import pytest import numpy as np from scipy._lib._array_api import make_xp_test_case, xp_default_dtype, is_jax from scipy._lib._array_api_no_0d import xp_assert_close from scipy import stats from scipy.stats._axis_nan_policy import SmallSampleWarning @make_xp_test_case(stats.chatterjeexi) class TestChatterjeeXi: @pytest.mark.parametrize('case', [ dict(y_cont=True, statistic=-0.303030303030303, pvalue=0.9351329808526656), dict(y_cont=False, statistic=0.07407407407407396, pvalue=0.3709859367123997)]) @pytest.mark.parametrize('dtype', ['float32', 'float64', None]) def test_against_R_XICOR(self, case, dtype, xp): # Test against R package XICOR, e.g. # library(XICOR) # options(digits=16) # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335, # 0.6651317324378386, 0.6905752850115503, 0.06115250587536558, # 0.5209906494474178, 0.3155763519785274, 0.18405731803625924, # 0.8613557911541495) # y = c(0.8402081904493103, 0.5946972833914318, 0.23481606164114155, # 0.49754786197715384, 0.9146460831206026, 0.5848057749217579, # 0.7620801065573549, 0.31410063302647495, 0.7935620302236199, # 0.5423085761365468) # xicor(x, y, ties=FALSE, pvalue=TRUE) if dtype == 'float32' and np.__version__ < "2": pytest.skip("Scalar dtypes only respected after NEP 50.") dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype) rng = np.random.default_rng(25982435982346983) x = rng.random(size=10) y = (rng.random(size=10) if case['y_cont'] else rng.integers(0, 5, size=10)) x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype) res = stats.chatterjeexi(x, y, y_continuous=case['y_cont']) xp_assert_close(res.statistic, xp.asarray(case['statistic'], dtype=dtype)) xp_assert_close(res.pvalue, xp.asarray(case['pvalue'], dtype=dtype)) @pytest.mark.parametrize('y_continuous', (False, True)) def test_permutation_asymptotic(self, y_continuous): # XICOR doesn't seem to perform the permutation test as advertised, so # compare the result of a permutation test against an asymptotic test. rng = np.random.default_rng(2524579827426) n = np.floor(rng.uniform(100, 150)).astype(int) shape = (2, n) x = rng.random(size=shape) y = (rng.random(size=shape) if y_continuous else rng.integers(0, 10, size=shape)) method = stats.PermutationMethod(rng=rng) res = stats.chatterjeexi(x, y, method=method, y_continuous=y_continuous, axis=-1) ref = stats.chatterjeexi(x, y, y_continuous=y_continuous, axis=-1) np.testing.assert_allclose(res.statistic, ref.statistic, rtol=1e-15) np.testing.assert_allclose(res.pvalue, ref.pvalue, rtol=2e-2) def test_input_validation(self, xp): rng = np.random.default_rng(25932435798274926) x, y = rng.random(size=(2, 10)) x, y = xp.asarray(x), xp.asarray(y) message = 'Array shapes are incompatible for broadcasting.|Incompatible shapes' with pytest.raises((ValueError, TypeError), match=message): stats.chatterjeexi(x, y[:-1]) if not is_jax(xp): # jax misses out on some input validation from _axis_nan_policy decorator message = '...axis 10 is out of bounds for array...|out of range' with pytest.raises((ValueError, IndexError), match=message): stats.chatterjeexi(x, y, axis=10) message = '`y_continuous` must be boolean.' with pytest.raises(ValueError, match=message): stats.chatterjeexi(x, y, y_continuous='a herring') message = "`method` must be 'asymptotic' or" with pytest.raises(ValueError, match=message): stats.chatterjeexi(x, y, method='ekki ekii') @pytest.mark.skip_xp_backends('jax.numpy', reason='no SmallSampleWarning (lazy)') def test_special_cases(self, xp): message = 'One or more sample arguments is too small...' with pytest.warns(SmallSampleWarning, match=message): res = stats.chatterjeexi(xp.asarray([1]), xp.asarray([2])) assert xp.isnan(res.statistic) assert xp.isnan(res.pvalue) @make_xp_test_case(stats.spearmanrho) class TestSpearmanRho: @pytest.mark.parametrize('alternative, statistic, pvalue', [ ('two-sided', -0.2727272727272727, 0.4458383415428), ('greater', -0.2727272727272727, 0.7770808292286), ('less', -0.2727272727272727, 0.2229191707714), ]) @pytest.mark.parametrize('dtype', ['float32', 'float64', None]) def test_against_R_cor_test(self, alternative, statistic, pvalue, dtype, xp): # Test against R cor.test, e.g. # options(digits=16) # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335, # 0.6651317324378386, 0.6905752850115503, 0.06115250587536558, # 0.5209906494474178, 0.3155763519785274, 0.18405731803625924, # 0.8613557911541495) # y = c(0.8402081904493103, 0.5946972833914318, 0.23481606164114155, # 0.49754786197715384, 0.9146460831206026, 0.5848057749217579, # 0.7620801065573549, 0.31410063302647495, 0.7935620302236199, # 0.5423085761365468) # cor.test(x, y, method='spearman', alternative='t', exact=FALSE) if dtype == 'float32' and np.