test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((570517, 3), (564569, 3), (5948, 3))"]},"metadata":{},"execution_count":25}]},{"cell_type":"markdown","metadata":{"id":"REsycBTbZvni"},"source":["### Model architecture"]},{"cell_type":"code","metadata":{"id":"N8qLutMpZvnj"},"source":["class EASE:\n","    def __init__(self):\n","        self.user_enc = LabelEncoder()\n","        self.item_enc = LabelEncoder()\n","\n","    def _get_users_and_items(self, df):\n","        users = self.user_enc.fit_transform(df.loc[:, 'userId'])\n","        items = self.item_enc.fit_transform(df.loc[:, 'movieId'])\n","        return users, items\n","\n","    def fit(self, df, lambda_: float = 0.5, implicit=True):\n","        \"\"\"\n","        df: pandas.DataFrame with columns user_id, item_id and (rating)\n","        lambda_: l2-regularization term\n","        implicit: if True, ratings are ignored and taken as 1, else normalized ratings are used\n","        \"\"\"\n","        users, items = self._get_users_and_items(df)\n","        values = np.ones(df.shape[0]) if implicit else df['rating'].to_numpy() / df['rating'].max()\n","\n","        X = csr_matrix((values, (users, items)))\n","        self.X = X\n","\n","        G = X.T.dot(X).toarray()\n","        diagIndices = np.diag_indices(G.shape[0])\n","        G[diagIndices] += lambda_\n","        P = np.linalg.inv(G)\n","        B = P / (-np.diag(P))\n","        B[diagIndices] = 0\n","\n","        self.B = B\n","        self.pred = X.dot(B)\n","\n","    def predict(self, train, users, items, k):\n","        df = pd.DataFrame()\n","        items = self.item_enc.transform(items)\n","        dd = train.loc[train['userId'].isin(users)]\n","        dd['ci'] = self.item_enc.transform(dd['movieId'])\n","        dd['cu'] = self.user_enc.transform(dd['userId'])\n","        g = dd.groupby('userId')\n","        for user, group in tqdm(g):\n","            watched = set(group['ci'])\n","            candidates = [item for item in items if item not in watched]\n","            u = group['cu'].iloc[0]\n","            pred = np.take(self.pred[u, :], candidates)\n","            res = np.argpartition(pred, -k)[-k:]\n","            r = pd.DataFrame({\n","                \"userId\": [user] * len(res),\n","                \"movieId\": np.take(candidates, res),\n","                \"score\": np.take(pred, res)\n","            }).sort_values('score', ascending=False)\n","            df = df.append(r, ignore_index=True)\n","        df['movieId'] = self.item_enc.inverse_transform(df['movieId'])\n","        return df"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KwfcFM2yZvnj"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"zEzkqTbWVCzD"},"source":["ease = EASE()\n","ease.fit(train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5I5877tBU7yB","executionInfo":{"status":"ok","timestamp":1630834999318,"user_tz":-330,"elapsed":644,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"dcb8117a-e70e-42b8-d713-4da7dd8cc4e4"},"source":["uid = 0\n","ease.user_enc.inverse_transform([0])[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZdpFQsXgV1Mp","executionInfo":{"status":"ok","timestamp":1630835007002,"user_tz":-330,"elapsed":551,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"80c3d15c-99a2-4603-b91f-f5c7bb2045f7"},"source":["ease.item_enc.inverse_transform(np.argsort(ease.pred[0]))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2845, 1204, 1502, ...,  957,  574,  581], dtype=int16)"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d9fCVWvOV2sK","executionInfo":{"status":"ok","timestamp":1630835012996,"user_tz":-330,"elapsed":785,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"8b52e238-bc6f-47d4-ce62-2e5602f4c4f7"},"source":["np.argsort(-ease.pred[0])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 565,  559,  903, ..., 1420, 1138, 2716])"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j-arIMZHV4e5","executionInfo":{"status":"ok","timestamp":1630835020095,"user_tz":-330,"elapsed":508,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"257943c7-262d-4aee-99fa-a97e53ec23fe"},"source":["ease.pred[0][np.argsort(-ease.pred[0])]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0.88894744,  0.86783598,  0.76730558, ..., -0.26904345,\n","       -0.29024257, -0.29286189])"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GTdoYucRU30u","executionInfo":{"status":"ok","timestamp":1630835023395,"user_tz":-330,"elapsed":738,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"7a35e7f5-8f17-4200-b8bb-a18c5cf8b4d9"},"source":["np.unique(train[train['userId']==0]['movieId'])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([   0,   47,  144,  253,  513,  517,  574,  580,  581,  593,  740,\n","        858,  877,  957,  963,  964,  970, 1025, 1104, 1117, 1154, 1178,\n","       1195, 1421, 1439, 1574, 1658, 1727, 1781, 1782, 1838, 1848, 2102,\n","       2162, 2205, 2488, 2557, 2586, 2592, 2599, 2710, 2889, 2969, 3177],\n","      