mirror of
https://github.com/slsdetectorgroup/aare.git
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9661aef013
## Summary Makes the minimizer behind `FitModel` selectable and adds two built-in, dependency-free solvers next to Minuit2's Migrad and Fumili. `Minimizer.Migrad` stays the default, so existing fits behave as before. - **`Minimizer` enum** (C++ `FitModel::SetMinimizer`, Python `minimizer` constructor argument and property): `Migrad`, `Fumili`, `LevenbergMarquardt`, `VarPro`. Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Co-authored-by: Khalil Ferjaoui <khalilferjaoui@yahoo.fr>
402 lines
14 KiB
Python
402 lines
14 KiB
Python
# SPDX-License-Identifier: MPL-2.0
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import numpy as np
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import pytest
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import aare
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MINIMIZERS = [
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aare.Minimizer.Migrad,
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aare.Minimizer.Fumili,
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aare.Minimizer.LevenbergMarquardt,
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aare.Minimizer.VarPro,
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]
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def gaussian(x, A, mu, sigma):
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return A * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
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def uses_minuit2(minimizer):
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return minimizer not in (
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aare.Minimizer.LevenbergMarquardt,
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aare.Minimizer.VarPro,
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)
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@pytest.fixture(params=MINIMIZERS, ids=lambda m: m.name)
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def minimizer(request):
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return request.param
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@pytest.fixture
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def gaussian_data():
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x = np.linspace(-6.0, 6.0, 61)
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y = gaussian(x, 120.0, 0.8, 1.3)
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y_err = np.ones_like(y)
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return x, y, y_err
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def test_gaussian_model_evaluates_and_fits_data(minimizer):
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x = np.linspace(-5.0, 5.0, 51)
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expected = np.array([20.0, 0.5, 1.2])
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model = aare.Gaussian(minimizer=minimizer)
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y = model(x, expected)
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result = model.fit(x, y)
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np.testing.assert_allclose(result["par"], expected, atol=2e-3)
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def test_minimizer_can_be_selected():
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assert aare.Gaussian().minimizer == aare.Minimizer.Migrad
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assert set(aare.Minimizer.__members__) == {
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"Migrad",
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"Fumili",
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"LevenbergMarquardt",
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"VarPro",
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}
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for minimizer in MINIMIZERS:
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assert aare.Gaussian(minimizer=minimizer).minimizer == minimizer
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model = aare.Gaussian()
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model.minimizer = aare.Minimizer.LevenbergMarquardt
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assert model.minimizer == aare.Minimizer.LevenbergMarquardt
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def test_fit_returns_errors_and_chi2(gaussian_data, minimizer):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True, minimizer=minimizer)
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result = model.fit(x, y, y_err)
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assert set(result) == {"par", "par_err", "chi2"}
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assert result["par"].shape == (3,)
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assert result["par_err"].shape == (3,)
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assert result["chi2"].shape == (1,)
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np.testing.assert_allclose(result["par"], [120.0, 0.8, 1.3], rtol=1e-4)
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np.testing.assert_allclose(
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result["par_err"], [0.361, 0.00451, 0.00451], rtol=2e-2
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)
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assert result["chi2"][0] == pytest.approx(0.0, abs=1e-3)
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without_errors = aare.fit(aare.Gaussian(minimizer=minimizer), x, y)
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assert set(without_errors) == {"par", "chi2"}
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def test_pol1_weighted_errors_match_analytic_formula(minimizer):
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x = np.arange(20, dtype=float)
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y = 2.0 + 0.5 * x
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y_err = 1.0 + 0.1 * x
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w = 1.0 / y_err**2
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S, Sx, Sxx = w.sum(), (w * x).sum(), (w * x * x).sum()
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delta = S * Sxx - Sx * Sx
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result = aare.Pol1(compute_errors=True, minimizer=minimizer).fit(x, y, y_err)
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np.testing.assert_allclose(result["par"], [2.0, 0.5], rtol=1e-6)
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np.testing.assert_allclose(
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result["par_err"],
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[np.sqrt(Sxx / delta), np.sqrt(S / delta)],
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rtol=1e-6,
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)
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def test_fixed_parameter_reports_zero_error(gaussian_data, minimizer):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True, minimizer=minimizer)
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model.FixParameter("sigma", 1.5)
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result = model.fit(x, y, y_err)
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assert result["par"][2] == 1.5
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assert result["par_err"][2] == 0.0
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assert result["par"][0] == pytest.approx(111.15, rel=1e-3)
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assert result["chi2"][0] == pytest.approx(1684.25, rel=1e-3)
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model.ReleaseParameter("sigma")
