diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 9efb5087..6ac28be7 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -3,6 +3,7 @@ on: push: branches: - master + - hw1 pull_request: types: [opened, synchronize] diff --git a/Makefile b/Makefile index d4a99c1e..a559001e 100644 --- a/Makefile +++ b/Makefile @@ -58,7 +58,7 @@ lint: isort flake mypy pylint # Test .pytest: - poetry run pytest $(TESTS) + poetry run pytest $(TESTS) -W ignore::DeprecationWarning test: .venv .pytest diff --git a/metric_validation .ipynb b/metric_validation .ipynb new file mode 100644 index 00000000..7e148aca --- /dev/null +++ b/metric_validation .ipynb @@ -0,0 +1,2007 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "8b94f641", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from rectools.dataset import Interactions, Dataset, DenseFeatures\n", + "from rectools.model_selection import Splitter, TimeRangeSplitter\n", + "from rectools.models.base import ModelBase\n", + "from rectools.models import RandomModel, PopularModel\n", + "from rectools.metrics.base import MetricAtK\n", + "from rectools.metrics import (\n", + " Precision,\n", + " Recall,\n", + " MAP\n", + " NDCG,\n", + " Serendipity,\n", + " MeanInvUserFreq,\n", + " IntraListDiversity,\n", + " PairwiseHammingDistanceCalculator,\n", + " calc_metrics,\n", + " MRR,\n", + " serendipity\n", + ")\n", + "from rectools import Columns\n", + "from typing import Dict, Union, Callable, Tuple, Any, List\n", + "from tqdm import tqdm\n", + "from copy import deepcopy\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7449fd99", + "metadata": {}, + "outputs": [], + "source": [ + "interactions_df = pd.read_csv('interactions.csv')\n", + "users = pd.read_csv('users.csv')\n", + "items = pd.read_csv('items.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a2d49bb0", + "metadata": {}, + "outputs": [], + "source": [ + "metrics = {\n", + " 'precision@1': Precision(k=1),\n", + " 'precision@5': Precision(k=5),\n", + " 'precision@10': Precision(k=10),\n", + " 'recall@1': Recall(k=1),\n", + " 'recall@5': Recall(k=5),\n", + " 'recall@10': Recall(k=10),\n", + " 'MAP@1': MAP(k=1, divide_by_k=False),\n", + " 'MAP@5': MAP(k=5, divide_by_k=False),\n", + " 'MAP@10': MAP(k=10, divide_by_k=False),\n", + " 'NDCG@1': NDCG(k=1, log_base=2),\n", + " 'NDCG@5': NDCG(k=5, log_base=2),\n", + " 'NDCG@10': NDCG(k=10, log_base=2),\n", + " 'novelty@1': MeanInvUserFreq(k=1),\n", + " 'novelty@5': MeanInvUserFreq(k=5),\n", + " 'novelty@10': MeanInvUserFreq(k=10),\n", + " \"mrr@1\": MRR(k=1),\n", + " \"mrr@5\": MRR(k=5),\n", + " \"mrr@10\": MRR(k=10)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bb9fb057", + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validate(models, metrics, interactions, splitter, k_recos):\n", + " columns = ['k_fold', 'model'] + [metric_name for metric_name in metrics]\n", + " results_df = pd.DataFrame(columns=columns)\n", + "\n", + " fold_iterator = splitter.split(interactions, collect_fold_stats=True)\n", + "\n", + " for id_train, id_test, k_fold in fold_iterator:\n", + " print(f\"Split Index: {k_fold['i_split']}\")\n", + " print(f\"Start Date: {k_fold['start']}\")\n", + " print(f\"End Date: {k_fold['end']}\")\n", + " print(f\"Train Set Size: {k_fold['train']}\")\n", + " print(f\"Train Users: {k_fold['train_users']}\")\n", + " print(f\"Train Items: {k_fold['train_items']}\")\n", + " print(f\"Test Set Size: {k_fold['test']}\")\n", + " print(f\"Test Users: {k_fold['test_users']}\")\n", + " print(f\"Test Items: {k_fold['test_items']}\")\n", + " print(\"-\" * 40) \n", + "\n", + " train = interactions.df.iloc[id_train]\n", + " dataset = Dataset.construct(train)\n", + " val = interactions.df.iloc[id_test][Columns.UserItem]\n", + " val_id = np.unique(val[Columns.User])\n", + "\n", + " catalog = train[Columns.Item].unique()\n", + " \n", + " for model_name, model in models.items():\n", + " model = deepcopy(model)\n", + " model.fit(dataset)\n", + " recos = model.recommend(\n", + " users=val_id,\n", + " dataset=dataset,\n", + " k=k_recos,\n", + " filter_viewed=True,\n", + " )\n", + " metric_values = calc_metrics(\n", + " metrics,\n", + " reco=recos,\n", + " interactions=val,\n", + " prev_interactions=train,\n", + " catalog=catalog,\n", + " )\n", + " temp_df = pd.DataFrame([{\"k_fold\": k_fold[\"i_split\"], \"model\": model_name, **metric_values}])\n", + " results_df = pd.concat([results_df, temp_df], ignore_index=True)\n", + "\n", + " return results_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dc874002", + "metadata": {}, + "outputs": [], + "source": [ + "interactions_df.rename(\n", + " columns={\"last_watch_dt\": Columns.Datetime, \"total_dur\": rectools.Columns.Weight}, inplace=True\n", + ")\n", + "interactions = Interactions(interactions_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b8b23879", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split Index: 0\n", + "Start Date: 2021-07-27 00:00:00\n", + "End Date: 2021-08-05 00:00:00\n", + "Train Set Size: 3941089\n", + "Train Users: 749447\n", + "Train Items: 15109\n", + "Test Set Size: 314621\n", + "Test Users: 108975\n", + "Test Items: 7055\n", + "----------------------------------------\n", + "Split Index: 1\n", + "Start Date: 2021-08-05 00:00:00\n", + "End Date: 2021-08-14 00:00:00\n", + "Train Set Size: 4423600\n", + "Train Users: 819309\n", + "Train Items: 15331\n", + "Test Set Size: 335644\n", + "Test Users: 114204\n", + "Test Items: 7022\n", + "----------------------------------------\n", + "Split Index: 2\n", + "Start Date: 2021-08-14 00:00:00\n", + "End Date: 2021-08-23 00:00:00\n", + "Train Set Size: 4923625\n", + "Train Users: 888245\n", + "Train Items: 15554\n", + "Test Set Size: 379102\n", + "Test Users: 127438\n", + "Test Items: 7095\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "splitter = TimeRangeSplitter(\"9D\", 3)\n", + "models = {\"random\": RandomModel(random_state=32), \"popular\": PopularModel()}\n", + "result = cross_validate(models, metrics, interactions, splitter, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9a3a91a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_iditem_idscoreranktitlegenresnum_of_views
