project_2_recommender_and_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "alike-morgan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display, HTML\n",
    "from collections import defaultdict\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from livelossplot import PlotLosses\n",
    "\n",
    "# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
    "import os\n",
    "\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-knitting",
   "metadata": {},
   "source": [
    "# Load the dataset for recommenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "victorian-bottom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>term</th>\n      <th>length_of_stay_bucket</th>\n      <th>rate_plan</th>\n      <th>room_segment</th>\n      <th>n_people_bucket</th>\n      <th>weekend_stay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>3</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>4</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[0-160]</td>\n      <td>[2-2]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>5</td>\n      <td>Easter</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>6</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>8</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[0-160]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>10</td>\n      <td>9</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>11</td>\n      <td>9</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>12</td>\n      <td>10</td>\n      <td>HighSeason</td>\n      <td>[8-inf]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>11</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_path = os.path.join(\"data\", \"hotel_data\")\n",
    "\n",
    "interactions_df = pd.read_csv(os.path.join(data_path, \"hotel_data_interactions_df.csv\"), index_col=0)\n",
    "\n",
    "base_item_features = ['term', 'length_of_stay_bucket', 'rate_plan', 'room_segment', 'n_people_bucket', 'weekend_stay']\n",
    "\n",
    "column_values_dict = {\n",
    "    'term': ['WinterVacation', 'Easter', 'OffSeason', 'HighSeason', 'LowSeason', 'MayLongWeekend', 'NewYear',\n",
    "             'Christmas'],\n",
    "    'length_of_stay_bucket': ['[0-1]', '[2-3]', '[4-7]', '[8-inf]'],\n",
    "    'rate_plan': ['Standard', 'Nonref'],\n",
    "    'room_segment': ['[0-160]', '[160-260]', '[260-360]', '[360-500]', '[500-900]'],\n",
    "    'n_people_bucket': ['[1-1]', '[2-2]', '[3-4]', '[5-inf]'],\n",
    "    'weekend_stay': ['True', 'False']\n",
    "}\n",
    "\n",
    "interactions_df.loc[:, 'term'] = pd.Categorical(\n",
    "    interactions_df['term'], categories=column_values_dict['term'])\n",
    "interactions_df.loc[:, 'length_of_stay_bucket'] = pd.Categorical(\n",
    "    interactions_df['length_of_stay_bucket'], categories=column_values_dict['length_of_stay_bucket'])\n",
    "interactions_df.loc[:, 'rate_plan'] = pd.Categorical(\n",
    "    interactions_df['rate_plan'], categories=column_values_dict['rate_plan'])\n",
    "interactions_df.loc[:, 'room_segment'] = pd.Categorical(\n",
    "    interactions_df['room_segment'], categories=column_values_dict['room_segment'])\n",
    "interactions_df.loc[:, 'n_people_bucket'] = pd.Categorical(\n",
    "    interactions_df['n_people_bucket'], categories=column_values_dict['n_people_bucket'])\n",
    "interactions_df.loc[:, 'weekend_stay'] = interactions_df['weekend_stay'].astype('str')\n",
    "interactions_df.loc[:, 'weekend_stay'] = pd.Categorical(\n",
    "    interactions_df['weekend_stay'], categories=column_values_dict['weekend_stay'])\n",
    "\n",
    "display(HTML(interactions_df.head(15).to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "realistic-third",
   "metadata": {},
   "source": [
    "# (Optional) Prepare numerical user features\n",
    "\n",
    "The method below is left here for convenience if you want to experiment with content-based user features as an input for your neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "variable-jaguar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['user_term_WinterVacation', 'user_term_Easter', 'user_term_OffSeason', 'user_term_HighSeason', 'user_term_LowSeason', 'user_term_MayLongWeekend', 'user_term_NewYear', 'user_term_Christmas', 'user_length_of_stay_bucket_[0-1]', 'user_length_of_stay_bucket_[2-3]', 'user_length_of_stay_bucket_[4-7]', 'user_length_of_stay_bucket_[8-inf]', 'user_rate_plan_Standard', 'user_rate_plan_Nonref', 'user_room_segment_[0-160]', 'user_room_segment_[160-260]', 'user_room_segment_[260-360]', 'user_room_segment_[360-500]', 'user_room_segment_[500-900]', 'user_n_people_bucket_[1-1]', 'user_n_people_bucket_[2-2]', 'user_n_people_bucket_[3-4]', 'user_n_people_bucket_[5-inf]', 'user_weekend_stay_True', 'user_weekend_stay_False']\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>user_term_WinterVacation</th>\n      <th>user_term_Easter</th>\n      <th>user_term_OffSeason</th>\n      <th>user_term_HighSeason</th>\n      <th>user_term_LowSeason</th>\n      <th>user_term_MayLongWeekend</th>\n      <th>user_term_NewYear</th>\n      <th>user_term_Christmas</th>\n      <th>user_length_of_stay_bucket_[0-1]</th>\n      <th>user_length_of_stay_bucket_[2-3]</th>\n      <th>user_length_of_stay_bucket_[4-7]</th>\n      <th>user_length_of_stay_bucket_[8-inf]</th>\n      <th>user_rate_plan_Standard</th>\n      <th>user_rate_plan_Nonref</th>\n      <th>user_room_segment_[0-160]</th>\n      <th>user_room_segment_[160-260]</th>\n      <th>user_room_segment_[260-360]</th>\n      <th>user_room_segment_[360-500]</th>\n      <th>user_room_segment_[500-900]</th>\n      <th>user_n_people_bucket_[1-1]</th>\n      <th>user_n_people_bucket_[2-2]</th>\n      <th>user_n_people_bucket_[3-4]</th>\n      <th>user_n_people_bucket_[5-inf]</th>\n      <th>user_weekend_stay_True</th>\n      <th>user_weekend_stay_False</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.090909</td>\n      <td>0.0</td>\n      <td>0.681818</td>\n      <td>0.090909</td>\n      <td>0.136364</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.590909</td>\n      <td>0.409091</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.863636</td>\n      <td>0.136364</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.727273</td>\n      <td>0.181818</td>\n      <td>0.090909</td>\n      <td>0.681818</td>\n      <td>0.318182</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>50</td>\n      <td>0.043478</td>\n      <td>0.0</td>\n      <td>0.434783</td>\n      <td>0.304348</td>\n      <td>0.217391</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.913043</td>\n      <td>0.086957</td>\n      <td>0.000000</td>\n      <td>0.260870</td>\n      <td>0.739130</td>\n      <td>0.000000</td>\n      <td>0.565217</td>\n      <td>0.434783</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.173913</td>\n      <td>0.521739</td>\n      <td>0.304348</td>\n      <td>0.782609</td>\n      <td>0.217391</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>96</td>\n      <td>0.090909</td>\n      <td>0.0</td>\n      <td>0.681818</td>\n      <td>0.136364</td>\n      <td>0.045455</td>\n      <td>0.045455</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.272727</td>\n      <td>0.636364</td>\n      <td>0.045455</td>\n      <td>0.045455</td>\n      <td>0.272727</td>\n      <td>0.727273</td>\n      <td>0.045455</td>\n      <td>0.863636</td>\n      <td>0.090909</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.045455</td>\n      <td>0.272727</td>\n      <td>0.590909</td>\n      <td>0.090909</td>\n      <td>0.727273</td>\n      <td>0.272727</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>115</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>371</th>\n      <td>706</td>\n      <td>0.095238</td>\n      <td>0.0</td>\n      <td>0.511905</td>\n      <td>0.190476</td>\n      <td>0.142857</td>\n      <td>0.047619</td>\n      <td>0.011905</td>\n      <td>0.0</td>\n      <td>0.297619</td>\n      <td>0.380952</td>\n      <td>0.238095</td>\n      <td>0.083333</td>\n      <td>0.976190</td>\n      <td>0.023810</td>\n      <td>0.035714</td>\n      <td>0.857143</td>\n      <td>0.107143</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.130952</td>\n      <td>0.154762</td>\n      <td>0.583333</td>\n      <td>0.130952</td>\n      <td>0.559524</td>\n      <td>0.440476</td>\n    </tr>\n    <tr>\n      <th>1383</th>\n      <td>1736</td>\n      <td>0.034483</td>\n      <td>0.0</td>\n      <td>0.482759</td>\n      <td>0.206897</td>\n      <td>0.275862</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.241379</td>\n      <td>0.551724</td>\n      <td>0.206897</td>\n      <td>0.000000</td>\n      <td>0.172414</td>\n      <td>0.827586</td>\n      <td>0.000000</td>\n      <td>0.931034</td>\n      <td>0.068966</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.379310</td>\n      <td>0.413793</td>\n      <td>0.206897</td>\n      <td>0.000000</td>\n      <td>0.413793</td>\n      <td>0.586207</td>\n    </tr>\n    <tr>\n      <th>7301</th>\n      <td>7779</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.250000</td>\n      <td>0.500000</td>\n      <td>0.250000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.750000</td>\n      <td>0.250000</td>\n      <td>0.750000</td>\n      <td>0.250000</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_p(xs, values):\n",
    "    totals = [0] * len(values)\n",
    "    for x in xs:\n",
    "        totals[values.index(x)] += 1\n",
    "    n = sum(totals)\n",
    "    return [x / n for x in totals] if n > 0 else totals\n",
    "\n",
    "\n",
    "def parse_column(column, interactions_df, user_features, users_df):\n",
    "    df = interactions_df.loc[:, ['user_id', column]]\n",
    "    df = df.groupby('user_id').aggregate(lambda x: list(x)).reset_index(drop=False)\n",
    "\n",
    "    column_values = column_values_dict[column]\n",
    "    df.loc[:, column] = df[column].apply(lambda x: get_p(x, column_values))\n",
    "    ps = []\n",
    "\n",
    "    for i in range(len(column_values)):\n",
    "        ps.append(\"user_\" + column + \"_\" + column_values[i])\n",
    "        df.loc[:, ps[i]] = df[column].apply(lambda x: x[i])\n",
    "        user_features.append(ps[i])\n",
    "    users_df = pd.merge(users_df, df.loc[:, ['user_id'] + ps], on=[\"user_id\"])\n",
    "\n",
