examples/12_neural_ts_exp.ipynb

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   "source": [
    "We will show the main RePlay functionality and compare performance of RePlay models on well-known MovieLens dataset. For simplicity we consider here only the variou bandit algorithms with context. The list of considered strategies for comparison:\n",
    "\n",
    "1) Linear UCB\n",
    "\n",
    "2) Linear Thompson Sampling\n",
    "\n",
    "3) Logistic Thompson Sampling\n",
    "\n",
    "### Dataset\n",
    "\n",
    "We will compare RePlay models on MovieLens 1m.\n",
    "\n",
    "### Dataset preprocessing:\n",
    "\n",
    "Ratings greater than or equal to 3 are considered as positive interactions.\n",
    "\n",
    "### Data split\n",
    "\n",
    "Dataset is split by date so that 20% of the last interactions as are placed in the test part. Cold items and users are dropped.\n",
    "\n",
    "### Predict:\n",
    "We will predict top-10 most relevant films for each user.\n",
    "\n",
    "### Metrics\n",
    "\n",
    "Quality metrics used: ndcg@k, hitrate@k, map@k, mrr@k for k = 1, 5, 10 Additional metrics used: coverage@k and surprisal@k.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "518b0d1a-a574-413b-928f-5755c763c405",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install rs-datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7dd87a23-af97-44ca-b3f4-db88eb6bfb35",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42bf7dd6-e79f-4d73-a6be-f8df5d75d6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d9abd75-4180-4a67-9b07-eddac41ebbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from optuna.exceptions import ExperimentalWarning\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=ExperimentalWarning)\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9cdf0fa-0e15-4a5f-966d-d65e3eac7f2e",
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    {
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   "source": [
    "import logging\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pyspark.sql import functions as sf, types as st\n",
    "from pyspark.sql.types import IntegerType\n",
    "\n",
    "from replay.experimental.preprocessing.data_preparator import Indexer, DataPreparator\n",
    "from replay.metrics import Coverage, HitRate, MRR, MAP, NDCG, Surprisal, Experiment\n",
    "from replay.experimental.models import (\n",
    "    NeuralTS\n",
    ")\n",
    "\n",
    "\n",
    "from replay.utils.session_handler import State\n",
    "from replay.splitters import TimeSplitter\n",
    "from replay.utils.spark_utils import get_log_info\n",
    "from rs_datasets import MovieLens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9490b442-0f13-4106-ab81-c0c8c0e14ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = State().session\n",
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "176cba50-02f2-4a0b-90da-31d6a4ca3be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.sparkContext.setLogLevel('ERROR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de6cfeb3-e9a8-40f0-a86a-29c3351c19e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = logging.getLogger(\"replay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2e59462-943b-49e4-ad29-47c68655db10",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 10\n",
    "K_list_metrics = [1, 5, 10]\n",
    "BUDGET = 20\n",
    "BUDGET_NN = 10\n",
    "SEED = 12345"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60db72f-6060-4727-9916-ffe170777cb6",
   "metadata": {},
   "source": [
    "## Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4d61c4-3f36-4422-b4ea-8938fe53bcb3",
   "metadata": {},
   "source": [
    "### Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afb72229-1f12-412e-bb6d-65e49f2acb5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratings\n"
     ]
    },
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       "   user_id  item_id  rating  timestamp\n",
       "0        1     1193       5  978300760\n",
       "1        1      661       3  978302109\n",
       "2        1      914       3  978301968"
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "users\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
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       "      <td>70072</td>\n",
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      ],
      "text/plain": [
       "   user_id gender  age  occupation zip_code\n",
       "0        1      F    1          10    48067\n",
       "1        2      M   56          16    70072\n",
       "2        3      M   25          15    55117"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "items\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
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       "   item_id                    title                        genres\n",
       "0        1         Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2           Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3  Grumpier Old Men (1995)                Comedy|Romance"
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    {
     "name": "stdout",
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data = MovieLens(\"1m\")\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "799925a2-9b6d-485e-b373-a7118a32fbf7",
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    {
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       "      <th>item_id</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "</table>\n",
       "<p>1000209 rows × 4 columns</p>\n",
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      ],
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       "         user_id  item_id  rating  timestamp\n",
       "0              1     1193       5  978300760\n",
       "1              1      661       3  978302109\n",
       "2              1      914       3  978301968\n",