0955527496101Воскресший Эртугрулбоевики, драмы, приключения12.0
1955527420592Дело гастронома №1 (Операция Беркут)драмы, русские3.0
29555271082283Она защищает Родинудрамы, советские, военные2.0
39555271091474Великолепнаязарубежные, комедии, мелодрамы136.0
4955527399965Джиперс криперсужасы, триллеры1.0
59555271575656Ремнант: Всё ещё вижу тебя (жестовым языком)фантастика, зарубежные, триллеры23.0
69555271496147Битва за Землюбоевики, ужасы, фантастика, триллерыNaN
79555271373438Сексуальный массаж и Фантазиидля взрослых28.0
8955527340729Черный капитанбоевики, русские, военные47.0
995552714614110Настямелодрамы, комедии99.0
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"7 для взрослых 28.0 \n", + "8 боевики, русские, военные 47.0 \n", + "9 мелодрамы, комедии 99.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "visualize(model, interactions_df, users=user_ids, k_recos=10, item_data=items)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5092edcb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/__init__.py b/notebooks/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/notebooks/__init__.py @@ -0,0 +1 @@ + diff --git a/notebooks/hw3.ipynb b/notebooks/hw3.ipynb new file mode 100644 index 00000000..1c9f9318 --- /dev/null +++ b/notebooks/hw3.ipynb @@ -0,0 +1,1289 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "id": "8b94f641", + "metadata": {}, + "outputs": [], + "source": [ + "import dill\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from rectools import Columns\n", + "from rectools.dataset import Dataset, Interactions, DenseFeatures\n", + "from rectools.metrics import (\n", + " MAP,\n", + " Serendipity,\n", + " MeanInvUserFreq,\n", + " calc_metrics,\n", + ")\n", + "from rectools.metrics.base import MetricAtK\n", + "from rectools.metrics import (\n", + " Precision,\n", + " Recall,\n", + " NDCG,\n", + " PairwiseHammingDistanceCalculator,\n", + " MRR,\n", + " serendipity,\n", + " IntraListDiversity,\n", + ")\n", + "from rectools.model_selection import Splitter, TimeRangeSplitter\n", + "from rectools.models import RandomModel, PopularModel, ModelBase\n", + "from implicit.nearest_neighbours import (\n", + " BM25Recommender,\n", + " CosineRecommender,\n", + " TFIDFRecommender,\n", + ")\n", + "from tqdm import tqdm\n", + "from copy import deepcopy\n", + "from scipy.stats import mode\n", + "from pprint import pprint\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_colwidth', 200)\n", + "pd.set_option('display.float_format', lambda x: f'{x:,.6f}')\n", + "\n", + "\n", + "from userknn import UserKnn\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7449fd99", + "metadata": {}, + "outputs": [], + "source": [ + "interactions_df = pd.read_csv('interactions.csv')\n", + "users = pd.read_csv('users.csv')\n", + "items = pd.read_csv('items.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a2d49bb0", + "metadata": {}, + "outputs": [], + "source": [ + "metrics = {\n", + " 'precision@1': Precision(k=1),\n", + " 'precision@5': Precision(k=5),\n", + " 'precision@10': Precision(k=10),\n", + " 'recall@1': Recall(k=1),\n", + " 'recall@5': Recall(k=5),\n", + " 'recall@10': Recall(k=10),\n", + " 'MAP@1': MAP(k=1, divide_by_k=False),\n", + " 'MAP@5': MAP(k=5, divide_by_k=False),\n", + " 'MAP@10': MAP(k=10, divide_by_k=False),\n", + " 'NDCG@1': NDCG(k=1, log_base=2),\n", + " 'NDCG@5': NDCG(k=5, log_base=2),\n", + " 'NDCG@10': NDCG(k=10, log_base=2),\n", + " 'novelty@1': MeanInvUserFreq(k=1),\n", + " 'novelty@5': MeanInvUserFreq(k=5),\n", + " 'novelty@10': MeanInvUserFreq(k=10),\n", + " \"mrr@1\": MRR(k=1),\n", + " \"mrr@5\": MRR(k=5),\n", + " \"mrr@10\": MRR(k=10)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bb9fb057", + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validate(models, metrics, interactions, splitter, k_recos):\n", + " columns = ['k_fold', 'model'] + [metric_name for metric_name in metrics]\n", + " results_df = pd.DataFrame(columns=columns)\n", + "\n", + " fold_iterator = splitter.split(interactions, collect_fold_stats=True)\n", + "\n", + " for id_train, id_test, k_fold in fold_iterator:\n", + " print(f\"Split Index: {k_fold['i_split']}\")\n", + " print(f\"Start Date: {k_fold['start']}\")\n", + " print(f\"End Date: {k_fold['end']}\")\n", + " print(f\"Train Set Size: {k_fold['train']}\")\n", + " print(f\"Train Users: {k_fold['train_users']}\")\n", + " print(f\"Train Items: {k_fold['train_items']}\")\n", + " print(f\"Test Set Size: {k_fold['test']}\")\n", + " print(f\"Test Users: {k_fold['test_users']}\")\n", + " print(f\"Test Items: {k_fold['test_items']}\")\n", + " print(\"-\" * 40) \n", + "\n", + " train = interactions.df.iloc[id_train]\n", + " dataset = Dataset.construct(train)\n", + " val = interactions.df.iloc[id_test][Columns.UserItem]\n", + " val_id = np.unique(val[Columns.User])\n", + "\n", + " catalog = train[Columns.Item].unique()\n", + " \n", + " for model_name, model in models.items():\n", + " model.fit(train)\n", + " recos = model.predict(val)\n", + " metric_values = calc_metrics(\n", + " metrics,\n", + " reco=recos,\n", + " interactions=val,\n", + " prev_interactions=train,\n", + " catalog=catalog,\n", + " )\n", + " temp_df = pd.DataFrame([{\"k_fold\": k_fold[\"i_split\"], \"model\": model_name, **metric_values}])\n", + " results_df = pd.concat([results_df, temp_df], ignore_index=True)\n", + "\n", + " return results_df" + ] + }, + { + "cell_type": "markdown", + "id": "d7c02b04", + "metadata": {}, + "source": [ + "# 1. Обучим модель для \"холодных\" пользователей, просто сделаем популярное" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "8f25ed21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = Dataset.construct(\n", + " interactions_df=interactions_df,\n", + " user_features_df=None,\n", + " item_features_df=None\n", + ")\n", + "model = PopularModel()\n", + "model.