Rename gcm stats unit tests
Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>
This commit is contained in:
Patrick Bloebaum
Patrick Blöbaum
Родитель
86e0b1c78d
Коммит
3b5989078a
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@ -15,7 +15,7 @@ from dowhy.gcm.util.general import geometric_median
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@flaky(max_runs=5)
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def test_estimate_geometric_median():
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def test_when_estimate_geometric_median_then_returns_correct_median_vector():
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a = np.random.normal(10, 1, 100)
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a = np.hstack([a, np.random.normal(10000, 1, 20)])
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b = np.random.normal(-5, 1, 100)
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@ -27,7 +27,7 @@ def test_estimate_geometric_median():
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assert gm[1] == approx(-5, abs=0.5)
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def test_quantile_based_fwer():
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def test_when_apply_quantile_based_fwer_control_then_returns_single_adjusted_pvalue():
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p_values = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1])
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assert quantile_based_fwer(p_values, quantile=0.5) == 0.055 / 0.5
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assert quantile_based_fwer(p_values, quantile=0.25) == 0.0325 / 0.25
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@ -47,7 +47,7 @@ def test_given_p_values_with_nans_when_using_quantile_based_fwer_then_ignores_th
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assert quantile_based_fwer(p_values, quantile=0.5) == 0.055 / 0.5
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def test_quantile_based_fwer_scaling():
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def test_given_p_values_with_scaling_when_apply_quantile_based_fwer_control_then_returns_single_adjusted_pvalue():
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p_values = np.array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1])
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p_values_scaling = np.array([2, 2, 1, 2, 1, 3, 1, 2, 4, 1])
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@ -56,7 +56,7 @@ def test_quantile_based_fwer_scaling():
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assert quantile_based_fwer(p_values, p_values_scaling, quantile=0.75) == approx(0.193, abs=0.001)
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def test_quantile_based_fwer_raises_error():
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def test_given_invalid_inputs_when_apply_quantile_based_fwer_control_then_raises_error():
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with pytest.raises(ValueError):
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assert quantile_based_fwer(np.array([0.1, 0.5, 1]), quantile=0)
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@ -70,7 +70,7 @@ def test_quantile_based_fwer_raises_error():
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assert quantile_based_fwer(np.array([0.1, 0.5, 1]), quantile=-0.5)
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def test_marginal_expectation_returns_all_results():
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def test_when_evaluate_marginal_expectation_without_averaging_result_then_returned_results_have_correct_format():
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# Just checking formats, i.e. no need for correlation.
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X = np.random.normal(0, 1, (1000, 3))
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Y = np.random.normal(0, 1, (1000, 1))
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@ -84,7 +84,7 @@ def test_marginal_expectation_returns_all_results():
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assert results.shape[2] == 1
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def test_marginal_expectation_returns_reduced_results():
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def test_when_evaluate_marginal_expectation_with_averaging_result_then_returned_results_have_correct_format():
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# Just checking formats, i.e. no need for correlation.