dtype=int16)"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"pV3bdkthZvnn"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":451,"referenced_widgets":["2f741acf5a964291af3d1314627871e7","508e94b448894be08342221ff5ca5515","4de8e9ae4b8e4e4c954bec8acd09d992","c5882e7ec03a4e978341e8406e7ec604","23ef87acab4e46bf9eb6e11870fbb6fc","ee3e841f043d404fa1a8a8409cc3f421","386ab749cd954d36a9c7ecebccf5f2df","eacf302707954850878334ff6a5c1fae","ad83565fcfd34dfc824ab4c16ad6cd7a","f9ef7106318146e89ecbcde0ad71bcd7","6c3ee40cc767411d98efd6d4af1ca2eb"]},"id":"01b3WkGrV9Nk","executionInfo":{"status":"ok","timestamp":1630835080341,"user_tz":-330,"elapsed":40605,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"b6635be8-427b-4694-84bf-90d422c9d30e"},"source":["pred = ease.predict(train, train['userId'].unique(), train['movieId'].unique(), 100)\n","pred"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2f741acf5a964291af3d1314627871e7","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/5947 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>354</td>\n","      <td>0.659144</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>2511</td>\n","      <td>0.420217</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>2058</td>\n","      <td>0.397685</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0.382166</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>892</td>\n","      <td>0.325232</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>594695</th>\n","      <td>6037</td>\n","      <td>1729</td>\n","      <td>0.105471</td>\n","    </tr>\n","    <tr>\n","      <th>594696</th>\n","      <td>6037</td>\n","      <td>1978</td>\n","      <td>0.104400</td>\n","    </tr>\n","    <tr>\n","      <th>594697</th>\n","      <td>6037</td>\n","      <td>1172</td>\n","      <td>0.104144</td>\n","    </tr>\n","    <tr>\n","      <th>594698</th>\n","      <td>6037</td>\n","      <td>27</td>\n","      <td>0.103118</td>\n","    </tr>\n","    <tr>\n","      <th>594699</th>\n","      <td>6037</td>\n","      <td>2128</td>\n","      <td>0.102213</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>594700 rows × 3 columns</p>\n","</div>"],"text/plain":["        userId  movieId     score\n","0            0      354  0.659144\n","1            0     2511  0.420217\n","2            0     2058  0.397685\n","3            0      853  0.382166\n","4            0      892  0.325232\n","...        ...      ...       ...\n","594695    6037     1729  0.105471\n","594696    6037     1978  0.104400\n","594697    6037     1172  0.104144\n","594698    6037       27  0.103118\n","594699    6037     2128  0.102213\n","\n","[594700 rows x 3 columns]"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"ggRtLpiNV_c2","executionInfo":{"status":"ok","timestamp":1630835091546,"user_tz":-330,"elapsed":588,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"744a2a4c-d13f-4211-eafa-1dea1e4671ee"},"source":["uid = 1\n","df[(df['userId']==uid) & (df['movieId'].isin(pred[pred['userId']==uid]['movieId']))]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [userId, movieId, rating]\n","Index: []"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"QPkVz-gnWCQW","executionInfo":{"status":"ok","timestamp":1630835091967,"user_tz":-330,"elapsed":13,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"def7da36-b6ea-4131-a7ac-bbf81ae15a3a"},"source":["train[(train['userId']==uid) & (train['movieId'].isin(pred[pred['userId']==uid]['movieId']))]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [userId, movieId, rating]\n","Index: []"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"UcilNIv2WD_n"},"source":["for uid in range(942):\n","    pdf = df[(df['userId']==uid) & (df['movieId'].isin(pred[pred['userId']==uid]['movieId']))]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8WvaXEwZWFTu","executionInfo":{"status":"ok","timestamp":1630835114633,"user_tz":-330,"elapsed":726,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"3eb88b98-6150-41d2-fad5-c97043016ef2"},"source":["ease.pred.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5947, 3532)"]},"metadata":{},"execution_count":42}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WudrTlyWZvnq","executionInfo":{"status":"ok","timestamp":1630835116746,"user_tz":-330,"elapsed":11,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"528edaa4-2162-45fe-e108-41d0c5de33bb"},"source":["train['userId'].unique().shape, train['movieId'].unique().shape, "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((5947,), (3532,))"]},"metadata":{},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"dQUI23C-WWWO"},"source":["## MultiVAE"]},{"cell_type":"markdown","metadata":{"id":"hA2o80xvWlah"},"source":["### Load data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"7NKMTvN_Wlai","executionInfo":{"status":"ok","timestamp":1630835218743,"user_tz":-330,"elapsed":6555,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"a5b11636-ea6a-458e-e482-49b72a12d954"},"source":["df = load_data('./ml-1m/ratings.dat', threshold=3)\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>639</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   userId  movieId  rating\n","0       0     1104       1\n","1       0      639       0\n","2       0      853       0\n","3       0     3177       1\n","4       0     2162       1"]},"metadata":{},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"vm3oUHOcWlaj"},"source":["### Preprocessing"]},{"cell_type":"code","metadata":{"id":"nxvgVlESWlaj"},"source":["df = df[df['rating']==1].