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "70a1e8e9", + "metadata": {}, + "outputs": [], + "source": [ + "hold_data = model.recommend(\n", + " dataset.user_id_map.external_ids,\n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "fbe13127", + "metadata": {}, + "outputs": [], + "source": [ + "hold_reco = hold_data.item_id.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "273beca8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10440, 15297, 9728, 13865, 4151, 3734, 2657, 4880, 142,\n", + " 6809])" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hold_reco" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "67315a1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idageincomesexkids_flg
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..................
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95909 rows × 5 columns

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" + ], + "text/plain": [ + " user_id age income sex kids_flg\n", + "2 1047345 age_45_54 income_40_60 Ж 0\n", + "6 391756 age_25_34 income_0_20 М 0\n", + "7 15878 age_25_34 income_40_60 М 1\n", + "10 99952 NaN NaN М 0\n", + "19 1067802 age_35_44 income_40_60 М 0\n", + "... ... ... ... .. ...\n", + "840180 157810 age_25_34 income_20_40 Ж 0\n", + "840185 1021814 age_45_54 income_20_40 Ж 0\n", + "840191 365945 age_25_34 income_20_40 Ж 0\n", + "840193 983617 age_18_24 income_20_40 Ж 1\n", + "840196 166555 age_65_inf income_20_40 Ж 0\n", + "\n", + "[95909 rows x 5 columns]" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users[~users.user_id.isin(interactions_df.user_id)]" + ] + }, + { + "cell_type": "markdown", + "id": "c01620b6", + "metadata": {}, + "source": [ + "### Вообще, пользователей без просмотров довольно много, поэтому на лидерборде популярное уже дает неплохой результат" + ] + }, + { + "cell_type": "markdown", + "id": "25539c28", + "metadata": {}, + "source": [ + "# 3. Попробуем сделать cv на 3 фолдах на двух разныз моделях и выберем разное K, по дефолту K = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "5778dfdc", + "metadata": {}, + "outputs": [], + "source": [ + "models = {\n", + " 'cosine_userknn_10': UserKnn(CosineRecommender(K=10)), \n", + " 'cosine_userknn_50': UserKnn(CosineRecommender()),\n", + " 'tfidf_userknn_10': UserKnn(TFIDFRecommender(K=10)),\n", + " 'tfidf_userknn_50': UserKnn(TFIDFRecommender())\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "38d128a5", + "metadata": {}, + "outputs": [], + "source": [ + "splitter = TimeRangeSplitter(\"7D\", 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "fdfa1d16", + "metadata": {}, + "outputs": [], + "source": [ + "interactions_df.rename(\n", + " columns={\"last_watch_dt\": Columns.Datetime, \"total_dur\": rectools.Columns.Weight}, inplace=True\n", + ")\n", + "interactions = Interactions(interactions_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "b8b23879", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split Index: 0\n", + "Start Date: 2021-08-02 00:00:00\n", + "End Date: 2021-08-09 00:00:00\n", + "Train Set Size: 4266013\n", + "Train Users: 797423\n", + "Train Items: 15237\n", + "Test Set Size: 263681\n", + "Test Users: 98184\n", + "Test Items: 6602\n", + "----------------------------------------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2948b4890c1e4611a22e9e8881979ba9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/797423 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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model
cosine_userknn_100.0008650.0028730.0041420.0003350.0071270.0200410.0003350.0022220.0039580.0008650.0023690.0034429.1207147.6376496.9925810.0008650.0045730.007867
cosine_userknn_500.0007600.0026030.0042790.0002910.0064650.0209650.0002910.0019940.0039050.0007600.0021370.0034679.6558368.2474157.5408230.0007600.0041120.007659
tfidf_userknn_100.0002930.0044340.0058900.0000900.0115850.0295030.0000900.0032090.0056590.0002930.0034000.0047999.9358108.2012037.4336050.0002930.0060540.010469
tfidf_userknn_500.0002810.0047700.0065290.0000870.0127150.0332530.0000870.0035480.0063340.0002810.0036680.00529210.0070468.4154957.6385980.0002810.0065500.011438
