Bert4rec

examples/bert4rec.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/data/home/maspirina1/tasks/repo/RecTools/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import threadpoolctl\n",
+    "from pathlib import Path\n",
+    "from lightning_fabric import seed_everything\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from rectools import Columns\n",
+    "\n",
+    "\n",
+    "from rectools.dataset import Dataset\n",
+    "from rectools.metrics import MAP, calc_metrics, MeanInvUserFreq, Serendipity\n",
+    "from rectools.models.bert4rec import IdEmbeddingsItemNet, BERT4RecModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<threadpoolctl.threadpool_limits at 0x7fb340f9bfa0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n",
+    "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\"\n",
+    "threadpoolctl.threadpool_limits(1, \"blas\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prepare data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# !wget -q https://github.com/irsafilo/KION_DATASET/raw/f69775be31fa5779907cf0a92ddedb70037fb5ae/data_original.zip -O data_original.zip\n",
+    "# !unzip -o data_original.zip\n",
+    "# !rm data_original.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_PATH = Path(\"data_original\")\n",
+    "\n",
+    "interactions = (\n",
+    "    pd.read_csv(DATA_PATH / 'interactions.csv', parse_dates=[\"last_watch_dt\"])\n",
+    "    .rename(columns={\"last_watch_dt\": \"datetime\"})\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interactions[Columns.Weight] = np.where(interactions['watched_pct'] > 10, 3, 1)\n",
+    "\n",
+    "# Split to train / test\n",
+    "max_date = interactions[Columns.Datetime].max()\n",
+    "train = interactions[interactions[Columns.Datetime] < max_date - pd.Timedelta(days=7)].copy()\n",
+    "test = interactions[interactions[Columns.Datetime] >= max_date - pd.Timedelta(days=7)].copy()\n",
+    "train.drop(train.query(\"total_dur < 300\").index, inplace=True)\n",
+    "\n",
+    "# drop items with less than 20 interactions in train\n",
+    "items = train[\"item_id\"].value_counts()\n",
+    "items = items[items >= 20]\n",
+    "items = items.index.to_list()\n",
+    "train = train[train[\"item_id\"].isin(items)]\n",
+    "    \n",
+    "# drop users with less than 2 interactions in train\n",
+    "users = train[\"user_id\"].value_counts()\n",
+    "users = users[users >= 2]\n",
+    "users = users.index.to_list()\n",
+    "train = train[(train[\"user_id\"].isin(users))]\n",
+    "\n",
+    "users = train[\"user_id\"].drop_duplicates().to_list()\n",
+    "\n",
+    "# drop cold users from test\n",
+    "test_users_sasrec = test[Columns.User].unique()\n",
+    "cold_users = set(test[Columns.User]) - set(train[Columns.User])\n",
+    "test.drop(test[test[Columns.User].isin(cold_users)].index, inplace=True)\n",
+    "test_users = test[Columns.User].unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = pd.read_csv(DATA_PATH / 'items.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process item features to the form of a flatten dataframe\n",
+    "items = items.loc[items[Columns.Item].isin(train[Columns.Item])].copy()\n",
+    "items[\"genre\"] = items[\"genres\"].str.lower().str.replace(\", \", \",\", regex=False).str.split(\",\")\n",
+    "genre_feature = items[[\"item_id\", \"genre\"]].explode(\"genre\")\n",
+    "genre_feature.columns = [\"id\", \"value\"]\n",
+    "genre_feature[\"feature\"] = \"genre\"\n",
+    "content_feature = items.reindex(columns=[Columns.Item, \"content_type\"])\n",
+    "content_feature.columns = [\"id\", \"value\"]\n",
+    "content_feature[\"feature\"] = \"content_type\"\n",
+    "item_features = pd.concat((genre_feature, content_feature))\n",
+    "\n",
+    "candidate_items = interactions['item_id'].drop_duplicates().astype(int)\n",
+    "test[\"user_id\"] = test[\"user_id\"].astype(int)\n",
+    "test[\"item_id\"] = test[\"item_id\"].astype(int)\n",
+    "\n",
+    "catalog=train[Columns.Item].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_no_features = Dataset.construct(\n",
+    "    interactions_df=train,\n",
+    ")\n",
+    "\n",
+    "dataset_item_features = Dataset.construct(\n",
+    "    interactions_df=train,\n",
+    "    item_features_df=item_features,\n",
+    "    cat_item_features=[\"genre\", \"content_type\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics_name = {\n",
+    "    'MAP': MAP,\n",
+    "    'MIUF': MeanInvUserFreq,\n",
+    "    'Serendipity': Serendipity\n",
+    "    \n",
+    "\n",
+    "}\n",
+    "metrics = {}\n",
+    "for metric_name, metric in metrics_name.items():\n",
+    "    for k in (1, 5, 10):\n",
+    "        metrics[f'{metric_name}@{k}'] = metric(k=k)\n",
+    "\n",
+    "# list with metrics results of all models\n",
+    "features_results = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BERT4Rec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Seed set to 32\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RANDOM_SEED = 32\n",
+    "torch.use_deterministic_algorithms(True)\n",
+    "seed_everything(RANDOM_SEED, workers=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BERT4Rec with item ids embeddings in ItemNetBlock"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Trainer will use only 1 of 2 GPUs because it is running inside an interactive / notebook environment. You may try to set `Trainer(devices=2)` but please note that multi-GPU inside interactive / notebook environments is considered experimental and unstable. Your mileage may vary.\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = BERT4RecModel(\n",
+    "    n_blocks=3,\n",
+    "    n_heads=4,\n",
+    "    dropout_rate=0.2,\n",
+    "    session_max_len=32,\n",
+    "    lr=1e-3,\n",
+    "    epochs=5,\n",
+    "    verbose=1,\n",
+    "    mask_prob=0.5,\n",
+    "    deterministic=True,\n",
+    "    item_net_block_types=(IdEmbeddingsItemNet, ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "model.fit(dataset_no_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "recos = model.recommend(\n",
+    "    users=test_users_sasrec, \n",
+    "    dataset=dataset_item_features,\n",
+    "    k=10,\n",
+    "    filter_viewed=True,\n",
+    "    on_unsupported_targets=\"warn\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recos[\"item_id\"] = recos[\"item_id\"].apply(str)\n",
+    "test[\"item_id\"] = test[\"item_id\"].astype(str)\n",
+    "metric_values = calc_metrics(metrics, recos[[\"user_id\", \"item_id\", \"rank\"]], test, train, catalog)\n",
+    "metric_values[\"model\"] = \"bert4rec_ids\"\n",
+    "features_results.append(metric_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'MAP@1': 0.04153170911358253,\n",
+       "  'MAP@5': 0.07096106984411608,\n",
+       "  'MAP@10': 0.07874644762957389,\n",
+       "  'MIUF@1': 18.824620072061013,\n",
+       "  'MIUF@5': 18.824620072061013,\n",
+       "  'MIUF@10': 18.824620072061013,\n",
+       "  'Serendipity@1': 0.08494799640990444,\n",
+       "  'Serendipity@5': 0.05316937762913509,\n",
+       "  'Serendipity@10': 0.03892074762532452,\n",
+       "  'model': 'bert4rec_ids'}]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}