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","\n","cnt = tdf.sum(1)\n","df = df[df['userId'].isin(np.where(cnt >= 10)[0])].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","tdf.iloc[:,:] = 0\n","test_idx = []\n","\n","for i in tdf.index:\n","    test_idx += list(np.random.choice(df[df['userId']==i].index, 1))\n","    \n","train = df.iloc[list(set(df.index)-set(test_idx)),:]\n","test = df.iloc[test_idx, :]\n","\n","for uid, iid in zip(train['userId'].values, train['movieId'].values):\n","    tdf.loc[uid, iid] = 1\n","\n","train =  tdf.copy()\n","\n","def sampling(args):\n","    z_mean, z_log_var = args\n","    batch = tf.shape(z_mean)[0]\n","    dim = tf.shape(z_mean)[1]\n","    epsilon = tf.random.normal(shape=(batch, dim), stddev=0.01)\n","    return z_mean + tf.exp(0.5 * z_log_var) * epsilon"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Lr6hS321Wlak","executionInfo":{"status":"ok","timestamp":1630835304957,"user_tz":-330,"elapsed":20,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"7e9aa5fa-d0ee-47c7-c909-daca50c8b412"},"source":["df.shape, train.shape, test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((570517, 3), (5948, 3533), (5948, 3))"]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"Q3XcxcpoWlak"},"source":["### Model architecture"]},{"cell_type":"code","metadata":{"id":"25ZXRGkfXLjd"},"source":["class MultVAE(tf.keras.models.Model):\n","    def __init__(self, input_dim, latent_dim, lamda=1e-4):\n","        super().__init__()\n","        self.input_dim = input_dim\n","        self.latent_dim = latent_dim\n","        self.anneal = 0.\n","        \n","        self.model = self.build()\n","\n","    def compile(self, optimizer, loss_fn=None):\n","        super().compile()\n","        self.optimizer = optimizer\n","        self.loss_fn = loss_fn\n","\n","        \n","    def build(self):\n","        self.encoder = self.build_encoder()\n","        self.decoder = self.build_decoder()\n","        \n","        inputs = self.encoder.input\n","        \n","        mu, log_var = self.encoder(inputs)\n","        h = sampling([mu, log_var])\n","        \n","        outputs = self.decoder(h)\n","    \n","        return Model(inputs, outputs)\n","    \n","    def build_encoder(self):\n","        inputs = Input(shape = (self.input_dim, ))\n","        h = Dropout(0.2)(inputs)\n","        \n","        mu = Dense(self.latent_dim)(h)\n","        log_var = Dense(self.latent_dim)(h)\n","        \n","        return Model(inputs, [mu, log_var])\n","    \n","    def build_decoder(self):\n","        inputs = Input(shape = (self.latent_dim, ))\n","        \n","        outputs = Dense(self.input_dim, activation='sigmoid')(inputs)\n","\n","        return Model(inputs, outputs)\n","    \n","    def train_step(self, data):\n","        x = data\n","        with tf.GradientTape() as tape:\n","            mu, log_var = self.encoder(x)\n","            pred = self.model(x)\n","            \n","            kl_loss = tf.reduce_mean(tf.reduce_sum(0.5*(log_var + tf.exp(log_var) + tf.pow(mu, 2)-1), 1, keepdims=True))\n","            ce_loss = -tf.reduce_mean(tf.reduce_sum(tf.nn.log_softmax(pred) * x, -1))\n","            \n","            loss = ce_loss + kl_loss*self.anneal\n","            \n","        grads = tape.gradient(loss, self.model.trainable_weights)\n","        self.optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n","        \n","        return {'loss': loss}\n","    \n","    def predict(self, data):\n","        mu, log_var = self.encoder(data)\n","        return self.decoder(mu)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zh7u-CfiXNwo"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"OMgAqDDAXQ0s"},"source":["loader = tf.data.Dataset.from_tensor_slices(train.values.astype(np.float32))\n","loader = loader.batch(8, drop_remainder=True).shuffle(len(train))\n","\n","model = MultVAE(train.shape[1], 200)\n","model.compile(optimizer=tf.optimizers.Adam())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rRtvceiYXXmX"},"source":["class AnnealCallback(callbacks.Callback):\n","    def __init__(self):\n","        super().