Module neuralops.tests.test_analysis_stats

Unit tests for statistical correction and hypothesis testing utilities.

Classes

class TestHypothesisTests

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class TestHypothesisTests:
    """Test non-parametric paired tests and contingency table tests."""

    def test_wilcoxon_signed_rank_test(self):
        x = np.array([1.2, 2.5, 3.1, 4.0, 5.2, 6.1])
        y = np.array([1.0, 2.2, 2.9, 3.8, 5.0, 5.9])
        res = wilcoxon_signed_rank_test(x, y)
        assert isinstance(res, dict)
        assert "statistic" in res
        assert "p_value" in res
        assert 0.0 <= res["p_value"] <= 1.0

    def test_sign_test(self):
        x = np.array([10.0, 12.0, 15.0, 14.0, 16.0])
        y = np.array([9.0, 11.0, 13.0, 13.5, 15.5])
        res = sign_test(x, y)
        assert isinstance(res, dict)
        assert "wins_a" in res
        assert "wins_b" in res
        assert 0.0 <= res["p_value"] <= 1.0

    def test_mcnemar_test(self):
        y_true = np.array([1, 0, 1, 1, 0, 1, 0, 0, 1, 1])
        pred_a = np.array([1, 0, 1, 0, 0, 1, 0, 1, 1, 1])
        pred_b = np.array([1, 1, 1, 1, 0, 0, 0, 0, 1, 1])
        res = mcnemar_test(y_true, pred_a, pred_b)
        assert isinstance(res, dict)
        assert "chi2" in res
        assert "p_value" in res
        assert 0.0 <= res["p_value"] <= 1.0

Test non-parametric paired tests and contingency table tests.

Methods

def test_mcnemar_test(self)

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def test_mcnemar_test(self):
    y_true = np.array([1, 0, 1, 1, 0, 1, 0, 0, 1, 1])
    pred_a = np.array([1, 0, 1, 0, 0, 1, 0, 1, 1, 1])
    pred_b = np.array([1, 1, 1, 1, 0, 0, 0, 0, 1, 1])
    res = mcnemar_test(y_true, pred_a, pred_b)
    assert isinstance(res, dict)
    assert "chi2" in res
    assert "p_value" in res
    assert 0.0 <= res["p_value"] <= 1.0

def test_sign_test(self)

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def test_sign_test(self):
    x = np.array([10.0, 12.0, 15.0, 14.0, 16.0])
    y = np.array([9.0, 11.0, 13.0, 13.5, 15.5])
    res = sign_test(x, y)
    assert isinstance(res, dict)
    assert "wins_a" in res
    assert "wins_b" in res
    assert 0.0 <= res["p_value"] <= 1.0

def test_wilcoxon_signed_rank_test(self)

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def test_wilcoxon_signed_rank_test(self):
    x = np.array([1.2, 2.5, 3.1, 4.0, 5.2, 6.1])
    y = np.array([1.0, 2.2, 2.9, 3.8, 5.0, 5.9])
    res = wilcoxon_signed_rank_test(x, y)
    assert isinstance(res, dict)
    assert "statistic" in res
    assert "p_value" in res
    assert 0.0 <= res["p_value"] <= 1.0

class TestMultipleTestingCorrections

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class TestMultipleTestingCorrections:
    """Test family-wise error rate and false discovery rate corrections."""

    def test_holm_correction_monotonicity(self):
        p_vals = np.array([0.01, 0.04, 0.03, 0.005])
        adj_p = holm_correction(p_vals)
        assert len(adj_p) == len(p_vals)
        assert np.all(adj_p >= p_vals)
        assert np.all(adj_p <= 1.0)

    def test_hochberg_correction(self):
        p_vals = np.array([0.001, 0.02, 0.08])
        adj_p = hochberg_correction(p_vals)
        assert len(adj_p) == len(p_vals)
        assert adj_p[0] < adj_p[2]

    def test_finner_correction(self):
        p_vals = np.array([0.002, 0.01, 0.05, 0.2])
        adj_p = finner_correction(p_vals)
        assert len(adj_p) == len(p_vals)
        assert np.all(adj_p >= 0.0)
        assert np.all(adj_p <= 1.0)

Test family-wise error rate and false discovery rate corrections.

Methods

def test_finner_correction(self)

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def test_finner_correction(self):
    p_vals = np.array([0.002, 0.01, 0.05, 0.2])
    adj_p = finner_correction(p_vals)
    assert len(adj_p) == len(p_vals)
    assert np.all(adj_p >= 0.0)
    assert np.all(adj_p <= 1.0)

def test_hochberg_correction(self)

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def test_hochberg_correction(self):
    p_vals = np.array([0.001, 0.02, 0.08])
    adj_p = hochberg_correction(p_vals)
    assert len(adj_p) == len(p_vals)
    assert adj_p[0] < adj_p[2]

def test_holm_correction_monotonicity(self)

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def test_holm_correction_monotonicity(self):
    p_vals = np.array([0.01, 0.04, 0.03, 0.005])
    adj_p = holm_correction(p_vals)
    assert len(adj_p) == len(p_vals)
    assert np.all(adj_p >= p_vals)
    assert np.all(adj_p <= 1.0)