diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 110d1dc1..ee21e3a6 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -29,10 +29,10 @@ jobs: with: path: .venv key: venv-lint-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} - + - name: Install dependencies if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: make install + run: make install - name: Static analysis run: make lint @@ -62,10 +62,10 @@ jobs: with: path: .venv key: venv-test-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('**/poetry.lock') }} - + - name: Install dependencies if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: make install + run: make install - name: Run tests run: make test diff --git a/README.md b/README.md index b383177b..9e9fb0b7 100644 --- a/README.md +++ b/README.md @@ -89,6 +89,7 @@ The default version doesn't contain all the dependencies, because some of them a - `torch`: adds models based on neural nets, - `visuals`: adds visualization tools, - `nmslib`: adds fast ANN recommenders. +- `catboost`: adds Catboost as a reranker for `CandidateRankingModel` Install extension: ``` diff --git a/examples/tutorials/candidate_ranking_model_tutorial.ipynb b/examples/tutorials/candidate_ranking_model_tutorial.ipynb new file mode 100644 index 00000000..dd7e802c --- /dev/null +++ b/examples/tutorials/candidate_ranking_model_tutorial.ipynb @@ -0,0 +1,2249 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Candidate ranking model tutorial\n", + "\n", + "`CandidateRankingModel` from RecTools is a fully funcitonal two-stage recommendation pipeline. \n", + "\n", + "On the first stage simple models generate candidates from their usual recommendations. On the second stage, a \"reranker\" (usually Gradient Boosting Decision Trees model) learns how to rank these candidates to predict user actual interactions.\n", + "\n", + "Main features of our implementation:\n", + "- Ranks and scores from first-stage models can be added as features for the second-stage reranker.\n", + "- Explicit features for user-items candidate pairs can be added using `CandidateFeatureCollector`\n", + "- Custom negative samplers for creating second-stage train can be used.\n", + "- Custom splitters for creating second-stage train targets can be used.\n", + "- CatBoost models as second-stage reranking models are supported out of the box.\n", + "\n", + "**You can treat `CandidateRankingModel` as any other RecTools model and easily pass it to cross-validation. All of the complicated logic for fitting first-stage and second-stage models and recommending through the whole pipeline will happen under the hood.**\n", + "\n", + "**Table of Contents**\n", + "\n", + "* Load data: kion\n", + "* Initialization of CandidateRankingModel\n", + "* What if we want to easily add user/item features to candidates?\n", + " * From external source\n", + "* Using boosings from well-known libraries as a ranking model\n", + " * CandidateRankingModel with gradient boosting from sklearn\n", + " * Features of constructing model\n", + " * CandidateRankingModel with gradient boosting from catboost\n", + " * Features of constructing model\n", + " * Using CatBoostClassifier\n", + " * Using CatBoostRanker\n", + " * CandidateRankingModel with gradient boosting from lightgbm\n", + " * Features of constructing model\n", + " * Using LGBMClassifier\n", + " * Using LGBMRanker\n", + " * An example of creating a custom class for reranker\n", + "* CrossValidate\n", + " * Evaluating the metrics of candidate ranking models and candidate generator models" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from rectools.models import PopularModel, ImplicitItemKNNWrapperModel\n", + "from implicit.nearest_neighbours import CosineRecommender\n", + "from rectools.model_selection import TimeRangeSplitter\n", + "from rectools.dataset import Dataset\n", + "from sklearn.linear_model import RidgeClassifier\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "import numpy as np\n", + "from rectools import Columns\n", + "from lightgbm import LGBMClassifier, LGBMRanker\n", + "from catboost import CatBoostClassifier, CatBoostRanker\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from rectools.metrics import Precision, Recall, MeanInvUserFreq, Serendipity, calc_metrics\n", + "from rectools.model_selection import cross_validate\n", + "from rectools.models.ranking import (\n", + " CandidateRankingModel,\n", + " CandidateGenerator,\n", + " Reranker,\n", + " CatBoostReranker, \n", + " CandidateFeatureCollector,\n", + " PerUserNegativeSampler\n", + ")\n", + "from rectools.models.base import ExternalIds\n", + "import typing as tp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data: kion" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archive: data_original.zip\n", + " creating: data_original/\n", + " inflating: data_original/interactions.csv \n", + " inflating: __MACOSX/data_original/._interactions.csv \n", + " inflating: data_original/users.csv \n", + " inflating: __MACOSX/data_original/._users.csv \n", + " inflating: data_original/items.csv \n", + " inflating: __MACOSX/data_original/._items.csv \n", + "CPU times: user 644 ms, sys: 183 ms, total: 827 ms\n", + "Wall time: 49.3 s\n" + ] + } + ], + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare dataset\n", + "\n", + "DATA_PATH = Path(\"data_original\")\n", + "users = pd.read_csv(DATA_PATH / 'users.csv')\n", + "items = pd.read_csv(DATA_PATH / 'items.csv')\n", + "interactions = (\n", + " pd.read_csv(DATA_PATH / 'interactions.csv', parse_dates=[\"last_watch_dt\"])\n", + " .rename(columns={\"last_watch_dt\": Columns.Datetime})\n", + ")\n", + "interactions[\"weight\"] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = Dataset.construct(interactions)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "RANDOM_STATE = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization of `CandidateRankingModel`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare first stage models. They will be used to generate candidates for reranking\n", + "first_stage = [\n", + " CandidateGenerator(PopularModel(), num_candidates=30, keep_ranks=True, keep_scores=True), \n", + " CandidateGenerator(\n", + " ImplicitItemKNNWrapperModel(CosineRecommender()), \n", + " num_candidates=30, \n", + " keep_ranks=True, \n", + " keep_scores=True\n", + " )\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare reranker. This model is used to rerank candidates from first stage models. \n", + "# It is usually trained on classification or ranking task\n", + "\n", + "reranker = CatBoostReranker()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare splitter for selecting reranker train. Only one fold is expected!\n", + "# This fold data will be used to define targets for training\n", + "\n", + "splitter = TimeRangeSplitter(\"7D\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize CandidateRankingModel\n", + "# We can also pass negative sampler but here we are just using the default one\n", + "\n", + "two_stage = CandidateRankingModel(first_stage, splitter, reranker)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What data is reranker trained on? \n", + "\n", + "We can explicitly call `get_train_with_targets_for_reranker` method to look at the actual \"train\" for reranker.\n", + "\n", + "Here' what happends under the hood during this call:\n", + "- Dataset interactions are split using provided splitter (usually on time basis) to history dataset and holdout interactions\n", + "- First stage models are fitted on history dataset\n", + "- First stage models generate recommendations -> These pairs become candidates for reranker\n", + "- All candidate pairs are assigned targets from holdout interactions. (`1` if interactions actually happend, `0` otherwise)\n", + "- Negative targets are sampled (here defult PerUserNegativeSampler is used which keeps a fixed number of negative samples per user)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "candidates = two_stage.get_train_with_targets_for_reranker(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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They can be added from dataset or from external sources and they also can be time-dependent (e.g. item popularity).\n", + "\n", + "To let the CandidateRankingModel join these features to train data for reranker, you need to create a custom feature collector. Inherit if from `CandidateFeatureCollector` which is used by default.\n", + "\n", + "You can overwrite the following methods:\n", + "- `_get_user_features`\n", + "- `_get_item_features`\n", + "- `_get_user_item_features`\n", + "\n", + "Each of the methods receives:\n", + "- `dataset` with all interactions that are available for model in this particular moment (no leak from the future). You can use it to collect user ot items stats on the current moment.\n", + "- `fold_info` with fold stats if you need to know that date that model considers as current date. You can join time-dependant features from external source that are valid on this particular date.