    "    return users_df\n",
    "\n",
    "\n",
    "def prepare_users_df(interactions_df):\n",
    "    ########################\n",
    "    # Write your code here #\n",
    "    ########################\n",
    "    users_df = interactions_df.loc[:, [\"user_id\"]]\n",
    "    users_df = users_df.groupby(\"user_id\").first().reset_index(drop=False)\n",
    "    user_features = []\n",
    "    for column in base_item_features:\n",
    "        users_df = parse_column(column, interactions_df, user_features, users_df)\n",
    "    return users_df, user_features\n",
    "\n",
    "\n",
    "users_df, user_features = prepare_users_df(interactions_df)\n",
    "\n",
    "print(user_features)\n",
    "\n",
    "display(HTML(users_df.loc[users_df['user_id'].isin([706, 1736, 7779, 96, 1, 50, 115])].head(15).to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-keyboard",
   "metadata": {},
   "source": [
    "# (Optional) Prepare numerical item features\n",
    "\n",
    "The method below is left here for convenience if you want to experiment with content-based item features as an input for your neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "formal-munich",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['term_WinterVacation', 'term_Easter', 'term_OffSeason', 'term_HighSeason', 'term_LowSeason', 'term_MayLongWeekend', 'term_NewYear', 'term_Christmas', 'length_of_stay_bucket_[0-1]', 'length_of_stay_bucket_[2-3]', 'length_of_stay_bucket_[4-7]', 'length_of_stay_bucket_[8-inf]', 'rate_plan_Standard', 'rate_plan_Nonref', 'room_segment_[0-160]', 'room_segment_[160-260]', 'room_segment_[260-360]', 'room_segment_[360-500]', 'room_segment_[500-900]', 'n_people_bucket_[1-1]', 'n_people_bucket_[2-2]', 'n_people_bucket_[3-4]', 'n_people_bucket_[5-inf]', 'weekend_stay_True', 'weekend_stay_False']\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>item_id</th>\n      <th>term_WinterVacation</th>\n      <th>term_Easter</th>\n      <th>term_OffSeason</th>\n      <th>term_HighSeason</th>\n      <th>term_LowSeason</th>\n      <th>term_MayLongWeekend</th>\n      <th>term_NewYear</th>\n      <th>term_Christmas</th>\n      <th>length_of_stay_bucket_[0-1]</th>\n      <th>length_of_stay_bucket_[2-3]</th>\n      <th>length_of_stay_bucket_[4-7]</th>\n      <th>length_of_stay_bucket_[8-inf]</th>\n      <th>rate_plan_Standard</th>\n      <th>rate_plan_Nonref</th>\n      <th>room_segment_[0-160]</th>\n      <th>room_segment_[160-260]</th>\n      <th>room_segment_[260-360]</th>\n      <th>room_segment_[360-500]</th>\n      <th>room_segment_[500-900]</th>\n      <th>n_people_bucket_[1-1]</th>\n      <th>n_people_bucket_[2-2]</th>\n      <th>n_people_bucket_[3-4]</th>\n      <th>n_people_bucket_[5-inf]</th>\n      <th>weekend_stay_True</th>\n      <th>weekend_stay_False</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prepare_items_df(interactions_df):\n",
    "    ########################\n",
    "    # Write your code here #\n",
    "    ########################\n",
    "    item_features = [\"item_id\"] + base_item_features\n",
    "    interactions_df = interactions_df.copy().reset_index()\n",
    "    df = interactions_df.loc[:, item_features].drop_duplicates()\n",
    "    one_hot_encoded = pd.get_dummies(df.loc[:, base_item_features])\n",
    "    df = df.drop(base_item_features, axis=1)\n",
    "    return df.join(one_hot_encoded), list(one_hot_encoded.columns)\n",
    "\n",
    "\n",
    "items_df, item_features = prepare_items_df(interactions_df)\n",
    "\n",
    "print(item_features)\n",
    "\n",
    "display(HTML(items_df.loc[items_df['item_id'].isin([0, 1, 2, 3, 4, 5, 6])].head(15).to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "figured-imaging",
   "metadata": {},
   "source": [
    "# Neural network recommender\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "Code a recommender based on a neural network model. You are free to choose any network architecture you find appropriate. The network can use the interaction vectors for users and items, embeddings of users and items, as well as user and item features (you can use the features you developed in the first project).\n",
    "\n",
    "Remember to keep control over randomness - in the init method add the seed as a parameter and initialize the random seed generator with that seed (both for numpy and pytorch):\n",
    "\n",
    "```python\n",
    "self.seed = seed\n",
    "self.rng = np.random.RandomState(seed=seed)\n",
    "```\n",
    "in the network model:\n",
    "```python\n",
    "self.seed = torch.manual_seed(seed)\n",
    "```\n",
    "\n",
    "You are encouraged to experiment with:\n",
    "  - the number of layers in the network, the number of neurons and different activation functions,\n",
    "  - different optimizers and their parameters,\n",
    "  - batch size and the number of epochs,\n",
    "  - embedding layers,\n",
    "  - content-based features of both users and items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "unlike-recipient",
   "metadata": {},
   "outputs": [],
   "source": [
    "from recommenders.recommender import Recommender\n",
    "\n",
    "\n",