\n", + "" + ], + "text/plain": [ + " precision@1 precision@5 precision@10 recall@1 recall@5 \\\n", + "model \n", + "cosine_userknn_10 0.000865 0.002873 0.004142 0.000335 0.007127 \n", + "cosine_userknn_50 0.000760 0.002603 0.004279 0.000291 0.006465 \n", + "tfidf_userknn_10 0.000293 0.004434 0.005890 0.000090 0.011585 \n", + "tfidf_userknn_50 0.000281 0.004770 0.006529 0.000087 0.012715 \n", + "\n", + " recall@10 MAP@1 MAP@5 MAP@10 NDCG@1 NDCG@5 \\\n", + "model \n", + "cosine_userknn_10 0.020041 0.000335 0.002222 0.003958 0.000865 0.002369 \n", + "cosine_userknn_50 0.020965 0.000291 0.001994 0.003905 0.000760 0.002137 \n", + "tfidf_userknn_10 0.029503 0.000090 0.003209 0.005659 0.000293 0.003400 \n", + "tfidf_userknn_50 0.033253 0.000087 0.003548 0.006334 0.000281 0.003668 \n", + "\n", + " NDCG@10 novelty@1 novelty@5 novelty@10 mrr@1 \\\n", + "model \n", + "cosine_userknn_10 0.003442 9.120714 7.637649 6.992581 0.000865 \n", + "cosine_userknn_50 0.003467 9.655836 8.247415 7.540823 0.000760 \n", + "tfidf_userknn_10 0.004799 9.935810 8.201203 7.433605 0.000293 \n", + "tfidf_userknn_50 0.005292 10.007046 8.415495 7.638598 0.000281 \n", + "\n", + " mrr@5 mrr@10 \n", + "model \n", + "cosine_userknn_10 0.004573 0.007867 \n", + "cosine_userknn_50 0.004112 0.007659 \n", + "tfidf_userknn_10 0.006054 0.010469 \n", + "tfidf_userknn_50 0.006550 0.011438 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.groupby('model').mean()" + ] + }, + { + "cell_type": "markdown", + "id": "c7f5b662", + "metadata": {}, + "source": [ + "### Лучше всего использовать tfidf_userknn_50, эту модель и отправим в сервис (выбрал по MAP@10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0c4a8f0", + "metadata": {}, + "outputs": [], + "source": [ + "userknn_model = UserKnn(model=TFIDFRecommender(), N_users=50)\n", + "userknn_model.fit(interactions.df)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "98e716f4", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(userknn_model, open('baseknn.pkl', \"wb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "689a0850", + "metadata": {}, + "outputs": [], + "source": [ + "def recommend(model, user_id:int, N_recs:int=10):\n", + " \"\"\"\n", + " Outputs recommendations for a certain user\n", + " \"\"\"\n", + " df = pd.DataFrame({\"user_id\": [user_id], \"item_id\": [user_id]})\n", + " return model.predict(df, N_recs=N_recs).item_id.to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "47e9c7a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10515]" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pickled_model = pickle.load(open('baseknn.pkl', \"rb\"))\n", + "recommend(pickled_model, 31)" + ] + }, + { + "cell_type": "markdown", + "id": "ede89d07", + "metadata": {}, + "source": [ + "# 2. Попробуем сделать всегда минимум 5 рекомендаций ( можно и побольше просто тогда популярных побольше собрать )" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "c5212628", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10515, 10440, 15297, 9728, 13865]\n" + ] + } + ], + "source": [ + "import pickle\n", + "\n", + "def rec_fix(user_id, n, hold_reco):\n", + " pickled_model = pickle.load(open('baseknn.pkl', \"rb\"))\n", + "\n", + " recommendations = recommend(pickled_model, user_id)\n", + " \n", + " # Удаляем элементы из hold_reco, которые уже присутствуют в recommendations\n", + " hold_reco = [item for item in hold_reco if item not in recommendations]\n", + " \n", + " # Если рекомендаций все еще недостаточно, добавьте дополнительные из hold_reco\n", + " additional_recommendations = hold_reco[:n - len(recommendations)]\n", + " recommendations += additional_recommendations\n", + "\n", + " return recommendations\n", + "\n", + "user_id = 31 \n", + "result = rec_fix(user_id, n=5, hold_reco = hold_reco)\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "01471e17", + "metadata": {}, + "source": [ + "### Получается порекомендовали одно и добавили 4 популярных в этой ситуации" + ] + }, + { + "cell_type": "markdown", + "id": "a03060c3", + "metadata": {}, + "source": [ + "# 4. Возьмем какой-то другой способ например BM25, который сделан для учета длины документов и частоты терминов в коллекции" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "494b1b10", + "metadata": {}, + "outputs": [], + "source": [ + "result = cross_validate(models, metrics, interactions, splitter, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2814cf3", + "metadata": {}, + "outputs": [], + "source": [ + "splitter = TimeRangeSplitter(\"7D\", 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "38cb9da9", + "metadata": {}, + "outputs": [], + "source": [ + "models = {\n", + " 'bm25' : UserKnn(model=BM25Recommender())\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "bbb4089d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split Index: 0\n", + "Start Date: 2021-08-02 00:00:00\n", + "End Date: 2021-08-09 00:00:00\n", + "Train Set Size: 4266013\n", + "Train Users: 797423\n", + "Train Items: 15237\n", + "Test Set Size: 263681\n", + "Test Users: 98184\n", + "Test Items: 6602\n", + "----------------------------------------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3c6a38516c8940779040517d52c3248d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/797423 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
precision@1precision@5precision@10recall@1recall@5recall@10MAP@1MAP@5MAP@10NDCG@1NDCG@5NDCG@10novelty@1novelty@5novelty@10mrr@1mrr@5mrr@10
model
bm250.0005770.0019510.0030500.0002340.0044600.0138280.0002340.0014600.0026990.0005770.0016260.00250211.0162269.8999819.2831690.0005770.0031510.005584