\n", + "\n", + "In the example below we will simply collect users age, sex and income features from external csv file:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Write custome feature collecting funcs for users, items and user/item pairs\n", + "class CustomFeatureCollector(CandidateFeatureCollector):\n", + " \n", + " def __init__(self, cat_cols: tp.List[str])-> None: \n", + " self.cat_cols = cat_cols\n", + " \n", + " # your any helper functions for working with loaded data\n", + " def _encode_cat_cols(self, df: pd.DataFrame) -> pd.DataFrame: \n", + " df_cat_cols = self.cat_cols\n", + " df[df_cat_cols] = df[df_cat_cols].astype(\"category\")\n", + "\n", + " for col in df_cat_cols:\n", + " cat_col = df[col].astype(\"category\").cat\n", + " df[col] = cat_col.codes.astype(\"category\")\n", + " return df\n", + " \n", + " def _get_user_features(\n", + " self, users: ExternalIds, dataset: Dataset, fold_info: tp.Optional[tp.Dict[str, tp.Any]]\n", + " ) -> pd.DataFrame:\n", + " columns = self.cat_cols.copy()\n", + " columns.append(Columns.User)\n", + " user_features = pd.read_csv(DATA_PATH / \"users.csv\")[columns] \n", + " \n", + " users_without_features = pd.DataFrame(\n", + " np.setdiff1d(dataset.user_id_map.external_ids, user_features[Columns.User].unique()),\n", + " columns=[Columns.User]\n", + " ) \n", + " user_features = pd.concat([user_features, users_without_features], axis=0)\n", + " user_features = self._encode_cat_cols(user_features)\n", + " \n", + " return user_features[user_features[Columns.User].isin(users)]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Now we specify our custom feature collector for CandidateRankingModel\n", + "\n", + "two_stage = CandidateRankingModel(\n", + " first_stage,\n", + " splitter,\n", + " Reranker(RidgeClassifier()),\n", + " feature_collector=CustomFeatureCollector(cat_cols = [\"age\", \"income\", \"sex\"])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "candidates = two_stage.get_train_with_targets_for_reranker(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id PopularModel_1_score PopularModel_1_rank \\\n", + "0 168379 11640 NaN NaN \n", + "1 462121 3734 69687.0 6.0 \n", + "2 826617 14809 NaN NaN \n", + "3 184867 2657 66415.0 7.0 \n", + "4 716827 4436 16846.0 23.0 \n", + "5 729424 10440 189923.0 1.0 \n", + "6 1080167 11863 16231.0 18.0 \n", + "7 22315 13865 115095.0 3.0 \n", + "8 865689 7107 16279.0 27.0 \n", + "9 276952 9728 119797.0 3.0 \n", + "10 220905 8636 34148.0 11.0 \n", + "11 910378 7102 17110.0 23.0 \n", + "12 188204 14741 20232.0 20.0 \n", + "13 218646 11769 NaN NaN \n", + "14 763920 1844 24009.0 15.0 \n", + "15 292610 7444 NaN NaN \n", + "16 179061 7417 17346.0 23.0 \n", + "17 791167 7571 26242.0 12.0 \n", + "18 164915 12192 31907.0 14.0 \n", + "19 150282 16228 16213.0 24.0 \n", + "\n", + " ImplicitItemKNNWrapperModel_1_score ImplicitItemKNNWrapperModel_1_rank \\\n", + "0 0.144429 13.0 \n", + "1 NaN NaN \n", + "2 0.147328 4.0 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 0.403324 6.0 \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 0.142324 16.0 \n", + "11 0.408631 16.0 \n", + "12 NaN NaN \n", + "13 0.237359 13.0 \n", + "14 NaN NaN \n", + "15 0.275426 20.0 \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 0.071363 11.0 \n", + "19 0.319129 23.0 \n", + "\n", + " target age income sex \n", + "0 0 0 2 0 \n", + "1 1 0 2 0 \n", + "2 1 0 2 0 \n", + "3 0 2 3 1 \n", + "4 0 5 2 1 \n", + "5 0 0 2 0 \n", + "6 0 -1 -1 -1 \n", + "7 0 1 2 0 \n", + "8 0 1 2 0 \n", + "9 0 1 3 0 \n", + "10 0 2 3 1 \n", + "11 0 2 3 1 \n", + "12 0 -1 -1 -1 \n", + "13 1 -1 -1 -1 \n", + "14 0 -1 -1 -1 \n", + "15 0 1 2 0 \n", + "16 0 2 3 0 \n", + "17 0 0 2 1 \n", + "18 0 3 3 1 \n", + "19 0 2 2 0 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now our candidates also have features for users: age, sex and income\n", + "candidates.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using boosings from well-known libraries as a ranking model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CandidateRankingModel with gradient boosting from sklearn\n", + "\n", + "**Features of constructing model:**\n", + " - `GradientBoostingClassifier` works correctly with Reranker\n", + " - `GradientBoostingClassifier` cannot work with missing values. When initializing CandidateGenerator, specify the parameter values `scores_fillna_value` and `ranks_fillna_value`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare first stage models\n", + "first_stage_gbc = [\n", + " CandidateGenerator(\n", + " model=PopularModel(),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " scores_fillna_value=1.01, # when working with the GradientBoostingClassifier, you need to fill in the empty scores (e.g. max score)\n", + " ranks_fillna_value=31 # when working with the GradientBoostingClassifier, you need to fill in the empty ranks (e.g. min rank)\n", + " ), \n", + " CandidateGenerator(\n", + " model=ImplicitItemKNNWrapperModel(CosineRecommender()),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " scores_fillna_value=1.01, # when working with the GradientBoostingClassifier, you need to fill in the empty scores (e.g. max score)\n", + " ranks_fillna_value=31 # when working with the GradientBoostingClassifier, you need to fill in the empty ranks (e.g. min rank)\n", + " )\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "two_stage_gbc = CandidateRankingModel(\n", + " first_stage_gbc,\n", + " splitter,\n", + " Reranker(GradientBoostingClassifier(random_state=RANDOM_STATE)),\n", + " sampler=PerUserNegativeSampler(n_negatives=3, random_state=RANDOM_STATE) # pass sampler to fix random_state\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_stage_gbc.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "reco_gbc = two_stage_gbc.recommend(\n", + " users=dataset.user_id_map.external_ids, \n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id score rank\n", + "0 1097557 10440 0.613872 1\n", + "1 1097557 13865 0.506201 2\n", + "2 1097557 9728 0.472571 3\n", + "3 1097557 3734 0.349941 4\n", + "4 1097557 2657 0.287745 5" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reco_gbc.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CandidateRankingModel with gradient boosting from catboost\n", + "\n", + "**Features of constructing model:**\n", + "- for `CatBoostClassifier` and `CatBoostRanker` it is necessary to process categorical features: fill in empty values (if there are categorical features in the training sample for Rerankers). You can do this with CustomFeatureCollector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using CatBoostClassifier**\n", + "- `CatBoostClassifier` works correctly with CatBoostReranker" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare first stage models\n", + "first_stage_catboost = [\n", + " CandidateGenerator(\n", + " model=PopularModel(),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " ), \n", + " CandidateGenerator(\n", + " model=ImplicitItemKNNWrapperModel(CosineRecommender()),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " )\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = [\"age\", \"income\", \"sex\"]\n", + "\n", + "# Categorical features are definitely transferred to the pool_kwargs\n", + "pool_kwargs = {\n", + " \"cat_features\": cat_cols \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# To transfer CatBoostClassifier we use CatBoostReranker (for faster work with large amounts of data)\n", + "# You can also pass parameters in fit_kwargs and pool_kwargs in CatBoostReranker\n", + "\n", + "two_stage_catboost_classifier = CandidateRankingModel(\n", + " candidate_generators=first_stage_catboost,\n", + " splitter=splitter,\n", + " reranker=CatBoostReranker(CatBoostClassifier(verbose=False, random_state=RANDOM_STATE), pool_kwargs=pool_kwargs),\n", + " sampler=PerUserNegativeSampler(n_negatives=3, random_state=RANDOM_STATE) # pass sampler to fix random_state\n", + " feature_collector=CustomFeatureCollector(cat_cols)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_stage_catboost_classifier.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "reco_catboost_classifier = two_stage_catboost_classifier.recommend(\n", + " users=dataset.user_id_map.external_ids, \n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id score rank\n", + "0 1097557 10440 0.590609 1\n", + "1 1097557 7417 0.585314 2\n", + "2 1097557 9728 0.454810 3\n", + "3 1097557 13865 0.453770 4\n", + "4 1097557 3734 0.364262 5" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reco_catboost_classifier.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using CatBoostRanker**\n", + "- `CatBoostRanker` works correctly with CatBoostReranker" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# To transfer CatBoostRanker we use CatBoostReranker\n", + "\n", + "two_stage_catboost_ranker = CandidateRankingModel(\n", + " candidate_generators=first_stage_catboost,\n", + " splitter=splitter,\n", + " reranker=CatBoostReranker(CatBoostRanker(verbose=False, random_state=RANDOM_STATE), pool_kwargs=pool_kwargs),\n", + " sampler=PerUserNegativeSampler(n_negatives=3, random_state=RANDOM_STATE) # pass sampler to fix random_state\n", + " feature_collector=CustomFeatureCollector(cat_cols), \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_stage_catboost_ranker.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "reco_catboost_ranker = two_stage_catboost_ranker.recommend(\n", + " users=dataset.user_id_map.external_ids, \n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id score rank\n", + "0 1097557 10440 2.420927 1\n", + "1 1097557 13865 1.738958 2\n", + "2 1097557 9728 1.571645 3\n", + "3 1097557 3734 1.190009 4\n", + "4 1097557 142 1.030506 5" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reco_catboost_ranker.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CandidateRankingModel with gradient boosting from lightgbm\n", + "**Features of constructing model:**\n", + "- `LGBMClassifier` and `LGBMRanker` cannot work with missing values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using LGBMClassifier**\n", + "- `LGBMClassifier` works correctly with Reranker" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare first stage models\n", + "first_stage_lgbm = [\n", + " CandidateGenerator(\n", + " model=PopularModel(),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " scores_fillna_value=1.01, # when working with the LGBMClassifier, you need to fill in the empty scores (e.g. max score)\n", + " ranks_fillna_value=31 # when working with the LGBMClassifier, you need to fill in the empty ranks (e.g. min rank)\n", + " ), \n", + " CandidateGenerator(\n", + " model=ImplicitItemKNNWrapperModel(CosineRecommender()),\n", + " num_candidates=30,\n", + " keep_ranks=True,\n", + " keep_scores=True,\n", + " scores_fillna_value=1, # when working with the LGBMClassifier, you need to fill in the empty scores\n", + " ranks_fillna_value=31 # when working with the LGBMClassifier, you need to fill in the empty ranks\n", + " )\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = [\"age\", \"income\", \"sex\"]\n", + "\n", + "# example parameters for running model training \n", + "# more valid parameters here https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier.fit\n", + "fit_params = {\n", + " \"categorical_feature\": cat_cols,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "two_stage_lgbm_classifier = CandidateRankingModel(\n", + " candidate_generators=first_stage_lgbm,\n", + " splitter=splitter,\n", + " reranker=Reranker(LGBMClassifier(random_state=RANDOM_STATE), fit_params),\n", + " sampler=PerUserNegativeSampler(n_negatives=3, random_state=RANDOM_STATE) # pass sampler to fix random_state\n", + " feature_collector=CustomFeatureCollector(cat_cols)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 78233, number of negative: 330228\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003245 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 395\n", + "[LightGBM] [Info] Number of data points in the train set: 408461, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.191531 -> initscore=-1.440092\n", + "[LightGBM] [Info] Start training from score -1.440092\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_stage_lgbm_classifier.