2. Movie_Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T7NA8UpqUnnV"
   },
   "source": [
    "# Process\n",
    "In this notebook, we will train all the algorithms on MovieLens1M using <a href=\"https://cornac.readthedocs.io/en/latest/\">Cornac</a>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6HyLhxzyUu6w"
   },
   "source": [
    "## A. Import lbraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "5YRmDovpU54c"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import matplotlib.pyplot as plt\n",
    "import random as rd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import pickle as pkl\n",
    "import time\n",
    "\n",
    "# Cornac imports\n",
    "import cornac\n",
    "from cornac.eval_methods import RatioSplit\n",
    "from cornac.data import Reader as CornacReader\n",
    "from cornac.models import MostPop, MF, PMF, BPR, NeuMF, WMF, HPF, VAECF, ItemKNN, UserKNN, BaselineOnly \n",
    "from cornac.models import NMF as CornacNMF \n",
    "from cornac.metrics import MAE, MSE, RMSE, Precision, Recall, NDCG, AUC, MAP, FMeasure, MRR\n",
    "\n",
    "from collections import defaultdict\n",
    "from scipy import stats\n",
    "from sklearn.metrics import mean_squared_error, precision_score, recall_score, ndcg_score\n",
    "from numpy.linalg import norm\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# set plot style: grey grid in the background:\n",
    "sns.set(style=\"darkgrid\")\n",
    "pd.set_option(\"display.precision\", 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wJr35vAGVuM4"
   },
   "source": [
    "## B. Set hyperparameters\n",
    "There are certain hyperparameters that need to be tuned before the run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Wz1OKVKoVkkD"
   },
   "outputs": [],
   "source": [
    "# constants and initialization\n",
    "rating_threshold = 1.0 # needed for the cornac library: Threshold used to binarize rating values into positive or negative feedback for model evaluation using ranking metrics (rating metrics are not affected). \n",
    "my_seed = 0\n",
    "test_size = 0.2 # the percentage of \"hold out\" data that are used for testing\n",
    "predict_col = \"rating\"\n",
    "user_col = \"user\"\n",
    "top_fraction = 0.2 # the amount of items that will be considered \"popular\"\n",
    "\n",
    "rd.seed(my_seed) #seed for random functions\n",
    "np.random.seed(my_seed) #seed for all numpy stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Z0PCJ5LZen3"
   },
   "source": [
    "## C. Read files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xWjNXmvmZcTw",
    "outputId": "473bad69-afa1-47d3-9c7a-57abcb33ac30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of user events: 1000209\n"
     ]
    }
   ],
   "source": [
    "user_events_file = 'https://raw.githubusercontent.com/SavvinaDaniil/UnfairnessOfPopularityBias/main/data/ratings_movies.dat'\n",
    "\n",
    "# read user events\n",
    "cols = ['user', 'movie', 'rating', 'timestamp']\n",
    "df_events = pd.read_table(user_events_file, sep = \"::\", engine=\"python\", names=cols)\n",
    "item_col = \"movie\"\n",
    "print('No. of user events: ' + str(len(df_events)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "afPDrweUqAgb",
    "outputId": "1da09f21-e2c6-4a5e-a05f-c7378b3d561f"
   },
   "outputs": [
    {
     "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>user</th>\n",
       "      <th>movie</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>"
      ],
      "text/plain": [
       "   user  movie  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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iMVCVOTmaozs"
   },
   "source": [
    "## D. Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "WpGdgR_iam3y"
   },
   "outputs": [],
   "source": [
    "def users_and_items(df_events, user_col, item_col):\n",
    "    print('No. user events: ' + str(len(df_events)))\n",
    "    print('No. items: ' + str(len(df_events[item_col].unique())))\n",
    "    print('No. users: ' + str(len(df_events[user_col].unique())))\n",
    "    print(\"\\n\")\n",
    "def user_distribution(df_events, user_col, item_col):\n",
    "    user_dist = df_events[user_col].value_counts() \n",
    "    num_users = len(user_dist)\n",
    "    print('Mean '+item_col+'s per user: ' + str(np.round(user_dist.mean(),1))) \n",
    "    print('Min '+item_col+'s per user: ' + str(np.round(user_dist.min(),1))) \n",
    "    print('Max '+item_col+'s per user: ' + str(np.round(user_dist.max(),1)))\n",
    "    print(\"\\n\")\n",
    "    return user_dist, num_users\n",
    "def item_distribution(df_events, user_col, item_col):\n",
    "    item_dist = df_events[item_col].value_counts()\n",
    "    num_items = len(item_dist)\n",
    "    print('Mean users per '+item_col+': ' + str(np.round(item_dist.mean(),1))) \n",
    "    print('Min users per '+item_col+': ' + str(np.round(item_dist.min(),1))) \n",
    "    print('Max users per '+item_col+': ' + str(np.round(item_dist.max(),1))) \n",
    "    print(\"\\n\")\n",
    "    return item_dist, num_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1jwJq8rda1jS",
    "outputId": "c72b7146-0d64-49b0-b411-56dc5afec979"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. user events: 1000209\n",
      "No. items: 3706\n",
      "No. users: 6040\n",
      "\n",
      "\n",
      "Mean movies per user: 165.6\n",
      "Min movies per user: 20\n",
      "Max movies per user: 2314\n",
      "\n",
      "\n",
      "Mean users per movie: 269.9\n",
      "Min users per movie: 1\n",
      "Max users per movie: 3428\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "users_and_items(df_events, user_col, item_col)\n",
    "user_dist, num_users = user_distribution(df_events, user_col, item_col)\n",
    "item_dist, num_items = item_distribution(df_events, user_col, item_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "C7wdClok5BPv"
   },
   "outputs": [],
   "source": [
    "# create item dataframe with normalized item counts\n",
    "df_item_dist = pd.DataFrame(item_dist) # turn the series into a dataframe\n",
    "df_item_dist.columns = ['count']\n",
    "df_item_dist['count'] /= num_users # basically the popularity of every item\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "UMNYiSh35KG8",
