TSATabularPreprocessing¶

TSA (Temporal Self-Attention) tabular preprocessing with explicit output declarations for processed_data and preprocessor artifacts


SageMaker step type	`Processing`
Node type	internal (consumes upstream, produces downstream)
Container entry point	`tsa_tabular_preprocessing.py`
Interface file	`steps/interfaces/tsa_tabular_preprocessing.step.yaml`

Compute¶


Compute kind	`framework`
SDK class	`PyTorch` (SageMaker DLC via `image_uris.retrieve`)

Functionality¶

TSA tabular preprocessing script that combines data shards, loads column signature, applies TSA-domain feature engineering (label encoding, ID field handling, date-based feature extraction), splits data into train/test/val, serialises the fitted sklearn preprocessor pipeline to preprocessor.pkl, and outputs both processed CSV and the preprocessor artifact. Supports streaming mode for large datasets.

Inputs (dependencies)¶

Input	Type	Required	Compatible producers
`DATA`	`processing_output`	yes	CradleDataLoading, DummyDataLoading, RedshiftDataLoading, DataLoad, ProcessingStep, BedrockProcessing, StratifiedSampling
`DATA_SECONDARY`	`processing_output`	no	CradleDataLoading, DummyDataLoading, RedshiftDataLoading, DataLoad, ProcessingStep, BedrockProcessing, StratifiedSampling
`SIGNATURE`	`processing_output`	no	CradleDataLoading, DummyDataLoading

Outputs¶

Output	Type
`tsa_processed_data`	`processing_output`
`preprocessor`	`processing_output`

Consumers (downstream steps)¶

Steps that declare this step as a compatible input source:

Framework requirements¶

Package	Version
`pandas`	`>=1.3.0`
`numpy`	`>=1.21.0`
`scikit-learn`	`>=1.0.0`

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