def test_statistic_value_asym_method(self):
statistic, pval, method = ddm.distance_covariance_test(
self.x, self.y, method="asym")
assert method == "asym"
assert_almost_equal(statistic, self.test_stat_asym_exp, 4)
assert_almost_equal(pval, self.pval_asym_exp, 3)
def test_results_on_the_quakes_dataset(self):
"""
R code:
------
> data("quakes")
> x = quakes[1:50, 1:3]
> y = quakes[51:100, 1:3]
> dcov.test(x, y, R=200)
dCov independence test (permutation test)
data: index 1, replicates 200
nV^2 = 45046, p-value = 0.4577
sample estimates:
dCov
30.01526
"""
quakes = get_rdataset("quakes").data.values[:, :3]
x = quakes[:50]
y = quakes[50:100]
stats = ddm.distance_statistics(x, y)
assert_almost_equal(np.round(stats.test_statistic), 45046, 0)
assert_almost_equal(stats.distance_correlation, 0.1894193, 4)
assert_almost_equal(stats.distance_covariance, 30.01526, 4)
assert_almost_equal(stats.dvar_x, 170.1702, 4)
assert_almost_equal(stats.dvar_y, 147.5545, 4)
assert_almost_equal(stats.S, 52265, 0)
test_statistic, _, method = ddm.distance_covariance_test(x, y, B=199)
assert_almost_equal(np.round(test_statistic), 45046, 0)
assert method == "emp"
def test_results_on_the_iris_dataset(self):
"""
R code example from the `energy` package documentation for
`energy::distance_covariance.test`:
> x <- iris[1:50, 1:4]
> y <- iris[51:100, 1:4]
> set.seed(1)
> dcov.test(x, y, R=200)
dCov independence test (permutation test)
data: index 1, replicates 200
nV^2 = 0.5254, p-value = 0.9552
sample estimates:
dCov
0.1025087
"""
iris = get_rdataset("iris").data.values[:, :4]
x = iris[:50]
y = iris[50:100]
stats = ddm.distance_statistics(x, y)
assert_almost_equal(stats.test_statistic, 0.5254, 4)
assert_almost_equal(stats.distance_correlation, 0.3060479, 4)
assert_almost_equal(stats.distance_covariance, 0.1025087, 4)
assert_almost_equal(stats.dvar_x, 0.2712927, 4)
assert_almost_equal(stats.dvar_y, 0.4135274, 4)
assert_almost_equal(stats.S, 0.667456, 4)
test_statistic, _, method = ddm.distance_covariance_test(x, y, B=199)
assert_almost_equal(test_statistic, 0.5254, 4)
assert method == "emp"
def test_fallback_to_asym_method(self):
match_text = "The asymptotic approximation will be used"
with pytest.warns(UserWarning, match=match_text):
statistic, pval, _ = ddm.distance_covariance_test(
self.x, self.y, method="emp", B=200
)
assert_almost_equal(statistic, self.test_stat_emp_exp, 0)
assert_almost_equal(pval, self.pval_asym_exp, 3)
def test_statistic_value_emp_method(self):
statistic, pval, method = ddm.distance_covariance_test(self.x,
self.y,
method="emp")
assert method == "emp"
assert_almost_equal(statistic, self.test_stat_emp_exp, 0)
assert_almost_equal(pval, self.pval_emp_exp, 3)
def test_statistic_value_emp_method(self):
with pytest.warns(HypothesisTestWarning):
statistic, pval, method = ddm.distance_covariance_test(
self.x, self.y, method="emp")
assert method == "emp"
assert_almost_equal(statistic, self.test_stat_emp_exp, 0)
assert_almost_equal(pval, self.pval_emp_exp, 3)
def test_results_on_the_quakes_dataset(self):
"""
R code:
------
> data("quakes")
> x = quakes[1:50, 1:3]
> y = quakes[51:100, 1:3]
> dcov.test(x, y, R=200)
dCov independence test (permutation test)
data: index 1, replicates 200
nV^2 = 45046, p-value = 0.4577
sample estimates:
dCov
30.01526
"""
try:
quakes = get_rdataset("quakes").data.values[:, :3]
except IGNORED_EXCEPTIONS:
pytest.skip('Failed with HTTPError or URLError, these are random')
x = np.asarray(quakes[:50], dtype=float)
y = np.asarray(quakes[50:100], dtype=float)
stats = ddm.distance_statistics(x, y)
assert_almost_equal(np.round(stats.test_statistic), 45046, 0)
assert_almost_equal(stats.distance_correlation, 0.1894193, 4)
assert_almost_equal(stats.distance_covariance, 30.01526, 4)
assert_almost_equal(stats.dvar_x, 170.1702, 4)
assert_almost_equal(stats.dvar_y, 147.5545, 4)
assert_almost_equal(stats.S, 52265, 0)
test_statistic, _, method = ddm.distance_covariance_test(x, y, B=199)
assert_almost_equal(np.round(test_statistic), 45046, 0)
assert method == "emp"
def test_results_on_the_iris_dataset(self):
"""
R code example from the `energy` package documentation for
`energy::distance_covariance.test`:
> x <- iris[1:50, 1:4]
> y <- iris[51:100, 1:4]
> set.seed(1)
> dcov.test(x, y, R=200)
dCov independence test (permutation test)
data: index 1, replicates 200
nV^2 = 0.5254, p-value = 0.9552
sample estimates:
dCov
0.1025087
"""
try:
iris = get_rdataset("iris").data.values[:, :4]
except IGNORED_EXCEPTIONS:
pytest.skip('Failed with HTTPError or URLError, these are random')
x = np.asarray(iris[:50], dtype=float)
y = np.asarray(iris[50:100], dtype=float)
stats = ddm.distance_statistics(x, y)
assert_almost_equal(stats.test_statistic, 0.5254, 4)
assert_almost_equal(stats.distance_correlation, 0.3060479, 4)
assert_almost_equal(stats.distance_covariance, 0.1025087, 4)
assert_almost_equal(stats.dvar_x, 0.2712927, 4)
assert_almost_equal(stats.dvar_y, 0.4135274, 4)
assert_almost_equal(stats.S, 0.667456, 4)
test_statistic, _, method = ddm.distance_covariance_test(x, y, B=199)
assert_almost_equal(test_statistic, 0.5254, 4)
assert method == "emp"
def test_input_validation_unknown_method(self):
with pytest.raises(ValueError, match="Unknown 'method' parameter"):
ddm.distance_covariance_test(self.x, self.y, method="wrong_name")
def test_input_validation_nobs(self):
with pytest.raises(ValueError, match="same number of observations"):
ddm.distance_covariance_test(self.x[:2, :], self.y)