__init__()\n","        self.anneal_cap = 0.3\n","        \n","    def on_train_batch_end(self, batch, logs=None):\n","        self.model.anneal =  min(self.anneal_cap, self.model.anneal+1e-4)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vPj4AythXUo6","executionInfo":{"status":"ok","timestamp":1630835847202,"user_tz":-330,"elapsed":429687,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"23552135-8011-439d-a61e-22d007fb7f44"},"source":["model.fit(loader, epochs=25, callbacks=[AnnealCallback()])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/25\n","743/743 [==============================] - 13s 16ms/step - loss: 730.4190\n","Epoch 2/25\n","743/743 [==============================] - 12s 16ms/step - loss: 722.0552\n","Epoch 3/25\n","743/743 [==============================] - 12s 16ms/step - loss: 719.0447\n","Epoch 4/25\n","743/743 [==============================] - 12s 16ms/step - loss: 717.2124\n","Epoch 5/25\n","743/743 [==============================] - 12s 17ms/step - loss: 715.4193\n","Epoch 6/25\n","743/743 [==============================] - 12s 16ms/step - loss: 713.9064\n","Epoch 7/25\n","743/743 [==============================] - 12s 17ms/step - loss: 711.3033\n","Epoch 8/25\n","743/743 [==============================] - 12s 16ms/step - loss: 709.6291\n","Epoch 9/25\n","743/743 [==============================] - 12s 16ms/step - loss: 707.6846\n","Epoch 10/25\n","743/743 [==============================] - 12s 16ms/step - loss: 707.0621\n","Epoch 11/25\n","743/743 [==============================] - 12s 16ms/step - loss: 705.8719\n","Epoch 12/25\n","743/743 [==============================] - 12s 16ms/step - loss: 704.4416\n","Epoch 13/25\n","743/743 [==============================] - 12s 16ms/step - loss: 703.5001\n","Epoch 14/25\n","743/743 [==============================] - 12s 16ms/step - loss: 703.4260\n","Epoch 15/25\n","743/743 [==============================] - 12s 16ms/step - loss: 703.5530\n","Epoch 16/25\n","743/743 [==============================] - 12s 16ms/step - loss: 701.2676\n","Epoch 17/25\n","743/743 [==============================] - 12s 16ms/step - loss: 700.5692\n","Epoch 18/25\n","743/743 [==============================] - 12s 16ms/step - loss: 700.5253\n","Epoch 19/25\n","743/743 [==============================] - 12s 16ms/step - loss: 699.8253\n","Epoch 20/25\n","743/743 [==============================] - 12s 16ms/step - loss: 700.0319\n","Epoch 21/25\n","743/743 [==============================] - 12s 16ms/step - loss: 699.0198\n","Epoch 22/25\n","743/743 [==============================] - 12s 16ms/step - loss: 699.0835\n","Epoch 23/25\n","743/743 [==============================] - 12s 16ms/step - loss: 698.7805\n","Epoch 24/25\n","743/743 [==============================] - 12s 16ms/step - loss: 698.1454\n","Epoch 25/25\n","743/743 [==============================] - 12s 16ms/step - loss: 698.6210\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fe770aee110>"]},"metadata":{},"execution_count":49}]},{"cell_type":"markdown","metadata":{"id":"OutR1NzaXZ4p"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["356fbd553cd74283bd40f67e43b24e2f","4e71c9f1b0a74ffbae2c04f977efd6a6","2b4e62ee18cf43a59467818ebb76289e","39515edfa1324e2e8b3ded22fd6f1384","a7a16d4486fb4a9ab1ff40b1b61a41a4","e2d3d44b598242ed84c780fab3cea768","0f589d8eceff4716a2029420f1da243c","c0dc1db826314529aadd1884683d4eda","59269807a50b4aecac1472fdece6a0be","ef2b338adc7140a8856263406b28c8d3","388c8dca4a624437a32b04844a53e84d"]},"id":"EGuXlAcpXtCA","executionInfo":{"status":"ok","timestamp":1630835990749,"user_tz":-330,"elapsed":143578,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"9dc168b8-fc8d-46f8-ff4f-3aef4f32cc5b"},"source":["top_k = 10\n","np.random.seed(42)\n","\n","scores = []\n","for idx, i in tqdm(enumerate(np.random.choice(train.index, 100))):\n","    item_to_pred = {item: pred for item, pred in zip(train.columns, model.model.predict(train.values)[idx])}\n","    test_ = test[(test['userId']==i) & (test['rating']==1)]['movieId'].values\n","    items = list(np.random.choice(list(filter(lambda x: x not in np.argwhere(train.values[idx]).flatten(), item_to_pred.keys())), 100)) + list(test_)\n","    top_k_items = heapq.nlargest(top_k, items, key=item_to_pred.get)\n","    \n","    score = eval_NDCG(test_, top_k_items)\n","    scores.append(score)\n","    \n","np.mean(scores)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"356fbd553cd74283bd40f67e43b24e2f","version_minor":0,"version_major":2},"text/plain":["0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["0.22758533052845625"]},"metadata":{},"execution_count":50}]},{"cell_type":"markdown","metadata":{"id":"gyrB_wB1aXGf"},"source":["## DAE"]},{"cell_type":"markdown","metadata":{"id":"p27z1a9RaXGg"},"source":["### Load data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"FUCzohpraXGh","executionInfo":{"status":"ok","timestamp":1630833675434,"user_tz":-330,"elapsed":5144,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"28528ff8-0aec-41dd-8355-823359e5f830"},"source":["df = load_data('./ml-1m/ratings.dat', threshold=3)\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>639</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   userId  movieId  rating\n","0       0     1104       1\n","1       0      639       0\n","2       0      853       0\n","3       0     3177       1\n","4       0     2162       1"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"VYGBPKuKaXGj"},"source":["### Preprocessing"]},{"cell_type":"code","metadata":{"id":"95emN_MmaXGk"},"source":["df = df[df['rating']==1].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","\n","cnt = tdf.sum(1)\n","df = df[df['userId'].isin(np.where(cnt >= 10)[0])].