    "class NNRecommender(Recommender):\n",
    "    \"\"\"\n",
    "    Linear recommender class based on user and item features.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self,\n",
    "            seed=6789,\n",
    "            n_neg_per_pos=1,\n",
    "            drop_out=0.9,\n",
    "            lr=0.001,\n",
    "            epochs=10,\n",
    "            neurons=32,\n",
    "            batch_size=10\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Initialize recommender params and variables.\n",
    "        \"\"\"\n",
    "        self.model = None\n",
    "        self.n_neg_per_pos = n_neg_per_pos\n",
    "\n",
    "        self.recommender_df = pd.DataFrame(columns=['user_id', 'item_id', 'score'])\n",
    "        self.users_df = None\n",
    "        self.user_features = None\n",
    "\n",
    "        self.seed = seed\n",
    "        self.rng = np.random.RandomState(seed=seed)\n",
    "\n",
    "        # You can add more arguments if needed\n",
    "        ########################\n",
    "        # Write your code here #\n",
    "        ########################\n",
    "\n",
    "        self.drop_out = drop_out\n",
    "        self.neurons = neurons\n",
    "        self.lr = lr\n",
    "        self.batch_size = batch_size\n",
    "        self.epochs = epochs\n",
    "\n",
    "    def fit(self, interactions_df, users_df, items_df):\n",
    "        \"\"\"\n",
    "        Training of the recommender.\n",
    "\n",
    "        :param pd.DataFrame interactions_df: DataFrame with recorded interactions between users and items\n",
    "            defined by user_id, item_id and features of the interaction.\n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features defined by user_id and the user feature columns.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features defined by item_id and the item feature columns.\n",
    "        \"\"\"\n",
    "\n",
    "        interactions_df = interactions_df.copy()\n",
    "\n",
    "        # Prepare users_df and items_df\n",
    "        # (optional - use only if you want to train a hybrid model with content-based features)\n",
    "\n",
    "        users_df, user_features = prepare_users_df(interactions_df)\n",
    "\n",
    "        self.users_df = users_df\n",
    "        self.user_features = user_features\n",
    "\n",
    "        items_df, item_features = prepare_items_df(interactions_df)\n",
    "        items_df = items_df.loc[:, ['item_id'] + item_features]\n",
    "\n",
    "        self.interactions_df = interactions_df.copy()\n",
    "        n_users = np.max(interactions_df['user_id']) + 1\n",
    "        n_items = np.max(interactions_df['item_id']) + 1\n",
    "\n",
    "        # Get the user-item interaction matrix (mapping to int is necessary because of how iterrows works)\n",
    "        r = np.zeros(shape=(n_users, n_items))\n",
    "        for idx, interaction in interactions_df.iterrows():\n",
    "            r[int(interaction['user_id'])][int(interaction['item_id'])] = 1\n",
    "\n",
    "        self.r = r\n",
    "\n",
    "        interactions_df = interactions_df.loc[:, ['user_id', 'item_id']]\n",
    "\n",
    "        interactions_df.loc[:, 'interacted'] = 1\n",
    "\n",
    "        # Generate negative interactions\n",
    "        ########################\n",
    "        # Write your code here #\n",
    "        ########################\n",
    "\n",
    "        negative_interactions = []\n",
    "\n",
    "        i = 0\n",
    "        while i < self.n_neg_per_pos * len(interactions_df):\n",
    "            size = 1000\n",
    "            user_ids, item_ids = self.rng.choice(np.arange(n_users), size=size),  self.rng.choice(np.arange(n_items), size=size)\n",
    "            j = 0\n",
    "            while j < size and i < self.n_neg_per_pos * len(interactions_df):\n",
    "                if r[user_ids[j]][item_ids[j]] == 0:\n",
    "                    negative_interactions.append([user_ids[j], item_ids[j], 0])\n",
    "                    i += 1\n",
    "                j += 1\n",
    "\n",
    "        interactions_df = pd.concat(\n",
    "            [interactions_df, pd.DataFrame(negative_interactions, columns=['user_id', 'item_id', 'interacted'])])\n",
    "\n",
    "        # Merge user and item features\n",
    "        # (optional - use only if you want to train a hybrid model with content-based features)\n",
    "\n",
    "        interactions_df = pd.merge(interactions_df, users_df, on=['user_id'])\n",
    "        interactions_df = pd.merge(interactions_df, items_df, on=['item_id'])\n",
    "\n",
    "        # Initialize the neural network model\n",
    "\n",
    "        ########################\n",
    "        # Write your code here #\n",
    "        ########################\n",
    "\n",
    "        x = interactions_df.loc[:, user_features + item_features].values\n",
    "        y = interactions_df['interacted'].values\n",
    "\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(len(x[0]), self.neurons),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(self.drop_out),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Dropout(self.drop_out),\n",