    "outputId": "7184403f-43de-4501-af1f-c9ff6502914a"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>0.56754967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.49519868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>0.49503311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>0.47731788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>0.44238411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count\n",
       "2858  0.56754967\n",
       "260   0.49519868\n",
       "1196  0.49503311\n",
       "1210  0.47731788\n",
       "480   0.44238411"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_item_dist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "G7o_VBaUa4lH"
   },
   "outputs": [],
   "source": [
    "def plot_data_distribution(item_dist, item_col,log = False):\n",
    "    plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax.spines['bottom'].set_color('w')\n",
    "    ax.spines['top'].set_color('w')\n",
    "    ax.spines['right'].set_color('w')\n",
    "    ax.spines['left'].set_color('w')\n",
    "    ax.spines['left'].set_zorder(0)\n",
    "    ax.xaxis.set_ticks_position('none') \n",
    "    ax.yaxis.set_ticks_position('none') \n",
    "    ax.set_facecolor(\"aliceblue\")\n",
    "    plt.grid(color = \"w\",linewidth = 2 )\n",
    "    plt.plot(item_dist.values)\n",
    "    plt.xticks(fontsize='13')\n",
    "    plt.yticks(fontsize='13')\n",
    "    add = \"\"\n",
    "    if log:\n",
    "        plt.xscale('log')\n",
    "        plt.yscale('log')\n",
    "        add = \" (log)\"\n",
    "    plt.xlabel(item_col+add, fontsize='14')\n",
    "    plt.ylabel('Number of users' + add, fontsize='15')\n",
    "    plt.show(block=True)\n",
    "\n",
    "def plot_popularity_distribution(pop_fraq, item_col):\n",
    "    plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax.spines['bottom'].set_color('w')\n",
    "    ax.spines['top'].set_color('w')\n",
    "    ax.spines['right'].set_color('w')\n",
    "    ax.spines['left'].set_color('w')\n",
    "    ax.spines['left'].set_zorder(0)\n",
    "    ax.xaxis.set_ticks_position('none') \n",
    "    ax.yaxis.set_ticks_position('none') \n",
    "    \n",
    "    ax.set_facecolor(\"aliceblue\")\n",
    "    plt.grid(color = \"w\",linewidth = 2 )\n",
    "    plt.plot(sorted(pop_fraq))\n",
    "    plt.xlabel('User', fontsize='15')\n",
    "    plt.xticks(fontsize='13')\n",
    "    plt.ylabel('Ratio of popular '+item_col+'s', fontsize='15')\n",
    "    plt.yticks(fontsize='13')\n",
    "    plt.axhline(y=0.8, color='black', linestyle='--', label='80% ratio of popular '+item_col+'s')\n",
    "    plt.legend(fontsize='15')\n",
    "    plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "COATHnNGa_G5",
    "outputId": "acf8403a-0296-4636-bef2-27c1fc958728"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_distribution(item_dist, item_col)\n",
    "plot_data_distribution(item_dist, item_col, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KMiAQybFbFSj"
   },
   "source": [
    "## E. Popularity in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jYCqPRLjbByM",
    "outputId": "fe2d54c4-226d-4d82-e855-4495748d6a35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. items labeled as \"popular\": 741\n"
     ]
    }
   ],
   "source": [
    "num_top = int(top_fraction * num_items)\n",
    "top_item_dist = item_dist[:num_top]\n",
    "print('No. items labeled as \"popular\": ' + str(len(top_item_dist)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "T0vchbD9bIie"
   },
   "outputs": [],
   "source": [
    "def calculate_popularity(df_events, top_item_dist, item_dist, num_users, user_col, item_col):\n",
    "    pop_count = [] # number of top items per user\n",
    "    user_hist = [] # user history sizes\n",
    "    pop_fraq = [] # relative number of top items per user\n",
    "    pop_item_fraq = [] # average popularity of items in user profiles\n",
    "    i=0\n",
    "    for u, df in df_events.groupby(user_col):\n",
    "        no_user_items = len(set(df[item_col]))\n",
    "        no_user_pop_items = len(set(df[item_col]) & set(top_item_dist.index))\n",
    "        pop_count.append(no_user_pop_items)\n",
    "        user_hist.append(no_user_items) \n",
    "        pop_fraq.append(no_user_pop_items / no_user_items)\n",
    "        user_pop_item_fraq = sum(item_dist[df[item_col]] / num_users) / no_user_items\n",
    "        pop_item_fraq.append(user_pop_item_fraq)\n",
    "        i+=1\n",
    "        if i%1000==0:\n",
    "            print(i)\n",
    "    return pop_count,user_hist,pop_fraq, pop_item_fraq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bp0tmyMHbQJf",
    "outputId": "8c632e67-3cd4-417d-d199-694a5c24f8d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n"
     ]
    }
   ],
   "source": [
    "pop_count,user_hist,pop_fraq, pop_item_fraq = calculate_popularity(df_events, top_item_dist, item_dist, num_users, user_col, item_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RZwgf9kEbR52"
   },
   "outputs": [],
   "source": [
    "def plot_popularity_distribution(pop_fraq, item_col):\n",
    "    plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax.spines['bottom'].set_color('w')\n",
    "    ax.spines['top'].set_color('w')\n",
    "    ax.spines['right'].set_color('w')\n",
    "    ax.spines['left'].set_color('w')\n",
    "    ax.spines['left'].set_zorder(0)\n",
    "    ax.xaxis.set_ticks_position('none') \n",
    "    ax.yaxis.set_ticks_position('none') \n",
    "    \n",
    "    ax.set_facecolor(\"aliceblue\")\n",
    "    plt.grid(color = \"w\",linewidth = 2 )\n",
    "    plt.plot(sorted(pop_fraq))\n",
    "    plt.xlabel('User', fontsize='15')\n",
    "    plt.xticks(fontsize='13')\n",
    "    plt.ylabel('Ratio of popular '+item_col+'s', fontsize='15')\n",
    "    plt.yticks(fontsize='13')\n",
    "    plt.axhline(y=0.8, color='black', linestyle='--', label='80% ratio of popular '+item_col+'s')\n",
    "    plt.legend(fontsize='15')\n",
    "    plt.show(block=True)\n",
    "\n",
    "def plot_profile_size_vs_popularity(pop_metric, user_hist, way, item_col):\n",
    "    plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax.spines['bottom'].set_color('w')\n",
    "    ax.spines['top'].set_color('w')\n",
    "    ax.spines['right'].set_color('w')\n",
    "    ax.spines['left'].set_color('w')\n",
    "    ax.spines['left'].set_zorder(0)\n",
    "    ax.xaxis.set_ticks_position('none') \n",
    "    ax.yaxis.set_ticks_position('none') \n",
    "    \n",
    "    ax.set_facecolor(\"aliceblue\")\n",
    "    plt.grid(color = \"w\",linewidth = 2 )\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(user_hist, pop_metric)\n",
