GH-36: feature: Serialize pipeline feature sets as DataFrames

[Urfoex]

Refactor pipeline serialization to handle transformed features as full getML DataFrames instead of simple NumPy arrays.

This provides a more robust and informative serialization format, preserving column names, types, and other metadata for the feature sets.

Key changes:

• Rename transforms to feature_sets and predicts to predictions throughout the codebase for clarity.
• pipeline.transform() now produces a getml.DataFrame.
• serialize_feature_sets saves the resulting DataFrame using the existing serialize_dataframe_or_view logic.
• Update PipelineInformation metadata, on-disk folder structure, and all related tests to reflect the new model.
• Remove generic serialization helpers in favor of more explicit and readable implementations for predictions and feature sets.

[Copilot]

Pull Request Overview

This PR refactors pipeline serialization to use getML DataFrames for transformed features, while renaming keys and updating related tests accordingly.

• Renames parameters and keys from “predicts” to “predictions” and “transforms” to “feature_sets”.
• Updates serialization functions, metadata, and tests to support the new DataFrame-based feature sets.

Reviewed Changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
tests/unit/serialize/test_pipeline_information.py	Updates JSON keys to “predictions” and “feature_sets”.
tests/unit/serialize/test_pipeline.py	Renames test functions and assertions to validate feature sets and predictions.
tests/unit/conftest.py	Updates fixtures and mock pipeline to support new naming and data types.
tests/integration/test_serialize_robot.py	Updates integration test directory and file naming for feature sets and predictions.
src/getml_io/serialize/pipeline.py	Replaces deprecated serialization helpers with new functions for predictions and feature sets.
src/getml_io/metadata/*	Renames type aliases and metadata keys to correspond to the new nomenclature.
src/getml_io/metadata/feature_sets.py	Introduces a new type alias for structured feature sets.

[gemini-code-assist]

Summary of Changes

Hello @Urfoex, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly refactors the pipeline serialization mechanism to enhance the robustness and informativeness of serialized feature sets. Previously stored as simple NumPy arrays, transformed features will now be serialized as full getML DataFrames, preserving crucial metadata like column names and types. This change also introduces clearer terminology by renaming 'transforms' to 'feature_sets' and 'predicts' to 'predictions' throughout the codebase and on-disk storage.

Highlights

• Enhanced Feature Set Serialization: Transformed features, now termed 'feature sets,' are serialized as getML DataFrames instead of NumPy arrays. This change ensures that column names, types, and other metadata are preserved, leading to a more robust and informative serialization format.
• Terminology Renaming: The terms transforms and predicts have been consistently renamed to feature_sets and predictions, respectively. This renaming applies across the codebase, metadata structures (e.g., PipelineInformation), and the on-disk storage paths for improved clarity and consistency.
• Refactored Serialization Logic: Generic serialization helpers for pipeline actions have been removed. They are replaced with explicit, dedicated functions (serialize_predictions and serialize_feature_sets) that handle the specific serialization requirements for each type of output, simplifying the code and making it more readable.
• Metadata and Storage Updates: The PipelineInformation metadata structures and the on-disk folder layout have been updated to reflect the new predictions and feature_sets terminology. Feature set filenames now include a features. prefix (e.g., features.subset_name.parquet) to distinguish them clearly.

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[gemini-code-assist]

Code Review

The pull request refactors pipeline serialization to handle transformed features as DataFrames, enhancing robustness and information preservation. Key changes include renaming variables for clarity, updating pipeline.transform() to produce DataFrames, and adjusting metadata and tests accordingly. The changes look good overall, with a couple of suggestions to enhance robustness by adding checks for None values before serialization.