218 lines
7.8 KiB
Python
218 lines
7.8 KiB
Python
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import pytest
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from pytest import raises as assert_raises, warns as assert_warns
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import numpy as np
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from numpy.testing import assert_approx_equal, assert_allclose, assert_equal
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from scipy.spatial.distance import cdist
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from scipy import stats
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class TestMGCErrorWarnings:
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""" Tests errors and warnings derived from MGC.
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"""
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def test_error_notndarray(self):
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# raises error if x or y is not a ndarray
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x = np.arange(20)
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y = [5] * 20
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assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
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assert_raises(ValueError, stats.multiscale_graphcorr, y, x)
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def test_error_shape(self):
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# raises error if number of samples different (n)
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x = np.arange(100).reshape(25, 4)
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y = x.reshape(10, 10)
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assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
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def test_error_lowsamples(self):
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# raises error if samples are low (< 3)
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x = np.arange(3)
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y = np.arange(3)
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assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
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def test_error_nans(self):
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# raises error if inputs contain NaNs
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x = np.arange(20, dtype=float)
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x[0] = np.nan
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assert_raises(ValueError, stats.multiscale_graphcorr, x, x)
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y = np.arange(20)
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assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
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def test_error_wrongdisttype(self):
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# raises error if metric is not a function
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x = np.arange(20)
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compute_distance = 0
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assert_raises(ValueError, stats.multiscale_graphcorr, x, x,
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compute_distance=compute_distance)
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@pytest.mark.parametrize("reps", [
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-1, # reps is negative
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'1', # reps is not integer
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])
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def test_error_reps(self, reps):
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# raises error if reps is negative
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x = np.arange(20)
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assert_raises(ValueError, stats.multiscale_graphcorr, x, x, reps=reps)
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def test_warns_reps(self):
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# raises warning when reps is less than 1000
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x = np.arange(20)
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reps = 100
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assert_warns(RuntimeWarning, stats.multiscale_graphcorr, x, x, reps=reps)
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def test_error_infty(self):
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# raises error if input contains infinities
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x = np.arange(20)
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y = np.ones(20) * np.inf
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assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
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class TestMGCStat:
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""" Test validity of MGC test statistic
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"""
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def _simulations(self, samps=100, dims=1, sim_type=""):
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# linear simulation
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if sim_type == "linear":
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x = np.random.uniform(-1, 1, size=(samps, 1))
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y = x + 0.3 * np.random.random_sample(size=(x.size, 1))
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# spiral simulation
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elif sim_type == "nonlinear":
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unif = np.array(np.random.uniform(0, 5, size=(samps, 1)))
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x = unif * np.cos(np.pi * unif)
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y = (unif * np.sin(np.pi * unif) +
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0.4*np.random.random_sample(size=(x.size, 1)))
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# independence (tests type I simulation)
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elif sim_type == "independence":
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u = np.random.normal(0, 1, size=(samps, 1))
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v = np.random.normal(0, 1, size=(samps, 1))
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u_2 = np.random.binomial(1, p=0.5, size=(samps, 1))
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v_2 = np.random.binomial(1, p=0.5, size=(samps, 1))
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x = u/3 + 2*u_2 - 1
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y = v/3 + 2*v_2 - 1
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# raises error if not approved sim_type
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else:
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raise ValueError("sim_type must be linear, nonlinear, or "
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"independence")
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# add dimensions of noise for higher dimensions
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if dims > 1:
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dims_noise = np.random.normal(0, 1, size=(samps, dims-1))
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x = np.concatenate((x, dims_noise), axis=1)
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return x, y
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@pytest.mark.xslow
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@pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
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("linear", 0.97, 1/1000), # test linear simulation
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("nonlinear", 0.163, 1/1000), # test spiral simulation
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("independence", -0.0094, 0.78) # test independence simulation
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])
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def test_oned(self, sim_type, obs_stat, obs_pvalue):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type=sim_type)
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
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assert_approx_equal(stat, obs_stat, significant=1)
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assert_approx_equal(pvalue, obs_pvalue, significant=1)
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@pytest.mark.xslow
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@pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
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("linear", 0.184, 1/1000), # test linear simulation
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("nonlinear", 0.0190, 0.117), # test spiral simulation
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])
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def test_fived(self, sim_type, obs_stat, obs_pvalue):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=5, sim_type=sim_type)
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
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assert_approx_equal(stat, obs_stat, significant=1)
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assert_approx_equal(pvalue, obs_pvalue, significant=1)
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@pytest.mark.xslow
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def test_twosamp(self):
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np.random.seed(12345678)
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# generate x and y
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x = np.random.binomial(100, 0.5, size=(100, 5))
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y = np.random.normal(0, 1, size=(80, 5))
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y)
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assert_approx_equal(stat, 1.0, significant=1)
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assert_approx_equal(pvalue, 0.001, significant=1)
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# generate x and y
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y = np.random.normal(0, 1, size=(100, 5))
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y, is_twosamp=True)
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assert_approx_equal(stat, 1.0, significant=1)
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assert_approx_equal(pvalue, 0.001, significant=1)
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@pytest.mark.xslow
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def test_workers(self):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type="linear")
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y, workers=2)
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assert_approx_equal(stat, 0.97, significant=1)
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assert_approx_equal(pvalue, 0.001, significant=1)
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@pytest.mark.xslow
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def test_random_state(self):
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type="linear")
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# test stat and pvalue
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stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
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assert_approx_equal(stat, 0.97, significant=1)
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assert_approx_equal(pvalue, 0.001, significant=1)
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@pytest.mark.xslow
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def test_dist_perm(self):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type="nonlinear")
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distx = cdist(x, x, metric="euclidean")
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disty = cdist(y, y, metric="euclidean")
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stat_dist, pvalue_dist, _ = stats.multiscale_graphcorr(distx, disty,
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compute_distance=None,
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random_state=1)
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assert_approx_equal(stat_dist, 0.163, significant=1)
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assert_approx_equal(pvalue_dist, 0.001, significant=1)
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@pytest.mark.fail_slow(10) # all other tests are XSLOW; we need at least one to run
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@pytest.mark.slow
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def test_pvalue_literature(self):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type="linear")
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# test stat and pvalue
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_, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
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assert_allclose(pvalue, 1/1001)
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@pytest.mark.xslow
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def test_alias(self):
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np.random.seed(12345678)
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# generate x and y
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x, y = self._simulations(samps=100, dims=1, sim_type="linear")
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res = stats.multiscale_graphcorr(x, y, random_state=1)
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assert_equal(res.stat, res.statistic)
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