    "    print('R-value: ' + str(r_value))\n",
    "    line = slope * np.array(user_hist) + intercept\n",
    "    plt.plot(user_hist, pop_metric, 'o', user_hist, line)\n",
    "    plt.xlabel('User profile size', fontsize='15')\n",
    "    plt.xticks(fontsize='13')\n",
    "    if way == \"count\":\n",
    "        ylabel = \"Number of popular \"+item_col+\"s\"\n",
    "    elif way == \"percentage\":\n",
    "        ylabel = 'Percentage of popular '+item_col+'s'\n",
    "    else:\n",
    "        ylabel = \"Average popularity of \"+item_col+\"s\"\n",
    "    plt.ylabel(ylabel, fontsize='15')\n",
    "    plt.yticks(fontsize='13')\n",
    "    plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "WDFRIFsibZTI",
    "outputId": "7264bfe7-d802-45f7-d18f-20a96575e76b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-value: 0.9662364112421091\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-value: -0.40259977494843086\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-value: -0.48167705404310895\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_popularity_distribution(pop_fraq, item_col)\n",
    "plot_profile_size_vs_popularity(pop_count,user_hist, \"count\", item_col)\n",
    "plot_profile_size_vs_popularity(pop_fraq,user_hist, \"percentage\", item_col)\n",
    "plot_profile_size_vs_popularity(pop_item_fraq,user_hist, \"average\", item_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nZ-KPsOzbduG"
   },
   "source": [
    "## F. User groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "n_hQEsRFbbhG"
   },
   "outputs": [],
   "source": [
    "def sort_user_dist(user_dist,pop_count, user_hist,pop_fraq,pop_item_fraq, by = \"pop_fraq\"):\n",
    "    user_dist = user_dist.sort_index()\n",
    "    user_dist_sorted = pd.DataFrame(data = user_dist)\n",
    "    \n",
    "    user_dist_sorted.columns = [\"count\"]\n",
    "    \n",
    "    user_dist_sorted[\"pop_count\"] = pop_count\n",
    "    user_dist_sorted[\"user_hist\"] = user_hist\n",
    "    user_dist_sorted[\"pop_fraq\"] = pop_fraq\n",
    "    user_dist_sorted[\"pop_item_fraq\"] = pop_item_fraq\n",
    "    \n",
    "    user_dist_sorted = user_dist_sorted.sort_values(by=[by])\n",
    "    return user_dist_sorted\n",
    "\n",
    "def split(user_dist_sorted, top_fraction):\n",
    "    low, med, high = np.split(user_dist_sorted, [int(top_fraction*len(user_dist_sorted)), int((1-top_fraction)*len(user_dist_sorted))])\n",
    "    return low, med, high\n",
    "def calculate_group_characteristics(low, med, high):\n",
    "    low_profile_size = low.user_hist.mean()\n",
    "    med_profile_size = med.user_hist.mean()\n",
    "    high_profile_size = high.user_hist.mean()\n",
    "    \n",
    "    low_nr_users = len(low)\n",
    "    med_nr_users = len(med)\n",
    "    high_nr_users = len(high)\n",
    "    \n",
    "    low_GAP = low.pop_item_fraq.mean()\n",
    "    med_GAP = med.pop_item_fraq.mean()\n",
    "    high_GAP = high.pop_item_fraq.mean()\n",
    "    \n",
    "    return low_profile_size, med_profile_size, high_profile_size, low_nr_users, med_nr_users, high_nr_users, low_GAP, med_GAP, high_GAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IVpmIvO0PjHm"
   },
   "source": [
    "### Make notion of popularity propensity choice\n",
    "The reader has to manually choose how to divide the users into groups according to their propensity for popular items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "OzQ9eNt1Pj9w"
   },
   "outputs": [],
   "source": [
    "popularity_function = {'pop_one': \"pop_fraq\", # divide by percentage of popular items in profile\n",
    "             'pop_two': \"pop_item_fraq\"} # divide by average popularity in profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "FehS1Z1pPnLk"
   },
   "outputs": [],
   "source": [
    "pop_notion = \"pop_one\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "RYwIQwD6bi3G"
   },
   "outputs": [],
   "source": [
    "user_dist_sorted = sort_user_dist(user_dist,pop_count, user_hist,pop_fraq,pop_item_fraq, by = popularity_function[pop_notion])\n",
    "low, med, high = split(user_dist_sorted, top_fraction)\n",
    "low_profile_size, med_profile_size, high_profile_size, low_nr_users, med_nr_users, high_nr_users, low_GAP, med_GAP, high_GAP = calculate_group_characteristics(low, med, high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7rmTLMN7bpDo",
    "outputId": "bffa9560-a34d-4f5f-afa5-02189f765f77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Niche GAP: 0.1080159187333548\n",
      "Diverse GAP: 0.1569755573676999\n",
      "Blockbuster-focused GAP: 0.20907251963505036\n"
     ]
    }
   ],
   "source": [
    "print('Niche GAP: ' + str(low_GAP))\n",
    "print('Diverse GAP: ' + str(med_GAP))\n",
    "print('Blockbuster-focused GAP: ' + str(high_GAP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Dmf4J023byK4"
   },
   "outputs": [],
   "source": [
    "def plot_group_characteristics(low_nr, med_nr, high_nr, way, item_col):\n",
    "    plt.figure()\n",
    "    ax = plt.axes()\n",
    "    ax.spines['bottom'].set_color('w')\n",
    "    ax.spines['top'].set_color('w')\n",
    "    ax.spines['right'].set_color('w')\n",
    "    ax.spines['left'].set_color('w')\n",
    "    ax.spines['left'].set_zorder(0)\n",
    "    ax.xaxis.set_ticks_position('none') \n",
    "    ax.yaxis.set_ticks_position('none') \n",
    "    \n",
    "    ax.set_facecolor(\"aliceblue\")\n",
    "    plt.bar(np.arange(3), [low_nr, med_nr, high_nr])\n",
    "    plt.xticks(np.arange(3), ['Niche', 'Diverse', 'Blockbuster-focused'])\n",
    "    plt.xlabel('User group')\n",
    "    if way==\"size\":\n",
    "        ylabel = 'Average user profile size'\n",
    "    else:\n",
    "        ylabel = \"Number of users per group\"\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    print('Niche: ' + str(low_nr))\n",
    "    print('Diverse: ' + str(med_nr))\n",
    "    print('Blockbuster-focused: ' + str(high_nr))\n",
    "    plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 660
    },
    "id": "GByQlxuOb245",
    "outputId": "a7a2eca4-68b4-4d21-d735-9d310bc080ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Niche: 1208\n",
      "Diverse: 3624\n",
      "Blockbuster-focused: 1208\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Niche: 301.737582781457\n",
      "Diverse: 152.3543046357616\n",
