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feat(ga): Overhaul GA for multi-root robustness and CPU performance
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### 🚀 Performance (CPU)
* Replaces `np.polyval` with a parallel Numba JIT function (`_calculate_ranks_numba`).
* Replaces $O(N \log N)$ `np.argsort` with $O(N)$ `np.argpartition` in the GA loop.
* Adds `numba` as a core dependency.

### 🧠 Robustness (Algorithm)
* Implements Blend Crossover (BLX-$\alpha$) for better, extrapolative exploration.
* Uses a hybrid selection model (top X% for crossover, 100% for mutation) to preserve root niches.
* Adds `selection_percentile` and `blend_alpha` to `GA_Options` for tuning.
This commit was merged in pull request #21.
This commit is contained in:
Jonathan Rampersad
2025-10-30 11:31:00 -04:00
parent 1318006959
commit 9d967210fa
4 changed files with 132 additions and 51 deletions
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14
tests/test_polysolve.py
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@@ -1,5 +1,6 @@
import pytest
import numpy as np
import numpy.testing as npt
# Try to import cupy to check for CUDA availability
try:
@@ -101,7 +102,7 @@ def test_get_real_roots_numpy(quadratic_func):
Tests that the NumPy-based genetic algorithm approximates the roots correctly.
"""
# Using more generations for higher accuracy in testing
ga_opts = GA_Options(num_of_generations=50, data_size=200000, root_precision=3)
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
roots = quadratic_func.get_real_roots(ga_opts, use_cuda=False)
@@ -109,11 +110,7 @@ def test_get_real_roots_numpy(quadratic_func):
# We don't know which order they'll be in, so we check for presence.
expected_roots = np.array([-1.0, 2.5])
# Check that at least one found root is close to -1.0
assert np.any(np.isclose(roots, expected_roots[0], atol=1e-2))
# Check that at least one found root is close to 2.5
assert np.any(np.isclose(roots, expected_roots[1], atol=1e-2))
npt.assert_allclose(np.sort(roots), np.sort(expected_roots), atol=1e-2)
@pytest.mark.skipif(not _CUPY_AVAILABLE, reason="CuPy is not installed, skipping CUDA test.")
@@ -124,13 +121,12 @@ def test_get_real_roots_cuda(quadratic_func):
It will be skipped automatically if CuPy is not available.
"""
ga_opts = GA_Options(num_of_generations=50, data_size=200000, root_precision=3)
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
roots = quadratic_func.get_real_roots(ga_opts, use_cuda=True)
expected_roots = np.array([-1.0, 2.5])
# Verify that the CUDA implementation also finds the correct roots within tolerance.
assert np.any(np.isclose(roots, expected_roots[0], atol=1e-2))
assert np.any(np.isclose(roots, expected_roots[1], atol=1e-2))
npt.assert_allclose(np.sort(roots), np.sort(expected_roots), atol=1e-2)