\n", + "" + ], + "text/plain": [ + " precision@1 precision@5 precision@10 recall@1 recall@5 recall@10 \\\n", + "model \n", + "bm25 0.000577 0.001951 0.003050 0.000234 0.004460 0.013828 \n", + "\n", + " MAP@1 MAP@5 MAP@10 NDCG@1 NDCG@5 NDCG@10 novelty@1 \\\n", + "model \n", + "bm25 0.000234 0.001460 0.002699 0.000577 0.001626 0.002502 11.016226 \n", + "\n", + " novelty@5 novelty@10 mrr@1 mrr@5 mrr@10 \n", + "model \n", + "bm25 9.899981 9.283169 0.000577 0.003151 0.005584 " + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm25.groupby('model').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "9c325030", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b50aa2c09d834b578d6b669bc6725754", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/962179 [00:00= 300)]\n", + "test = interactions[interactions['datetime'] >= test_start_date]\n", + "\n", + "train_users = train['user_id'].unique()\n", + "warm_test = test[test['user_id'].isin(train_users)]\n", + "cold_test = test[~test['user_id'].isin(train_users)]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T17:52:36.105041Z", + "iopub.status.busy": "2023-12-05T17:52:36.104496Z", + "iopub.status.idle": "2023-12-05T17:52:36.405626Z", + "shell.execute_reply": "2023-12-05T17:52:36.404764Z", + "shell.execute_reply.started": "2023-12-05T17:52:36.104984Z" + } + }, + "outputs": [], + "source": [ + "dataset = Dataset.construct(\n", + " interactions_df=train\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T17:49:39.437218Z", + "iopub.status.busy": "2023-12-05T17:49:39.436772Z", + "iopub.status.idle": "2023-12-05T17:49:45.267292Z", + "shell.execute_reply": "2023-12-05T17:49:45.266380Z", + "shell.execute_reply.started": "2023-12-05T17:49:39.437183Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = ImplicitALSWrapperModel(\n", + " model=AlternatingLeastSquares(\n", + " factors=32,\n", + " random_state=42,\n", + " num_threads=2,\n", + " ))\n", + "model.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:35:31.763381Z", + "iopub.status.busy": "2023-12-05T18:35:31.762743Z", + "iopub.status.idle": "2023-12-05T18:35:31.767983Z", + "shell.execute_reply": "2023-12-05T18:35:31.766990Z", + "shell.execute_reply.started": "2023-12-05T18:35:31.763343Z" + } + }, + "outputs": [], + "source": [ + "metric = {\n", + " 'MAP@5': MAP(k=5),\n", + " 'MAP@10': MAP(k=10)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Реализуем функцию для optuna, сюда можем передавать и другие модели и перебирать их гиперпараметры, делаю на кагле с GPU поэтому можно попробовать для ALS что-то быстро перебрать**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T17:33:15.851341Z", + "iopub.status.busy": "2023-12-05T17:33:15.850937Z", + "iopub.status.idle": "2023-12-05T17:40:30.541867Z", + "shell.execute_reply": "2023-12-05T17:40:30.540864Z", + "shell.execute_reply.started": "2023-12-05T17:33:15.851309Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-12-05 17:33:15,856] A new study created in memory with name: no-name-88be229f-ad35-4025-8d46-765ee8a694da\n", + "[I 2023-12-05 17:34:43,082] Trial 0 finished with value: 0.009340079047551512 and parameters: {'factors': 90, 'regularization': 0.03}. Best is trial 0 with value: 0.009340079047551512.\n", + "[I 2023-12-05 17:34:43,139] Trial 1 finished with value: 0.0069911689091521545 and parameters: {'factors': 80, 'regularization': 0.08}. Best is trial 0 with value: 0.009340079047551512.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning NaN Detected in row 37854 of 756562\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-12-05 17:36:12,884] Trial 2 finished with value: 0.007641304978851978 and parameters: {'factors': 100, 'regularization': 0.06999999999999999}. Best is trial 0 with value: 0.009340079047551512.\n", + "[I 2023-12-05 17:36:12,917] Trial 3 finished with value: 0.00746093202122258 and parameters: {'factors': 90, 'regularization': 0.06999999999999999}. Best is trial 0 with value: 0.009340079047551512.\n", + "[I 2023-12-05 17:37:40,214] Trial 4 finished with value: 0.007460577617827046 and parameters: {'factors': 90, 'regularization': 0.09}. Best is trial 0 with value: 0.009340079047551512.\n", + "[I 2023-12-05 17:37:40,240] Trial 5 finished with value: 0.01488696682378543 and parameters: {'factors': 40, 'regularization': 0.03}. Best is trial 5 with value: 0.01488696682378543.\n", + "[I 2023-12-05 17:39:03,524] Trial 7 finished with value: 0.020141784512617993 and parameters: {'factors': 30, 'regularization': 0.01}. Best is trial 7 with value: 0.020141784512617993.\n", + "[I 2023-12-05 17:39:03,555] Trial 6 finished with value: 0.013052139264831257 and parameters: {'factors': 40, 'regularization': 0.06999999999999999}. Best is trial 7 with value: 0.020141784512617993.\n", + "[I 2023-12-05 17:40:30,506] Trial 8 finished with value: 0.007163492747554648 and parameters: {'factors': 90, 'regularization': 0.09999999999999999}. Best is trial 7 with value: 0.020141784512617993.\n", + "[I 2023-12-05 17:40:30,536] Trial 9 finished with value: 0.007369816264266614 and parameters: {'factors': 80, 'regularization': 0.08}. Best is trial 7 with value: 0.020141784512617993.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best MAP@10: 0.020141784512617993\n", + "Best Hyperparameters: {'factors': 30, 'regularization': 0.01}\n" + ] + } + ], + "source": [ + "import optuna\n", + "from implicit.als import AlternatingLeastSquares\n", + "K_RECOS = 10\n", + "RANDOM_STATE = 42\n", + "NUM_THREADS = 16\n", + "N_FACTORS = 4\n", + "def objective(trial):\n", + " factors = trial.suggest_int('factors', 10, 100, step=10)\n", + " regularization = trial.suggest_float('regularization', 0.01, 0.1, step=0.01)\n", + "\n", + " model = ImplicitALSWrapperModel(\n", + " model=AlternatingLeastSquares(\n", + " factors=factors,\n", + " regularization=regularization,\n", + " random_state=42,\n", + " num_threads=2,\n", + " )\n", + " )\n", + " \n", + " model.fit(dataset)\n", + " recs = model.recommend(\n", + " users=warm_test['user_id'].unique(),\n", + " dataset=dataset,\n", + " k=K_RECOS,\n", + " filter_viewed=True\n", + " )\n", + " metric_value = calc_metrics(metric, recs, warm_test, train)['MAP@10']\n", + " return metric_value\n", + "\n", + "study = optuna.create_study(direction='maximize', sampler=optuna.samplers.TPESampler())\n", + "study.optimize(objective, n_trials=10, n_jobs=2)\n", + "\n", + "best_params = study.best_params\n", + "best_metric_value = study.best_value\n", + "\n", + "print(\"Best MAP@10:\", best_metric_value)\n", + "print(\"Best Hyperparameters:\", best_params)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Воспользуемся приблеженным поиском ближайших соседей**" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T17:54:31.024698Z", + "iopub.status.busy": "2023-12-05T17:54:31.023892Z", + "iopub.status.idle": "2023-12-05T17:54:31.110607Z", + "shell.execute_reply": "2023-12-05T17:54:31.109496Z", + "shell.execute_reply.started": "2023-12-05T17:54:31.024661Z" + } + }, + "outputs": [], + "source": [ + "user_vectors, item_vectors = model.get_vectors()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:36:03.444060Z", + "iopub.status.busy": "2023-12-05T18:36:03.443322Z", + "iopub.status.idle": "2023-12-05T18:36:04.069072Z", + "shell.execute_reply": "2023-12-05T18:36:04.068242Z", + "shell.execute_reply.started": "2023-12-05T18:36:03.444006Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alsup = UserToItemAnnRecommender(\n", + " user_vectors=user_vectors,\n", + " item_vectors=item_vectors,\n", + " user_id_map=dataset.user_id_map,\n", + " item_id_map=dataset.item_id_map,\n", + ")\n", + "alsup.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:36:39.377598Z", + "iopub.status.busy": "2023-12-05T18:36:39.377128Z", + "iopub.status.idle": "2023-12-05T18:36:39.406591Z", + "shell.execute_reply": "2023-12-05T18:36:39.405579Z", + "shell.execute_reply.started": "2023-12-05T18:36:39.377556Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([11132, 12598, 5651, 15423, 254, 111, 297, 3419, 1986,\n", + " 623])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alsup.get_item_list_for_user(123123, top_n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Воспользуемся информацией о пользователях и о айтемах**" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:41:22.801359Z", + "iopub.status.busy": "2023-12-05T18:41:22.800961Z", + "iopub.status.idle": "2023-12-05T18:41:23.242266Z", + "shell.execute_reply": "2023-12-05T18:41:23.241459Z", + "shell.execute_reply.started": "2023-12-05T18:41:22.801328Z" + } + }, + "outputs": [], + "source": [ + "selected_users = users[users[Columns.User].isin(interactions[Columns.User])]\n", + "selected_items = items[items[Columns.Item].isin(interactions[Columns.Item])]\n", + "\n", + "def create_user_feature_frame(users, feature):\n", + " frame = users[[Columns.User, feature]].rename(columns={Columns.User: \"id\", feature: \"value\"})\n", + " frame[\"feature\"] = feature\n", + " return frame\n", + "\n", + "user_features = pd.concat([create_user_feature_frame(selected_users, feature) for feature in ['sex', 'income', 'age']])\n", + "\n", + "selected_items[\"genre\"] = selected_items[\"genres\"].str.lower().replace(\", \", \",\", regex=False).str.split(\",\")\n", + "\n", + "def create_item_feature_frame(items, feature_name, column_name):\n", + " frame = items.explode(feature_name) if feature_name == \"genre\" else items\n", + " return frame[[Columns.Item, feature_name]].rename(columns={Columns.Item: \"id\", feature_name: \"value\"}).assign(feature=column_name)\n", + "\n", + "item_features = pd.concat([\n", + " create_item_feature_frame(selected_items, \"genre\", \"genre\"),\n", + " create_item_feature_frame(selected_items, \"content_type\", \"content_type\")\n", + "])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:12:53.302218Z", + "iopub.status.busy": "2023-12-05T18:12:53.301831Z", + "iopub.status.idle": "2023-12-05T18:12:54.958918Z", + "shell.execute_reply": "2023-12-05T18:12:54.958120Z", + "shell.execute_reply.started": "2023-12-05T18:12:53.302184Z" + } + }, + "outputs": [], + "source": [ + "dataset = Dataset.construct(\n", + " interactions_df=interactions,\n", + " user_features_df=user_features,\n", + " cat_user_features=[\"sex\", \"age\", \"income\"],\n", + " item_features_df=item_features,\n", + " cat_item_features=[\"genre\", \"content_type\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:13:14.850790Z", + "iopub.status.busy": "2023-12-05T18:13:14.849909Z", + "iopub.status.idle": "2023-12-05T18:13:24.993902Z", + "shell.execute_reply": "2023-12-05T18:13:24.992786Z", + "shell.execute_reply.started": "2023-12-05T18:13:14.850753Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2927428b8b914849b0ccafa52ef189c4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = ImplicitALSWrapperModel(\n", + " model=AlternatingLeastSquares(\n", + " factors=32,\n", + " random_state=42,\n", + " num_threads=2,\n", + " ))\n", + "model.fit(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Ну и как всегда сделаем PopularModel, чтобы было что-то для холодных пользователей о которых нет никакой информации**" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-05T18:25:05.100877Z", + "iopub.status.busy": "2023-12-05T18:25:05.100455Z", + "iopub.status.idle": "2023-12-05T18:25:06.995579Z", + "shell.execute_reply": "2023-12-05T18:25:06.994484Z", + "shell.execute_reply.started": "2023-12-05T18:25:05.100845Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_model = PopularModel()\n", + "pop_model.fit(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Сохраним в csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_recos = model.recommend(\n", + " 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"^0.27.0" httpx = "^0.22.0" # for starlette.testclient pydantic-settings = "^2.0.3" +pandas = "1.4.1" +jupyter = "^1.0.0" [tool.poetry.group.dev.dependencies] pytest = "7.4.3" diff --git