      "Blockbuster-focused: 69.18708609271523\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_group_characteristics(low_nr_users, med_nr_users, high_nr_users, way = \"number\", item_col = item_col)\n",
    "plot_group_characteristics(low_profile_size, med_profile_size, high_profile_size, way = \"size\", item_col = item_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gbQUQuRbcJ5H"
   },
   "source": [
    "## G. Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "yJewyI61nSAv",
    "outputId": "da855818-0878-404a-a898-361d571c2695"
   },
   "outputs": [
    {
     "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>user</th>\n",
       "      <th>movie</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>"
      ],
      "text/plain": [
       "   user  movie  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"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yQyAB2jFcMnk"
   },
   "source": [
    "### G.1 Cornac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "id": "uI-XJAWQpHkL"
   },
   "outputs": [],
   "source": [
    "df_events_cornac = df_events.copy()\n",
    "mapping_dict = {} # Create a dictionary that maps each movie number to an integer - necessary for Cornac.\n",
    "i=0\n",
    "for mov in df_events_cornac[item_col].unique():\n",
    "    mapping_dict[mov] = i\n",
    "    i+=1\n",
    "df_events_cornac[item_col] = df_events_cornac[item_col].map(lambda x: mapping_dict.get(x,x)) # Map in the ratings file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "id": "SVrTTaHBpZ04"
   },
   "outputs": [],
   "source": [
    "df_events_cornac.user = df_events_cornac.user.astype(\"string\")\n",
    "df_events_cornac.movie = df_events_cornac.movie.astype(\"string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "id": "Gom3rbXz5844"
   },
   "outputs": [],
   "source": [
    "df_item_dist_cornac = df_item_dist.rename(index = mapping_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "id": "O8irZQHgcAV3"
   },
   "outputs": [],
   "source": [
    "data = list(df_events_cornac[[user_col,item_col,predict_col]].to_records(index = False)) # ready to import in Cornac."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "id": "cZ0JGW8ppnn7"
   },
   "outputs": [],
   "source": [
    "# Split the data based on ratio\n",
    "rs = RatioSplit(data=data, test_size=test_size, rating_threshold=rating_threshold, seed=123, exclude_unknowns=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "id": "qS5k9wNprWvm"
   },
   "outputs": [],
   "source": [
    "all_items = set(range(rs.total_items)) # needed for later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "id": "uxPCqVf9rYky"
   },
   "outputs": [],
   "source": [
    "# Define the two different options for UserKNN, with and without mean centering\n",
    "usKNN = UserKNN(k=40, similarity = 'cosine', mean_centered=False, seed = my_seed, verbose=True)\n",
    "usKNN_means = UserKNN(k=40, similarity = 'cosine', mean_centered=True, seed = my_seed, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WOD-sjM9rZ_Z"
   },
   "source": [
    "#### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TXCo0O5sbjck"
   },
   "source": [
    "Google Colab no longer supports Tensorflow v1, which creates dependency issues with Cornac which is built on Tensorflow v1. Until we find a solution, we will exclude the models that do not run for this reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "id": "rVXlmaIUqV0I"
   },
   "outputs": [],
   "source": [
    "# initialize models, here we are comparing: simple, traditional, and neural networks based models\n",
    "# initialize models, here we are comparing: simple, traditional, and neural networks based models\n",
    "models = [\n",
    "          # 1: Random\n",
    "          # 2: MostPop\n",
    "          MostPop(),\n",
    "          # 3: UserKNN\n",
    "          usKNN,\n",
    "          # 4: ItemKNN\n",
    "          ItemKNN(k=40, similarity = 'cosine', mean_centered=False, seed = my_seed, verbose=True),\n",
    "          # 5: UserKNN with means\n",
    "          usKNN_means,\n",
    "          ## 6: BaselineOnly\n",
    "          #BaselineOnly(verbose=True),\n",
    "          # 7: BPR\n",
    "          BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123, verbose=True),\n",
    "          # 8: MF\n",
    "          MF(k=30, max_iter=100, learning_rate=0.01, lambda_reg=0.001, seed=123, verbose=True),\n",
    "          # 9: PMF\n",
    "          PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, verbose=True),\n",
    "          # 10: NMF\n",
    "          CornacNMF(k=15, max_iter=50, learning_rate=0.005, lambda_u=0.06, lambda_v=0.06, lambda_bu=0.02, lambda_bi=0.02, use_bias=False, verbose=True, seed=123),\n",
    "          # 11: WMF\n",
    "          WMF(k=50, max_iter=50, learning_rate=0.001, lambda_u=0.01, lambda_v=0.01, verbose=True, seed=123),\n",
    "          # 12: PF\n",
    "          HPF(k=50, seed=123, hierarchical=False, name=\"PF\", verbose=True),\n",
    "          # 13: NueMF\n",
    "          NeuMF(num_factors=8, layers=[32, 16, 8], act_fn=\"tanh\", num_epochs=1, num_neg=3, batch_size=256, lr=0.001, seed=42, verbose=True),\n",
    "          # 14: VAECF\n",
    "          VAECF(k=10, autoencoder_structure=[20], act_fn=\"tanh\", likelihood=\"mult\", n_epochs=100, batch_size=100, learning_rate=0.001, beta=1.0, seed=123, use_gpu=True, verbose=True)\n",
    "          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "id": "06Irv0BurifH"
   },
   "outputs": [],
   "source": [
    "algo_names = [\"Random\",\n",
    "              \"MostPop\", \n",
    "              \"UserKNN\",\n",
    "              \"ItemKNN\", \n",
    "              \"UserKNN with means\", \n",
    "              \"BPR\",\n",
    "              \"MF\",\n",
    "              \"PMF\",\n",
    "              \"NMF\",\n",
    "              \"WMF\",\n",
    "              \"HPF\",\n",
    "              \"NeuMF\",\n",
    "              \"VAECF\"\n",
    "              ] # in the order that they are in the model list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "id": "w-4yhJQOqXv3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cca39025fda343128c1c782823e2fd87",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6040 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b27c42cec634ec6a988bf93df5faf2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3679 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2db6d0f756524309ab130570866dc78b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6040 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7312777399324294ad90c7b6e06b34f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization finished!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad2a3516fe1a4ad2a8f4d232104bfcd5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization finished!\n",