       "3              1     3408       4  978300275\n",
       "4              1     2355       5  978824291\n",
       "...          ...      ...     ...        ...\n",
       "1000204     6040     1091       1  956716541\n",
       "1000205     6040     1094       5  956704887\n",
       "1000206     6040      562       5  956704746\n",
       "1000207     6040     1096       4  956715648\n",
       "1000208     6040     1097       4  956715569\n",
       "\n",
       "[1000209 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "367862be-9ccd-4ae9-847b-6cd0ce4b82df",
   "metadata": {},
   "outputs": [],
   "source": [
    "preparator = DataPreparator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba5deeec-7890-4273-b77f-2733c2c1c1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21-Oct-24 09:47:34, replay, INFO: Columns with ids of users or items are present in mapping. The dataframe will be treated as an interactions log.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 235 ms, sys: 31.7 ms, total: 267 ms\n",
      "Wall time: 17.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "log = preparator.transform(columns_mapping={'user_id': 'user_id',\n",
    "                                      'item_id': 'item_id',\n",
    "                                      'relevance': 'rating',\n",
    "                                      'timestamp': 'timestamp'\n",
    "                                     }, \n",
    "                           data=data.ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1f43241-03d3-44b6-9208-c9e40523d91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "836478"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# will consider ratings >= 3 as positive feedback. A positive feedback is treated with relevance = 1\n",
    "only_positives_log = log.filter(sf.col('relevance') >= 3).withColumn('relevance', sf.lit(1))\n",
    "only_positives_log.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3007ddfc-1b7b-45d6-b550-dc628b24d176",
   "metadata": {},
   "source": [
    "### Indexing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "102618c4-c2d1-4083-aef4-3329abeb798a",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexer = Indexer(user_col='user_id', item_col='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35619648-b44b-4b53-8809-0cc20e1ee7f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 14:>                                                       (0 + 48) / 48]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30 ms, sys: 10.8 ms, total: 40.8 ms\n",
      "Wall time: 30.3 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "indexer.fit(users=log.select('user_id'),\n",
    "           items=log.select('item_id'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a040889-0bff-476d-af92-3af77b323cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------+---------+-------------------+\n",
      "|user_idx|item_idx|relevance|          timestamp|\n",
      "+--------+--------+---------+-------------------+\n",
      "|    4131|      43|        1|2001-01-01 01:12:40|\n",
      "|    4131|     585|        1|2001-01-01 01:35:09|\n",
      "+--------+--------+---------+-------------------+\n",
      "only showing top 2 rows\n",
      "\n",
      "CPU times: user 46.4 ms, sys: 9.75 ms, total: 56.1 ms\n",
      "Wall time: 1min 12s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "836478"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "log_replay = indexer.transform(df=only_positives_log)\n",
    "log_replay.show(2)\n",
    "log_replay.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8d487f-8a46-4470-9006-01c24ffc7b87",
   "metadata": {},
   "source": [
    "### Data split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90514332-4396-4b0f-a497-e28b679a9e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train info:\n",
      " total lines: 669181, total users: 5397, total items: 3569\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test info:\n",
      " total lines: 86542, total users: 1139, total items: 3279\n"
     ]
    }
   ],
   "source": [
    "# train/test split \n",
    "train_spl = TimeSplitter(\n",
    "    time_threshold=0.2,\n",
    "    drop_cold_items=True,\n",
    "    drop_cold_users=True,\n",
    "    query_column=\"user_idx\",\n",
    "    item_column=\"item_idx\",\n",
    ")\n",
    "\n",
    "train, test = train_spl.split(log_replay)\n",
    "print('train info:\\n', get_log_info(train))\n",
    "print('test info:\\n', get_log_info(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbef1ae0-df6f-42e8-8679-63ef36458dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.is_cached"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "843be418-7120-41d8-85a4-c4587872224f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(535343, 24241)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train/test split for hyperparameters selection\n",
    "opt_train, opt_val = train_spl.split(train)\n",
    "opt_train.count(), opt_val.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86661982-34f3-4c5a-bf4c-a543837f4d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_train.is_cached"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4502490b-59cb-4896-8b0f-e8985c6ad32c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "798993"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# negative feedback will be used for Wilson and UCB models\n",
    "only_negatives_log = indexer.transform(df=log.filter(sf.col('relevance') < 3).withColumn('relevance', sf.lit(0.)))\n",
    "test_start = test.agg(sf.min('timestamp')).collect()[0][0]\n",
    "\n",
    "# train with both positive and negative feedback\n",
    "pos_neg_train=(train\n",
    "              .withColumn('relevance', sf.lit(1.))\n",
    "              .union(only_negatives_log.filter(sf.col('timestamp') < test_start))\n",