    "            nn.Linear(self.neurons, 1)\n",
    "        )\n",
    "        self.loss = torch.nn.MSELoss()\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)\n",
    "\n",
    "        # Train the model using an optimizer\n",
    "\n",
    "        ########################\n",
    "        # Write your code here #\n",
    "        ########################\n",
    "\n",
    "        for epoch in range(self.epochs):\n",
    "            for b in range((y.shape[0] - 1) // self.batch_size + 1):\n",
    "                start = b * self.batch_size\n",
    "                end = start + self.batch_size\n",
    "\n",
    "                X_train = torch.tensor(x[start:end].astype(np.float32))\n",
    "                Y_train = torch.tensor(y[start:end].astype(np.float32)).reshape(-1, 1)\n",
    "\n",
    "                Y_predict = self.model(X_train)\n",
    "\n",
    "                loss = self.loss(Y_predict, Y_train)\n",
    "                loss.backward()\n",
    "\n",
    "                self.optimizer.step()\n",
    "                self.optimizer.zero_grad()\n",
    "\n",
    "    def recommend(self, users_df, items_df, n_recommendations=1):\n",
    "        \"\"\"\n",
    "        Serving of recommendations. Scores items in items_df for each user in users_df and returns\n",
    "        top n_recommendations for each user.\n",
    "\n",
    "        :param pd.DataFrame users_df: DataFrame with users and their features for which recommendations should be generated.\n",
    "        :param pd.DataFrame items_df: DataFrame with items and their features which should be scored.\n",
    "        :param int n_recommendations: Number of recommendations to be returned for each user.\n",
    "        :return: DataFrame with user_id, item_id and score as columns returning n_recommendations top recommendations\n",
    "            for each user.\n",
    "        :rtype: pd.DataFrame\n",
    "        \"\"\"\n",
    "\n",
    "        # Clean previous recommendations (iloc could be used alternatively)\n",
    "        self.recommender_df = self.recommender_df[:0]\n",
    "\n",
    "        # Prepare users_df and items_df\n",
    "        # (optional - use only if you want to train a hybrid model with content-based features)\n",
    "\n",
    "        users_df = users_df.loc[:, 'user_id']\n",
    "        users_df = pd.merge(users_df, self.users_df, on=['user_id'], how='left').fillna(0)\n",
    "\n",
    "        items_df, item_features = prepare_items_df(items_df)\n",
    "        items_df = items_df.loc[:, ['item_id'] + item_features]\n",
    "\n",
    "        # Score the items\n",
    "\n",
    "        for ix, user in users_df.iterrows():\n",
    "\n",
    "            # Calculate the score for the user and every item in items_df\n",
    "            ########################\n",
    "            # Write your code here #\n",
    "            ########################\n",
    "            cart = users_df.loc[users_df['user_id'] == user['user_id'], self.user_features].merge(\n",
    "                items_df[item_features], how='cross')\n",
    "\n",
    "            self.model.eval()\n",
    "            scores = self.model(torch.tensor(cart.loc[:, user_features + item_features].values.astype(np.float32)))\n",
    "            scores = scores.detach().numpy()[:, 0]\n",
    "\n",
    "            item_ids = np.argsort(-scores)[:n_recommendations]\n",
    "\n",
    "            recommendations = []\n",
    "            for item_id in item_ids:\n",
    "                recommendations.append(\n",
    "                    {\n",
    "                        'user_id': user['user_id'],\n",
    "                        'item_id': item_id,\n",
    "                        'score': scores[item_id]\n",
    "                    }\n",
    "                )\n",
    "\n",
    "            user_recommendations = pd.DataFrame(recommendations)\n",
    "\n",
    "            self.recommender_df = pd.concat([self.recommender_df, user_recommendations])\n",
    "\n",
    "        return self.recommender_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "copyrighted-relative",
   "metadata": {},
   "source": [
    "# Quick test of the recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "greatest-canon",
   "metadata": {},
   "outputs": [],
   "source": [
    "items_df = interactions_df.loc[:, ['item_id'] + base_item_features].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "initial-capital",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit method\n",
    "nn_recommender = NNRecommender()\n",
