Clutter_chuva/etc/tests/test_statistics.py

import numpy as np
import pytest
from scipy.stats import gamma, expon, norm
import sys
import os

sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

from tools.statistics import aic_statistic, bic_statistic
from fitting.fitter import Fitter


RNG = np.random.default_rng(42)
GAMMA_DATA = RNG.gamma(shape=2.0, scale=1.5, size=200)


# ── aic_statistic unit tests ──────────────────────────────────────────────────


class TestAicStatistic:
    def _fitted_dist(self, dist, data, **kwargs):
        """Return a frozen distribution fitted to data."""
        params = dist.fit(data, **kwargs)
        return dist(*params)

    def test_returns_float(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        result = aic_statistic(frozen, GAMMA_DATA, axis=0)
        assert isinstance(float(result), float)

    def test_formula_correct(self):
        """AIC = 2k - 2*log_likelihood."""
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        k = len(frozen.args)
        log_likelihood = np.sum(frozen.logpdf(GAMMA_DATA), axis=0)
        expected = 2 * k - 2 * log_likelihood
        assert pytest.approx(aic_statistic(frozen, GAMMA_DATA, axis=0)) == expected

    def test_penalises_more_parameters(self):
        """gamma (3 params) should have higher AIC penalty term than expon (2 params)
        when both are fitted to the same data with identical log-likelihood contribution."""
        gamma_frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        expon_frozen = self._fitted_dist(expon, GAMMA_DATA, floc=0)
        # penalty term alone: 2*k; gamma has more params so its penalty is larger
        assert 2 * len(gamma_frozen.args) > 2 * len(expon_frozen.args)

    def test_better_fit_has_lower_aic(self):
        """Gamma fitted to gamma data should have lower AIC than normal fitted to gamma data."""
        gamma_frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        norm_frozen = self._fitted_dist(norm, GAMMA_DATA)
        aic_gamma = aic_statistic(gamma_frozen, GAMMA_DATA, axis=0)
        aic_norm = aic_statistic(norm_frozen, GAMMA_DATA, axis=0)
        assert aic_gamma < aic_norm

    def test_works_with_axis_none(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        result = aic_statistic(frozen, GAMMA_DATA, axis=None)
        assert np.isfinite(result)

    def test_result_is_finite(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        assert np.isfinite(aic_statistic(frozen, GAMMA_DATA, axis=0))


# ── Integration: aic_statistic as callable in Fitter ─────────────────────────


class TestAicStatisticInFitter:
    def test_fitter_accepts_aic_callable(self):
        f = Fitter([gamma], statistic_method=aic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert f["gamma"].test_result is not None

    def test_fitter_aic_statistic_is_finite(self):
        f = Fitter([gamma], statistic_method=aic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert np.isfinite(f["gamma"].gof_statistic)

    def test_fitter_aic_pvalue_in_range(self):
        f = Fitter([gamma], statistic_method=aic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        pval = f["gamma"].pvalue
        assert 0.0 <= pval <= 1.0

    def test_fitter_aic_vs_ad_different_statistic_values(self):
        """AIC and AD statistics should differ numerically."""
        f_aic = Fitter(
            [gamma], statistic_method=aic_statistic, gamma_params={"floc": 0}
        )
        f_ad = Fitter([gamma], statistic_method="ad", gamma_params={"floc": 0})
        f_aic.fit(GAMMA_DATA)
        f_ad.fit(GAMMA_DATA)
        f_aic.validate(n_mc_samples=99)
        f_ad.validate(n_mc_samples=99)
        assert f_aic["gamma"].gof_statistic != pytest.approx(
            f_ad["gamma"].gof_statistic
        )

    def test_fitter_aic_multiple_distributions(self):
        f = Fitter(
            [gamma, expon],
            statistic_method=aic_statistic,
            gamma_params={"floc": 0},
            expon_params={"floc": 0},
        )
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert f["gamma"].test_result is not None
        assert f["expon"].test_result is not None


# ── bic_statistic unit tests ──────────────────────────────────────────────────


class TestBicStatistic:
    def _fitted_dist(self, dist, data, **kwargs):
        """Return a frozen distribution fitted to data."""
        params = dist.fit(data, **kwargs)
        return dist(*params)

    def test_returns_float(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        result = bic_statistic(frozen, GAMMA_DATA, axis=0)
        assert isinstance(float(result), float)

    def test_formula_correct(self):
        """BIC = ln(n)*k - 2*log_likelihood."""
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        n = len(GAMMA_DATA)
        k = len(frozen.args)
        log_likelihood = np.sum(frozen.logpdf(GAMMA_DATA), axis=0)
        expected = np.log(n) * k - 2 * log_likelihood
        assert pytest.approx(bic_statistic(frozen, GAMMA_DATA, axis=0)) == expected

    def test_penalises_more_parameters(self):
        """gamma (3 params) should have higher BIC penalty term than expon (2 params)."""
        gamma_frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        expon_frozen = self._fitted_dist(expon, GAMMA_DATA, floc=0)
        n = len(GAMMA_DATA)
        assert np.log(n) * len(gamma_frozen.args) > np.log(n) * len(expon_frozen.args)

    def test_better_fit_has_lower_bic(self):
        """Gamma fitted to gamma data should have lower BIC than normal fitted to gamma data."""
        gamma_frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        norm_frozen = self._fitted_dist(norm, GAMMA_DATA)
        bic_gamma = bic_statistic(gamma_frozen, GAMMA_DATA, axis=0)
        bic_norm = bic_statistic(norm_frozen, GAMMA_DATA, axis=0)
        assert bic_gamma < bic_norm

    def test_works_with_axis_none(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        result = bic_statistic(frozen, GAMMA_DATA, axis=None)
        assert np.isfinite(result)

    def test_result_is_finite(self):
        frozen = self._fitted_dist(gamma, GAMMA_DATA, floc=0)
        assert np.isfinite(bic_statistic(frozen, GAMMA_DATA, axis=0))


# ── Integration: bic_statistic as callable in Fitter ─────────────────────────


class TestBicStatisticInFitter:
    def test_fitter_accepts_bic_callable(self):
        f = Fitter([gamma], statistic_method=bic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert f["gamma"].test_result is not None

    def test_fitter_bic_statistic_is_finite(self):
        f = Fitter([gamma], statistic_method=bic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert np.isfinite(f["gamma"].gof_statistic)

    def test_fitter_bic_pvalue_in_range(self):
        f = Fitter([gamma], statistic_method=bic_statistic, gamma_params={"floc": 0})
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        pval = f["gamma"].pvalue
        assert 0.0 <= pval <= 1.0

    def test_fitter_bic_vs_ad_different_statistic_values(self):
        """BIC and AD statistics should differ numerically."""
        f_bic = Fitter(
            [gamma], statistic_method=bic_statistic, gamma_params={"floc": 0}
        )
        f_ad = Fitter([gamma], statistic_method="ad", gamma_params={"floc": 0})
        f_bic.fit(GAMMA_DATA)
        f_ad.fit(GAMMA_DATA)
        f_bic.validate(n_mc_samples=99)
        f_ad.validate(n_mc_samples=99)
        assert f_bic["gamma"].gof_statistic != pytest.approx(
            f_ad["gamma"].gof_statistic
        )

    def test_fitter_bic_multiple_distributions(self):
        f = Fitter(
            [gamma, expon],
            statistic_method=bic_statistic,
            gamma_params={"floc": 0},
            expon_params={"floc": 0},
        )
        f.fit(GAMMA_DATA)
        f.validate(n_mc_samples=99)
        assert f["gamma"].test_result is not None
        assert f["expon"].test_result is not None