      "Learning...\n",
      "epoch 0, loss: 74507.760547\n",
      "epoch 1, loss: 58688.467291\n",
      "epoch 2, loss: 52678.747320\n",
      "epoch 3, loss: 50128.612106\n",
      "epoch 4, loss: 48638.692067\n",
      "epoch 5, loss: 47552.996439\n",
      "epoch 6, loss: 46602.354240\n",
      "epoch 7, loss: 45706.298393\n",
      "epoch 8, loss: 44892.622352\n",
      "epoch 9, loss: 44200.995180\n",
      "epoch 10, loss: 43633.593588\n",
      "epoch 11, loss: 43168.020233\n",
      "epoch 12, loss: 42778.554077\n",
      "epoch 13, loss: 42444.117638\n",
      "epoch 14, loss: 42148.739790\n",
      "epoch 15, loss: 41880.417562\n",
      "epoch 16, loss: 41630.244486\n",
      "epoch 17, loss: 41391.966497\n",
      "epoch 18, loss: 41161.701966\n",
      "epoch 19, loss: 40937.535107\n",
      "epoch 20, loss: 40718.851572\n",
      "epoch 21, loss: 40505.555427\n",
      "epoch 22, loss: 40297.467211\n",
      "epoch 23, loss: 40094.107682\n",
      "epoch 24, loss: 39894.832366\n",
      "epoch 25, loss: 39699.116261\n",
      "epoch 26, loss: 39506.788203\n",
      "epoch 27, loss: 39318.101296\n",
      "epoch 28, loss: 39133.630307\n",
      "epoch 29, loss: 38954.070643\n",
      "epoch 30, loss: 38780.035464\n",
      "epoch 31, loss: 38611.921470\n",
      "epoch 32, loss: 38449.865955\n",
      "epoch 33, loss: 38293.776800\n",
      "epoch 34, loss: 38143.401803\n",
      "epoch 35, loss: 37998.405959\n",
      "epoch 36, loss: 37858.436980\n",
      "epoch 37, loss: 37723.169802\n",
      "epoch 38, loss: 37592.329612\n",
      "epoch 39, loss: 37465.697837\n",
      "epoch 40, loss: 37343.107055\n",
      "epoch 41, loss: 37224.430170\n",
      "epoch 42, loss: 37109.567922\n",
      "epoch 43, loss: 36998.437470\n",
      "epoch 44, loss: 36890.963211\n",
      "epoch 45, loss: 36787.070457\n",
      "epoch 46, loss: 36686.681650\n",
      "epoch 47, loss: 36589.714490\n",
      "epoch 48, loss: 36496.081527\n",
      "epoch 49, loss: 36405.690571\n",
      "epoch 50, loss: 36318.445549\n",
      "epoch 51, loss: 36234.247489\n",
      "epoch 52, loss: 36152.995550\n",
      "epoch 53, loss: 36074.587924\n",
      "epoch 54, loss: 35998.922615\n",
      "epoch 55, loss: 35925.898110\n",
      "epoch 56, loss: 35855.413946\n",
      "epoch 57, loss: 35787.371088\n",
      "epoch 58, loss: 35721.672467\n",
      "epoch 59, loss: 35658.223111\n",
      "epoch 60, loss: 35596.930528\n",
      "epoch 61, loss: 35537.704834\n",
      "epoch 62, loss: 35480.459017\n",
      "epoch 63, loss: 35425.108961\n",
      "epoch 64, loss: 35371.573625\n",
      "epoch 65, loss: 35319.775063\n",
      "epoch 66, loss: 35269.638511\n",
      "epoch 67, loss: 35221.092370\n",
      "epoch 68, loss: 35174.068131\n",
      "epoch 69, loss: 35128.500476\n",
      "epoch 70, loss: 35084.327077\n",
      "epoch 71, loss: 35041.488657\n",
      "epoch 72, loss: 34999.928771\n",
      "epoch 73, loss: 34959.593830\n",
      "epoch 74, loss: 34920.432930\n",
      "epoch 75, loss: 34882.397763\n",
      "epoch 76, loss: 34845.442477\n",
      "epoch 77, loss: 34809.523598\n",
      "epoch 78, loss: 34774.599892\n",
      "epoch 79, loss: 34740.632190\n",
      "epoch 80, loss: 34707.583406\n",
      "epoch 81, loss: 34675.418262\n",
      "epoch 82, loss: 34644.103305\n",
      "epoch 83, loss: 34613.606734\n",
      "epoch 84, loss: 34583.898349\n",
      "epoch 85, loss: 34554.949408\n",
      "epoch 86, loss: 34526.732578\n",
      "epoch 87, loss: 34499.221832\n",
      "epoch 88, loss: 34472.392404\n",
      "epoch 89, loss: 34446.220646\n",
      "epoch 90, loss: 34420.684026\n",
      "epoch 91, loss: 34395.761067\n",
      "epoch 92, loss: 34371.431235\n",
      "epoch 93, loss: 34347.674891\n",
      "epoch 94, loss: 34324.473311\n",
      "epoch 95, loss: 34301.808550\n",
      "epoch 96, loss: 34279.663461\n",
      "epoch 97, loss: 34258.021591\n",
      "epoch 98, loss: 34236.867180\n",
      "epoch 99, loss: 34216.185154\n",
      "Learning completed\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c953c499efa471b86d225cbb8ce1e12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization finished!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cd6066f7c554b35a16276e6340f56c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning completed!\n",
      "Learning...\n",
      "Learning completed!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0ed17560ee44bd4bdf441f741ffbd67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6146ac2f91d4d7080f11c549d22fd80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TEST:\n",
      "...\n",
      "        |    MAE | Train (s) | Test (s)\n",
      "------- + ------ + --------- + --------\n",
      "MostPop | 1.2974 |    0.0565 |   2.9612\n",
      "UserKNN | 0.7669 |    5.3674 |  33.5459\n",
      "ItemKNN | 0.8037 |    2.4507 |  30.2063\n",
      "UserKNN | 0.7491 |    5.2670 |  33.7682\n",
      "BPR     | 2.0852 |   29.9988 |   3.7406\n",
      "MF      | 0.7904 |    4.3080 |   4.1373\n",
      "PMF     | 0.7011 |   30.5374 |   4.0983\n",
      "NMF     | 0.7496 |    2.8358 |   3.9620\n",
      "WMF     | 1.1277 |   22.4081 |   3.5737\n",
      "PF      | 2.4691 |  800.5093 |   3.8290\n",
      "NeuMF   | 2.7023 |   71.3622 |  62.2430\n",
      "VAECF   | 2.7023 |  211.1795 |  87.3983\n",
      "\n",
      "1460 seconds.\n"
     ]
    }
   ],
   "source": [
    "# define metrics to evaluate the models\n",
    "metrics = [MAE()]\n",
    "\n",
    "# put it together in an experiment, voilà!\n",
    "start = time.time()\n",
    "exp = cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True)\n",
    "exp.run()\n",
    "end = time.time()\n",
    "print(round(end-start), \"seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lDyNWKN3rpbX"
   },
   "source": [
    "#### Ranking and recommendation\n",
    "We have identified three evaluation strategies:\n",
    "1. For every user in the **train set** rank **all items**. | Book paper, *eva_one*\n",
    "2. For every user in the **test set** rank **only the items this user has rated in the test set**. | Music paper, *eva_two*\n",