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","tdf.iloc[:,:] = 0\n","\n","test_idx = []\n","for i in tdf.index:\n","    test_idx += list(np.random.choice(df[df['userId']==i].index, 1))\n","    \n","train = df.loc[list(set(df.index)-set(test_idx)),:]\n","test = df.loc[test_idx, :]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"3Qjyrw5SaXGl","executionInfo":{"status":"ok","timestamp":1630833683218,"user_tz":-330,"elapsed":31,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"6286da55-c46b-42d7-de7e-bcfda08b3227"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>1195</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2599</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>570512</th>\n","      <td>6037</td>\n","      <td>346</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570513</th>\n","      <td>6037</td>\n","      <td>1120</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570514</th>\n","      <td>6037</td>\n","      <td>1133</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570515</th>\n","      <td>6037</td>\n","      <td>1204</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>570516</th>\n","      <td>6037</td>\n","      <td>1007</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>570517 rows × 3 columns</p>\n","</div>"],"text/plain":["        userId  movieId  rating\n","0            0     1104       1\n","1            0     3177       1\n","2            0     2162       1\n","3            0     1195       1\n","4            0     2599       1\n","...        ...      ...     ...\n","570512    6037      346       1\n","570513    6037     1120       1\n","570514    6037     1133       1\n","570515    6037     1204       1\n","570516    6037     1007       1\n","\n","[570517 rows x 3 columns]"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tFJKSzzfaXGm","executionInfo":{"status":"ok","timestamp":1630833683220,"user_tz":-330,"elapsed":27,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"bc4ae887-d201-46be-be65-6fab4a39d805"},"source":["df.shape, train.shape, test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((570517, 3), (564569, 3), (5948, 3))"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"id":"24m_wWovaXGo","executionInfo":{"status":"ok","timestamp":1630833732782,"user_tz":-330,"elapsed":49579,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"0bc3342b-926b-41be-c4f6-19764a138450"},"source":["for uid, iid in zip(train['userId'].values, train['movieId'].values):\n","    tdf.loc[uid, iid] = 1\n","train =  tdf.copy()\n","train"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>movieId</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","      <th>26</th>\n","      <th>27</th>\n","      <th>28</th>\n","      <th>29</th>\n","      <th>30</th>\n","      <th>31</th>\n","      <th>32</th>\n","      <th>33</th>\n","      <th>34</th>\n","      <th>35</th>\n","      <th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>39</th>\n","      <th>...</th>\n","      <th>3666</th>\n","      <th>3667</th>\n","      <th>3668</th>\n","      <th>3669</th>\n","      <th>3670</th>\n","      <th>3671</th>\n","      <th>3672</th>\n","      <th>3673</th>\n","      <th>3674</th>\n","      <th>3675</th>\n","      <th>3676</th>\n","      <th>3677</th>\n","      <th>3678</th>\n","      <th>3679</th>\n","      <th>3680</th>\n","      <th>3681</th>\n","      <th>3682</th>\n","      <th>3683</th>\n","      <th>3684</th>\n","      <th>3685</th>\n","      <th>3686</th>\n","      <th>3687</th>\n","      <th>3688</th>\n","      <th>3689</th>\n","      <th>3690</th>\n","      <th>3691</th>\n","      <th>3692</th>\n","      <th>3693</th>\n","      <th>3694</th>\n","      <th>3695</th>\n","      <th>3696</th>\n","      <th>3697</th>\n","      <th>3698</th>\n","      <th>3699</th>\n","      <th>3700</th>\n","      <th>3701</th>\n","      <th>3702</th>\n","      <th>3703</th>\n","      <th>3704</th>\n","      <th>3705</th>\n","    </tr>\n","    <tr>\n","      <th>userId</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      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      super().__init__()\n","        self.input_dim = input_dim\n","        self.latent_dim = latent_dim\n","        self.lamda = lamda\n","        self.model = self.build()\n","        \n","    def compile(self, optimizer, loss_fn=None):\n","        super().compile()\n","        self.optimizer = optimizer\n","        self.loss_fn = loss_fn\n","\n","        \n","    def build(self):\n","        self.encoder = self.build_encoder()\n","        self.decoder = self.build_decoder()\n","        inputs = self.encoder.input\n","        outputs = self.decoder(self.encoder(inputs))\n","        \n","        return Model(inputs, outputs)\n","    \n","    def build_encoder(self):\n","        inputs = Input(shape = (self.input_dim, ))\n","        \n","        encoder = Sequential()\n","        encoder.add(Dropout(0.2))\n","        encoder.add(Dense(self.latent_dim, activation='tanh'))\n","        \n","        outputs = encoder(inputs)\n","        \n","        return Model(inputs, outputs)\n","    \n","    def build_decoder(self):\n","        inputs = Input(shape = (self.latent_dim, ))\n","        \n","        encoder = Sequential()\n","        encoder.add(Dense(self.input_dim, activation='sigmoid'))\n","        \n","        outputs = encoder(inputs)\n","        \n","        return Model(inputs, outputs)\n","    \n","    def train_step(self, x):\n","        with tf.GradientTape() as tape:\n","            pred = self.model(x)\n","            \n","            rec_loss = tf.losses.binary_crossentropy(x, pred)\n","            loss = rec_loss\n","\n","        grads = tape.gradient(loss, self.model.trainable_weights)\n","        self.optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n","        \n","        return {'loss': loss}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0Nx3cmNKaXGr"},"source":["### Training"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_O0JYFMeaXGr","executionInfo":{"status":"ok","timestamp":1630833850254,"user_tz":-330,"elapsed":113676,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"00b48b7c-859a-478d-f0a9-4349db9ca679"},"source":["loader = tf.data.Dataset.from_tensor_slices(train.values)\n","loader = loader.batch(32, drop_remainder=True).shuffle(len(df))\n","model = DAE(train.shape[1], 200)\n","model.compile(optimizer=tf.optimizers.Adam())\n","model.fit(loader, epochs = 25)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/25\n","185/185 [==============================] - 4s 16ms/step - loss: 0.1585\n","Epoch 2/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.1032\n","Epoch 3/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0940\n","Epoch 4/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0876\n","Epoch 5/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0823\n","Epoch 6/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0785\n","Epoch 7/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0758\n","Epoch 8/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0737\n","Epoch 9/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0719\n","Epoch 10/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0705\n","Epoch 11/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0693\n","Epoch 12/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0684\n","Epoch 13/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0675\n","Epoch 14/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0668\n","Epoch 15/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0661\n","Epoch 16/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0654\n","Epoch 17/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0649\n","Epoch 18/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0640\n","Epoch 19/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0635\n","Epoch 20/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0628\n","Epoch 21/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0621\n","Epoch 22/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0613\n","Epoch 23/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0606\n","Epoch 24/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0601\n","Epoch 25/25\n","185/185 [==============================] - 3s 16ms/step - loss: 0.0595\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f9bd8942850>"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"UG7OWzoCaXGt"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["54ec6362c43f4c9fb0da5fae155dc21e","b5bae646ec9141fa858b0f24e55c3919","26275847d59a42ef9a5199401a59e6d5","f4e0639d4d194b15aeeb3e8fcf17b0ee","79e2ba356105419aba51015935b1206c","a569e072c0dc409eb91f27bab7ad4c4f","4276f2e7a5aa4042ac0fea7a4433341a","e0c831a414ab443ab1b2d5053d34ca43","55d8f25f6840463689954ae7061546cf","bb840c41a45e44b78d9dffb837ef2872","ac9ba7893cbd4c44aacd670a5ab2ac1b"]},"id":"zaBPGF_paXGt","executionInfo":{"status":"ok","timestamp":1630836102534,"user_tz":-330,"elapsed":131749,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"a05cf1da-f629-41b6-8a38-755acaf0ad39"},"source":["top_k = 10\n","np.random.seed(42)\n","\n","scores = []\n","for idx, i in tqdm(enumerate(np.random.choice(train.index, 100))):\n","    item_to_pred = {item: pred for item, pred in zip(train.columns, model.model.predict(train.values)[idx])}\n","    test_ = test[(test['userId']==i) & (test['rating']==1)]['movieId'].values\n","    items = list(np.random.choice(list(filter(lambda x: x not in np.argwhere(train.values[idx]).flatten(), item_to_pred.keys())), 100)) + list(test_)\n","    top_k_items = heapq.nlargest(top_k, items, key=item_to_pred.get)\n","    \n","    score = eval_NDCG(test_, top_k_items)\n","    scores.append(score)\n","    \n","np.mean(scores)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"54ec6362c43f4c9fb0da5fae155dc21e","version_minor":0,"version_major":2},"text/plain":["0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["0.2853871661973964"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"3WzMlytjXENu"},"source":["## RecVAE"]},{"cell_type":"markdown","metadata":{"id":"3JGgb4cpX-8S"},"source":["### Load data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"iJY1toVZX-8S","executionInfo":{"status":"ok","timestamp":1630835582915,"user_tz":-330,"elapsed":6008,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"5d82f1ef-db9e-4ce1-a201-ec5f3ed4b04e"},"source":["df = load_data('./ml-1m/ratings.dat', threshold=3)\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>userId</th>\n","      <th>movieId</th>\n","      <th>rating</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>1104</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>639</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>853</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>3177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>2162</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   userId  movieId  rating\n","0       0     1104       1\n","1       0      639       0\n","2       0      853       0\n","3       0     3177       1\n","4       0     2162       1"]},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"_er5KwLBX-8T"},"source":["### Preprocessing"]},{"cell_type":"code","metadata":{"id":"STkKDPOiX-8U"},"source":["df = df[df['rating']==1].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","\n","cnt = tdf.sum(1)\n","df = df[df['userId'].isin(np.where(cnt >= 10)[0])].reset_index(drop=True)\n","tdf = pd.pivot_table(df, index='userId', values='rating', columns='movieId').fillna(0)\n","tdf.iloc[:,:] = 0\n","test_idx = []\n","for i in tdf.index:\n","    test_idx += list(np.random.choice(df[df['userId']==i].index, 1))\n","    \n","train = df.iloc[list(set(df.index)-set(test_idx)),:]\n","test = df.iloc[test_idx, :]\n","\n","for uid, iid in zip(train['userId'].values, train['movieId'].values):\n","    tdf.loc[uid, iid] = 1\n","train =  tdf.copy().astype(np.float32)\n","\n","loader = tf.data.Dataset.from_tensor_slices(train.values.astype(np.float32))\n","loader = loader.batch(8, drop_remainder=True).shuffle(len(train))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6ZDpx3i9X-8V"},"source":["### Model architecture"]},{"cell_type":"code","metadata":{"id":"axGJNldkYLEe"},"source":["def log_norm_pdf(x, mu, logvar):\n","    return -0.5*(logvar + tf.math.log(2 * np.pi) + tf.pow((x - mu), 2) / tf.exp(logvar))\n","\n","def sampling(args):\n","    z_mean, z_log_var = args\n","    batch = tf.shape(z_mean)[0]\n","    dim = tf.shape(z_mean)[1]\n","    epsilon = tf.random.normal(shape=(batch, dim), stddev=0.01)\n","    return z_mean + tf.exp(0.5 * z_log_var) * epsilon"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"48AlS5OtX-8W"},"source":["class CompositePrior(tf.keras.models.Model):\n","    def __init__(self, x_dim, latent_dim, mixture_weights = [3/20, 15/20, 2/20]):\n","        super().__init__()\n","        self.encoder_old = Encoder(x_dim, latent_dim, dropout_rate=0)\n","        self.latent_dim = latent_dim\n","        self.mixture_weights = mixture_weights\n","        \n","        self.mu_prior = self.add_weight(shape=(self.latent_dim, ), initializer = tf.zeros_initializer(), trainable=False)\n","        self.logvar_prior  = self.add_weight(shape=(self.latent_dim, ), initializer = tf.zeros_initializer(), trainable=False)\n","        self.logvar_unif_prior = self.add_weight(shape=(self.latent_dim, ), initializer = tf.constant_initializer(10), trainable=False)\n","        \n","    def call(self, x, z):\n","        post_mu, post_logvar = self.encoder_old(x)\n","        \n","        stnd_prior = log_norm_pdf(z, self.mu_prior, self.logvar_prior)\n","        post_prior = log_norm_pdf(z, post_mu, post_logvar)\n","        unif_prior = log_norm_pdf(z, self.mu_prior, self.logvar_unif_prior)\n","        \n","        gaussians = [stnd_prior, post_prior, unif_prior]\n","        gaussians = [g+tf.math.log(w) for g, w in zip(gaussians, self.mixture_weights)]\n","        \n","        density = tf.stack(gaussians, -1)\n","        return tf.math.log(tf.reduce_sum(tf.exp(density), -1)) # logsumexp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CQG9eIBxYPWl"},"source":["class Encoder(tf.keras.models.Model):\n","    def __init__(self, x_dim, latent_dim, dropout_rate = 0.1):\n","        super().__init__()\n","        self.latent_dim = latent_dim\n","        self.x_dim = x_dim\n","        self.dropout_rate = dropout_rate\n","        self.model = self.build_model()\n","        \n","    def build_model(self): # now just shallow net\n","        x_in = Input(shape=(self.x_dim, ))\n","        \n","        h = Dense(1024, activation='relu')(x_in)\n","        mu = Dense(self.latent_dim)(h)\n","        logvar = Dense(self.latent_dim)(h)\n","        \n","        return Model(x_in, [mu, logvar])\n","        \n","    def call(self, x):\n","        norm = tf.sqrt(tf.reduce_sum(tf.pow(x, 2), -1, keepdims=True))\n","        x = x/norm\n","        if self.dropout_rate>0:\n","            x = Dropout(self.dropout_rate)(x)\n","        \n","        return self.model(x)\n","\n","class RecVAE(tf.keras.models.Model):\n","    def __init__(self, x_dim, latent_dim):\n","        super().