a/recmodels/__init__.py b/recmodels/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/recmodels/__init__.py @@ -0,0 +1 @@ + diff --git a/recmodels/model_proc.py b/recmodels/model_proc.py new file mode 100644 index 00000000..d8e24de7 --- /dev/null +++ b/recmodels/model_proc.py @@ -0,0 +1,31 @@ +import os +import pickle +import pandas as pd + +class CustomUnpickler(pickle.Unpickler): + def find_class(self, module, name): + if name == 'UserKnn': + from recmodels.userknn import UserKnn + return UserKnn + return super().find_class(module, name) + +class CustomUnpickler(pickle.Unpickler): + def find_class(self, module, name): + if name == 'UserKnn': + return UserKnn + return super().find_class(module, name) + +def load_model(path: str): + if not os.path.exists(path): + raise FileNotFoundError(f"The file '{path}' does not exist.") + + with open(path, 'rb') as f: + try: + return CustomUnpickler(f).load() + except Exception as e: + raise RuntimeError(f"Failed to load the model from '{path}': {e}") + +def get_recommendations_from_csv(user_id: int, n_items: int = 10): + reco_df = pd.read_csv('rico.csv') + filtered_reco = reco_df[reco_df['user_id'] == user_id]['item_id'].tolist() + return filtered_reco[:n_items] diff --git a/recmodels/userknn.py b/recmodels/userknn.py new file mode 100644 index 00000000..790df147 --- /dev/null +++ b/recmodels/userknn.py @@ -0,0 +1,116 @@ +from collections import Counter +from typing import Dict + +import numpy as np +import pandas as pd +import scipy as sp +from implicit.nearest_neighbours import ItemItemRecommender + + +class UserKnn(): + """Class for fit-perdict UserKNN model + based on ItemKNN model from implicit.nearest_neighbours + """ + + def __init__(self, model: ItemItemRecommender, N_users: int = 50): + self.N_users = N_users + self.model = model + self.is_fitted = False + + def get_mappings(self, train): + self.users_inv_mapping = dict(enumerate(train['user_id'].unique())) + self.users_mapping = {v: k for k, v in self.users_inv_mapping.items()} + + self.items_inv_mapping = dict(enumerate(train['item_id'].unique())) + self.items_mapping = {v: k for k, v in self.items_inv_mapping.items()} + + def get_matrix(self, df: pd.DataFrame, + user_col: str = 'user_id', + item_col: str = 'item_id', + weight_col: str = None, + users_mapping: Dict[int, int] = None, + items_mapping: Dict[int, int] = None): + + if weight_col: + weights = df[weight_col].astype(np.float32) + else: + weights = np.ones(len(df), dtype=np.float32) + + self.interaction_matrix = sp.sparse.coo_matrix(( + weights, + ( + df[item_col].map(self.items_mapping.get), + df[user_col].map(self.users_mapping.get) + ) + )) + + self.watched = df \ + .groupby(user_col, as_index=False) \ + .agg({item_col: list}) \ + .rename(columns={user_col: 'sim_user_id'}) + + return self.interaction_matrix + + def idf(self, n: int, x: float): + return np.log((1 + n) / (1 + x) + 1) + + def _count_item_idf(self, df: pd.DataFrame): + item_cnt = Counter(df['item_id'].values) + item_idf = pd.DataFrame.from_dict(item_cnt, orient='index', + columns=['doc_freq']).reset_index() + item_idf['idf'] = item_idf['doc_freq'].apply( + lambda x: self.idf(self.n, x)) + self.item_idf = item_idf + + def fit(self, train: pd.DataFrame): + self.user_knn = self.model + self.get_mappings(train) + self.weights_matrix = self.get_matrix(train, + users_mapping=self.users_mapping, + items_mapping=self.items_mapping) + + self.n = train.shape[0] + self._count_item_idf(train) + + self.user_knn.fit(self.weights_matrix) + self.is_fitted = True + + def _generate_recs_mapper(self, model: ItemItemRecommender, + user_mapping: Dict[int, int], + user_inv_mapping: Dict[int, int], N: int): + def _recs_mapper(user): + user_id = self.users_mapping[user] + users, sim = model.similar_items(user_id, N=N) + return [self.users_inv_mapping[user] for user in users], sim + + return _recs_mapper + + def predict(self, test: pd.DataFrame, N_recs: int = 10): + + if not self.is_fitted: + raise ValueError("Please call fit before predict") + + mapper = self._generate_recs_mapper( + model=self.user_knn, + user_mapping=self.users_mapping, + user_inv_mapping=self.users_inv_mapping, + N=self.N_users + ) + + recs = pd.DataFrame({'user_id': test['user_id'].unique()}) + recs['sim_user_id'], recs['sim'] = zip(*recs['user_id'].map(mapper)) + recs = recs.set_index('user_id').apply(pd.Series.explode).reset_index() + + recs = recs[~(recs['user_id'] == recs['sim_user_id'])] \ + .merge(self.watched, on=['sim_user_id'], how='left') \ + .explode('item_id') \ + .sort_values(['user_id', 'sim'], ascending=False) \ + .drop_duplicates(['user_id', 'item_id'], keep='first') \ + .merge(self.item_idf, left_on='item_id', right_on='index', + how='left') + + recs['score'] = recs['sim'] * recs['idf'] + recs = recs.sort_values(['user_id', 'score'], ascending=False) + recs['rank'] = recs.groupby('user_id').cumcount() + 1 + return recs[recs['rank'] <= N_recs][ + ['user_id', 'item_id', 'score', 'rank']] diff --git a/service/api/views.py b/service/api/views.py index 24cf4a7f..f8a331d2 100644 --- a/service/api/views.py +++ b/service/api/views.py @@ -1,11 +1,49 @@ +import os +import random +from http import HTTPStatus from typing import List -from fastapi import APIRouter, FastAPI, Request +import pandas as pd +from fastapi import APIRouter, FastAPI, HTTPException, Request, Security +from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer from pydantic import BaseModel +from recmodels.model_proc import get_recommendations_from_csv, load_model from service.api.exceptions import UserNotFoundError from service.log import app_logger +MODEL_PATH = "recmodels/baseknn.pkl" +if os.path.exists(MODEL_PATH): + userknn = load_model(MODEL_PATH) +else: + userknn = None + + +class NotFoundModel(BaseModel): + detail: str + + +class UnauthorizedModel(BaseModel): + detail: str + + +class InternalServerErrorModel(BaseModel): + detail: str + + +security = HTTPBearer() +API_KEY = "i_love_recsys" +VALID_MODELS = ['some_model', 'best_random', 'knn'] + + +async def verify_token( + http_authorization_credentials: HTTPAuthorizationCredentials = Security( + security)): + token = http_authorization_credentials.credentials + if token != API_KEY: + raise HTTPException(status_code=HTTPStatus.UNAUTHORIZED, + detail="Invalid or missing token") + class RecoResponse(BaseModel): user_id: int @@ -18,6 +56,15 @@ class RecoResponse(BaseModel): @router.get( path="/health", tags=["Health"], + responses={ + 200: { + "description": "Successful health response", + }, + 500: { + "description": "Internal server error", + "model": InternalServerErrorModel, + } + } ) async def health() -> str: return "I am alive" @@ -27,21 +74,62 @@ async def health() -> str: path="/reco/{model_name}/{user_id}", tags=["Recommendations"], response_model=RecoResponse, + dependencies=[Security(verify_token)], + responses={ + 200: { + "description": "Successful response with recommendations", + "model": RecoResponse, + }, + 404: { + "description": "Model not found or user not found", + "model": NotFoundModel, + }, + 401: { + "description": "Unauthorized access", + "model": UnauthorizedModel, + }, + 500: { + "description": "Internal server error", + "model": InternalServerErrorModel, + } + } ) async def get_reco( request: Request, model_name: str, user_id: int, ) -> RecoResponse: - app_logger.info(f"Request for model: {model_name}, user_id: {user_id}") + app_logger.info(f"Запрос на модель: {model_name}, user_id: {user_id}") + + if model_name not in VALID_MODELS and model_name != "best_random": + raise HTTPException( + status_code=HTTPStatus.NOT_FOUND, + detail=f"Модель {model_name} не найдена" + ) + + if user_id > 10 ** 9: + raise UserNotFoundError( + error_message=f"Пользователь {user_id} не найден" + ) - # Write your code here + reco = [] - if user_id > 10**9: - raise UserNotFoundError(error_message=f"User {user_id} not found") + if model_name == "best_random": + reco = random.sample(range(0, 10), 10) + elif model_name == 'knn': + reco = userknn.predict(pd.DataFrame([user_id], columns=['user_id']), N_recs=10)['item_id'].tolist() + elif model_name == "ALS": + reco = get_recommendations_from_csv(user_id)[:10] + seen = set(reco) + additional_recommendations = [10440, 15297, 9728, 13865, 4151, 3734, 2657, 4880, 142, 6809] + for recommendation in additional_recommendations: + if len(reco) < 10 and recommendation not in seen: + reco.append(recommendation) + seen.add(recommendation) + else: + k_recs = request.app.state.k_recs + reco = list(range(k_recs)) - k_recs = request.app.state.k_recs - reco = list(range(k_recs)) return RecoResponse(user_id=user_id, items=reco) diff --git a/tests/api/test_views.py b/tests/api/test_views.py index 50516b47..d3182955 100644 --- a/tests/api/test_views.py +++ b/tests/api/test_views.py @@ -7,22 +7,19 @@ GET_RECO_PATH = "/reco/{model_name}/{user_id}" -def test_health( - client: TestClient, -) -> None: - with client: - response = client.get("/health") +def test_health(client: TestClient) -> None: + response = client.get("/health") assert response.status_code == HTTPStatus.OK -def test_get_reco_success( - client: TestClient, - service_config: ServiceConfig, -) -> None: +API_KEY = "i_love_recsys" + + +def test_get_reco_success(client: TestClient, service_config: ServiceConfig) -> None: user_id = 123 path = GET_RECO_PATH.format(model_name="some_model", user_id=user_id) - with client: - response = client.get(path) + headers = {"Authorization": f"Bearer {API_KEY}"} + response = client.get(path, headers=headers) assert response.status_code == HTTPStatus.OK response_json = response.json() assert response_json["user_id"] == user_id @@ -30,12 +27,36 @@ def test_get_reco_success( assert all(isinstance(item_id, int) for item_id in response_json["items"]) -def test_get_reco_for_unknown_user( - client: TestClient, -) -> None: +def test_get_reco_for_unknown_user(client: TestClient) -> None: user_id = 10**10 path = GET_RECO_PATH.format(model_name="some_model", user_id=user_id) - with client: - response = client.get(path) + headers = {"Authorization": f"Bearer {API_KEY}"} + response = client.get(path, headers=headers) assert response.status_code == HTTPStatus.NOT_FOUND assert response.json()["errors"][0]["error_key"] == "user_not_found" + + +def test_get_reco_unauthorized(client: TestClient) -> None: + user_id = 123 + path = GET_RECO_PATH.format(model_name="some_model", user_id=user_id) + response = client.get(path) + assert response.status_code == HTTPStatus.FORBIDDEN + + +def test_get_reco_authorized(client: TestClient) -> None: + user_id = 123 + path = GET_RECO_PATH.format(model_name="some_model", user_id=user_id) + headers = {"Authorization": f"Bearer {API_KEY}"} + response = client.get(path, headers=headers) + assert response.status_code == HTTPStatus.OK + response_json = response.json() + assert response_json["user_id"] == user_id + + +def test_get_reco_invalid_model(client: TestClient) -> None: + user_id = 123 + invalid_model_name = "invalid_model" + path = GET_RECO_PATH.format(model_name=invalid_model_name, user_id=user_id) + headers = {"Authorization": f"Bearer {API_KEY}"} + response = client.get(path, headers=headers) + assert response.status_code == HTTPStatus.NOT_FOUND