| """Tests for LogisticModel: eval, derivative correctness, and numerical gradient check.""" |
|
|
| from __future__ import annotations |
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|
| import math |
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| from mlenergy_data.modeling import LogisticModel |
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| def test_eval_at_midpoint(): |
| """At x = x0, sigmoid = 0.5, so y = b0 + L/2.""" |
| lp = LogisticModel(L=100.0, x0=5.0, k=1.0, b0=50.0) |
| assert abs(lp.eval_x(5.0) - 100.0) < 1e-10 |
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|
| def test_eval_at_extremes(): |
| """Far from x0, sigmoid approaches 0 or 1.""" |
| lp = LogisticModel(L=100.0, x0=5.0, k=2.0, b0=10.0) |
| |
| assert abs(lp.eval_x(100.0) - 110.0) < 1e-5 |
| |
| assert abs(lp.eval_x(-100.0) - 10.0) < 1e-5 |
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|
| def test_eval_batch(): |
| """eval(batch) should equal eval_x(log2(batch)).""" |
| lp = LogisticModel(L=200.0, x0=7.0, k=0.5, b0=30.0) |
| for batch in [8, 16, 32, 64, 128, 256, 512]: |
| x = math.log2(batch) |
| assert abs(lp.eval(batch) - lp.eval_x(x)) < 1e-12 |
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|
| def test_derivative_numerical_gradient(): |
| """Analytical derivative should match numerical finite-difference gradient.""" |
| lp = LogisticModel(L=150.0, x0=6.0, k=1.5, b0=20.0) |
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|
| eps = 1e-7 |
| for x in [3.0, 5.0, 6.0, 7.0, 9.0]: |
| analytical = lp.deriv_wrt_x(x) |
| numerical = (lp.eval_x(x + eps) - lp.eval_x(x - eps)) / (2 * eps) |
| assert abs(analytical - numerical) < 1e-4, ( |
| f"Gradient mismatch at x={x}: analytical={analytical:.8f}, numerical={numerical:.8f}" |
| ) |
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|
| def test_derivative_sign(): |
| """Positive k and L means derivative is positive (increasing sigmoid).""" |
| lp = LogisticModel(L=100.0, x0=5.0, k=1.0, b0=0.0) |
| for x in [3.0, 5.0, 7.0]: |
| assert lp.deriv_wrt_x(x) > 0 |
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| |
| lp_neg = LogisticModel(L=-100.0, x0=5.0, k=1.0, b0=200.0) |
| for x in [3.0, 5.0, 7.0]: |
| assert lp_neg.deriv_wrt_x(x) < 0 |
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|
| def test_derivative_peak_at_midpoint(): |
| """Derivative is maximized at x = x0.""" |
| lp = LogisticModel(L=100.0, x0=5.0, k=2.0, b0=0.0) |
| d_mid = lp.deriv_wrt_x(5.0) |
| d_off = lp.deriv_wrt_x(3.0) |
| assert d_mid > d_off |
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|
| def test_numerical_stability_large_input(): |
| """Should not overflow for large inputs.""" |
| lp = LogisticModel(L=100.0, x0=5.0, k=1.0, b0=0.0) |
| assert math.isfinite(lp.eval_x(1000.0)) |
| assert math.isfinite(lp.eval_x(-1000.0)) |
| assert math.isfinite(lp.deriv_wrt_x(1000.0)) |
| assert math.isfinite(lp.deriv_wrt_x(-1000.0)) |
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