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "reco_lgbm_classifier = two_stage_lgbm_classifier.recommend(\n", + " users=dataset.user_id_map.external_ids, \n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id score rank\n", + "0 1097557 10440 0.610178 1\n", + "1 1097557 13865 0.510029 2\n", + "2 1097557 9728 0.479905 3\n", + "3 1097557 3734 0.347386 4\n", + "4 1097557 2657 0.290810 5" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reco_lgbm_classifier.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Using LGBMRanker**\n", + "- `LGBMRanker` does not work correctly with Reranker!\n", + "\n", + "When using LGBMRanker, you need to correctly compose groups. To do this, you can create a class inheriting from Reranker and override method `prepare_fit_kwargs` in it.\n", + "\n", + "Documentation on how to form groups for LGBMRanker (read about `group`):\n", + "https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRanker.html#lightgbm.LGBMRanker.fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**An example of creating a custom class for reranker**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "class LGBMReranker(Reranker):\n", + " def __init__(\n", + " self,\n", + " model: LGBMRanker,\n", + " fit_kwargs: tp.Optional[tp.Dict[str, tp.Any]] = None,\n", + " ):\n", + " super().__init__(model)\n", + " self.fit_kwargs = fit_kwargs\n", + " \n", + " def _get_group(self, df: pd.DataFrame) -> np.ndarray:\n", + " return df.groupby(by=[\"user_id\"])[\"item_id\"].count().values\n", + "\n", + " def prepare_fit_kwargs(self, candidates_with_target: pd.DataFrame) -> tp.Dict[str, tp.Any]:\n", + " candidates_with_target = candidates_with_target.sort_values(by=[Columns.User])\n", + " groups = self._get_group(candidates_with_target)\n", + " candidates_with_target = candidates_with_target.drop(columns=Columns.UserItem)\n", + "\n", + " \n", + " fit_kwargs = {\n", + " \"X\": candidates_with_target.drop(columns=Columns.Target),\n", + " \"y\": candidates_with_target[Columns.Target],\n", + " \"group\": groups,\n", + " }\n", + "\n", + " if self.fit_kwargs is not None:\n", + " fit_kwargs.update(self.fit_kwargs)\n", + "\n", + " return fit_kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = [\"age\", \"income\", \"sex\"]\n", + "\n", + "# example parameters for running model training \n", + "# more valid parameters here\n", + "# https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRanker.html#lightgbm.LGBMRanker.fit\n", + "fit_params = {\n", + " \"categorical_feature\": cat_cols,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Now we specify our custom feature collector for CandidateRankingModel\n", + "\n", + "two_stage_lgbm_ranker = CandidateRankingModel(\n", + " candidate_generators=first_stage_lgbm,\n", + " splitter=splitter,\n", + " reranker=LGBMReranker(LGBMRanker(random_state=RANDOM_STATE), fit_kwargs=fit_params),\n", + " sampler=PerUserNegativeSampler(n_negatives=3, random_state=RANDOM_STATE) # pass sampler to fix random_state\n", + " feature_collector=CustomFeatureCollector(cat_cols)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003223 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 396\n", + "[LightGBM] [Info] Number of data points in the train set: 408461, number of used features: 7\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "two_stage_lgbm_ranker.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "reco_lgbm_ranker = two_stage_lgbm_ranker.recommend(\n", + " users=dataset.user_id_map.external_ids, \n", + " dataset=dataset,\n", + " k=10,\n", + " filter_viewed=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id score rank\n", + "0 1097557 10440 2.095641 1\n", + "1 1097557 13865 1.503235 2\n", + "2 1097557 9728 1.420993 3\n", + "3 1097557 3734 0.806803 4\n", + "4 1097557 142 0.725385 5" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reco_lgbm_ranker.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CrossValidate\n", + "### Evaluating the metrics of candidate ranking models and candidate generator models." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Take few models to compare\n", + "models = {\n", + " \"popular\": PopularModel(),\n", + " \"cosine_knn\": ImplicitItemKNNWrapperModel(CosineRecommender()),\n", + " \"two_stage_gbc\": two_stage_gbc,\n", + " \"two_stage_catboost_classifier\": two_stage_catboost_classifier,\n", + " \"two_stage_catboost_ranker\": two_stage_catboost_ranker,\n", + " \"two_stage_lgbm_classifier\": two_stage_lgbm_classifier,\n", + " \"two_stage_lgbm_ranker\": two_stage_lgbm_ranker\n", + "}\n", + "\n", + "# We will calculate several classic (precision@k and recall@k) and \"beyond accuracy\" metrics\n", + "metrics = {\n", + " \"prec@1\": Precision(k=1),\n", + " \"prec@10\": Precision(k=10),\n", + " \"recall@10\": Recall(k=10),\n", + " \"novelty@10\": MeanInvUserFreq(k=10),\n", + " \"serendipity@10\": Serendipity(k=10),\n", + "}\n", + "\n", + "K_RECS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 73891, number of negative: 310533\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002992 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 394\n", + "[LightGBM] [Info] Number of data points in the train set: 384424, number of used features: 7\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.192212 -> initscore=-1.435699\n", + "[LightGBM] [Info] Start training from score -1.435699\n", + "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003532 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 395\n", + "[LightGBM] [Info] Number of data points in the train set: 384424, number of used features: 7\n", + "CPU times: user 23min, sys: 51.8 s, total: 23min 52s\n", + "Wall time: 8min 49s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "cv_results = cross_validate(\n", + " dataset=dataset,\n", + " splitter=splitter,\n", + " models=models,\n", + " metrics=metrics,\n", + " k=K_RECS,\n", + " filter_viewed=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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popular0.0708060.0326550.1660893.7156590.000002
cosine_knn0.0793720.0367570.1766095.7586600.000189
two_stage_gbc0.0856230.0396090.1944384.8319110.000155
two_stage_catboost_classifier0.0844600.0386670.1894904.8977150.000154
two_stage_catboost_ranker0.0887110.0395780.1939054.8633400.000155
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" + ], + "text/plain": [ + " prec@1 prec@10 recall@10 novelty@10 \\\n", + " mean mean mean mean \n", + "model \n", + "popular 0.070806 0.032655 0.166089 3.715659 \n", + "cosine_knn 0.079372 0.036757 0.176609 5.758660 \n", + "two_stage_gbc 0.085623 0.039609 0.194438 4.831911 \n", + "two_stage_catboost_classifier 0.084460 0.038667 0.189490 4.897715 \n", + "two_stage_catboost_ranker 0.088711 0.039578 0.193905 4.863340 \n", + "two_stage_lgbm_classifier 0.086795 0.039282 0.192634 4.843057 \n", + "two_stage_lgbm_ranker 0.087085 0.039757 0.195510 4.754899 \n", + "\n", + " serendipity@10 \n", + " mean \n", + "model \n", + "popular 0.000002 \n", + "cosine_knn 0.000189 \n", + "two_stage_gbc 0.000155 \n", + "two_stage_catboost_classifier 0.000154 \n", + "two_stage_catboost_ranker 0.000155 \n", + "two_stage_lgbm_classifier 0.000154 \n", + "two_stage_lgbm_ranker 0.000144 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pivot_results = (\n", + " pd.DataFrame(cv_results[\"metrics\"])\n", + " .drop(columns=\"i_split\")\n", + " .groupby([\"model\"], sort=False)\n", + " .agg([\"mean\"])\n", + ")\n", + "pivot_results" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "two_stage", + "language": "python", + "name": "two_stage" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index 0c485a58..d28c0794 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,97 +13,97 @@ files = [ [[package]] name = "aiohttp" -version = "3.12.14" +version = "3.12.15" description = "Async http client/server framework (asyncio)" optional = true python-versions = ">=3.9" files = [ - {file = "aiohttp-3.12.14-cp310-cp310-macosx_10_9_universal2.whl", 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"nmslib-metabrainz", "torch", "pytorch-lightning", + "catboost", "ipywidgets", "plotly", "nbformat", "cupy-cuda12x", ] [tool.poetry.group.dev.dependencies] -black = "24.10.0" +black = "25.1.0" isort = "5.13.2" pylint = "3.1.0" mypy = "1.13.0" diff --git a/rectools/columns.py b/rectools/columns.py index 55b6eb41..013a24eb 100644 --- a/rectools/columns.py +++ b/rectools/columns.py @@ -26,6 +26,7 @@ class Columns: Rank = "rank" Score = "score" Model = "model" + Target = "target" Split = "i_split" UserItem = [User, Item] Interactions = [User, Item, Weight, Datetime] diff --git a/rectools/compat.py b/rectools/compat.py index 3185a148..d1cd3861 100644 --- a/rectools/compat.py +++ b/rectools/compat.py @@ -58,6 +58,12 @@ class BERT4RecModel(RequirementUnavailable): requirement = "torch" +class CatBoostReranker(RequirementUnavailable): + """Dummy class, which is returned if there are no dependencies required for the model""" + + requirement = "catboost" + + class ItemToItemAnnRecommender(RequirementUnavailable): """Dummy class, which is returned if there are no dependencies required for the model""" diff --git a/rectools/exceptions.py b/rectools/exceptions.py index c506b68b..bdced022 100644 --- a/rectools/exceptions.py +++ b/rectools/exceptions.py @@ -24,3 +24,20 @@ def __init__(self, obj_name: str) -> None: def __str__(self) -> str: return f"{self.obj_name} isn't fitted, call method `fit` first." + + +class NotFittedForStageError(Exception): + """ + The error is raised when some fittable object is attempted to be used without fitting first. + Only specific stage in pipeline is taken into account. + """ + + def __init__(self, obj_name: str, stage_name: str) -> None: + super().