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result = model.fit(x, y, y_err)
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assert result["par"][2] == pytest.approx(1.3, rel=1e-4)
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def test_active_limit_ends_on_the_limit(gaussian_data, minimizer):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True, minimizer=minimizer)
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model.SetParLimits("mu", -5.0, 0.5)
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result = model.fit(x, y, y_err)
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margin = 1e-6 if uses_minuit2(minimizer) else 1e-9
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assert result["par"][1] == pytest.approx(0.5, abs=margin)
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assert result["par_err"][1] == 0.0
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assert result["par"][0] == pytest.approx(116.88, rel=1e-3)
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assert result["chi2"][0] == pytest.approx(4301.95, rel=1e-3)
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inactive = aare.Gaussian(minimizer=minimizer)
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inactive.SetParLimits(1, -5.0, 5.0)
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np.testing.assert_allclose(
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inactive.fit(x, y)["par"], [120.0, 0.8, 1.3], rtol=1e-4
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)
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def test_user_start_value(gaussian_data, minimizer):
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x, y, _ = gaussian_data
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model = aare.Gaussian(minimizer=minimizer)
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model.SetParameter("mu", 0.0)
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np.testing.assert_allclose(model.fit(x, y)["par"], [120.0, 0.8, 1.3], rtol=1e-4)
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def test_max_calls_failure_returns_zeros(gaussian_data, minimizer):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(max_calls=3, compute_errors=True, minimizer=minimizer)
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# start far from the minimum so that no minimizer converges in 3 calls
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model.SetParameter("mu", -3.0)
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model.SetParameter("sigma", 3.0)
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result = model.fit(x, y, y_err)
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assert np.all(result["par"] == 0.0)
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assert np.all(result["par_err"] == 0.0)
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assert np.all(result["chi2"] == 0.0)
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def test_strategy_is_ignored_by_levenberg_marquardt(gaussian_data):
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x, y, _ = gaussian_data
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lm = aare.Minimizer.LevenbergMarquardt
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a = aare.Gaussian(strategy=0, minimizer=lm).fit(x, y)
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b = aare.Gaussian(strategy=1, minimizer=lm).fit(x, y)
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np.testing.assert_array_equal(a["par"], b["par"])
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def test_invalid_parameter_access_raises():
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model = aare.Gaussian()
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assert model.par_names == ["A", "mu", "sigma"]
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assert model.n_par == 3
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with pytest.raises(RuntimeError):
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model.SetParameter("nope", 1.0)
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with pytest.raises(IndexError):
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model.SetParameter(3, 1.0)
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with pytest.raises(RuntimeError):
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model.SetParLimits("mu", 1.0, 1.0)
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def test_mismatched_array_sizes_raise(gaussian_data, minimizer):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(minimizer=minimizer)
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with pytest.raises(RuntimeError):
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model.fit(x, y[:-1])
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with pytest.raises(RuntimeError):
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model.fit(x, y, y_err[:-1])
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@pytest.mark.parametrize("cls", [aare.RisingScurve, aare.FallingScurve])
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def test_scurve_fit(cls, minimizer):
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truth = np.array([10.0, 0.1, 50.0, 3.0, 1000.0, 2.0])
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x = np.linspace(0.0, 100.0, 100)
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model = cls(max_calls=500, compute_errors=True, minimizer=minimizer)
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y = model(x, truth)
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result = model.fit(x, y, np.ones_like(y))
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np.testing.assert_allclose(result["par"], truth, rtol=1e-3)
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assert np.all(result["par_err"] > 0.0)
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@pytest.mark.parametrize("with_errors", [False, True])
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@pytest.mark.parametrize("weighted", [False, True])
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def test_fit_3d_matches_fit_1d(with_errors, weighted, minimizer):
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x = np.linspace(-6.0, 6.0, 61)
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rows, cols = 4, 3
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y = np.empty((rows, cols, x.size))
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for r in range(rows):
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for c in range(cols):
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y[r, c] = gaussian(x, 80.0 + 10.0 * r, -0.6 + 0.3 * c, 0.9 + 0.1 * r)
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y_err = np.ones_like(y) if weighted else None
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model = aare.Gaussian(compute_errors=with_errors, minimizer=minimizer)
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cube = model.fit(x, y, y_err, n_threads=3)
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expected_keys = {"par", "par_err", "chi2"} if with_errors else {"par", "chi2"}
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assert set(cube) == expected_keys
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assert cube["par"].shape == (rows, cols, 3)
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assert cube["chi2"].shape == (rows, cols)
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if with_errors:
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assert cube["par_err"].shape == (rows, cols, 3)
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for r in range(rows):
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for c in range(cols):
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single = model.fit(x, y[r, c], None if y_err is None else y_err[r, c])