    "3. For every user in the **test set** rank **only the items this user has NOT rated in the train set**. | Movie(?) paper, *eva_three*\n",
    "\n",
    "For every evaluation strategy, we define a function that recommends the appropriate items to the appropriate users as described above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aZbCVBNArrWy"
   },
   "source": [
    "##### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "id": "1Ft6Cw5sqxYp"
   },
   "outputs": [],
   "source": [
    "def get_top_n_eva_one(algo_name,i, n=10):\n",
    "  model = exp.models[i]\n",
    "  print(algo_name + \" model is selected:\")\n",
    "  top_n = defaultdict(list)\n",
    "  for uid in tqdm(model.train_set.uid_map.values()): # every user in the train set\n",
    "    user_id = list(model.train_set.user_ids)[uid] # so this is the actual user_id\n",
    "    item_rank = model.rank(user_idx=uid)[0] # all\n",
    "    # collect top N items\n",
    "    item_rank_top = item_rank[:n]\n",
    "    for iid in item_rank_top:\n",
    "      item_id = list(model.train_set.item_ids)[iid]\n",
    "      if isinstance(model.score(uid, iid), (np.ndarray)): # for NEUMF\n",
    "            score = model.score(uid, iid)[0]\n",
    "      else:\n",
    "            score = model.score(uid, iid)\n",
    "        \n",
    "      top_n[int(user_id)].append((int(item_id), score))\n",
    "  return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "id": "7ReRcdQCrveT"
   },
   "outputs": [],
   "source": [
    "def get_top_n_eva_two(algo_name,i, n=10):\n",
    "  #print(\"HI\")\n",
    "  model = exp.models[i]\n",
    "  print(algo_name + \" model is selected:\")\n",
    "  top_n = defaultdict(list)\n",
    "  for uid in tqdm(rs.test_set.uid_map.values()): # every user in the test set\n",
    "    user_id = list(rs.train_set.user_ids)[uid]\n",
    "    user_items_in_the_test_set = rs.test_set.user_data[uid][0] \n",
    "    #print(user_items_in_the_test_set)\n",
    "    item_rank = model.rank(user_idx=uid, item_indices = user_items_in_the_test_set)[0] # items the user has rated in the test set \n",
    "    item_rank_top = item_rank[:n]\n",
    "    for iid in item_rank_top:\n",
    "      item_id = list(model.train_set.item_ids)[iid]\n",
    "      if isinstance(model.score(uid, iid), (np.ndarray)): # for NEUMF\n",
    "            score = model.score(uid, iid)[0]\n",
    "      else:\n",
    "            score = model.score(uid, iid)\n",
    "        \n",
    "      top_n[int(user_id)].append((int(item_id), score))\n",
    "      \n",
    "  return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "id": "cGjFAhr6rxXI"
   },
   "outputs": [],
   "source": [
    "def get_top_n_eva_three(algo_name,i, n=10):\n",
    "  model = exp.models[i]\n",
    "  print(algo_name + \" model is selected:\")\n",
    "  top_n = defaultdict(list)\n",
    "\n",
    "  for uid in tqdm(rs.test_set.uid_map.values()): # every user in the test set\n",
    "    user_id = list(rs.train_set.user_ids)[uid]\n",
    "    user_items_in_the_train_set = set(rs.train_set.user_data[uid][0])\n",
    "    user_items_not_in_the_train_set = list(all_items.difference(user_items_in_the_train_set))\n",
    "    \n",
    "    item_rank = model.rank(user_idx=uid, item_indices = user_items_not_in_the_train_set)[0] # items the user has NOT rated in the TRAIN set\n",
    "    item_rank_top = item_rank[:n]\n",
    "    for iid in item_rank_top:\n",
    "      item_id = list(model.train_set.item_ids)[iid]\n",
    "      if isinstance(model.score(uid, iid), (np.ndarray)): # for NEUMF\n",
    "            score = model.score(uid, iid)[0]\n",
    "      else:\n",
    "            score = model.score(uid, iid)\n",
    "        \n",
    "      top_n[int(user_id)].append((int(item_id), score))\n",
    "  return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "id": "nU1sU9Q_qy_J"
   },
   "outputs": [],
   "source": [
    "# random recommendation algorithm, same for every strategy \n",
    "def get_top_n_random(n=10):\n",
    "    print(\"Random model is selected:\")\n",
    "    top_n = defaultdict(list)\n",
    "\n",
    "    for uid in tqdm(rs.test_set.uid_map.values()): # every user in the test set\n",
    "      if uid not in top_n.keys():\n",
    "        user_id = list(rs.train_set.user_ids)[uid]\n",
    "        user_items_in_the_train_set = set(rs.train_set.user_data[uid][0])\n",
    "        user_items_not_in_the_train_set = list(all_items.difference(user_items_in_the_train_set)) # random choice out of the items that the user has NOT rated in the train set\n",
    "        for i in range(0, n):\n",
    "          top_n[int(user_id)].append((int(rd.choice(user_items_not_in_the_train_set)), i))\n",
    "          \n",
    "    return top_n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4rMlWVflr3UH"
   },
   "source": [
    "##### Recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qL0I-4HSr5GJ"
   },
   "source": [
    "Choose evaluation strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "id": "tLQUNsGer7YQ"
   },
   "outputs": [],
   "source": [
    "evaluation_function = {'eva_one':get_top_n_eva_one,\n",
    "             'eva_two': get_top_n_eva_two,\n",
    "             'eva_three': get_top_n_eva_three}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "id": "OJk2R7C-r-Ii"
   },
   "outputs": [],
   "source": [
    "choice = 'eva_one'\n",
    "# to be manually set!!! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad7L_oQhedR_"
   },
   "source": [
    "### Prediction\n",
    "Run it when different evaluation strategy is chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "id": "f9hSXCNpecsP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:02<00:00, 2260.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MostPop model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:33<00:00, 181.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserKNN model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [04:51<00:00, 20.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ItemKNN model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [06:33<00:00, 15.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserKNN with means model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [04:19<00:00, 23.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPR model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:42<00:00, 142.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:42<00:00, 143.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:40<00:00, 149.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:44<00:00, 135.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:40<00:00, 147.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HPF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:42<00:00, 142.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [01:43<00:00, 58.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAECF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [01:42<00:00, 58.79it/s]\n"