    "nn_recommender.fit(interactions_df, None, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "digital-consolidation",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>score</th>\n      <th>term</th>\n      <th>length_of_stay_bucket</th>\n      <th>rate_plan</th>\n      <th>room_segment</th>\n      <th>n_people_bucket</th>\n      <th>weekend_stay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>575</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>218</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>594</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>262</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>565</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.0</td>\n      <td>211</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1.0</td>\n      <td>296</td>\n      <td>0.223296</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.0</td>\n      <td>344</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1.0</td>\n      <td>353</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1.0</td>\n      <td>167</td>\n      <td>0.223296</td>\n      <td>OffSeason</td>\n      <td>[8-inf]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2.0</td>\n      <td>394</td>\n      <td>0.223296</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[360-500]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2.0</td>\n      <td>193</td>\n      <td>0.223296</td>\n      <td>Easter</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2.0</td>\n      <td>469</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2.0</td>\n      <td>83</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2.0</td>\n      <td>175</td>\n      <td>0.223296</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2.0</td>\n      <td>505</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2.0</td>\n      <td>167</td>\n      <td>0.223296</td>\n      <td>OffSeason</td>\n      <td>[8-inf]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2.0</td>\n      <td>558</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2.0</td>\n      <td>559</td>\n      <td>0.223296</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2.0</td>\n      <td>415</td>\n      <td>0.223296</td>\n      <td>LowSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>3.0</td>\n      <td>262</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>3.0</td>\n      <td>353</td>\n      <td>0.223287</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>3.0</td>\n      <td>565</td>\n      <td>0.223091</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>3.0</td>\n      <td>594</td>\n      <td>0.223020</td>\n      <td>WinterVacation</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>176</td>\n      <td>0.222649</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>3.0</td>\n      <td>575</td>\n      <td>0.222519</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>3.0</td>\n      <td>57</td>\n      <td>0.222486</td>\n      <td>OffSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>3.0</td>\n      <td>211</td>\n      <td>0.222484</td>\n      <td>HighSeason</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>3.0</td>\n      <td>175</td>\n      <td>0.222455</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>3.0</td>\n      <td>343</td>\n      <td>0.222447</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>4.0</td>\n      <td>394</td>\n      <td>0.223296</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[360-500]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>4.0</td>\n      <td>294</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>4.0</td>\n      <td>175</td>\n      <td>0.223296</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>4.0</td>\n      <td>597</td>\n      <td>0.223296</td>\n      <td>Easter</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>4.0</td>\n      <td>780</td>\n      <td>0.223296</td>\n      <td>Easter</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>4.0</td>\n      <td>173</td>\n      <td>0.223296</td>\n      <td>MayLongWeekend</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>4.0</td>\n      <td>778</td>\n      <td>0.223296</td>\n      <td>HighSeason</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[0-160]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>4.0</td>\n      <td>230</td>\n      <td>0.223296</td>\n      <td>MayLongWeekend</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>4.0</td>\n      <td>101</td>\n      <td>0.223296</td>\n      <td>OffSeason</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>4.0</td>\n      <td>296</td>\n      <td>0.223296</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>5.0</td>\n      <td>279</td>\n      <td>0.223198</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>5.0</td>\n      <td>262</td>\n      <td>0.223079</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>5.0</td>\n      <td>344</td>\n      <td>0.222995</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>5.0</td>\n      <td>479</td>\n      <td>0.222949</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>5.0</td>\n      <td>353</td>\n      <td>0.222875</td>\n      <td>HighSeason</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>5.0</td>\n      <td>565</td>\n      <td>0.222829</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>5.0</td>\n      <td>211</td>\n      <td>0.222820</td>\n      <td>HighSeason</td>\n      <td>[4-7]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>5.0</td>\n      <td>566</td>\n      <td>0.222746</td>\n      <td>WinterVacation</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>5.0</td>\n      <td>594</td>\n      <td>0.222627</td>\n      <td>WinterVacation</td>\n      <td>[0-1]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>5.0</td>\n      <td>284</td>\n      <td>0.222540</td>\n      <td>HighSeason</td>\n      <td>[8-inf]</td>\n      <td>Nonref</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recommender method\n",
