def test_kendalltau_1D(self):
"""
Assert that a 2D matrix is required as input
"""
with pytest.raises(IndexError, match="tuple index out of range"):
X = 0.1 * np.arange(10)
kendalltau(X)
def test_kendalltau(self):
"""
Test results returned match expectations
"""
X, _ = load_energy(return_dataset=True).to_numpy()
expected = np.array([
[1.0, -1.0, -0.2724275, -0.7361443, 0.7385489, 0.0, 0.0, 0.0],
[-1.0, 1.0, 0.2724275, 0.7361443, -0.7385489, 0.0, 0.0, 0.0],
[
-0.2724275, 0.2724275, 1.0, -0.15192004, 0.19528337, 0.0, 0.0,
0.0
],
[
-0.73614431, 0.73614431, -0.15192004, 1.0, -0.87518995, 0.0,
0.0, 0.0
],
[
0.73854895, -0.73854895, 0.19528337, -0.87518995, 1.0, 0.0,
0.0, 0.0
],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.15430335],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15430335, 1.0],
])
actual = kendalltau(X)
npt.assert_almost_equal(expected, actual)
def test_kendalltau_shape(self):
"""
Assert that a square correlation matrix is returned
"""
corr = kendalltau(self.dataset)
assert corr.shape[0] == corr.shape[1]
for (i, j), val in np.ndenumerate(corr):
assert corr[j][i] == pytest.approx(val)
def test_kendalltau_shape(self):
"""
Assert that a square correlation matrix is returned
"""
X, _ = load_energy(return_dataset=True).to_numpy()
corr = kendalltau(X)
assert corr.shape[0] == corr.shape[1]
for (i, j), val in np.ndenumerate(corr):
assert corr[j][i] == pytest.approx(val)
def test_kendalltau(self):
"""
Test results returned match expectations
"""
expected = np.array([
[
1.0,
-0.68,
-0.57454545,
0.49858586,
0.07555556,
-0.05858586,
0.02387848,
0.11357219,
],
[
-0.68,
1.0,
0.58666667,
-0.69090909,
-0.22262626,
-0.17171717,
-0.05059964,
-0.12397575,
],
[
-0.57454545,
0.58666667,
1.0,
-0.61050505,
0.18909091,
0.07515152,
0.00341121,
-0.0638663,
],
[
0.49858586,
-0.69090909,
-0.61050505,
1.0,
0.11070707,
0.3030303,
0.03013237,
0.07542581,
],
[
0.07555556,
-0.22262626,
0.18909091,
0.11070707,
1.0,
0.4610101,
0.01648752,
0.05982047,
],
[
-0.05858586,
-0.17171717,
0.07515152,
0.3030303,
0.4610101,
1.0,
0.03695479,
-0.02398599,
],
[
0.02387848,
-0.05059964,
0.00341121,
0.03013237,
0.01648752,
0.03695479,
1.0,
0.18298883,
],
[
0.11357219,
-0.12397575,
-0.0638663,
0.07542581,
0.05982047,
-0.02398599,
0.18298883,
1.0,
],
])
npt.assert_almost_equal(expected, kendalltau(self.dataset))