add streaming OLS implementation with duckdb arrow interop #17
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Pull Request Overview
This PR introduces a streaming OLS implementation that leverages DuckDB's Arrow IPC support for memory-efficient regression on large datasets. The implementation uses sufficient statistics to process data in chunks rather than loading everything into memory.
- Adds streaming regression functionality with DuckDB Arrow interop
- Implements sufficient statistics-based OLS and Ridge regression
- Provides comprehensive test coverage for the streaming implementation
Reviewed Changes
Copilot reviewed 3 out of 4 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| duckreg/stream.py | Core streaming regression implementation with Arrow IPC support |
| tests/test_stream.py | Test suite covering streaming regression functionality |
| duckreg/init.py | Exposes StreamingRegression in public API |
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| def solve_ols(self) -> np.ndarray: | ||
| """Compute OLS coefficients.""" | ||
| if self.n < self.k: |
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The condition self.n < self.k checks if there are fewer observations than features, but self.k could be None if no data has been processed yet. This will raise a TypeError when comparing int with NoneType.
| if self.n < self.k: | |
| if self.k is None or self.n < self.k: |
| feature_cols: list[str] = None, | ||
| target_col: str = None, |
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Using mutable default arguments (None) should be replaced with proper Optional type annotations and default to None explicitly. The type hints should be Optional[list[str]] and Optional[str].
| feature_cols: list[str] = None, | |
| target_col: str = None, | |
| feature_cols: Optional[list[str]] = None, | |
| target_col: Optional[str] = None, |
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