__init__() + self.obj_name = obj_name + self.stage_name = stage_name + + def __str__(self) -> str: + return f""" + {self.obj_name} isn't fitted for {self.stage_name} stage, call method `fit` for this stage first. + """ diff --git a/rectools/models/ranking/__init__.py b/rectools/models/ranking/__init__.py new file mode 100644 index 00000000..67a4f63f --- /dev/null +++ b/rectools/models/ranking/__init__.py @@ -0,0 +1,55 @@ +# Copyright 2024 MTS (Mobile Telesystems) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# pylint: disable=wrong-import-position + +""" +Two-stage ranking Recommendation models (:mod:`rectools.models.ranking`) +============================================== + +`CandidateRankingModel` and helper classes. + + +Models +------ +`models.ranking.CandidateRankingModel` +`models.ranking.CandidateGenerator` +`models.ranking.CandidateFeatureCollector` +`models.ranking.Reranker` +`models.ranking.CatBoostReranker` +`models.ranking.PerUserNegativeSampler` +""" + +from .candidate_ranking import ( + CandidateFeatureCollector, + CandidateGenerator, + CandidateRankingModel, + PerUserNegativeSampler, + Reranker, +) + +try: + from .catboost_reranker import CatBoostReranker +except ImportError: # pragma: no cover + from rectools.compat import CatBoostReranker # type: ignore + + +__all__ = ( + "CatBoostReranker", + "Reranker", + "CandidateRankingModel", + "CandidateGenerator", + "CandidateFeatureCollector", + "PerUserNegativeSampler", +) diff --git a/rectools/models/ranking/candidate_ranking.py b/rectools/models/ranking/candidate_ranking.py new file mode 100644 index 00000000..45ab6592 --- /dev/null +++ b/rectools/models/ranking/candidate_ranking.py @@ -0,0 +1,848 @@ +import typing as tp +from collections import defaultdict +from functools import reduce + +import numpy as np +import pandas as pd +import typing_extensions as tpe + +from rectools import Columns +from rectools.dataset import Dataset +from rectools.dataset.identifiers import ExternalIds +from rectools.exceptions import NotFittedForStageError +from rectools.model_selection import Splitter +from rectools.models.base import ErrorBehaviour, ModelBase + + +@tp.runtime_checkable +class ClassifierBase(tp.Protocol): + """ + A protocol that defines the interface for a classifier model. Classes implementing this protocol + should provide methods for fitting the model and predicting class probabilities. + + Methods + ------- + fit + Fit the classifier to the training data. + predict_proba + Predict class probabilities for the given input data. The implementation should return + an array where each element is a probability distribution over the classes. + """ + + def fit(self, *args: tp.Any, **kwargs: tp.Any) -> tpe.Self: + """ + Fit the classifier to the training data. + + Parameters + ---------- + *args : any + Positional arguments for fitting the classifier. + **kwargs : any + Keyword arguments for fitting the classifier. + + Returns + ------- + tpe.Self + The fitted classifier instance. + """ + + def predict_proba(self, *args: tp.Any, **kwargs: tp.Any) -> np.ndarray: + """ + Predict class probabilities for the given input data. + + Parameters + ---------- + *args : any + Positional arguments for predicting probabilities. + **kwargs : any + Keyword arguments for predicting probabilities. + + Returns + ------- + np.ndarray + An array of predicted probabilities, where each element is a distribution over the classes. + """ + + +@tp.runtime_checkable +class RankerBase(tp.Protocol): + """ + A protocol that defines the interface for a ranker model. Classes implementing this protocol + should provide methods for fitting the model and predicting scores for ranking. + + Methods + ------- + fit + Fit the ranker to the training data. + predict + Predict scores for the given input data. The implementation should return an array of + scores that can be used for ranking items. + """ + + def fit(self, *args: tp.Any, **kwargs: tp.Any) -> tpe.Self: + """ + Fit the ranker to the training data. + + Parameters + ---------- + *args : any + Positional arguments for fitting the ranker. + **kwargs : any + Keyword arguments for fitting the ranker. + + Returns + ------- + tpe.Self + The fitted ranker instance. + """ + + def predict(self, *args: tp.Any, **kwargs: tp.Any) -> np.ndarray: + """ + Predict scores for the given input data. + + Parameters + ---------- + *args : any + Positional arguments for predicting scores. + **kwargs : any + Keyword arguments for predicting scores. + + Returns + ------- + np.ndarray + An array of predicted scores, which can be used for ranking items. + """ + + +class Reranker: + """ + A class used to re-rank candidates from first stage using ranking model. + The model can be either a classifier or a ranker. + """ + + def __init__( + self, + model: tp.Union[ClassifierBase, RankerBase], + fit_kwargs: tp.Optional[tp.Dict[str, tp.Any]] = None, + ): + """ + Initialize the Reranker with `model` and `fit_kwargs`. + + Parameters + ---------- + model : ClassifierBase | RankerBase + Ranking model. It must implement `fit` and `predict` or `predict_proba`. + fit_kwargs : dict(str -> any), optional, default ``None`` + Additional keyword arguments to pass to the model's fit method. + """ + self.model = model + self.fit_kwargs = fit_kwargs + + def prepare_fit_kwargs(self, candidates_with_target: pd.DataFrame) -> tp.Dict[str, tp.Any]: + """ + Prepare the keyword arguments for fitting the model, based on the provided candidates with targets. + + Parameters + ---------- + candidates_with_target : pd.DataFrame + A DataFrame containing the features and target labels for the candidates. + + Returns + ------- + dict(str -> any) + A dictionary containing the features (`X`) and target labels (`y`) for fitting the model. + """ + candidates_with_target = candidates_with_target.drop(columns=Columns.UserItem) + + fit_kwargs = { + "X": candidates_with_target.drop(columns=Columns.Target), + "y": candidates_with_target[Columns.Target], + } + + if self.fit_kwargs is not None: + fit_kwargs.update(self.fit_kwargs) + + return fit_kwargs + + def fit(self, candidates_with_target: pd.DataFrame) -> None: + """ + Fit the model using the provided candidates with target labels. + + Parameters + ---------- + candidates_with_target : pd.DataFrame + A DataFrame containing the features and target labels for the candidates. + """ + fit_kwargs = self.prepare_fit_kwargs(candidates_with_target) + self.model.fit(**fit_kwargs) + + def predict_scores(self, candidates: pd.DataFrame) -> pd.Series: + """ + Predict scores for the provided candidates using the fitted model. + + Parameters + ---------- + candidates : pd.DataFrame + A DataFrame containing the features for the candidates. + + Returns + ------- + pd.Series + A series containing the predicted scores for each candidate. If the model is a classifier, the scores + represent probabilities for the positive class. + """ + x_full = candidates.drop(columns=Columns.UserItem) + + if isinstance(self.model, ClassifierBase): + return self.model.predict_proba(x_full)[:, 1] + + return self.model.predict(x_full) + + @classmethod + def recommend(cls, scored_pairs: pd.DataFrame, k: int, add_rank_col: bool = True) -> pd.DataFrame: + """ + Generate top-k recommendations for each user based on the provided scores. + + Parameters + ---------- + scored_pairs : pd.DataFrame + A DataFrame containing user-item pairs with associated scores. + The DataFrame must have columns `Columns.User` and `Columns.Score`. + k : int + The number of top items to recommend for each user. + add_rank_col : bool, default ``True`` + Whether to add a rank column to the resulting DataFrame, indicating the rank + of each item within the user's recommendations. + + Returns + ------- + pd.DataFrame + A DataFrame containing the top-k recommended items for each user. If `add_rank_col` is True, the DataFrame + will include an additional column `Columns.Score` for the rank of each item. + """ + # TODO: optimize computations and introduce polars + # Discussion here: https://github.com/MobileTeleSystems/RecTools/pull/209 + # Branch here: https://github.com/blondered/RecTools/tree/feature/polars + reco = ( + scored_pairs.groupby(Columns.User, sort=False) + .apply(lambda x: x.sort_values([Columns.Score], ascending=False).head(k)) + .reset_index(drop=True) + ) + + if add_rank_col: + reco[Columns.Rank] = reco.groupby(Columns.User, sort=False).cumcount() + 1 + + return reco + + +class CandidateFeatureCollector: + """ + Base class for collecting features for candidates user-item pairs. Useful for creating train with features for + CandidateRankingModel. + Using this in CandidateRankingModel will result in not adding any features at all. + Inherit from this class and rewrite private methods to grab features from dataset and external sources + """ + + # TODO: this class can be used in pipelines directly. it will keep scores and ranks and add nothing + # TODO: create an inherited class that will get all features from dataset? + + def _get_user_features( + self, users: ExternalIds, dataset: Dataset, fold_info: tp.Optional[tp.Dict[str, tp.Any]] + ) -> pd.DataFrame: + return pd.DataFrame(columns=[Columns.User]) + + def _get_item_features( + self, items: ExternalIds, dataset: Dataset, fold_info: tp.Optional[tp.Dict[str, tp.Any]] + ) -> pd.DataFrame: + return pd.DataFrame(columns=[Columns.Item]) + + def _get_user_item_features( + self, useritem: pd.DataFrame, dataset: Dataset, fold_info: tp.Optional[tp.Dict[str, tp.Any]] + ) -> pd.DataFrame: + return pd.DataFrame(columns=Columns.UserItem) + + def collect_features( + self, useritem: pd.DataFrame, dataset: Dataset, fold_info: tp.Optional[tp.Dict[str, tp.Any]] + ) -> pd.DataFrame: + """ + Collect features for users-item pairs from any desired sources. + + Parameters + ---------- + useritem : pd.DataFrame + Candidates with score/rank features from first stage. Ids are either external or 1x internal + dataset : Dataset + Dataset will have either external -> 2x internal id maps to internal -> 2x internal. + fold_info : dict(str -> any), optional, default ``None`` + Fold info from splitter can be used for adding time-based features. + + Returns + ------- + pd.DataFrame + `useritem` dataframe enriched with features for users, items and useritem pairs. + """ + user_features = self._get_user_features(useritem[Columns.User].unique(), dataset, fold_info) + item_features = self._get_item_features(useritem[Columns.Item].unique(), dataset, fold_info) + useritem_features = self._get_user_item_features(useritem, dataset, fold_info) + + res = ( + useritem.merge(user_features, on=Columns.User, how="left") + .merge(item_features, on=Columns.Item, how="left") + .merge(useritem_features, on=Columns.UserItem, how="left") + ) + return res + + +class NegativeSamplerBase: + """A base class for negative sampling.""" + + def sample_negatives(self, train: pd.DataFrame) -> pd.DataFrame: + """ + Sample negative examples from the given training data. + + Parameters + ---------- + train : pd.DataFrame + A DataFrame containing the training data from which negative examples will be sampled. + + Returns + ------- + pd.DataFrame + A DataFrame containing the sampled negative examples. + """ + raise NotImplementedError() + + +class PerUserNegativeSampler(NegativeSamplerBase): + """ + A negative sampler that samples a specified number of negative examples per user from the training data. + This class implements a per-user negative sampling strategy, where a fixed number of negative examples are + randomly selected for each user. + """ + + def __init__( + self, + n_negatives: int = 3, + random_state: tp.Optional[int] = None, + ): + """ + Initialize the PerUserNegativeSampler with `n_negatives` and `random_state`. + + Parameters + ---------- + n_negatives : int, default ``3`` + The number of negative examples to sample for each user. + random_state : int, optional, default ``None`` + An optional random seed for reproducibility of the sampling process. + """ + self.n_negatives = n_negatives + self.random_state = random_state + + def sample_negatives(self, train: pd.DataFrame) -> pd.DataFrame: + """ + Sample negative examples from the given training data for each user. + + Parameters + ---------- + train : pd.DataFrame + A DataFrame containing the training data with user-item interactions. + + Returns + ------- + pd.DataFrame + A DataFrame containing the sampled training data, which includes the specified number of negative + examples per user along with all positive examples. The resulting DataFrame is shuffled. + """ + # train: user_id, item_id, scores, ranks, target(1/0) + + # TODO: refactor for faster computations: avoid shuffle and apply + # https://github.com/MobileTeleSystems/RecTools/pull/209#discussion_r1842977064 + + negative_mask = train[Columns.Target] == 0 + pos = train[~negative_mask] + neg = train[negative_mask] + + # Some users might not have enough negatives for sampling + num_negatives = neg.groupby([Columns.User])[Columns.Item].count() + sampling_mask = train[Columns.User].isin(num_negatives[num_negatives > self.n_negatives].index) + + neg_for_sample = train[sampling_mask & negative_mask] + neg = neg_for_sample.groupby([Columns.User], sort=False).apply( + pd.DataFrame.sample, + n=self.n_negatives, + replace=False, + random_state=self.random_state, + ) + neg = pd.concat([neg, train[(~sampling_mask) & negative_mask]], axis=0) + sampled_train = pd.concat([neg, pos], ignore_index=True).sample(frac=1, random_state=self.random_state) + + return sampled_train + + +class CandidateGenerator: + """ + A class responsible for generating recommendation candidates using a specified model. The generator + can be configured to retain or discard ranks and scores, and it supports both training and recommendation + modes. + """ + + def __init__( + self, + model: ModelBase, + num_candidates: int, + keep_ranks: bool, + keep_scores: bool, + scores_fillna_value: tp.Optional[float] = None, + ranks_fillna_value: tp.Optional[float] = None, + ): + """ + Initialize the CandidateGenerator with model, num_candidates, keep_ranks, keep_scores, + scores_fillna_value and ranks_fillna_value. + + Parameters + ---------- + model : ModelBase + The model used for generating recommendation candidates. + num_candidates : int + The number of candidates to generate for each user. + keep_ranks : bool + Whether to include rank information in the generated candidates. + keep_scores : bool + Whether to include score information in the generated candidates. + scores_fillna_value : float, optional, default ``None`` + The value to fill missing scores with, if any. If None, missing scores are not filled. + ranks_fillna_value : float, optional, default ``None`` + The value to fill missing ranks with, if any. If None, missing ranks are not filled. + """ + self.model = model + self.num_candidates = num_candidates + self.keep_ranks = keep_ranks + self.keep_scores = keep_scores + self.scores_fillna_value = scores_fillna_value + self.ranks_fillna_value = ranks_fillna_value + self.is_fitted_for_train = False + self.is_fitted_for_recommend = False + + def fit(self, dataset: Dataset, for_train: bool) -> None: + """ + Fit the model using the provided dataset, configuring the generator for either training or recommendation. + + Parameters + ---------- + dataset : Dataset + The dataset to fit the model with. This should contain the necessary data for training or recommending. + for_train : bool + If True, configure the generator for training; otherwise, configure it for recommendation. + """ + self.model.fit(dataset) + if for_train: + self.is_fitted_for_train = True # TODO: keep multiple fitted instances? + self.is_fitted_for_recommend = False + else: + self.is_fitted_for_train = False + self.is_fitted_for_recommend = True + + def generate_candidates( + self, + users: ExternalIds, + dataset: Dataset, + filter_viewed: bool, + for_train: bool, + items_to_recommend: tp.Optional[ExternalIds] = None, + on_unsupported_targets: ErrorBehaviour = "raise", + ) -> pd.DataFrame: + """ + Generate candidates for recommendations. + + Parameters + ---------- + users : ExternalIds + The users for whom to generate recommendation candidates. + dataset : Dataset + The dataset containing user-item interactions and additional data needed for recommendation. + filter_viewed : bool + Whether to filter out items that have already been viewed by the user. + for_train : bool + Whether the candidates are being generated for training purposes. + items_to_recommend : ExternalIds, optional, default ``None`` + Specific items to recommend. If None, recommend from all available items. + on_unsupported_targets : ErrorBehaviour, default ``"raise"`` + Behavior when encountering unsupported targets. Can be "raise" to raise an error. + + Returns + ------- + pd.DataFrame + A DataFrame containing the generated recommendation candidates. + """ + if for_train and not self.is_fitted_for_train: + raise NotFittedForStageError(self.model.__class__.__name__, "train") + if not for_train and not self.is_fitted_for_recommend: + raise NotFittedForStageError(self.model.__class__.__name__, "recommend") + + candidates = self.model.recommend( + users=users, + dataset=dataset, + k=self.num_candidates, + filter_viewed=filter_viewed, + items_to_recommend=items_to_recommend, + add_rank_col=self.keep_ranks, + on_unsupported_targets=on_unsupported_targets, + ) + if not self.keep_scores: + candidates.drop(columns=Columns.Score, inplace=True) + return candidates + + +class CandidateRankingModel(ModelBase): + """Candidate Ranking Model for recommendation systems.""" + + def __init__( + self, + candidate_generators: tp.List[CandidateGenerator], + splitter: Splitter, + reranker: Reranker, + sampler: NegativeSamplerBase = PerUserNegativeSampler(), + feature_collector: CandidateFeatureCollector = CandidateFeatureCollector(), + verbose: int = 0, + ) -> None: + """ + Initialize the CandidateRankingModel with candidate generators, splitter, reranker, sampler + and feature collector. + + Parameters + ---------- + candidate_generators : list(CandidateGenerator) + List of candidate generators. + splitter : Splitter + Splitter for dataset splitting. + reranker : Reranker + Reranker for reranking candidates. + sampler : NegativeSamplerBase, default ``PerUserNegativeSampler()`` + Sampler for negative sampling. + feature_collector : CandidateFeatureCollector, default ``CandidateFeatureCollector()`` + Collector for user-item features. + verbose : int, default ``0`` + Verbosity level. + """ + super().__init__(verbose=verbose) + + if hasattr(splitter, "n_splits"): + assert splitter.n_splits == 1 # TODO: handle softly + self.splitter = splitter + self.sampler = sampler + self.reranker = reranker + self.cand_gen_dict = self._create_cand_gen_dict(candidate_generators) + self.feature_collector = feature_collector + + def _create_cand_gen_dict( + self, candidate_generators: tp.List[CandidateGenerator] + ) -> tp.Dict[str, CandidateGenerator]: + """ + Create a dictionary of candidate generators with unique identifiers. + + Parameters + ---------- + candidate_generators : list(CandidateGenerator) + List of candidate generators. + + Returns + ------- + dict(str -> CandidateGenerator) + Dictionary with candidate generator identifiers as keys and candidate generators as values. + """ + model_count: tp.Dict[str, int] = defaultdict(int) + cand_gen_dict = {} + for candgen in candidate_generators: + model_name = candgen.model.__class__.__name__ + model_count[model_name] += 1 + identifier = f"{model_name}_{model_count[model_name]}" + cand_gen_dict[identifier] = candgen + return cand_gen_dict + + def split_to_history_dataset_and_train_targets( + self, dataset: Dataset, splitter: Splitter + ) -> tp.Tuple[Dataset, pd.DataFrame, tp.Dict[str, tp.Any]]: + """ + Split interactions into history and train sets for first-stage and second-stage model training. + + Parameters + ---------- + dataset : Dataset + The dataset to split. + splitter : Splitter + The splitter to use for splitting the dataset. + + Returns + ------- + pd.DataFrame, pd.DataFrame, dict(str -> any) + Tuple containing the history dataset, train targets, and fold information. + """ + split_iterator = splitter.split(dataset.interactions, collect_fold_stats=True) + + train_ids, test_ids, fold_info = next(iter(split_iterator)) # splitter has only one fold + + history_dataset = dataset.filter_interactions(train_ids) + interactions = dataset.get_raw_interactions() + train_targets = interactions.iloc[test_ids] + + return history_dataset, train_targets, fold_info + + def _fit(self, dataset: Dataset, *args: tp.Any, refit_candidate_generators: bool = True, **kwargs: tp.Any) -> None: + """ + Fits all first-stage models on history dataset + Generates candidates + Sets targets + Samples negatives + Collects features for candidates + Trains reranker on prepared train + Fits all first-stage models on full dataset + """ + train_with_target = self.get_train_with_targets_for_reranker(dataset) + self.reranker.fit(train_with_target, **kwargs) # TODO: add a flag to keep user/item id features somewhere + if refit_candidate_generators: + self._fit_candidate_generators(dataset, for_train=False) + + def get_train_with_targets_for_reranker(self, dataset: Dataset) -> pd.DataFrame: + """ + Prepare training data for the reranker. + + Parameters + ---------- + dataset : Dataset + The dataset to prepare training data from. + + Returns + ------- + pd.DataFrame + DataFrame containing training data with targets and 2 extra columns: `Columns.User`, `Columns.Item`. + """ + history_dataset, train_targets, fold_info = self.split_to_history_dataset_and_train_targets( + dataset, self.splitter + ) + + candidates = self.get_full_candidates_with_targets(train_targets, history_dataset) + candidates = self.sampler.sample_negatives(candidates) + + train_with_target = self.feature_collector.collect_features(candidates, history_dataset, fold_info) + + return train_with_target + + def get_full_candidates_with_targets(self, train_targets: pd.DataFrame, history_dataset: Dataset) -> pd.DataFrame: + """ + Prepare candidates with target values set from first-stage candidate generators. + + Parameters + ---------- + train_targets : pd.DataFrame + DataFrame containing training targets. + history_dataset : Dataset + The dataset to fit the candidate generators on. + + Returns + ------- + pd.DataFrame + DataFrame with target values set. + """ + self._fit_candidate_generators(history_dataset, for_train=True) + + candidates = self._get_candidates_from_first_stage( + users=train_targets[Columns.User].unique(), + dataset=history_dataset, + filter_viewed=self.splitter.filter_already_seen, # TODO: think about it + for_train=True, + ) + candidates = self._set_targets_to_candidates(candidates, train_targets) + return candidates + + def _set_targets_to_candidates(self, candidates: pd.DataFrame, train_targets: pd.DataFrame) -> pd.DataFrame: + """ + Set target values to the candidate items. + + Parameters + ---------- + candidates : pd.DataFrame + DataFrame containing candidate items. + train_targets : pd.DataFrame + DataFrame containing training targets. + + Returns + ------- + pd.DataFrame + DataFrame with target values set. + """ + train_targets[Columns.Target] = 1 + + # Remember that this way we exclude positives that weren't present in candidates + train = pd.merge( + candidates, + train_targets[[Columns.User, Columns.Item, Columns.Target]], + how="left", + on=Columns.UserItem, + ) + + train[Columns.Target] = train[Columns.Target].fillna(0).astype("int32") + return train + + def _fit_candidate_generators(self, dataset: Dataset, for_train: bool) -> None: + """ + Fit the first-stage candidate generators on the dataset. + + Parameters + ---------- + dataset : Dataset + The dataset to fit the candidate generators on. + for_train : bool + Whether the fitting is for training or not. + """ + for candgen in self.cand_gen_dict.values(): + candgen.fit(dataset, for_train) + + def _get_candidates_from_first_stage( + self, + users: ExternalIds, + dataset: Dataset, + filter_viewed: bool, + for_train: bool, + items_to_recommend: tp.Optional[ExternalIds] = None, + on_unsupported_targets: ErrorBehaviour = "raise", + ) -> pd.DataFrame: + """ + Get candidates from the first-stage models. + + Parameters + ---------- + users : ExternalIds + List of user IDs to get candidates for. + dataset : Dataset + The dataset to get candidates from. + filter_viewed : bool + Whether to filter already viewed items. + for_train : bool + Whether the candidates are for training or not. + items_to_recommend : ExternalIds, optional, default ``None`` + List of items to recommend. + + Returns + ------- + pd.DataFrame + DataFrame containing the candidates. + """ + candidates_dfs = [] + + for identifier, candgen in self.cand_gen_dict.items(): + candidates = candgen.generate_candidates( + users=users, + dataset=dataset, + filter_viewed=filter_viewed, + for_train=for_train, + items_to_recommend=items_to_recommend, + on_unsupported_targets=on_unsupported_targets, + ) + + # Process ranks and scores as features + rank_col_name, score_col_name = f"{identifier}_rank", f"{identifier}_score" + + candidates.rename( + columns={Columns.Rank: rank_col_name, Columns.Score: score_col_name}, + inplace=True, + ) + candidates_dfs.append(candidates) + + # Merge all candidates together and process missing ranks and scores + all_candidates = reduce(lambda a, b: a.merge(b, how="outer", on=Columns.UserItem), candidates_dfs) + first_stage_results = self._process_ranks_and_scores(all_candidates) + + return first_stage_results + + def _process_ranks_and_scores( + self, + all_candidates: pd.DataFrame, + ) -> pd.DataFrame: + """ + Process ranks and scores of the candidates. + + Parameters + ---------- + all_candidates : pd.DataFrame + DataFrame containing all candidates. + + Returns + ------- + pd.DataFrame + DataFrame with processed ranks and scores. + """ + for identifier, candgen in self.cand_gen_dict.items(): + rank_col_name, score_col_name = f"{identifier}_rank", f"{identifier}_score" + if candgen.keep_ranks and candgen.ranks_fillna_value is not None: + all_candidates[rank_col_name] = all_candidates[rank_col_name].fillna(candgen.ranks_fillna_value) + if candgen.keep_scores and candgen.scores_fillna_value is not None: + all_candidates[score_col_name] = all_candidates[score_col_name].fillna(candgen.scores_fillna_value) + + return all_candidates + + def recommend( + self, + users: ExternalIds, + dataset: Dataset, + k: int, + filter_viewed: bool, + items_to_recommend: tp.Optional[ExternalIds] = None, + add_rank_col: bool = True, + on_unsupported_targets: ErrorBehaviour = "raise", + force_fit_candidate_generators: bool = False, + ) -> pd.DataFrame: + """ + Generate k recommendations for specified users using the dataset. + + Parameters + ---------- + users : ExternalIds + List of user ids for whom recommendations are generated. + dataset : Dataset + Dataset containing user-item interaction data and possibly additional features. + k : int + The number of recommendations to generate for each user. + filter_viewed : bool + If true, viewed items will be excluded from the recommendations. + items_to_recommend : ExternalIds, optional, default ``None`` + List of item ids from which recommendations should be generated. + If not provided, it will include all items available in the dataset. + add_rank_col : bool, default ``True`` + If true, a rank column is added to the returned DataFrame. + The rank column shows the position of the item in the sorted order of predictions. + on_unsupported_targets : ErrorBehaviour, default ``"raise"`` + Controls the behavior when a target is encountered during prediction, + for which the Model makes no prediction. + If "raise", a ValueError is raised. If "warn", it outputs a warning, + and if "ignore", it silently continues. + force_fit_candidate_generators : bool, default ``False`` + If true, the candidate generators are fitted even if they are already fitted. + + Returns + ------- + pd.DataFrame + DataFrame with the recommended items for users. + """ + self._check_is_fitted() + self._check_k(k) + + if force_fit_candidate_generators or not all( + generator.is_fitted_for_recommend for generator in self.cand_gen_dict.values() + ): + self._fit_candidate_generators(dataset, for_train=False) + + candidates = self._get_candidates_from_first_stage( + users=users, + dataset=dataset, + filter_viewed=filter_viewed, + items_to_recommend=items_to_recommend, + for_train=False, + on_unsupported_targets=on_unsupported_targets, + ) + + train = self.feature_collector.collect_features(candidates, dataset, fold_info=None) + + scored_pairs = candidates.reindex(columns=Columns.UserItem) + scored_pairs[Columns.Score] = self.reranker.predict_scores(train) + + return self.reranker.recommend(scored_pairs, k=k, add_rank_col=add_rank_col) diff --git a/rectools/models/ranking/catboost_reranker.py b/rectools/models/ranking/catboost_reranker.py new file mode 100644 index 00000000..d72954a0 --- /dev/null +++ b/rectools/models/ranking/catboost_reranker.py @@ -0,0 +1,98 @@ +import typing as tp + +import pandas as pd +from catboost import CatBoostClassifier, CatBoostRanker, Pool + +from rectools import Columns + +from .candidate_ranking import Reranker + + +class CatBoostReranker(Reranker): + """ + A reranker using CatBoost models for classification or ranking tasks. + + This class supports both `CatBoostClassifier` and `CatBoostRanker` models to rerank candidates + based on their features and optionally provided additional parameters for fitting and pool creation. + """ + + def __init__( + self, + model: tp.Union[CatBoostClassifier, CatBoostRanker], + fit_kwargs: tp.Optional[tp.Dict[str, tp.Any]] = None, + pool_kwargs: tp.Optional[tp.Dict[str, tp.Any]] = None, + ): + """ + Initialize the CatBoostReranker with `model`, `fit_kwargs` and `pool_kwargs`. + + Parameters + ---------- + model : ClassifierBase | RankerBase + A CatBoost model instance used for reranking. Can be either a classifier or a ranker. + fit_kwargs : dict(str -> any), optional, default ``None`` + Additional keyword arguments to be passed to the `fit` method of the CatBoost model. + pool_kwargs : dict(str -> any), optional, default ``None`` + Additional keyword arguments to be used when creating the CatBoost `Pool`. + """ + super().__init__(model) + self.is_classifier = isinstance(model, CatBoostClassifier) + self.fit_kwargs = fit_kwargs + self.pool_kwargs = pool_kwargs + + def prepare_training_pool(self, candidates_with_target: pd.DataFrame) -> Pool: + """ + Prepare a CatBoost `Pool` for training from the given candidates with target. + + Depending on whether the model is a classifier or a ranker, the pool is prepared differently. + For classifiers, only data and label are used. For rankers, group information is also included. + + Parameters + ---------- + candidates_with_target : pd.DataFrame + DataFrame containing candidate features and target values, along with user and item identifiers. + + Returns + ------- + Pool + A CatBoost Pool object ready for training. + """ + if self.is_classifier: + pool_kwargs = { + "data": candidates_with_target.drop(columns=Columns.UserItem + [Columns.Target]), + "label": candidates_with_target[Columns.Target], + } + else: + candidates_with_target = candidates_with_target.sort_values(by=[Columns.User]) + pool_kwargs = { + "data": candidates_with_target.drop(columns=Columns.UserItem + [Columns.Target]), + "label": candidates_with_target[Columns.Target], + "group_id": candidates_with_target[Columns.User].values, + } + + if self.pool_kwargs is not None: + pool_kwargs.update(self.pool_kwargs) + + return Pool(**pool_kwargs) + + def fit(self, candidates_with_target: pd.DataFrame) -> None: + """ + Fit the CatBoost model using the given candidates with target data. + + This method prepares the training pool and fits the model using the specified fit parameters. + + Parameters + ---------- + candidates_with_target : pd.DataFrame + DataFrame containing candidate features and target values, along with user and item identifiers. + + Returns + ------- + None + """ + training_pool = self.prepare_training_pool(candidates_with_target) + + fit_kwargs = {"X": training_pool} + if self.fit_kwargs is not None: + fit_kwargs.update(self.fit_kwargs) + + self.model.fit(**fit_kwargs) diff --git a/tests/models/ranking/test_candidate_ranking.py b/tests/models/ranking/test_candidate_ranking.py new file mode 100644 index 00000000..6f5e8b3d --- /dev/null +++ b/tests/models/ranking/test_candidate_ranking.py @@ -0,0 +1,347 @@ +import typing as tp +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest +from implicit.nearest_neighbours import CosineRecommender +from sklearn.ensemble import GradientBoostingClassifier + +from rectools import Columns +from rectools.dataset import Dataset, IdMap, Interactions +from rectools.exceptions import NotFittedForStageError +from rectools.model_selection import TimeRangeSplitter +from rectools.models import ImplicitItemKNNWrapperModel, PopularModel +from rectools.models.ranking import ( + CandidateFeatureCollector, + CandidateGenerator, + CandidateRankingModel, + PerUserNegativeSampler, + Reranker, +) + + +class TestPerUserNegativeSampler: + @pytest.fixture + def sample_data(self) -> pd.DataFrame: + data = { + Columns.User: [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], + Columns.Item: [101, 102, 103, 104, 201, 202, 203, 204, 301, 302, 303, 304], + Columns.Score: [0.9, 0.8, 0.7, 0.6, 0.9, 0.8, 0.7, 0.6, 0.9, 0.8, 0.7, 0.6], + Columns.Rank: [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], + Columns.Target: [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], + } + return pd.DataFrame(data) + + @pytest.mark.parametrize("n_negatives", (1, 2)) + def test_sample_negatives(self, sample_data: pd.DataFrame, n_negatives: int) -> None: + sampler = PerUserNegativeSampler(n_negatives=n_negatives, random_state=42) + sampled_df = sampler.sample_negatives(sample_data) + + # Check if the resulting DataFrame has the correct columns + assert set(sampled_df.columns) == set(sample_data.columns) + + # Check if the number of negatives per user is correct + n_negatives_per_user = sampled_df.groupby(Columns.User)[Columns.Target].agg(lambda target: (target == 0).sum()) + assert (n_negatives_per_user == n_negatives).all() + + # Check if positives were not changed + pd.testing.assert_frame_equal( + sampled_df[sampled_df[Columns.Target] == 1].sort_values(Columns.UserItem).reset_index(drop=True), + sample_data[sample_data[Columns.Target] == 1].sort_values(Columns.UserItem).reset_index(drop=True), + ) + + def test_sample_negatives_with_insufficient_negatives(self, sample_data: pd.DataFrame) -> None: + # Modify sample_data to have insufficient negatives for user 1 + sample_data.loc[sample_data[Columns.User] == 1, Columns.Target] = [1, 0, 1, 0] + + sampler = PerUserNegativeSampler(n_negatives=3, random_state=42) + sampled_df = sampler.sample_negatives(sample_data) + + # Check if the resulting DataFrame has the correct columns + assert set(sampled_df.columns) == set(sample_data.columns) + + # Check if the number of negatives per user is correct + n_negatives_per_user = sampled_df.groupby(Columns.User)[Columns.Target].agg(lambda target: (target == 0).sum()) + assert n_negatives_per_user.to_list() == [2, 3, 3] + + # Check if positives were not changed + pd.testing.assert_frame_equal( + sampled_df[sampled_df[Columns.Target] == 1].sort_values(Columns.UserItem).reset_index(drop=True), + sample_data[sample_data[Columns.Target] == 1].sort_values(Columns.UserItem).reset_index(drop=True), + ) + + +class TestCandidateGenerator: + @pytest.fixture + def dataset(self) -> Dataset: + interactions_df = pd.DataFrame( + [ + [70, 11, 1, "2021-11-30"], + [70, 12, 1, "2021-11-30"], + [10, 11, 1, "2021-11-30"], + [10, 12, 1, "2021-11-29"], + [10, 13, 9, "2021-11-28"], + [20, 11, 1, "2021-11-27"], + [20, 14, 2, "2021-11-26"], + [30, 11, 1, "2021-11-24"], + [30, 12, 1, "2021-11-23"], + [30, 14, 1, "2021-11-23"], + [30, 15, 5, "2021-11-21"], + [40, 11, 1, "2021-11-20"], + [40, 12, 1, "2021-11-19"], + ], + columns=Columns.Interactions, + ) + user_id_map = IdMap.from_values([10, 20, 30, 40, 50, 60, 70, 80]) + item_id_map = IdMap.from_values([11, 12, 13, 14, 15, 16]) + interactions = Interactions.from_raw(interactions_df, user_id_map, item_id_map) + return Dataset(user_id_map, item_id_map, interactions) + + @pytest.fixture + def users(self) -> tp.List[int]: + return [10, 20, 30] + + @pytest.fixture + def model(self) -> PopularModel: + return PopularModel() + + @pytest.fixture + def generator(self, model: PopularModel) -> CandidateGenerator: + return CandidateGenerator(model, 2, False, False) + + @pytest.mark.parametrize("for_train", (True, False)) + def test_not_fitted_errors( + self, for_train: bool, dataset: Dataset, generator: CandidateGenerator, users: tp.List[int] + ) -> None: + with pytest.raises(NotFittedForStageError): + generator.generate_candidates(users, dataset, filter_viewed=True, for_train=for_train) + + @pytest.mark.parametrize("for_train", (True, False)) + def test_not_fitted_errors_when_fitted_to_opposite_case( + self, for_train: bool, dataset: Dataset, generator: CandidateGenerator, users: tp.List[int] + ) -> None: + generator.fit(dataset, for_train=not for_train) + with pytest.raises(NotFittedForStageError): + generator.generate_candidates(users, dataset, filter_viewed=True, for_train=for_train) + + @pytest.mark.parametrize("for_train", (True, False)) + @pytest.mark.parametrize( + ("filter_viewed", "expected"), + ( + (True, pd.DataFrame({Columns.User: [10, 10, 20, 20, 30], Columns.Item: [14, 15, 12, 13, 13]})), + (False, pd.DataFrame({Columns.User: [10, 10, 20, 20, 30, 30], Columns.Item: [11, 12, 11, 12, 11, 12]})), + ), + ) + def test_happy_path( + self, + for_train: bool, + dataset: Dataset, + generator: CandidateGenerator, + users: tp.List[int], + filter_viewed: bool, + expected: pd.DataFrame, + ) -> None: + generator.fit(dataset, for_train=for_train) + actual = generator.generate_candidates(users, dataset, filter_viewed=filter_viewed, for_train=for_train) + pd.testing.assert_frame_equal(actual, expected) + + @pytest.mark.parametrize("keep_scores", (True, False)) + @pytest.mark.parametrize("keep_ranks", (True, False)) + def test_columns( + self, dataset: Dataset, model: PopularModel, users: tp.List[int], keep_scores: bool, keep_ranks: bool + ) -> None: + generator = CandidateGenerator(model, 2, keep_ranks=keep_ranks, keep_scores=keep_scores) + generator.fit(dataset, for_train=True) + candidates = generator.generate_candidates(users, dataset, filter_viewed=True, for_train=True) + + columns = candidates.columns.to_list() + assert Columns.User in columns + assert Columns.Item in columns + + if keep_scores: + assert Columns.Score in columns + else: + assert Columns.Score not in columns + + if keep_ranks: + assert Columns.Rank in columns + else: + assert Columns.Rank not in columns + + +class TestCandidateFeatureCollector: + def test_happy_path(self) -> None: + feature_collector = CandidateFeatureCollector() + candidates = pd.DataFrame( + { + Columns.User: [1, 1, 2, 2, 3, 3], + Columns.Item: [10, 20, 30, 40, 50, 60], + "some_model_rank": [1, 2, 1, 2, 1, 2], + } + ) + dataset = MagicMock() + fold_info = MagicMock() + actual = feature_collector.collect_features(candidates, dataset, fold_info) + pd.testing.assert_frame_equal(candidates, actual) + + +class TestCandidateRankingModel: + @pytest.fixture + def dataset(self) -> Dataset: + interactions_df = pd.DataFrame( + [ + [70, 11, 1, "2021-11-30"], + [70, 12, 1, "2021-11-30"], + [10, 11, 1, "2021-11-30"], + [10, 12, 1, "2021-11-29"], + [10, 13, 9, "2021-11-28"], + [20, 11, 1, "2021-11-27"], + [20, 14, 2, "2021-11-26"], + [30, 11, 1, "2021-11-24"], + [30, 12, 1, "2021-11-23"], + [30, 14, 1, "2021-11-23"], + [30, 15, 5, "2021-11-21"], + [40, 11, 1, "2021-11-20"], + [40, 12, 1, "2021-11-19"], + ], + columns=Columns.Interactions, + ) + user_id_map = IdMap.from_values([10, 20, 30, 40, 50, 60, 70, 80]) + item_id_map = IdMap.from_values([11, 12, 13, 14, 15, 16]) + interactions = Interactions.from_raw(interactions_df, user_id_map, item_id_map) + return Dataset(user_id_map, item_id_map, interactions) + + @pytest.fixture + def users(self) -> tp.List[int]: + return [10, 20, 30] + + @pytest.fixture + def model(self) -> PopularModel: + return PopularModel() + + def test_get_train_with_targets_for_reranker(self, model: PopularModel, dataset: Dataset) -> None: + candidate_generators = [CandidateGenerator(model, 2, False, False)] + splitter = TimeRangeSplitter("1D", n_splits=1) + sampler = PerUserNegativeSampler(1, 32) + two_stage_model = CandidateRankingModel( + candidate_generators, + splitter, + sampler=sampler, + reranker=Reranker(GradientBoostingClassifier(random_state=123)), + ) + actual = two_stage_model.get_train_with_targets_for_reranker(dataset) + expected = pd.DataFrame( + { + Columns.User: [10, 10], + Columns.Item: [14, 11], + Columns.Target: np.array([0, 1], dtype="int32"), + } + ) + pd.testing.assert_frame_equal(actual, expected) + + def test_recommend(self, model: PopularModel, dataset: Dataset) -> None: + cangen_1 = model + cangen_2 = ImplicitItemKNNWrapperModel(CosineRecommender()) + + scores_fillna_value = -100 + ranks_fillna_value = 3 + + candidate_generators = [ + CandidateGenerator(cangen_1, 2, True, True, scores_fillna_value, ranks_fillna_value), + CandidateGenerator(cangen_2, 2, True, True, scores_fillna_value, ranks_fillna_value), + ] + splitter = TimeRangeSplitter("1D", n_splits=1) + sampler = PerUserNegativeSampler(1, 32) + two_stage_model = CandidateRankingModel( + candidate_generators, + splitter, + sampler=sampler, + reranker=Reranker(GradientBoostingClassifier(random_state=123)), + ) + two_stage_model.fit(dataset) + + actual_reco = two_stage_model.recommend( + [10, 20, 30], dataset, k=3, filter_viewed=True, force_fit_candidate_generators=True + ) + expected_reco = pd.DataFrame( + { + Columns.User: [10, 10, 20, 20, 20, 30], + Columns.Item: [14, 15, 12, 15, 13, 13], + Columns.Score: [ + 0.999, + 0.412, + 0.999, + 0.412, + 0.000, + 0.999, + ], + Columns.Rank: [1, 2, 1, 2, 3, 1], + } + ) + pd.testing.assert_frame_equal(actual_reco, expected_reco, atol=0.001) + + +class TestReranker: + @pytest.fixture + def fit_kwargs(self) -> tp.Dict[str, tp.Any]: + fit_kwargs = {"sample_weight": np.array([1, 2])} + return fit_kwargs + + @pytest.fixture + def model(self) -> GradientBoostingClassifier: + return GradientBoostingClassifier(random_state=123) + + @pytest.fixture + def reranker(self, model: GradientBoostingClassifier, fit_kwargs: tp.Dict[str, tp.Any]) -> Reranker: + return Reranker(model, fit_kwargs) + + @pytest.fixture + def candidates_with_target(self) -> pd.DataFrame: + candidates_with_target = pd.DataFrame( + { + Columns.User: [10, 10], + Columns.Item: [14, 11], + Columns.Score: [0.1, 0.2], + Columns.Target: np.array([0, 1], dtype="int32"), + } + ) + return candidates_with_target + + def test_prepare_fit_kwargs(self, reranker: Reranker, candidates_with_target: pd.DataFrame) -> None: + expected_fit_kwargs = { + "X": pd.DataFrame( + { + Columns.Score: [0.1, 0.2], + } + ), + "y": pd.Series(np.array([0, 1], dtype="int32"), name=Columns.Target), + "sample_weight": np.array([1, 2]), + } + + actual_fit_kwargs = reranker.prepare_fit_kwargs(candidates_with_target) + pd.testing.assert_frame_equal(actual_fit_kwargs["X"], expected_fit_kwargs["X"]) + pd.testing.assert_series_equal(actual_fit_kwargs["y"], expected_fit_kwargs["y"]) + np.testing.assert_array_equal(actual_fit_kwargs["sample_weight"], expected_fit_kwargs["sample_weight"]) + + def test_predict_scores(self, reranker: Reranker, candidates_with_target: pd.DataFrame) -> None: + reranker.fit(candidates_with_target) + candidates = candidates_with_target.drop(columns=Columns.Target) + + actual_predict_scores = reranker.predict_scores(candidates) + expected_predict_scores = np.array([0.000029, 1.000000]) + np.testing.assert_allclose(actual_predict_scores, expected_predict_scores, rtol=0.015, atol=1.5e-05) + + def test_recommend(self) -> None: + scored_pairs = pd.DataFrame( + { + Columns.User: [1, 1, 1, 1, 2, 2, 2], + Columns.Item: [10, 20, 30, 40, 10, 20, 30], + Columns.Score: [1, 4, 2, 3, 2, 3, 1], + } + ) + actual = Reranker.recommend(scored_pairs, 2, add_rank_col=False) + expected = pd.DataFrame( + {Columns.User: [1, 1, 2, 2], Columns.Item: [20, 40, 20, 10], Columns.Score: [4, 3, 3, 2]} + ) + pd.testing.assert_frame_equal(actual, expected) diff --git a/tests/models/ranking/test_catboost_reranker.py b/tests/models/ranking/test_catboost_reranker.py new file mode 100644 index 00000000..af5510df --- /dev/null +++ b/tests/models/ranking/test_catboost_reranker.py @@ -0,0 +1,224 @@ +import typing as tp + +import numpy as np +import pandas as pd +import pytest +from catboost import CatBoostClassifier, CatBoostRanker, Pool +from implicit.nearest_neighbours import CosineRecommender +from pytest import FixtureRequest + +from rectools import Columns +from rectools.dataset import Dataset, IdMap, Interactions +from rectools.model_selection import TimeRangeSplitter +from rectools.models import ImplicitItemKNNWrapperModel, PopularModel +from rectools.models.ranking import CandidateGenerator, CandidateRankingModel, CatBoostReranker, PerUserNegativeSampler + + +class TestCatBoostReranker: + @pytest.fixture + def fit_kwargs(self) -> tp.Dict[str, tp.Any]: + fit_kwargs = {"early_stopping_rounds": 10} + return fit_kwargs + + @pytest.fixture + def pool_kwargs(self) -> tp.Dict[str, tp.Any]: + pool_kwargs = {"cat_features": ["age", "sex"]} + return pool_kwargs + + @pytest.fixture + def reranker_catboost_classifier( + self, pool_kwargs: tp.Dict[str, tp.Any], fit_kwargs: tp.Dict[str, tp.Any] + ) -> CatBoostReranker: + return CatBoostReranker( + CatBoostClassifier(verbose=False, random_state=123), pool_kwargs=pool_kwargs, fit_kwargs=fit_kwargs + ) + + @pytest.fixture + def reranker_catboost_ranker( + self, pool_kwargs: tp.Dict[str, tp.Any], fit_kwargs: tp.Dict[str, tp.Any] + ) -> CatBoostReranker: + return CatBoostReranker( + CatBoostRanker(verbose=False, random_state=123), pool_kwargs=pool_kwargs, fit_kwargs=fit_kwargs + ) + + @pytest.fixture + def candidates_with_target(self) -> pd.DataFrame: + candidates_with_target = pd.DataFrame( + { + Columns.User: [10, 10], + Columns.Item: [14, 11], + Columns.Score: [0.1, 0.2], + "sex": ["M", "F"], + "age": ["18_24", "25_34"], + Columns.Target: [0, 1], + } + ) + return candidates_with_target + + @pytest.fixture + def dataset(self) -> Dataset: + interactions_df = pd.DataFrame( + [ + [70, 11, 1, "2021-11-30"], + [70, 12, 1, "2021-11-30"], + [10, 11, 1, "2021-11-30"], + [10, 12, 1, "2021-11-29"], + [10, 13, 9, "2021-11-28"], + [20, 11, 1, "2021-11-27"], + [20, 14, 2, "2021-11-26"], + [30, 11, 1, "2021-11-24"], + [30, 12, 1, "2021-11-23"], + [30, 14, 1, "2021-11-23"], + [30, 15, 5, "2021-11-21"], + [40, 11, 1, "2021-11-20"], + [40, 12, 1, "2021-11-19"], + ], + columns=Columns.Interactions, + ) + user_id_map = IdMap.from_values([10, 20, 30, 40, 50, 60, 70, 80]) + item_id_map = IdMap.from_values([11, 12, 13, 14, 15, 16]) + interactions = Interactions.from_raw(interactions_df, user_id_map, item_id_map) + return Dataset(user_id_map, item_id_map, interactions) + + @pytest.mark.parametrize( + "reranker_fixture, expected_training_pool", + [ + ( + "reranker_catboost_ranker", + Pool( + data=pd.DataFrame( + { + Columns.Score: [0.1, 0.2], + "sex": ["M", "F"], + "age": ["18_24", "25_34"], + } + ), + label=[0, 1], + cat_features=["age", "sex"], + ), + ), + ( + "reranker_catboost_classifier", + Pool( + data=pd.DataFrame( + { + Columns.Score: [0.1, 0.2], + "sex": ["M", "F"], + "age": ["18_24", "25_34"], + } + ), + label=[0, 1], + cat_features=["age", "sex"], + group_id=[10, 10], + ), + ), + ], + ) + def test_prepare_training_pool( + self, + request: FixtureRequest, + reranker_fixture: str, + expected_training_pool: Pool, + candidates_with_target: pd.DataFrame, + ) -> None: + reranker = request.getfixturevalue(reranker_fixture) + actual_training_pool = reranker.prepare_training_pool(candidates_with_target) + + expected_labels = expected_training_pool.get_label() + actual_labels = actual_training_pool.get_label() + np.testing.assert_array_equal(expected_labels, actual_labels) + + expected_cat_features = expected_training_pool.get_cat_feature_indices() + actual_cat_features = actual_training_pool.get_cat_feature_indices() + np.testing.assert_array_equal(expected_cat_features, actual_cat_features) + + expected_feature_names = expected_training_pool.get_feature_names() + actual_feature_names = actual_training_pool.get_feature_names() + np.testing.assert_array_equal(expected_feature_names, actual_feature_names) + + @pytest.mark.parametrize( + "reranker_fixture, expected_predict_scores", + [ + ( + "reranker_catboost_ranker", + np.array([-23.397, 23.397]), + ), + ( + "reranker_catboost_classifier", + np.array([0.334, 0.665]), + ), + ], + ) + def test_predict_scores( + self, + request: FixtureRequest, + reranker_fixture: str, + expected_predict_scores: np.ndarray, + candidates_with_target: pd.DataFrame, + ) -> None: + reranker = request.getfixturevalue(reranker_fixture) + reranker.fit(candidates_with_target) + + candidates = candidates_with_target.drop(columns=Columns.Target) + actual_predict_scores = reranker.predict_scores(candidates) + np.testing.assert_allclose(actual_predict_scores, expected_predict_scores, atol=0.0007) + + @pytest.mark.parametrize( + "reranker, expected_reco", + [ + ( + CatBoostReranker(CatBoostRanker(random_state=32, verbose=False)), + pd.DataFrame( + { + Columns.User: [10, 10, 20, 20, 20, 30], + Columns.Item: [14, 15, 12, 15, 13, 13], + Columns.Score: [ + 11.909, + 1.020, + 23.396, + 1.020, + -23.396, + 11.909, + ], + Columns.Rank: [1, 2, 1, 2, 3, 1], + } + ), + ), + ( + CatBoostReranker(CatBoostClassifier(random_state=32, verbose=False)), + pd.DataFrame( + { + Columns.User: [10, 10, 20, 20, 20, 30], + Columns.Item: [14, 15, 12, 15, 13, 13], + Columns.Score: [0.588, 0.505, 0.665, 0.505, 0.334, 0.588], + Columns.Rank: [1, 2, 1, 2, 3, 1], + } + ), + ), + ], + ) + def test_recommend(self, reranker: CatBoostReranker, expected_reco: pd.DataFrame, dataset: Dataset) -> None: + cangen_1 = PopularModel() + cangen_2 = ImplicitItemKNNWrapperModel(CosineRecommender()) + + scores_fillna_value = -100 + ranks_fillna_value = 3 + + candidate_generators = [ + CandidateGenerator(cangen_1, 2, True, True, scores_fillna_value, ranks_fillna_value), + CandidateGenerator(cangen_2, 2, True, True, scores_fillna_value, ranks_fillna_value), + ] + splitter = TimeRangeSplitter("1D", n_splits=1) + sampler = PerUserNegativeSampler(1, 32) + two_stage_model_ranker = CandidateRankingModel( + candidate_generators, + splitter, + sampler=sampler, + reranker=reranker, + ) + two_stage_model_ranker.fit(dataset) + + actual_reco_ranker = two_stage_model_ranker.recommend( + [10, 20, 30], dataset, k=3, filter_viewed=True, force_fit_candidate_generators=True + ) + pd.testing.assert_frame_equal(actual_reco_ranker, expected_reco, atol=0.001) diff --git a/tests/models/test_serialization.py b/tests/models/test_serialization.py index a3568d92..2ef5c2af 100644 --- a/tests/models/test_serialization.py +++ b/tests/models/test_serialization.py @@ -27,7 +27,10 @@ except ImportError: LightFM = object # it's ok in case we're skipping the tests +from catboost import CatBoostRanker + from rectools.metrics import NDCG +from rectools.model_selection import TimeRangeSplitter from rectools.models import ( DSSMModel, EASEModel, @@ -44,6 +47,7 @@ ) from rectools.models.base import ModelBase, ModelConfig from rectools.models.nn.transformers.base import TransformerModelBase +from rectools.models.ranking import CandidateGenerator, CandidateRankingModel, CatBoostReranker from rectools.models.vector import VectorModel from rectools.utils.config import BaseConfig @@ -56,11 +60,18 @@ for cls in get_successors(ModelBase) if (cls.__module__.startswith("rectools.models") and cls not in INTERMEDIATE_MODEL_CLASSES) ) -CONFIGURABLE_MODEL_CLASSES = tuple(cls for cls in EXPOSABLE_MODEL_CLASSES if cls not in (DSSMModel,)) +CONFIGURABLE_MODEL_CLASSES = tuple( + cls for cls in EXPOSABLE_MODEL_CLASSES if cls not in (DSSMModel, CandidateRankingModel) +) def init_default_model(model_cls: tp.Type[ModelBase]) -> ModelBase: mandatory_params = { + CandidateRankingModel: { + "candidate_generators": [CandidateGenerator(PopularModel(), 2, False, False)], + "splitter": TimeRangeSplitter("1D", n_splits=1), + "reranker": CatBoostReranker(CatBoostRanker(random_state=32, verbose=False)), + }, ImplicitItemKNNWrapperModel: {"model": ItemItemRecommender()}, ImplicitALSWrapperModel: {"model": AlternatingLeastSquares()}, ImplicitBPRWrapperModel: {"model": BayesianPersonalizedRanking()}, diff --git a/tests/test_compat.py b/tests/test_compat.py index fc439e27..6bf09f3e 100644 --- a/tests/test_compat.py +++ b/tests/test_compat.py @@ -18,6 +18,7 @@ from rectools.compat import ( BERT4RecModel, + CatBoostReranker, DSSMModel, HSTUModel, ItemToItemAnnRecommender, @@ -45,6 +46,7 @@ ItemToItemVisualApp, MetricsApp, TorchRanker, + CatBoostReranker, ), ) def test_raise_when_model_not_available(