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X = np.random.normal(0, 1, (1000, 3))
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Y = np.random.normal(0, 1, (1000, 1))
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@ -98,7 +98,7 @@ def test_marginal_expectation_returns_reduced_results():
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@flaky(max_runs=5)
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def test_marginal_expectation_independent_continuous_linear():
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def test_given_linear_data_when_evaluate_marginal_expectation_then_returns_correct_marginal_expectations():
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X = np.random.normal(0, 1, (1000, 3))
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Y = 3 * X[:, 0] + 2 * X[:, 1] - X[:, 2]
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Y = Y.reshape(-1)
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@ -187,7 +187,7 @@ def test_marginal_expectation_independent_continuous_linear():
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@flaky(max_runs=5)
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def test_marginal_expectation_independent_categorical_linear():
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def test_given_linear_categorical_data_when_evaluate_marginal_expectation_then_returns_correct_marginal_expectations():
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X = np.random.normal(0, 1, (1000, 3))
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Y = 3 * X[:, 0] + 2 * X[:, 1] - X[:, 2]
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Y = (Y <= 0).reshape(-1)
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@ -271,7 +271,7 @@ def test_marginal_expectation_independent_categorical_linear():
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@flaky(max_runs=5)
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def test_marginal_expectation_independent_continuous_nonlinear():
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def test_given_nonlinear_data_when_evaluate_marginal_expectation_then_returns_correct_marginal_expectations():
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X = np.random.normal(0, 1, (2000, 3))
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Y = (2 * X[:, 0] + X[:, 1]) ** 2 + X[:, 2]
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Y = Y.reshape(-1)
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@ -327,7 +327,7 @@ def test_marginal_expectation_independent_continuous_nonlinear():
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@flaky(max_runs=5)
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def test_marginal_expectation_independent_categorical_nonlinear():
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def test_given_nonlinear_categorical_data_when_evaluate_marginal_expectation_then_returns_correct_marginal_expectations():
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X = np.random.normal(0, 1, (1000, 3))
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Y = (2 * X[:, 0] + X[:, 1]) ** 2 + X[:, 2]
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Y = (Y <= np.mean(Y)).reshape(-1)
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@ -410,7 +410,7 @@ def test_marginal_expectation_independent_categorical_nonlinear():
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) == approx(0.2, abs=1)
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def test_given_different_batch_sizes_when_estimating_marginal_expectation_then_returns_expected_result():
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def test_given_different_batch_sizes_when_estimating_marginal_expectation_then_returns_correct_marginal_expectations():
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X = np.random.normal(0, 1, (34, 3))
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feature_samples = np.random.normal(0, 1, (123, 3))
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expected_non_aggregated = np.array([repmat(X[i, :], feature_samples.shape[0], 1) for i in range(X.shape[0])])
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@ -448,8 +448,8 @@ def test_given_different_batch_sizes_when_estimating_marginal_expectation_then_r
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) == approx(expected_non_aggregated)
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@flaky(max_runs=2)
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def test_given_linear_dependent_data_when_estimate_ftest_pvalue_then_returns_expected_result():
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@flaky(max_runs=3)
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def test_given_linear_dependent_data_when_estimate_ftest_pvalue_then_returns_smaller_05():
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X_training = np.random.normal(0, 1, 1000)
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Y_training = X_training + np.random.normal(0, 0.05, 1000)
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@ -458,14 +458,20 @@ def test_given_linear_dependent_data_when_estimate_ftest_pvalue_then_returns_exp
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assert estimate_ftest_pvalue(X_training, np.array([]), Y_training, X_test, np.array([]), Y_test) < 0.05
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@flaky(max_runs=3)
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def test_given_linear_independent_data_when_estimate_ftest_pvalue_then_returns_pvalue_greater_05():
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X_training = np.random.normal(0, 1, 1000)
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Y_training = np.random.normal(0, 0.05, 1000)
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X_test = np.random.normal(0, 1, 1000)
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Y_test = np.random.normal(0, 0.05, 1000)
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assert estimate_ftest_pvalue(X_training, np.array([]), Y_training, X_test, np.array([]), Y_test) >= 0.05
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@flaky(max_runs=2)
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def test_given_multivariate_dependent_data_when_estimate_ftest_pvalue_then_returns_expected_result():
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@flaky(max_runs=3)
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def test_given_multivariate_dependent_data_when_estimate_ftest_pvalue_then_returns_pvalue_smaller_05():
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X1_training = np.random.normal(0, 1, 1000)
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X2_training = np.random.normal(0, 1, 1000)
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Y_training = X1_training + X2_training + np.random.normal(0, 0.05, 1000)
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@ -486,7 +492,15 @@ def test_given_multivariate_dependent_data_when_estimate_ftest_pvalue_then_retur
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< 0.05
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)
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@flaky(max_runs=3)
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def test_given_multivariate_independent_data_when_estimate_ftest_pvalue_then_returns_pvalue_greater_05():
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X1_training = np.random.normal(0, 1, 1000)
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X2_training = np.random.normal(0, 1, 1000)
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Y_training = X1_training + np.random.normal(0, 0.05, 1000)
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X1_test = np.random.normal(0, 1, 1000)
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X2_test = np.random.normal(0, 1, 1000)
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Y_test = X1_test + np.random.normal(0, 0.05, 1000)
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assert (
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