     ]
    }
   ],
   "source": [
    "algo_dict = {}\n",
    "for i in range(0, len(algo_names)):\n",
    "    if algo_names[i] == 'Random':\n",
    "      top_n = get_top_n_random(n=10)\n",
    "    else:\n",
    "      top_n = evaluation_function[choice](algo_names[i],i-1, n=10)\n",
    "    algo_dict[algo_names[i]] = top_n.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-NcU5xPfehSa"
   },
   "source": [
    "### Evaluation\n",
    "Run it when different popularity notion is chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NDCG@10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_NDCG_per_group(algo_name,i):\n",
    "    model = exp.models[i]\n",
    "    print(algo_name + \" model is selected:\")\n",
    "    \n",
    "    ndcg_low = []\n",
    "    ndcg_med = []\n",
    "    ndcg_high = []\n",
    "    \n",
    "    # nr_low = 0.0\n",
    "    # nr_med = 0.0\n",
    "    # nr_high = 0.0\n",
    "\n",
    "    top = algo_dict[algo_name]\n",
    "    user_ndcgs = []\n",
    "    for user_id, user_ratings in tqdm(top): # real, not inside\n",
    "        user_id = str(user_id)\n",
    "        if user_id  in rs.test_set.uid_map.keys(): # if the user is in the test set.\n",
    "            user_pred_scores = []\n",
    "            user_real_scores = []\n",
    "            \n",
    "            uid=rs.train_set.uid_map[user_id] #inside\n",
    "            real_rating_dict = dict(zip(*rs.test_set.user_data[uid])) # iid, real rating (dictionary)\n",
    "            for (item_id,pred_score) in user_ratings: # so for every item recommended, and its predicted score\n",
    "                \n",
    "                item_id = str(item_id)\n",
    "                user_pred_scores.append(pred_score)\n",
    "                # we need its real score also.\n",
    "                try:\n",
    "                    iid=rs.train_set.iid_map[item_id] #inside\n",
    "                    t=0\n",
    "                except:\n",
    "                    iid=rs.test_set.iid_map[item_id] #inside\n",
    "                    t=1\n",
    "                if iid in real_rating_dict.keys(): # if the item is in the test set of that user\n",
    "                    if t==1:\n",
    "                        print(item_id,user_id)\n",
    "                    real_score = real_rating_dict[iid]\n",
    "                else:\n",
    "                    \n",
    "                    real_score = 0.0 # LIKE EKSTRAND MANEL'S REFERENCE\n",
    "                user_real_scores.append(real_score)\n",
    "            \n",
    "            true_relevance = np.asarray([user_real_scores])\n",
    "            scores = np.asarray([user_pred_scores])\n",
    "            try: # IF THERE'S ONLY ONE ITEM RECOMMENDED, THERE'S NO POINT CALCULATING NDCG\n",
    "                user_ndcg = ndcg_score(true_relevance, scores,k=10)\n",
    "                #user_ndcgs.append(user_ndcg)\n",
    "                if int(user_id) in low.index: # some issue with string vs integer\n",
    "                    ndcg_low.append(user_ndcg)\n",
    "                    #nr_low += 1\n",
    "                elif int(user_id) in med.index:\n",
    "                    ndcg_med.append(user_ndcg)\n",
    "                    #nr_med += 1\n",
    "                elif int(user_id) in high.index:\n",
    "                    ndcg_high.append(user_ndcg)\n",
    "                    #nr_high += 1\n",
    "                else:\n",
    "                    print(\"NOWHERE?\")\n",
    "            \n",
    "            except:\n",
    "                pass\n",
    "\n",
    "\n",
    "    \n",
    "    ttests = [stats.ttest_ind(ndcg_low, ndcg_med, equal_var=False)[1],\n",
    "                            stats.ttest_ind(ndcg_low, ndcg_high, equal_var=False)[1],\n",
    "                            stats.ttest_ind(ndcg_med, ndcg_high, equal_var=False)[1]]\n",
    "    \n",
    "            \n",
    "    #return user_ndcgs\n",
    "    \n",
    "    # print(nr_low, nr_med, nr_high)\n",
    "    # ndcg_low/=nr_low\n",
    "    # ndcg_med/=nr_med\n",
    "    # ndcg_high/=nr_high\n",
    "    return np.mean(ndcg_low), np.mean(ndcg_med), np.mean(ndcg_high), ttests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MostPop model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1792.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.05805003167664107, 0.3985202495244651, 0.3099781881044263]\n",
      "UserKNN model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1837.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01712059940968206, 0.010121569329914804, 0.08378636450458862]\n",
      "ItemKNN model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1832.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.401932515376874e-34, 4.3789584046531283e-91, 1.8172193863980143e-104]\n",
      "UserKNN with means model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1845.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.023166033436380892, 0.023166033436380892, nan]\n",
      "BPR model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1756.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5282205886062767, 0.0007633282090106945, 0.00021573484733508372]\n",
      "MF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1827.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.343970698678331e-18, 8.506584401064903e-50, 6.92697488055804e-30]\n",
      "PMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1817.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0006521658179326321, 3.8365741655590535e-05, 0.10220543850452554]\n",
      "NMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1868.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.614427421050067e-05, 5.9646799691139695e-06, 0.13869097629937688]\n",
      "WMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1800.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6516211036387749, 0.6085638587382214, 0.8683579933904605]\n",
