Welcome to RePlay’s documentation!
Contents:
- Installation
- Development
- Modules
- Data
- Dataset
DatasetDataset.cache()Dataset.feature_schemaDataset.interactionsDataset.is_categorical_encodedDataset.item_countDataset.item_featuresDataset.item_idsDataset.load()Dataset.persist()Dataset.query_countDataset.query_featuresDataset.query_idsDataset.save()Dataset.subset()Dataset.to_pandas()Dataset.to_polars()Dataset.to_spark()Dataset.unpersist()
- DatasetLabelEncoder
DatasetLabelEncoderDatasetLabelEncoder.fit()DatasetLabelEncoder.fit_transform()DatasetLabelEncoder.get_encoder()DatasetLabelEncoder.interactions_encoderDatasetLabelEncoder.item_features_encoderDatasetLabelEncoder.item_id_encoderDatasetLabelEncoder.query_and_item_id_encoderDatasetLabelEncoder.query_features_encoderDatasetLabelEncoder.query_id_encoderDatasetLabelEncoder.transform()
- FeatureType
- FeatureSource
- FeatureHint
- FeatureInfo
- FeatureSchema
FeatureSchemaFeatureSchema.all_featuresFeatureSchema.categorical_featuresFeatureSchema.columnsFeatureSchema.copy()FeatureSchema.drop()FeatureSchema.filter()FeatureSchema.get()FeatureSchema.interaction_featuresFeatureSchema.interactions_rating_columnFeatureSchema.interactions_rating_featuresFeatureSchema.interactions_timestamp_columnFeatureSchema.interactions_timestamp_featuresFeatureSchema.item()FeatureSchema.item_featuresFeatureSchema.item_id_columnFeatureSchema.item_id_featureFeatureSchema.items()FeatureSchema.keys()FeatureSchema.numerical_featuresFeatureSchema.query_featuresFeatureSchema.query_id_columnFeatureSchema.query_id_featureFeatureSchema.subset()FeatureSchema.values()
- GetSchema
- Neural Networks
- TensorFeatureInfo
TensorFeatureInfoTensorFeatureInfo.cardinalityTensorFeatureInfo.embedding_dimTensorFeatureInfo.feature_hintTensorFeatureInfo.feature_sourceTensorFeatureInfo.feature_sourcesTensorFeatureInfo.feature_typeTensorFeatureInfo.is_catTensorFeatureInfo.is_listTensorFeatureInfo.is_numTensorFeatureInfo.is_seqTensorFeatureInfo.nameTensorFeatureInfo.padding_valueTensorFeatureInfo.tensor_dim
- TensorFeatureSource
- TensorSchema
TensorSchemaTensorSchema.all_featuresTensorSchema.categorical_featuresTensorSchema.filter()TensorSchema.get()TensorSchema.item()TensorSchema.item_id_feature_nameTensorSchema.item_id_featuresTensorSchema.items()TensorSchema.keys()TensorSchema.namesTensorSchema.numerical_featuresTensorSchema.query_id_feature_nameTensorSchema.query_id_featuresTensorSchema.rating_feature_nameTensorSchema.rating_featuresTensorSchema.sequential_featuresTensorSchema.subset()TensorSchema.timestamp_feature_nameTensorSchema.timestamp_featuresTensorSchema.values()
- SequenceTokenizer
SequenceTokenizerSequenceTokenizer.fit()SequenceTokenizer.fit_transform()SequenceTokenizer.interactions_encoderSequenceTokenizer.item_features_encoderSequenceTokenizer.item_id_encoderSequenceTokenizer.load()SequenceTokenizer.query_and_item_id_encoderSequenceTokenizer.query_features_encoderSequenceTokenizer.query_id_encoderSequenceTokenizer.save()SequenceTokenizer.tensor_schemaSequenceTokenizer.transform()
- PandasSequentialDataset
PandasSequentialDatasetPandasSequentialDataset.filter_by_query_id()PandasSequentialDataset.get_all_query_ids()PandasSequentialDataset.get_max_sequence_length()PandasSequentialDataset.get_query_id()PandasSequentialDataset.get_sequence()PandasSequentialDataset.get_sequence_by_query_id()PandasSequentialDataset.get_sequence_length()PandasSequentialDataset.keep_common_query_ids()PandasSequentialDataset.load()PandasSequentialDataset.schema
- TorchSequentialBatch
- TorchSequentialDataset
- TorchSequentialValidationBatch
- TorchSequentialValidationDataset
- Parquet processing
- ParquetModule (Lightning DataModule)
- TensorFeatureInfo
- Dataset
- Preprocessing
- Splitters
- Models
- RePlay Recommenders
- Redesigned Neural Networks recommenders
- SasRec
- TwoTower
- Losses
- Model Building Blocks
- Universal Lighting module
- Transforms for ParquetModule
- Easy training, validation and inference with Lightning
- Neural Networks recommenders
- Bert4Rec
- SasRec (legacy)
- Compiled sequential models
- Recommender interface
- Distributed models
- Popular Recommender
- Query Popular Recommender
- Wilson Recommender
- Random Recommender
- UCB Recommender
- KL-UCB Recommender
- LinUCB Recommender
- Thompson Sampling
- K Nearest Neighbours
- Alternating Least Squares
- Alternating Least Squares on Scala (Experimental)
- SLIM
- Word2Vec Recommender
- Association Rules Item-to-Item Recommender
- Cluster Recommender
- Neural models with distributed inference
- Hierarchical models
- Wrappers and other models with distributed inference
- Metrics
- Scenarios
- Utils
- Data
- Settings
- Useful Info