    "\n",
    "recommendations = nn_recommender.recommend(pd.DataFrame([[1], [2], [3], [4], [5]], columns=['user_id']), items_df, 10)\n",
    "\n",
    "recommendations = pd.merge(recommendations, items_df, on='item_id', how='left')\n",
    "display(HTML(recommendations.to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-eleven",
   "metadata": {},
   "source": [
    "# Tuning method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "strange-alaska",
   "metadata": {},
   "outputs": [],
   "source": [
    "from evaluation_and_testing.testing import evaluate_train_test_split_implicit\n",
    "\n",
    "seed = 6789"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "stable-theta",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import hp, fmin, tpe, Trials\n",
    "import traceback\n",
    "\n",
    "\n",
    "def tune_recommender(recommender_class, interactions_df, items_df,\n",
    "                     param_space, max_evals=1, show_progressbar=True, seed=6789):\n",
    "    # Split into train_validation and test sets\n",
    "\n",
    "    shuffle = np.arange(len(interactions_df))\n",
    "    rng = np.random.RandomState(seed=seed)\n",
    "    rng.shuffle(shuffle)\n",
    "    shuffle = list(shuffle)\n",
    "\n",
    "    train_test_split = 0.8\n",
    "    split_index = int(len(interactions_df) * train_test_split)\n",
    "\n",
    "    train_validation = interactions_df.iloc[shuffle[:split_index]]\n",
    "    test = interactions_df.iloc[shuffle[split_index:]]\n",
    "\n",
    "    # Tune\n",
    "\n",
    "    def loss(tuned_params):\n",
    "        recommender = recommender_class(seed=seed, **tuned_params)\n",
    "        hr1, hr3, hr5, hr10, ndcg1, ndcg3, ndcg5, ndcg10 = evaluate_train_test_split_implicit(\n",
    "            recommender, train_validation, items_df, seed=seed)\n",
    "        return -hr10\n",
    "\n",
    "    n_tries = 1\n",
    "    succeded = False\n",
    "    try_id = 0\n",
    "    while not succeded and try_id < n_tries:\n",
    "        try:\n",
    "            trials = Trials()\n",
    "            best_param_set = fmin(loss, space=param_space, algo=tpe.suggest,\n",
    "                                  max_evals=max_evals, show_progressbar=show_progressbar, trials=trials, verbose=True)\n",
    "            succeded = True\n",
    "        except:\n",
    "            traceback.print_exc()\n",
    "            try_id += 1\n",
    "\n",
    "    if not succeded:\n",
    "        return None\n",
    "\n",
    "    # Validate\n",
    "\n",
    "    recommender = recommender_class(seed=seed, **best_param_set)\n",
    "\n",
    "    results = [[recommender_class.__name__] + list(evaluate_train_test_split_implicit(\n",
    "        recommender, {'train': train_validation, 'test': test}, items_df, seed=seed))]\n",
    "\n",
    "    results = pd.DataFrame(results,\n",
    "                           columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5',\n",
    "                                    'NDCG@10'])\n",
    "\n",
    "    display(HTML(results.to_html()))\n",
    "\n",
    "    return best_param_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reliable-switzerland",
   "metadata": {},
   "source": [
    "## Tuning of the recommender\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "Tune your model using the code below. You only need to put the class name of your recommender and choose an appropriate parameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "obvious-astrology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [24:32<00:00, 147.24s/trial, best loss: -0.23529411764705882]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Recommender</th>\n      <th>HR@1</th>\n      <th>HR@3</th>\n      <th>HR@5</th>\n      <th>HR@10</th>\n      <th>NDCG@1</th>\n      <th>NDCG@3</th>\n      <th>NDCG@5</th>\n      <th>NDCG@10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NNRecommender</td>\n      <td>0.039036</td>\n      <td>0.098439</td>\n      <td>0.130686</td>\n      <td>0.241684</td>\n      <td>0.039036</td>\n      <td>0.074737</td>\n      <td>0.088015</td>\n      <td>0.123609</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'batch_size': 3, 'drop_out': 0.3162509666065092, 'epochs': 2, 'lr': 0.00013554246389291255, 'n_neg_per_pos': 1, 'neurons': 3}\n"
     ]
    }
   ],
   "source": [
    "param_space = {\n",
    "    ########################\n",
    "    # Write your code here #\n",
    "    ########################\n",
    "    'n_neg_per_pos': hp.choice('n_neg_per_pos', [1, 5, 10, 20]),\n",
    "    'drop_out': hp.uniform('drop_out', 0.0, 0.9),\n",
    "    'lr': hp.loguniform('lr', np.log(0.0001), np.log(0.01)),\n",
    "    'epochs': hp.choice('epochs', [10, 100, 500]),\n",
    "    'neurons': hp.choice('neurons', [32, 64, 128, 256]),\n",
    "    'batch_size': hp.choice('batch_size', [50, 100, 200, 500]),\n",