    "             )\n",
    "pos_neg_train.cache()\n",
    "pos_neg_train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "914f8905-b6e3-411e-b468-2932e8af97db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_neg_train.is_cached"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8ec5c4d-4302-49e6-8a97-0607a677cf13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------+\n",
      "|relevance| count|\n",
      "+---------+------+\n",
      "|      1.0|669181|\n",
      "|      0.0|129812|\n",
      "+---------+------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "pos_neg_train.groupBy('relevance').count().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d0faa9-35bf-4f39-baac-ed6ce1975e59",
   "metadata": {},
   "source": [
    "### Item features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a2bb8e3-838c-462d-8837-605beab5576d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21-Oct-24 09:59:18, replay, INFO: Column with ids of users or items is absent in mapping. The dataframe will be treated as a users'/items' features dataframe.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+----------------+--------------------+\n",
      "|item_idx|           title|              genres|\n",
      "+--------+----------------+--------------------+\n",
      "|      29|Toy Story (1995)|Animation|Childre...|\n",
      "|     393|  Jumanji (1995)|Adventure|Childre...|\n",
      "+--------+----------------+--------------------+\n",
      "only showing top 2 rows\n",
      "\n",
      "+-------------+\n",
      "|max(item_idx)|\n",
      "+-------------+\n",
      "|         3882|\n",
      "+-------------+\n",
      "\n",
      "+-------------+\n",
      "|min(item_idx)|\n",
      "+-------------+\n",
      "|            0|\n",
      "+-------------+\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3883"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_features_original = preparator.transform(columns_mapping={'item_id': 'item_id'}, \n",
    "                           data=data.items)\n",
    "item_features = indexer.transform(df=item_features_original)\n",
    "item_features.show(2)\n",
    "#different item features\n",
    "\n",
    "from pyspark.sql.functions import max,min\n",
    "item_features.select(max(item_features.item_idx)).show()\n",
    "item_features.select(min(item_features.item_idx)).show()\n",
    "#just to check that the indexing is dense between 0 and 3882\n",
    "item_features.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ef866ef5-5a58-435f-bd1a-98b1a84f791c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+----+\n",
      "|item_idx|year|\n",
      "+--------+----+\n",
      "|      29|1995|\n",
      "|     393|1995|\n",
      "+--------+----+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "year = item_features.withColumn('year', sf.substring(sf.col('title'), -5, 4).astype(st.IntegerType())).select('item_idx', 'year')\n",
    "year.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "584986c0-ca89-4340-9cb2-c925d2e8b69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "genres = (\n",
    "    item_features.select(\n",
    "        \"item_idx\",\n",
    "        sf.split(\"genres\", \"\\|\").alias(\"genres\")\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90f241fb-18e9-4824-b681-944bfb9d3173",
   "metadata": {},
   "outputs": [],
   "source": [
    "genres_list = (\n",
    "    genres.select(sf.explode(\"genres\").alias(\"genre\"))\n",
    "    .distinct().filter('genre <> \"(no genres listed)\"')\n",
    "    .toPandas()[\"genre\"].tolist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc361d69-2b2f-4e27-b477-a935b9695b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "item_features = genres\n",
    "for genre in genres_list:\n",
    "    item_features = item_features.withColumn(\n",
    "        genre,\n",
    "        sf.array_contains(sf.col(\"genres\"), genre).astype(IntegerType())\n",
    "    )\n",
    "item_features = item_features.drop(\"genres\").cache()\n",
    "item_features.count()\n",
    "item_features = item_features.join(year, on='item_idx', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "67b0e545-a8d1-4a53-8e08-122bd2dd2623",
   "metadata": {},
   "outputs": [],
   "source": [
    "item_features = item_features.withColumnRenamed(\"Children's\",\"Children\")\n",
    "item_features = item_features.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "659878de-012b-4ea0-89ce-84514dcf5bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['item_idx', 'Documentary', 'Adventure', 'Animation', 'Comedy',\n",
       "       'Thriller', 'Sci-Fi', 'Musical', 'Horror', 'Action', 'Fantasy', 'War',\n",
       "       'Mystery', 'Children', 'Drama', 'Crime', 'Romance', 'Film-Noir',\n",
       "       'Western', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_features.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4eddc96-9055-4ac1-8c54-47b19239973a",
   "metadata": {},
   "source": [
    "### Users features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b7a2fddb-3f5d-49c1-8e15-e7d158678739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>48067</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
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       "      <td>70072</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>M</td>\n",
       "      <td>25</td>\n",
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       "      <td>55117</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id gender  age  occupation zip_code\n",
       "0        1      F    1          10    48067\n",
       "1        2      M   56          16    70072\n",
       "2        3      M   25          15    55117\n",
       "3        4      M   45           7    02460\n",