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np.testing.assert_allclose(cube["par"][r, c], single["par"], rtol=1e-10)
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assert cube["chi2"][r, c] == pytest.approx(single["chi2"][0], abs=1e-10)
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if with_errors:
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np.testing.assert_allclose(
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cube["par_err"][r, c], single["par_err"], rtol=1e-10
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)
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np.testing.assert_allclose(cube["par"][3, 2], [110.0, 0.0, 1.2], rtol=1e-4, atol=1e-6)
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def test_all_minimizers_agree_on_3d_data_with_errors():
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x = np.linspace(-5.0, 5.0, 51)
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expected = np.array([20.0, 0.5, 1.2])
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single = aare.Gaussian()(x, expected)
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y = np.tile(single, (2, 3, 1))
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y_err = np.ones_like(y)
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results = {
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minimizer: aare.Gaussian(compute_errors=True, minimizer=minimizer).fit(
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x, y, y_err, n_threads=2
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)
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for minimizer in MINIMIZERS
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}
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reference = results[aare.Minimizer.Migrad]
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for minimizer, result in results.items():
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assert result["par"].shape == (2, 3, 3)
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assert result["par_err"].shape == (2, 3, 3)
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np.testing.assert_allclose(result["par"], np.broadcast_to(expected, (2, 3, 3)), atol=2e-3)
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np.testing.assert_allclose(result["chi2"], 0.0, atol=1e-3)
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np.testing.assert_allclose(result["par_err"], reference["par_err"], rtol=2e-2)
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# Regressions of the built-in solvers: fits that used to be reported as valid
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# although they had stopped away from the minimum or returned garbage.
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def test_gaussian_fit_to_noise_fails_or_stays_bounded(minimizer):
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# A width collapsing between two scan points let the amplitude diverge
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# (up to 1e159) in a fit that was still reported as valid.
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x = np.linspace(-6.0, 6.0, 61)
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y = np.random.default_rng(0).normal(0.0, 1.0, (20, 20, x.size))
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result = aare.Gaussian(minimizer=minimizer).fit(x, y, n_threads=2)
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par = result["par"]
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failed = np.all(par == 0.0, axis=-1)
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bounded = np.all(np.isfinite(par) & (np.abs(par) < 1e6), axis=-1)
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assert np.all(failed | bounded)
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assert np.all(np.isfinite(result["chi2"]))
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def test_step_reflected_onto_start_is_not_convergence(minimizer):
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# The step of +2 reflects at the limit 1 back onto the start 0; the
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# truncated step must still be tried.
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model = aare.Pol1(minimizer=minimizer)
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model.SetParameter("p0", 0.0)
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model.FixParameter("p1", 0.0)
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model.SetParLimits("p0", -1.0, 1.0)
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result = model.fit(np.arange(4.0), np.full(4, 2.2))
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assert result["par"][0] == pytest.approx(1.0, abs=1e-6)
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assert result["chi2"][0] == pytest.approx(5.76, rel=1e-6)
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@pytest.mark.parametrize(
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"amplitude, offset", [(1e8, 0.0), (1e10, 0.0), (120.0, 1e9)]
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)
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def test_large_parameter_does_not_stop_the_fit(minimizer, amplitude, offset):
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# One relative step norm over all parameters let a large amplitude or
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# position stop the fit at the start estimate of the width.
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x = np.linspace(-6.0, 6.0, 61)
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y = gaussian(x, amplitude, 0.8, 1.3)
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y_err = np.sqrt(y) + 1.0
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result = aare.Gaussian(minimizer=minimizer, max_calls=500).fit(
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x + offset, y, y_err
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)
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par = result["par"]
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assert par[0] == pytest.approx(amplitude, rel=1e-3)
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assert par[1] - offset == pytest.approx(0.8, abs=1e-3)
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assert par[2] == pytest.approx(1.3, rel=1e-3)
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def test_rejected_trial_on_limit_does_not_freeze_interior_parameter(minimizer):
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# A rejected trial truncated onto the lower limit froze mu at its start.
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x = np.linspace(-0.1, 0.1, 11)
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mu = np.sqrt(2.0 * np.log(2.0))
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y = gaussian(x, 10.0, mu, 1.0)
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model = aare.Gaussian(minimizer=minimizer)
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model.FixParameter("A", 10.0)
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model.FixParameter("sigma", 1.0)
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model.SetParameter("mu", 3.0)
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model.SetParLimits("mu", 0.0, 5.0)
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result = model.fit(x, y)
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assert result["par"][1] == pytest.approx(mu, abs=1e-3)
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assert result["chi2"][0] < 1e-4
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# Samples with a zero error are ignored, also when they hold NaN (a masked
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# dead frame): a zero weight does not remove a NaN from a sum.