    "}\n",
    "\n",
    "best_param_set = tune_recommender(NNRecommender, interactions_df, items_df,\n",
    "                                  param_space, max_evals=10, show_progressbar=True, seed=seed)\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(best_param_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accredited-strap",
   "metadata": {},
   "source": [
    "# Final evaluation\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "Run the final evaluation of your recommender and present its results against the Amazon and Netflix recommenders' results. You just need to give the class name of your recommender and its tuned parameters below.\n",
    "\n",
    "It's optional, but for better effect you can include here the results from all recommenders created during in this class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "given-homework",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Recommender</th>\n      <th>HR@1</th>\n      <th>HR@3</th>\n      <th>HR@5</th>\n      <th>HR@10</th>\n      <th>NDCG@1</th>\n      <th>NDCG@3</th>\n      <th>NDCG@5</th>\n      <th>NDCG@10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NNRecommender</td>\n      <td>0.054311</td>\n      <td>0.128649</td>\n      <td>0.18907</td>\n      <td>0.247794</td>\n      <td>0.054311</td>\n      <td>0.096547</td>\n      <td>0.121929</td>\n      <td>0.140516</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_recommender = NNRecommender(n_neg_per_pos=1, batch_size=500, drop_out=0.3162509666065092, epochs=500, lr=0.00013554246389291255, neurons=256)  # Initialize your recommender here\n",
    "\n",
    "# Give the name of your recommender in the line below\n",
    "nn_tts_results = [['NNRecommender'] + list(evaluate_train_test_split_implicit(\n",
    "    nn_recommender, interactions_df, items_df))]\n",
    "\n",
    "nn_tts_results = pd.DataFrame(\n",
    "    nn_tts_results, columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "\n",
    "display(HTML(nn_tts_results.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "suited-nomination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Recommender</th>\n      <th>HR@1</th>\n      <th>HR@3</th>\n      <th>HR@5</th>\n      <th>HR@10</th>\n      <th>NDCG@1</th>\n      <th>NDCG@3</th>\n      <th>NDCG@5</th>\n      <th>NDCG@10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AmazonRecommender</td>\n      <td>0.044128</td>\n      <td>0.118805</td>\n      <td>0.161236</td>\n      <td>0.221996</td>\n      <td>0.044128</td>\n      <td>0.086755</td>\n      <td>0.104479</td>\n      <td>0.124112</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from recommenders.amazon_recommender import AmazonRecommender\n",
    "\n",
    "amazon_recommender = AmazonRecommender()\n",
    "\n",
    "amazon_tts_results = [['AmazonRecommender'] + list(evaluate_train_test_split_implicit(\n",
    "    amazon_recommender, interactions_df, items_df))]\n",
    "\n",
    "amazon_tts_results = pd.DataFrame(\n",
    "    amazon_tts_results,\n",
    "    columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "\n",
    "display(HTML(amazon_tts_results.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conservative-remedy",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\ttraining         \t (min:    0.172, max:    0.254, cur:    0.172)\n",
      "\tvalidation       \t (min:    0.198, max:    0.293, cur:    0.198)\n"
     ]
    }
   ],
   "source": [
    "from recommenders.netflix_recommender import NetflixRecommender\n",
    "\n",
    "netflix_recommender = NetflixRecommender(embedding_dim=8, n_epochs=200, print_type='live')\n",
    "\n",
    "netflix_tts_results = [['NetflixRecommender'] + list(evaluate_train_test_split_implicit(\n",
    "    netflix_recommender, interactions_df, items_df))]\n",
    "\n",
    "netflix_tts_results = pd.DataFrame(\n",
    "    netflix_tts_results,\n",
    "    columns=['Recommender', 'HR@1', 'HR@3', 'HR@5', 'HR@10', 'NDCG@1', 'NDCG@3', 'NDCG@5', 'NDCG@10'])\n",
    "\n",
    "display(HTML(netflix_tts_results.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moderate-printing",
   "metadata": {},
   "outputs": [],
   "source": [
    "tts_results = pd.concat([nn_tts_results, amazon_tts_results, netflix_tts_results]).reset_index(drop=True)\n",
    "display(HTML(tts_results.to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uniform-vegetable",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "Write a summary of your experiments. What worked well and what did not? What are your thoughts how could you possibly further improve the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "declared-howard",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "# Write your summary here #\n",
    "###########################\n",
    "# I created a very simple Neural Network, the recommender could be improved with more sophisticated Neural Network architectures. I also tried to use `prepare_users_df` and `prepare_items_df` from the project 1, but the results were poor. I reckon that better data preparation would make the results better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}