       "4        5      M   25          20    55455"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "df210a6e-78ef-42d2-81ca-72edee25a4e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21-Oct-24 09:59:21, replay, INFO: Column with ids of users or items is absent in mapping. The dataframe will be treated as a users'/items' features dataframe.\n"
     ]
    },
    {
     "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_idx</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4131</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2364</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_idx gender  age  occupation zip_code\n",
       "0      4131      F    1          10    48067\n",
       "1      2364      M   56          16    70072"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#same preprocessing for users as was done in 2.4.1.\n",
    "user_features_original = preparator.transform(columns_mapping={'user_id': 'user_id'}, \n",
    "                           data=data.users)\n",
    "user_features = indexer.transform(df=user_features_original)\n",
    "#switch for a while into pandas\n",
    "user_features = user_features.toPandas()\n",
    "user_features.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e90b8cd9-9f70-4bf4-8466-f833c62c28c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import mean, stddev\n",
    "user_train_mean = pos_neg_train.groupBy('user_idx').mean('relevance').withColumnRenamed('avg(relevance)','user_mean_rel').toPandas()\n",
    "user_train_count = pos_neg_train.groupBy('user_idx').count().withColumnRenamed('count','user_count_rel').toPandas()\n",
    "user_train_std = pos_neg_train.groupBy('user_idx').agg({'relevance': 'stddev'}).withColumnRenamed('stddev(relevance)','user_std_rel').toPandas()\n",
    "user_train_median = pd.DataFrame(pos_neg_train.toPandas().groupby('user_idx', as_index = False)['relevance'].agg('median')).rename(columns={'relevance': 'user_median_rel'})\n",
    "user_train_kurt = pd.DataFrame(pos_neg_train.toPandas().groupby('user_idx', as_index = False)['relevance'].apply(pd.DataFrame.kurt)).rename(columns={'relevance': 'user_kurt_rel'})\n",
    "user_train_skew = pd.DataFrame(pos_neg_train.toPandas().groupby('user_idx', as_index = False)['relevance'].apply(pd.DataFrame.skew)).rename(columns={'relevance': 'user_skew_rel'})\n",
    "\n",
    "item_train_mean = pos_neg_train.groupBy('item_idx').mean('relevance').withColumnRenamed('avg(relevance)','item_mean_rel').toPandas()\n",
    "item_train_count = pos_neg_train.groupBy('item_idx').count().withColumnRenamed('count','item_count_rel').toPandas()\n",
    "item_train_std = pos_neg_train.groupBy('item_idx').agg({'relevance': 'stddev'}).withColumnRenamed('stddev(relevance)','item_std_rel').toPandas()\n",
    "item_train_median = pd.DataFrame(pos_neg_train.toPandas().groupby('item_idx', as_index = False)['relevance'].agg('median')).rename(columns={'relevance': 'item_median_rel'})\n",
    "item_train_kurt = pd.DataFrame(pos_neg_train.toPandas().groupby('item_idx', as_index = False)['relevance'].apply(pd.DataFrame.kurt)).rename(columns={'relevance': 'item_kurt_rel'})\n",
    "item_train_skew = pd.DataFrame(pos_neg_train.toPandas().groupby('item_idx', as_index = False)['relevance'].apply(pd.DataFrame.skew)).rename(columns={'relevance': 'item_skew_rel'})\n",
    "\n",
    "full_mean = pos_neg_train.select(mean('relevance')).toPandas().values.squeeze()\n",
    "full_count = 0\n",
    "full_std = pos_neg_train.agg({'relevance': 'stddev'}).toPandas().values.squeeze()\n",
    "full_median = pos_neg_train.toPandas()['relevance'].median().squeeze()\n",
    "full_kurt = pos_neg_train.toPandas()['relevance'].kurt().squeeze()\n",
    "full_skew = pos_neg_train.toPandas()['relevance'].skew().squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "422cedfe-76d7-4064-a713-6514d4b5deaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "user_train_sum = pos_neg_train.groupBy('user_idx').sum('relevance').withColumnRenamed('sum(relevance)','user_sum_rel').toPandas()\n",
    "item_train_sum = pos_neg_train.groupBy('item_idx').sum('relevance').withColumnRenamed('sum(relevance)','item_sum_rel').toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa549b76-ab84-4eb0-8e90-da49717868e5",
   "metadata": {},
   "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>item_idx</th>\n",
       "      <th>item_sum_rel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660</td>\n",
       "      <td>349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>783</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>619</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>612</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>263</td>\n",
       "      <td>581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>1864.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1229</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>186</td>\n",
       "      <td>822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>471</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>1432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_idx  item_sum_rel\n",
       "0       660         349.0\n",
       "1       783         312.0\n",
       "2       619         189.0\n",
       "3       612         399.0\n",
       "4       263         581.0\n",
       "5        18        1864.0\n",
       "6      1229         180.0\n",
       "7       186         822.0\n",
       "8       471         437.0\n",
       "9        38        1432.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_train_sum.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8b7f3532-4404-4b20-ab55-3a9bb54443be",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_all_true = pd.DataFrame({'user_idx': user_train_sum['user_idx'].values , \n",
    "                              'user_all_true': (user_train_sum['user_sum_rel'] == user_train_count['user_count_rel'].values).astype(int)})\n",