      "HPF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1738.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.010930211554453843, 1.5270478142262653e-09, 7.599757713914146e-07]\n",
      "NeuMF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1752.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2986859597871574, 0.024271541118164566, 5.879397079642728e-05]\n",
      "VAECF model is selected:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:03<00:00, 1726.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.41503969404915686, 6.734668633760207e-05, 4.749224965775177e-05]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "TTESTs = pd.DataFrame(index = algo_names[1:], columns = ['low-med','low-high','med-high']).fillna(0.0)\n",
    "NDCGs = pd.DataFrame(index = algo_names[1:], columns = ['low','med','high']).fillna(0.0)\n",
    "for i in range(1, len(algo_names)):\n",
    "    ndcg_low, ndcg_med, ndcg_high, ttests = calculate_NDCG_per_group(algo_names[i], i-1)\n",
    "    print(ttests)\n",
    "    NDCGs.loc[algo_names[i]] = ndcg_low, ndcg_med, ndcg_high\n",
    "    TTESTs.loc[algo_names[i]] =ttests\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1_TRKhBJq02Y",
    "outputId": "34921dd6-ef99-44c5-9c5b-75e3a647b24a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3815/3815 [00:06<00:00, 583.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MostPop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 586.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserKNN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 589.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ItemKNN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 587.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserKNN with means\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 587.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 588.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 586.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 586.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 586.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WMF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 584.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HPF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 589.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuMF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 594.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAECF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6038/6038 [00:10<00:00, 588.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130 seconds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "i = 0\n",
    "low_rec_gap_list = [] # one entry per algorithm\n",
    "medium_rec_gap_list = []\n",
    "high_rec_gap_list = []\n",
    "ttests_list = []\n",
    "\n",
    "for i in range(0, len(algo_names)):\n",
    "    print(algo_names[i])\n",
    "    df_item_dist_cornac[algo_names[i]] = 0.0\n",
    "    \n",
    "    low_rec_gap = []\n",
    "    medium_rec_gap = []\n",
    "    high_rec_gap = []\n",
    "    \n",
    "    # low_count = 0.0\n",
    "    # med_count = 0.0\n",
    "    # high_count = 0.0\n",
    "\n",
    "    top = algo_dict[algo_names[i]]\n",
    "    for uid, user_ratings in tqdm(top):\n",
    "        iid_list = []\n",
    "        for (iid, _) in user_ratings:\n",
    "            df_item_dist_cornac.loc[iid, algo_names[i]] += 1\n",
    "            iid_list.append(iid)\n",
    "        gap = sum(df_item_dist_cornac[\"count\"].loc[iid_list]) / len(iid_list)\n",
    "        if uid in low.index:\n",
    "            low_rec_gap.append(gap)\n",
    "            # low_count += 1\n",
    "        elif uid in med.index:\n",
    "            medium_rec_gap.append(gap)\n",
    "            # med_count += 1\n",
    "        elif uid in high.index:\n",
    "            high_rec_gap.append(gap)\n",
    "        #     high_count += 1\n",
    "        else:\n",
    "          print(\"hi\")\n",
    "    # print(low_count, med_count, high_count)\n",
    "    # ttests\n",
    "\n",
    "    ttests = [stats.ttest_ind((low_rec_gap-low_GAP)/low_GAP, (medium_rec_gap-med_GAP)/med_GAP, equal_var=False)[1],\n",
    "                            stats.ttest_ind((low_rec_gap-low_GAP)/low_GAP, (high_rec_gap-high_GAP)/high_GAP, equal_var=False)[1],\n",
    "                            stats.ttest_ind((medium_rec_gap-med_GAP)/med_GAP, (high_rec_gap-high_GAP)/high_GAP, equal_var=False)[1]]\n",
    "    \n",
    "    low_rec_gap_list.append(np.mean(low_rec_gap))\n",
    "    medium_rec_gap_list.append(np.mean(medium_rec_gap))\n",
    "    high_rec_gap_list.append(np.mean(high_rec_gap))\n",
    "    ttests_list.append(ttests)\n",
    "    i += 1 # next algorithm\n",
    "end = time.time()\n",
    "print(round(end-start), \"seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hRQ9ZID5-kfv"
   },
   "source": [
    "### Cornac results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "id": "tqyKuR-m-oaX"
   },
   "outputs": [],
   "source": [
    "low_gap_vals = []\n",
    "medium_gap_vals = []\n",
    "high_gap_vals = []\n",
    "\n",
    "for i in range(0, len(algo_names)):\n",
    "    low_gap_vals.append((low_rec_gap_list[i] - low_GAP) / low_GAP * 100)\n",
    "    medium_gap_vals.append((medium_rec_gap_list[i] - med_GAP) / med_GAP * 100)\n",
    "    high_gap_vals.append((high_rec_gap_list[i] - high_GAP) / high_GAP * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bX4L2rWt2Kyo"
   },
   "source": [
    "# Save!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "location = 'results/'\n",
    "df_item_dist_cornac.to_csv(location+'df_item_dist_cornac_movies_'+choice+'.csv') \n",
    "NDCGs.to_csv(location+'NDCGs_movies_'+choice+'_'+pop_notion+'.csv')\n",
    "TTESTs.to_csv(location+'NDCG_ttests_movies_'+choice+'_'+pop_notion+'.csv')\n",
    "with open(location+'low_gap_vals_movies_'+choice+'_'+pop_notion+'.pickle', 'wb') as handle:\n",
    "    pkl.dump(low_gap_vals, handle)\n",
    "with open(location+'med_gap_vals_movies_'+choice+'_'+pop_notion+'.pickle', 'wb') as handle:\n",
    "    pkl.dump(medium_gap_vals, handle)\n",
    "with open(location+'high_gap_vals_movies_'+choice+'_'+pop_notion+'.pickle', 'wb') as handle:\n",
    "    pkl.dump(high_gap_vals, handle)\n",
    "with open(location+'gap_ttests_movies_'+choice+'_'+pop_notion+'.pickle', 'wb') as handle:\n",
    "    pkl.dump(ttests_list, handle)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}