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@pytest.mark.parametrize("masked", [4, 0, 9])
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def test_masked_nan_sample_is_ignored(minimizer, masked):
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# The start estimate of Pol1 uses the first and the last sample.
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x = np.arange(10.0)
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y = 2.0 + 0.5 * x
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y_err = np.ones_like(y)
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y[masked] = np.nan
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y_err[masked] = 0.0
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result = aare.Pol1(minimizer=minimizer, compute_errors=True).fit(x, y, y_err)
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np.testing.assert_allclose(result["par"], [2.0, 0.5], atol=1e-5)
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assert np.all(result["par_err"] > 0.0)
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assert result["chi2"][0] == pytest.approx(0.0, abs=1e-6)
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def test_too_few_unmasked_samples_fail(minimizer):
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x = np.arange(10.0)
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y = np.full_like(x, np.nan)
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y_err = np.zeros_like(x)
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y[0], y_err[0] = 2.0, 1.0 # one sample left for two free parameters
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result = aare.Pol1(minimizer=minimizer).fit(x, y, y_err)
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assert np.all(result["par"] == 0.0)
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assert np.all(result["chi2"] == 0.0)
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def test_masked_samples_in_cube_match_1d_fits(minimizer):
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x = np.linspace(-6.0, 6.0, 61)
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y = np.stack([gaussian(x, 80.0 + 10.0 * c, 0.8, 1.2) for c in range(4)])
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y = y.reshape(1, 4, x.size)
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y_err = np.ones_like(y)
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for col, masked in [(1, 10), (2, 0), (3, 34)]:
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y[0, col, masked] = np.nan
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y_err[0, col, masked] = 0.0
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model = aare.Gaussian(minimizer=minimizer, compute_errors=True)
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cube = model.fit(x, y, y_err, n_threads=1)
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for col in range(4):
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single = model.fit(x, y[0, col], y_err[0, col])
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np.testing.assert_allclose(cube["par"][0, col], single["par"], atol=1e-12)
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np.testing.assert_allclose(cube["par_err"][0, col], single["par_err"], atol=1e-12)
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np.testing.assert_allclose(
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cube["par"][0, col], [80.0 + 10.0 * col, 0.8, 1.2], rtol=1e-3
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)
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def test_parameter_whose_minimum_lies_on_its_limit_reports_no_error(minimizer):
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# At a minimum that lies exactly on the limit the gradient is zero.
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x = np.array([-1.0, -1.0, 1.0, 1.0])
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model = aare.Pol1(minimizer=minimizer, compute_errors=True)
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model.SetParLimits("p0", 0.0, 1.0)
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result = model.fit(x, 1.0 + 2.0 * x)
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assert result["par"][0] == pytest.approx(1.0, abs=1e-6)
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assert result["par_err"][0] == 0.0
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assert result["par_err"][1] == pytest.approx(0.5, rel=1e-6)
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@pytest.mark.parametrize("amplitude", [0.1, 1e-4])
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def test_fit_does_not_stop_at_start_without_a_step(minimizer, amplitude):
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# The EDM tolerance is absolute in chi2 units: an unweighted fit of small
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# data passes it at the start estimate (sigma 1.3617) already.
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x = np.linspace(-6.0, 6.0, 61)
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y = gaussian(x, amplitude, 0.8, 1.3)
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result = aare.Gaussian(minimizer=minimizer).fit(x, y)
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assert abs(result["par"][2] - 1.3) < 0.03 # Migrad ends at 1.3205
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one_call = aare.Gaussian(minimizer=minimizer, max_calls=1).fit(x, y)
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assert np.all(one_call["par"] == 0.0)
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@pytest.mark.parametrize("lo, span", [(8000, 1000), (20000, 500), (60000, 200)])
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def test_polynomial_far_from_zero_keeps_its_precision(minimizer, lo, span):
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# Nearly collinear columns 1, x, x^2: VarPro's normal equations lost six
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# digits at x = 20000.
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x = np.linspace(lo, lo + span, 40)
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c = np.array([1.5 - 2e-3 * lo + 3e-7 * lo**2, 2e-3 - 6e-7 * lo, 3e-7])
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y = c[0] + c[1] * x + c[2] * x**2
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result = aare.Pol2(minimizer=minimizer).fit(x, y)
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np.testing.assert_allclose(result["par"], c, rtol=1e-9)
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