__init__()\n","        \n","        self.encoder = Encoder(x_dim, latent_dim)\n","        self.decoder = Dense(x_dim)\n","        self.prior = CompositePrior(x_dim, latent_dim)\n","        \n","    def call(self, data):\n","        mu, logvar = self.encoder(data)\n","        z = sampling([mu, logvar])\n","        recon = self.decoder(z)\n","        \n","        return mu, logvar, z, recon\n","    \n","    def predict(self, data):\n","        mu, logvar = self.encoder(data)\n","        z = sampling([mu, logvar])\n","        recon = self.decoder(z)\n","        \n","        return recon\n","    \n","    def update_prior(self):\n","        self.prior.encoder_old.set_weights(self.encoder.get_weights())"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HdkKDWyXX-8W"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"peb46Hd9X-8W"},"source":["def tf_train(model, loader, optimizer, target, gamma=1.):\n","    total_loss = 0.\n","    for x in loader:\n","        norm = tf.reduce_sum(x, -1, keepdims=True)\n","        kl_weight = gamma*norm\n","        \n","        with tf.GradientTape() as tape:\n","            mu, logvar, z, pred = model(x)\n","            \n","#             kl_loss = tf.reduce_mean(tf.reduce_sum(0.5*(logvar + tf.exp(logvar) + tf.pow(mu, 2)-1), 1, keepdims=True))\n","            kl_loss = tf.reduce_mean(log_norm_pdf(z, mu, logvar) - tf.multiply(model.prior(x, z), kl_weight))\n","            ce_loss = -tf.reduce_mean(tf.reduce_sum(tf.nn.log_softmax(pred) * x, -1))\n","            \n","            loss = ce_loss + kl_loss*kl_weight\n","            \n","        if target == 'encoder':\n","            grads = tape.gradient(loss, model.encoder.trainable_weights)\n","            optimizer.apply_gradients(zip(grads, model.encoder.trainable_weights))\n","        else:\n","            grads = tape.gradient(loss, model.decoder.trainable_weights)\n","            optimizer.apply_gradients(zip(grads, model.decoder.trainable_weights))\n","            \n","        total_loss += tf.reduce_sum(loss)\n","    return total_loss"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FzTZPD9CYfpj"},"source":["epochs = 25\n","\n","model = RecVAE(train.shape[1], 200)\n","enc_opt = optimizers.Adam()\n","dec_opt = optimizers.Adam()\n","\n","for e in range(epochs):\n","    # alternating \n","    ## train step\n","    tf_train(model, loader, enc_opt, 'encoder')\n","    model.update_prior()\n","    tf_train(model, loader, dec_opt, 'decoder')\n","    ## eval step"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bhZuJedDX-8X"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":66,"referenced_widgets":["87c9bc69718d40c0acd55be8b3d028c3","592a360bb8f74fd690223f2e4bb14f0e","291c4790efeb43b68b78eaef6a99ced7","8d0ed0b8a3e94733aad8d23ae0265d3c","4e554fb4f30a48faa6859d457d08ba1e","32727136c19e46558e6016eea4fa6fec","fa3e40aea0f14e4db3be250b51c8ede0","7a486090a89343168b6c82943865733c","57b0ddec4be6490b8da427622de4ebac","2d1191aacad24eba93d354e26e6ee37b","9940ceebfb384b6da8e6d5deffcce3a4"]},"id":"KgYQGSPBYUCb","executionInfo":{"status":"ok","timestamp":1630838390198,"user_tz":-330,"elapsed":184666,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"}},"outputId":"b760dcd0-9831-49d3-c3ee-3ab95dc24eda"},"source":["top_k = 10\n","np.random.seed(42)\n","\n","scores = []\n","for idx, i in tqdm(enumerate(np.random.choice(train.index, 100))):\n","    item_to_pred = {item: pred.numpy() for item, pred in zip(train.columns, model.predict(train.values)[idx])}\n","    test_ = test[(test['userId']==i) & (test['rating']==1)]['movieId'].values\n","    items = list(np.random.choice(list(filter(lambda x: x not in np.argwhere(train.values[idx]).flatten(), item_to_pred.keys())), 100)) + list(test_)\n","    top_k_items = heapq.nlargest(top_k, items, key=item_to_pred.get)\n","    \n","    score = eval_NDCG(test_, top_k_items)\n","    scores.append(score)\n","#     break\n","np.mean(scores)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"87c9bc69718d40c0acd55be8b3d028c3","version_minor":0,"version_major":2},"text/plain":["0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["0.0031546487678572877"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","source":["---"],"metadata":{"id":"RAaqLy1UtcIC"}},{"cell_type":"code","source":["!pip install -q watermark\n","%reload_ext watermark\n","%watermark -a \"Sparsh A.\" -m -iv -u -t -d"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jit1oP3jtd7k","executionInfo":{"status":"ok","timestamp":1639716362410,"user_tz":-330,"elapsed":4112,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"8470ae76-a435-4a4f-f909-351ed5e37fed"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Author: Sparsh A.\n","\n","Last updated: 2021-12-17 04:46:00\n","\n","Compiler    : GCC 7.5.0\n","OS          : Linux\n","Release     : 5.4.104+\n","Machine     : x86_64\n","Processor   : x86_64\n","CPU cores   : 2\n","Architecture: 64bit\n","\n","IPython   : 5.5.0\n","matplotlib: 3.2.2\n","sys       : 3.7.12 (default, Sep 10 2021, 00:21:48) \n","[GCC 7.5.0]\n","tensorflow: 2.7.0\n","pandas    : 1.1.5\n","numpy     : 1.19.5\n","seaborn   : 0.11.2\n","keras     : 2.7.0\n","google    : 2.0.3\n","\n"]}]},{"cell_type":"markdown","source":["---"],"metadata":{"id":"qrYL9Jx-tcIF"}},{"cell_type":"markdown","source":["**END**"],"metadata":{"id":"pZR6MBOZtcIG"}}]}