FeatureKit upgrade

[terbed]

Summary

This pull request introduces significant improvements to the feature engineering pipeline in FinMLKit, focusing on enhanced composability, efficiency, and extensibility. The main highlights are the addition of the ExternalFunction transform for integrating third-party libraries, improved caching and short-circuiting in mathematical operations, and new documentation and tutorials to support these capabilities.

Feature pipeline enhancements:

• Added ExternalFunction transform to allow wrapping external Python callables (including by import path) as pipeline steps, with full support for serialization, multiple outputs, and NumPy/pandas compatibility. This enables seamless integration of third-party libraries like TA-Lib into feature pipelines.
• Improved mathematical operation transforms (AddOp, ScalarOp, UnaryOp, MinMaxOp) to support output caching and short-circuiting: if a required output column is already present in the DataFrame, computation is skipped and the cached column is reused. This optimizes performance in complex, dependency-rich pipelines. [1] [2] [3] [4]
• All mathematical operation transforms now store their op_name for better introspection and debugging. [1] [2] [3] [4]
• JSON Serialization for FeatureKit to support FeatureKit configuration export and import to improve trancparency and reproducibility.
• Build a computational graph and topological execution to improve efficacy.

Documentation and tutorials:

• Added a comprehensive tutorial (feature_pipelines.rst) covering Compose, FeatureKit, the computation graph, caching, reproducibility, and integration with external libraries using ExternalFunction.
• Registered the new tutorial in the documentation index for discoverability.
• Expanded the README.md to document new capabilities: computational graph, optimized/caching-aware execution, reproducibility via JSON serialization, and external library integration.

Other improvements:

• Fixed naming in the SMA transform to use output_name for consistency.
• Updated type imports in transforms.py for clarity and future extensibility.

These changes make it easier to build, debug, and extend sophisticated feature pipelines, while ensuring efficient execution and reproducibility.

Related Issues

• Closes #

Testing

• NUMBA_DISABLE_JIT=1 pytest -q
• numba enabled tests

Documentation

• Added/updated docstrings
• Updated README or other docs

Checklist

• Tests added or updated
• Documentation added or updated
• Linting and type checks pass

[Copilot]

Pull Request Overview

This PR significantly enhances the FeatureKit framework in FinMLKit with major improvements to feature engineering capabilities, focusing on composability, performance optimization, and reproducibility. The key enhancements include adding support for external library integration, implementing caching-aware execution pipelines, and introducing comprehensive serialization capabilities.

Key changes:

• Added ExternalFunction transform for seamless integration of third-party libraries (e.g., TA-Lib, NumPy) with full serialization support
• Implemented intelligent caching and short-circuiting in mathematical operation transforms to optimize performance in complex pipelines
• Added JSON serialization/deserialization for Features and FeatureKit with complete configuration export/import capabilities

Reviewed Changes

Copilot reviewed 15 out of 15 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
finmlkit/feature/transforms.py	Added ExternalFunction transform class for external library integration
finmlkit/feature/kit.py	Major expansion with serialization framework, computation graph, and enhanced FeatureKit capabilities
finmlkit/feature/base.py	Enhanced mathematical operation transforms with caching and op_name storage
tests/features/test_*.py	Comprehensive test suite for new serialization, external functions, and caching features
docs/source/tutorials/feature_pipelines.rst	New tutorial covering advanced FeatureKit capabilities
examples/QuickStartGuide.ipynb	Updated with demonstrations of new features and capabilities
README.md	Updated to document new computational graph, caching, and external integration features

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