__version__ < "2": pytest.skip("Scalar dtypes only respected after NEP 50.") dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype) rng = np.random.default_rng(25982435982346983) x = rng.random(size=10) y = rng.random(size=10) x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype) res = stats.spearmanrho(x, y, alternative=alternative) xp_assert_close(res.statistic, xp.asarray(statistic, dtype=dtype)) xp_assert_close(res.pvalue, xp.asarray(pvalue, dtype=dtype)) @pytest.mark.parametrize('alternative, statistic, pvalue', [ ('two-sided', -0.4857142857142857, 0.3555555555556), ('greater', -0.4857142857142857, 0.8513888888889), ('less', -0.4857142857142857, 0.1777777777778), ]) def test_against_R_cor_test_exact(self, alternative, statistic, pvalue): xp = np # will test for multiple backends when gh-23772 merges # Test against R cor.test exact=TRUE, e.g. # options(digits=16) # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335, # 0.6651317324378386, 0.6905752850115503, 0.06115250587536558) # y = c(0.5209906494474178, 0.3155763519785274, 0.18405731803625924, # 0.8613557911541495, 0.8402081904493103, 0.5946972833914318) # cor.test(x, y, method='spearman', alternative='t', exact=TRUE) rng = np.random.default_rng(25982435982346983) x = rng.random(size=6) y = rng.random(size=6) x, y = xp.asarray(x), xp.asarray(y) method = stats.PermutationMethod() res = stats.spearmanrho(x, y, method=method, alternative=alternative) xp_assert_close(res.statistic, xp.asarray(statistic)) xp_assert_close(res.pvalue, xp.asarray(pvalue)) @pytest.mark.parametrize('alternative', ('two-sided', 'greater', 'less')) @pytest.mark.parametrize('n', [9, 99, 999]) def test_against_scipy_spearmanr(self, alternative, n, xp): rng = np.random.default_rng(5982435982346983) x = rng.integers(n//2, size=n) y = rng.integers(n//2, size=n) dtype = xp_default_dtype(xp) ref = stats.spearmanr(x, y, alternative=alternative) ref_statistic = xp.asarray(ref.statistic, dtype=dtype) ref_pvalue = xp.asarray(ref.pvalue, dtype=dtype) x = xp.asarray(x, dtype=dtype) y = xp.asarray(y, dtype=dtype) res = stats.spearmanrho(x, y, alternative=alternative) xp_assert_close(res.statistic, ref_statistic) xp_assert_close(res.pvalue, ref_pvalue) @pytest.mark.parametrize('axis', [-1, 0, 1]) def test_other_backends_nd(self, axis, xp): # NumPy n-d behavior is tested by `test_axis_nan_policy`; # check other backends against it. rng = np.random.default_rng(8243598346983259) shape = (8, 9, 10) x = rng.standard_normal(size=shape) y = rng.standard_normal(size=shape) res = stats.spearmanrho(xp.asarray(x), xp.asarray(y), axis=axis) ref = stats.spearmanrho(x, y, axis=axis) xp_assert_close(res.statistic, xp.asarray(ref.statistic), atol=1e-16) xp_assert_close(res.pvalue, xp.asarray(ref.pvalue), atol=1e-16) def test_input_validation(self, xp): rng = np.random.default_rng(25932435798274926) x, y = rng.random(size=(2, 10)) x, y = xp.asarray(x), xp.asarray(y) msg = 'incompatible for broadcasting|Incompatible shapes|must be broadcastable' with pytest.raises((ValueError, TypeError), match=msg): stats.spearmanrho(x, y[:-1]) if not is_jax(xp): # jax misses out on some input validation from _axis_nan_policy decorator message = '...axis 10 is out of bounds for array...|out of range' with pytest.raises((ValueError, IndexError), match=message): stats.spearmanrho(x, y, axis=10) message = "`alternative` must be 'less', 'greater', or 'two-sided'." with pytest.raises(ValueError, match=message): stats.spearmanrho(x, y, alternative='alternative') message = ("`method` must be...") with pytest.raises(ValueError, match=message): stats.spearmanrho(x, y, method='method') @pytest.mark.skip_xp_backends('jax.numpy', reason='no SmallSampleWarning (lazy)') def test_special_cases(self, xp): def check_nan(res): assert xp.isnan(res.statistic) assert xp.isnan(res.pvalue) message = 'One or more sample arguments is too small...' with pytest.warns(SmallSampleWarning, match=message): res = stats.spearmanrho(xp.asarray([1]), xp.asarray([2])) check_nan(res) x = xp.asarray([1, 1, 1, 1, 1]) y = xp.asarray([1, 2, 3, 4, 5]) message = 'An input array is constant; the correlation coefficient...' with pytest.warns(stats.ConstantInputWarning, match=message): res = stats.spearmanrho(x, y) check_nan(res) with pytest.warns(stats.ConstantInputWarning, match=message): res = stats.spearmanrho(y, x) check_nan(res)