replay-rec 0.13.0rc0

RecSys Library

RePlay

RePlay is a library providing tools for all stages of creating a recommendation system, from data preprocessing to model evaluation and comparison.

RePlay can use PySpark to handle big data.

You can

• Filter and split data
• Train models
• Optimize hyper parameters
• Evaluate predictions with metrics
• Combine predictions from different models
• Create a two-level model

Documentation is available here.

Table of Contents

• Installation
• Quickstart
• Resources
• Contributing to RePlay

Installation

Installation via pip package manager is recommended by default:

pip install replay-rec

In this case it will be installed the core package without PySpark and PyTorch dependencies. Also experimental submodule will not be installed.

To install experimental submodule please specify the version with rc0 suffix. For example:

pip install replay-rec==XX.YY.ZZrc0

Extras

In addition to the core package, several extras are also provided, including:

• [spark]: Install PySpark functionality
• [torch]: Install PyTorch and Lightning functionality
• [all]: [spark] [torch]

Example:

# Install core package with PySpark dependency
pip install replay-rec[spark]

# Install package with experimental submodule and PySpark dependency
pip install replay-rec[spark]==XX.YY.ZZrc0

To build RePlay from sources please use the instruction.

If you encounter an error during RePlay installation, check the troubleshooting guide.

Quickstart

from rs_datasets import MovieLens

from replay.data import Dataset, FeatureHint, FeatureInfo, FeatureSchema, FeatureType
from replay.data.dataset_utils import DatasetLabelEncoder
from replay.metrics import HitRate, NDCG, Experiment
from replay.models import ItemKNN
from replay.utils import convert2spark
from replay.utils.session_handler import State
from replay.splitters import RatioSplitter

spark = State().session

ml_1m = MovieLens("1m")
K=10

# data preprocessing
interactions = convert2spark(ml_1m.ratings)

# data splitting
splitter = RatioSplitter(
    test_size=0.3,
    divide_column="user_id",
    query_column="user_id",
    item_column="item_id",
    timestamp_column="timestamp",
    drop_cold_items=True,
    drop_cold_users=True,
)
train, test = splitter.split(interactions)

# dataset creating
feature_schema = FeatureSchema(
    [
        FeatureInfo(
            column="user_id",
            feature_type=FeatureType.CATEGORICAL,
            feature_hint=FeatureHint.QUERY_ID,
        ),
        FeatureInfo(
            column="item_id",
            feature_type=FeatureType.CATEGORICAL,
            feature_hint=FeatureHint.ITEM_ID,
        ),
        FeatureInfo(
            column="rating",
            feature_type=FeatureType.NUMERICAL,
            feature_hint=FeatureHint.RATING,
        ),
        FeatureInfo(
            column="timestamp",
            feature_type=FeatureType.NUMERICAL,
            feature_hint=FeatureHint.TIMESTAMP,
        ),
    ]
)

train_dataset = Dataset(
    feature_schema=feature_schema,
    interactions=train,
)
test_dataset = Dataset(
    feature_schema=feature_schema,
    interactions=test,
)

# data encoding
encoder = DatasetLabelEncoder()
train_dataset = encoder.fit_transform(train_dataset)
test_dataset = encoder.transform(test_dataset)

# model training
model = ItemKNN()
model.fit(train_dataset)

# model inference
encoded_recs = model.predict(
    dataset=train_dataset,
    k=K,
    queries=test_dataset.query_ids,
    filter_seen_items=True,
)

recs = encoder.query_and_item_id_encoder.inverse_transform(encoded_recs)

# model evaluation
metrics = Experiment(
    [NDCG(K), HitRate(K)],
    test,
    query_column="user_id",
    item_column="item_id",
    rating_column="rating",
)
metrics.add_result("ItemKNN", recs)
print(metrics.results)

Resources

Usage examples

1. 01_replay_basics.ipynb - get started with RePlay.
2. 02_models_comparison.ipynb - reproducible models comparison on MovieLens-1M dataset.
3. 03_features_preprocessing_and_lightFM.ipynb - LightFM example with pyspark for feature preprocessing.
4. 04_splitters.ipynb - An example of using RePlay data splitters.
5. 05_feature_generators.ipynb - Feature generation with RePlay.

Videos and papers

• Video guides:

  • Replay for offline recommendations, AI Journey 2021

• Research papers:

  • Yan-Martin Tamm, Rinchin Damdinov, Alexey Vasilev Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?

Contributing to RePlay

We welcome community contributions. For details please check our contributing guidelines.