    "item_all_true = pd.DataFrame({'item_idx': item_train_sum['item_idx'].values , \n",
    "                              'item_all_true': (item_train_sum['item_sum_rel'] == item_train_count['item_count_rel'].values).astype(int)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fae08c53-1ce3-4675-b2ab-86e91b437512",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_item = pd.DataFrame({'item_idx': item_train_mean['item_idx'].values , \n",
    "                              'item_is_pop': (item_train_mean['item_mean_rel'] > 0.9).astype(int)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a8cf10e7-6897-4ce0-9c9b-1426c4e746e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max ocupation index:  20\n",
      "min ocupation index:  0\n",
      "different zip codes:  3439\n"
     ]
    },
    {
     "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_idx</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>gender_occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4131</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;20</td>\n",
       "      <td>F_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2364</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>51-60</td>\n",
       "      <td>M_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4217</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20-29</td>\n",
       "      <td>M_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5916</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>40-49</td>\n",
       "      <td>M_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1603</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>20-29</td>\n",
       "      <td>M_20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_idx gender  age  occupation zip_code agegroup gender_occupation\n",
       "0      4131      F    1          10        4      <20              F_10\n",
       "1      2364      M   56          16        7    51-60              M_16\n",
       "2      4217      M   25          15        5    20-29              M_15\n",
       "3      5916      M   45           7        0    40-49               M_7\n",
       "4      1603      M   25          20        5    20-29              M_20"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "print(\"max ocupation index: \", user_features['occupation'].max())\n",
    "print(\"min ocupation index: \", user_features['occupation'].min())\n",
    "count_diff_zips = user_features['zip_code'].unique().size\n",
    "print(\"different zip codes: \", count_diff_zips) \n",
    "users_pd = user_features\n",
    "users_pd['zip_code'] = users_pd['zip_code'].apply(lambda x: x[0])\n",
    "\n",
    "#binarize age variable\n",
    "bins = [0, 20, 30, 40, 50, 60, np.inf]\n",
    "names = ['<20', '20-29', '30-39','40-49', '51-60', '60+']\n",
    "\n",
    "users_pd['agegroup'] = pd.cut(users_pd['age'], bins, labels=names)\n",
    "# users_pd = users_pd.drop([\"age\"], axis = 1)\n",
    "# users_pd.head()\n",
    "\n",
    "# #binarize following https://github.com/kfoofw/bandit_simulations/tree/master\n",
    "# columnsToEncode = [\"agegroup\",\"gender\",\"occupation\"]\n",
    "# myEncoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
    "# myEncoder.fit(users_pd[columnsToEncode])\n",
    "\n",
    "# users_pd = pd.concat([users_pd.drop(columnsToEncode, 1),\n",
    "#                            pd.DataFrame(myEncoder.transform(users_pd[columnsToEncode]), \n",
    "#                                         columns = myEncoder.get_feature_names_out(columnsToEncode))], axis=1).reindex()\n",
    "users_pd['gender_occupation'] = users_pd['gender'] + '_' + users_pd['occupation'].astype(str)\n",
    "users_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b669a3c4-6be1-438f-8db4-8b85003e79f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stats\n",
    "users_pd = users_pd.merge(user_train_mean, on='user_idx', how='left')\n",
    "users_pd['user_mean_rel'].fillna(full_mean, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_train_count, on='user_idx', how='left')\n",
    "users_pd['user_count_rel'].fillna(full_count, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_train_std, on='user_idx', how='left')\n",
    "users_pd['user_std_rel'].fillna(full_std, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_train_median, on='user_idx', how='left')\n",
    "users_pd['user_median_rel'].fillna(full_median, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_train_kurt, on='user_idx', how='left')\n",
    "users_pd['user_kurt_rel'].fillna(full_kurt, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_train_skew, on='user_idx', how='left')\n",
    "users_pd['user_skew_rel'].fillna(full_skew, inplace = True)\n",
    "\n",
    "users_pd = users_pd.merge(user_all_true, on='user_idx', how='left')\n",
    "users_pd['user_all_true'].fillna(0, inplace = True)\n",
    "\n",
    "\n",
    "item_features = item_features.merge(item_train_mean, on='item_idx', how='left')\n",
    "item_features['item_mean_rel'].fillna(full_mean, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_train_count, on='item_idx', how='left')\n",
    "item_features['item_count_rel'].fillna(full_count, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_train_std, on='item_idx', how='left')\n",
    "item_features['item_std_rel'].fillna(full_std, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_train_median, on='item_idx', how='left')\n",
    "item_features['item_median_rel'].fillna(full_median, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_train_kurt, on='item_idx', how='left')\n",
    "item_features['item_kurt_rel'].fillna(full_kurt, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_train_skew, on='item_idx', how='left')\n",
    "item_features['item_skew_rel'].fillna(full_skew, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(item_all_true, on='item_idx', how='left')\n",
    "item_features['item_all_true'].fillna(0, inplace = True)\n",
    "\n",
    "item_features = item_features.merge(pop_item, on='item_idx', how='left')\n",
    "item_features['item_is_pop'].fillna(0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7948ea64-1786-42f0-bc01-f0172d41fd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- user_idx: integer (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- age: integer (nullable = true)\n",
      " |-- occupation: integer (nullable = true)\n",
      " |-- zip_code: string (nullable = true)\n",
      " |-- agegroup: string (nullable = true)\n",
      " |-- gender_occupation: string (nullable = true)\n",
      " |-- user_mean_rel: double (nullable = true)\n",
      " |-- user_count_rel: double (nullable = true)\n",
      " |-- user_std_rel: double (nullable = true)\n",
      " |-- user_median_rel: double (nullable = true)\n",
      " |-- user_kurt_rel: double (nullable = true)\n",
      " |-- user_skew_rel: double (nullable = true)\n",
      " |-- user_all_true: double (nullable = true)\n",
      "\n",
      "total users:  6040\n"
     ]
    }
   ],
   "source": [
    "#make it pyspark\n",
    "user_features = spark.createDataFrame(users_pd) \n",
    "user_features.cache()\n",
    "user_features.printSchema()\n",
    "print(\"total users: \",user_features.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "005148e2-4bd9-4e4e-9bec-5622ca34be3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- item_idx: integer (nullable = true)\n",
      " |-- Documentary: integer (nullable = true)\n",
      " |-- Adventure: integer (nullable = true)\n",
      " |-- Animation: integer (nullable = true)\n",
      " |-- Comedy: integer (nullable = true)\n",
      " |-- Thriller: integer (nullable = true)\n",
      " |-- Sci-Fi: integer (nullable = true)\n",
      " |-- Musical: integer (nullable = true)\n",
      " |-- Horror: integer (nullable = true)\n",
      " |-- Action: integer (nullable = true)\n",
      " |-- Fantasy: integer (nullable = true)\n",
      " |-- War: integer (nullable = true)\n",
      " |-- Mystery: integer (nullable = true)\n",
      " |-- Children: integer (nullable = true)\n",
      " |-- Drama: integer (nullable = true)\n",
      " |-- Crime: integer (nullable = true)\n",
      " |-- Romance: integer (nullable = true)\n",
      " |-- Film-Noir: integer (nullable = true)\n",
      " |-- Western: integer (nullable = true)\n",
      " |-- year: integer (nullable = true)\n",
      " |-- item_mean_rel: double (nullable = true)\n",
      " |-- item_count_rel: double (nullable = true)\n",
      " |-- item_std_rel: double (nullable = true)\n",
      " |-- item_median_rel: double (nullable = true)\n",
      " |-- item_kurt_rel: double (nullable = true)\n",
      " |-- item_skew_rel: double (nullable = true)\n",
      " |-- item_all_true: double (nullable = true)\n",
      " |-- item_is_pop: double (nullable = true)\n",
      "\n",
      "total users:  3883\n"
     ]
    }
   ],
   "source": [
    "item_features = spark.createDataFrame(item_features) \n",
    "item_features.cache()\n",
    "item_features.printSchema()\n",
    "print(\"total users: \",item_features.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be6cf4d6-4ec2-40eb-a86d-4c82c7350069",
   "metadata": {},
   "outputs": [],
   "source": [
    "# item_features = indexer.transform(df=item_features)\n",
    "# user_features = indexer.transform(df=user_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3e45a708-0d64-4e1f-85e2-d2b91404cb9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_embed_cols = [\"agegroup\", 'gender', 'occupation', \"zip_code\"]\n",
    "continuous_cols = [\"Crime\", \"Sci-Fi\", \"Musical\", \n",
    "                   \"Mystery\", \"Documentary\", \"Fantasy\", \"Children\", \n",
    "                   \"Drama\", \"Horror\", \"Adventure\", \"Western\",\n",
    "                   \"Romance\", \"War\", \"Animation\", \"Action\", \"Comedy\", \"Thriller\",\n",
    "                   \"Film-Noir\"]\n",
    "                  #  , \"age\",\n",
    "                  #  'user_mean_rel', 'user_count_rel', 'user_std_rel',\n",
    "                  #  'user_median_rel', 'user_kurt_rel', 'user_skew_rel',\n",
    "                  # 'item_mean_rel', 'item_count_rel', 'item_std_rel',\n",
    "                  #  'item_median_rel', 'item_kurt_rel', 'item_skew_rel']\n",
    "wide_cols = ['gender', 'occupation']\n",
    "crossed_cols = ['gender_occupation']\n",
    "\n",
    "cols_item = {'continuous_cols':[], 'cat_embed_cols':[], 'wide_cols': []}\n",
    "\n",
    "cols_user = {'continuous_cols':[], 'cat_embed_cols':[], 'wide_cols': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8fc70e61-6e57-475b-a584-28f2143c978a",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = Experiment(\n",
    "    [ MAP(K), \n",
    "      NDCG(K), \n",
    "      HitRate(K_list_metrics), \n",
    "      Coverage(K),\n",
    "      Surprisal(K),\n",
    "      MRR(K)],\n",
    "    test,\n",
    "    pos_neg_train,\n",
    "    query_column=\"user_idx\", item_column=\"item_idx\", rating_column=\"relevance\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b1239639-00cd-4ab1-acdc-3c6d7b00870a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bandit_models = {\n",
    "    'Neural TS' : [\n",
    "        NeuralTS(\n",
    "            user_cols = cols_user,\n",
    "            item_cols = cols_item,\n",
    "            dim_head=20,\n",
    "            deep_out_dim=20,\n",
    "            hidden_layers=[32, 20],\n",
    "            embedding_sizes=[32, 32, 64],\n",
    "            wide_out_dim=1,\n",
    "            head_dropout=0.8,\n",
    "            deep_dropout=0.4,\n",
    "            n_epochs=2,\n",
    "            opt_lr = 3e-4,\n",
    "            lr_min = 1e-5,\n",
    "            use_gpu = False,\n",
    "            plot_dir='test.png',\n",
    "            use_warp_loss=True,\n",
    "            cnt_neg_samples=200,\n",
    "            cnt_samples_for_predict=10,\n",
    "            exploration_coef = +1.0,\n",
    "        ),\n",
    "        'no_opt'\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "df0171a4-8c59-4c30-8034-23644dd0fe06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_predict_add_res(name, model, experiment, train, test, suffix=''):\n",
    "    \"\"\"\n",
    "    Run fit_predict for the `model`, measure time on fit_predict and evaluate metrics\n",
    "    \"\"\"\n",
    "    start_time=time.time()\n",
    "    \n",
    "    logs = {'log': train}\n",
    "    predict_params = {'k': K, 'users': test.select('user_idx').distinct()}\n",
    "    \n",
    "    if isinstance(model, (NeuralTS)):\n",
    "        logs['log'] = pos_neg_train\n",
    "\n",
    "    if isinstance(model, (NeuralTS)):\n",
    "        logs['item_features'] = item_features\n",
    "        logs['user_features'] = user_features \n",
    "    predict_params.update(logs)\n",
    "\n",
    "    model.fit(**logs)\n",
    "    fit_time = time.time() - start_time\n",
    "    \n",
    "    pred=model.predict(**predict_params)\n",
    "    pred.show(100)\n",
    "    pred.cache()\n",
    "    pred.count()\n",
    "    predict_time = time.time() - start_time - fit_time\n",
    "\n",
    "    experiment.add_result(name + suffix, pred)\n",
    "    metric_time = time.time() - start_time - fit_time - predict_time\n",
    "    experiment.results.loc[name + suffix, 'fit_time'] = fit_time\n",
    "    experiment.results.loc[name + suffix, 'predict_time'] = predict_time\n",
    "    experiment.results.loc[name + suffix, 'metric_time'] = metric_time\n",
    "    experiment.results.loc[name + suffix, 'full_time'] = (fit_time + \n",
    "                                                          predict_time +\n",
    "                                                          metric_time)\n",
    "    pred.unpersist()\n",
    "    print(experiment.results[['NDCG@{}'.format(K), 'MRR@{}'.format(K), 'Coverage@{}'.format(K), 'fit_time']].sort_values('NDCG@{}'.format(K), ascending=False))\n",
    "    #add for me\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a5e270a6-1077-43ef-86c0-9eed0aadd269",
   "metadata": {},
   "outputs": [],
   "source": [
    "def full_pipeline(models, experiment, train, test, suffix='', budget=BUDGET):\n",
    "    \"\"\"\n",
    "    For each model:\n",
    "        -  if required: run hyperparameters search, set best params and save param values to `experiment`\n",
    "        - pass model to `fit_predict_add_res`        \n",
    "    \"\"\"\n",
    "    \n",
    "    for name, [model, params] in models.items():\n",
    "        model.logger.info(msg='{} started'.format(name))\n",
    "        if params != 'no_opt':\n",
    "            model.logger.info(msg='{} optimization started'.format(name))\n",
    "            best_params = model.optimize(opt_train, \n",
    "                                         opt_val, \n",
    "                                         param_borders=params, \n",
    "                                         item_features=item_features,\n",
    "                                         user_features=user_features,\n",
    "                                         k=K, \n",
    "                                         budget=budget)\n",
    "            logger.info(msg='best params for {} are: {}'.format(name, best_params))\n",
    "            model.set_params(**best_params)\n",
    "        \n",
    "        logger.info(msg='{} fit_predict started'.format(name))\n",
    "        pred = fit_predict_add_res(name, model, experiment, train, test, suffix)\n",
    "        # here we call protected attribute to get all parameters set during model initialization\n",
    "        experiment.results.loc[name + suffix, 'params'] = str(model._init_args)\n",
    "        #add for me\n",
    "        return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ca54985e-3e70-451a-bd03-25cd8c2c2326",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21-Oct-24 10:11:59, replay, INFO: ndcg val =0.1000\n",
      "100%|██████████| 1139/1139 [02:39<00:00,  7.13it/s]                             \n",
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------+------------------+\n",
      "|user_idx|item_idx|         relevance|\n",
      "+--------+--------+------------------+\n",
      "|      18|    1870|5.3775389406503695|\n",
      "|      18|      47| 4.739410579565877|\n",
      "|      18|     395| 4.541020278328126|\n",
      "|      18|     161|4.4218457112952425|\n",
      "|      18|    3868| 4.158440323365729|\n",
      "|      18|     507| 4.064827943320855|\n",
      "|      18|      48| 3.883049547540212|\n",
      "|      18|     633|3.8444350236367693|\n",
      "|      18|     943|3.8056227200041017|\n",
      "|      18|    1056| 3.797194694917845|\n",
      "|     186|      12| 6.420963270877239|\n",
      "|     186|     194| 5.656853085045034|\n",
      "|     186|     391| 5.453808038494393|\n",
      "|     186|      49| 5.335534083911569|\n",
      "|     186|     104|  4.99959438502184|\n",
      "|     186|      43| 4.954802449673658|\n",
      "|     186|      40|4.8434793129332245|\n",
      "|     186|     172| 4.813935521652629|\n",
      "|     186|     145|4.7580761479426545|\n",
      "|     186|      15| 4.704732956025126|\n",
      "|     471|       2| 5.780186942677461|\n",
      "|     471|      17| 5.615563203759828|\n",
      "|     471|      56| 5.497704504619465|\n",
      "|     471|      10|5.0609681882971636|\n",
      "|     471|      26| 4.963332303404584|\n",
      "|     471|      21| 4.671242982878783|\n",
      "|     471|      35| 4.554738592434478|\n",
      "|     471|      55| 4.246769581963848|\n",
      "|     471|    1870| 4.197333766451814|\n",
      "|     471|       1|4.1088095490122845|\n",
      "|     475|      10| 6.701779452074578|\n",
      "|     475|      37| 5.292274374073612|\n",
      "|     475|      47| 5.114361546553205|\n",
      "|     475|       1| 4.852021540246149|\n",
      "|     475|      57| 4.734576854374152|\n",
      "|     475|      21| 4.510590672858323|\n",
      "|     475|     104| 4.322961730192645|\n",
      "|     475|     265|  4.28847134185993|\n",
      "|     475|      22|  4.25265206884758|\n",
      "|     475|     142| 4.229137386934741|\n",
      "|     612|      10| 7.330814113274425|\n",
      "|     612|       7| 6.856444729509821|\n",
      "|     612|      34| 6.469022621530645|\n",
      "|     612|      23| 6.208986681175427|\n",
      "|     612|       0| 6.123002794066772|\n",
      "|     612|      52|5.3187449329939955|\n",
      "|     612|     107| 5.103535825434836|\n",
      "|     612|      17| 5.101795023569778|\n",
      "|     612|      80|  4.98305453620215|\n",
      "|     612|      47| 4.953340927200279|\n",
      "|     619|      35| 4.738846932058931|\n",
      "|     619|     395| 4.720097465853093|\n",
      "|     619|      34|4.6426619241744005|\n",
      "|     619|      56| 4.357526208103357|\n",
      "|     619|     181| 4.100114243079956|\n",
      "|     619|      38| 4.065488044304239|\n",
      "|     619|      21| 4.027138104286727|\n",
      "|     619|     218| 3.990815180751178|\n",
      "|     619|     369|3.8989457853372227|\n",
      "|     619|      76|3.8817515262443223|\n",
      "|     783|       7|5.8568744245892095|\n",
      "|     783|       2| 5.793882054628489|\n",
      "|     783|      17| 5.475194583318241|\n",
      "|     783|     107| 5.401114120622479|\n",
      "|     783|      12|  4.93190117248476|\n",
      "|     783|      10|  4.89251194100379|\n",
      "|     783|      21|  4.80735209843723|\n",
      "|     783|      43| 4.803914839816603|\n",
      "|     783|       1| 4.746171467041615|\n",
      "|     783|      23| 4.622379627118243|\n",
      "|    1229|      10| 7.264574236398227|\n",
      "|    1229|       2|  6.65464277975699|\n",
      "|    1229|       7| 6.652693645501403|\n",
      "|    1229|       0| 6.140432828956568|\n",
      "|    1229|     107| 5.053984667099723|\n",
      "|    1229|      35| 5.006709202046775|\n",
      "|    1229|     186|4.9359386146267585|\n",
      "|    1229|       1| 4.363536007138732|\n",
      "|    1229|    1870|  4.34529929744913|\n",
      "|    1229|      27| 4.333422964692474|\n",
      "|    1813|       0| 7.302196195827891|\n",
      "|    1813|      34|5.1506940569388995|\n",
      "|    1813|      12|4.8420815921814775|\n",
      "|    1813|       9| 4.730983578524336|\n",
      "|    1813|       7| 4.711336511911858|\n",
      "|    1813|      43| 4.613198489424418|\n",
      "|    1813|      35|4.3805694888266435|\n",
      "|    1813|       1| 4.341068564367505|\n",
      "|    1813|      32| 4.307821693757217|\n",
      "|    1813|    1870| 4.173657177532946|\n",
      "|    2146|      23| 6.301086135726802|\n",
      "|    2146|      10| 6.053079376499571|\n",
      "|    2146|      43|  5.86276937227346|\n",
      "|    2146|     107|  5.84950158361525|\n",
      "|    2146|      47| 5.412733170669306|\n",
      "|    2146|      15| 5.366695187697379|\n",
      "|    2146|      17| 5.314285618338058|\n",
      "|    2146|      37| 5.065666744326221|\n",
      "|    2146|      50| 4.829623170037843|\n",
      "|    2146|     186| 4.644570660604551|\n",
      "+--------+--------+------------------+\n",
      "only showing top 100 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            NDCG@10    MRR@10  Coverage@10    fit_time\n",
      "Neural TS  0.199511  0.361554     0.038777  704.733167\n",
      "CPU times: user 1h 33min 34s, sys: 3min 5s, total: 1h 36min 39s\n",
      "Wall time: 26min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred = full_pipeline(bandit_models, e, train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "425f2216-b01f-4c5a-bc63-dc5521bcefd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>NDCG@10</th>\n",
       "      <th>HitRate@1</th>\n",
       "      <th>HitRate@5</th>\n",
       "      <th>HitRate@10</th>\n",
       "      <th>Coverage@10</th>\n",
       "      <th>Surprisal@10</th>\n",
       "      <th>MRR@10</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>predict_time</th>\n",
       "      <th>metric_time</th>\n",
       "      <th>full_time</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Neural TS</th>\n",
       "      <td>0.115842</td>\n",
       "      <td>0.199511</td>\n",
       "      <td>0.246708</td>\n",
       "      <td>0.51712</td>\n",
       "      <td>0.623354</td>\n",
       "      <td>0.038777</td>\n",
       "      <td>0.16458</td>\n",
       "      <td>0.361554</td>\n",
       "      <td>704.733167</td>\n",
       "      <td>545.179961</td>\n",
       "      <td>336.674005</td>\n",
       "      <td>1586.587133</td>\n",
       "      <td>{'n_epochs': 2, 'union_cols': {'continuous_col...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MAP@10   NDCG@10  HitRate@1  HitRate@5  HitRate@10  Coverage@10  \\\n",
       "Neural TS  0.115842  0.199511   0.246708    0.51712    0.623354     0.038777   \n",
       "\n",
       "           Surprisal@10    MRR@10    fit_time  predict_time  metric_time  \\\n",
       "Neural TS       0.16458  0.361554  704.733167    545.179961   336.674005   \n",
       "\n",
       "             full_time                                             params  \n",
       "Neural TS  1586.587133  {'n_epochs': 2, 'union_cols': {'continuous_col...  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.results.sort_values('NDCG@10', ascending=False)"
   ]
  }
 ],
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