def test_fit_transform(self):
a = Autocorrelation(depths=[1], properties=['I'])
expected = numpy.array([
[8],
[8],
[104],
])
self.assertTrue((a.fit_transform(ALL_DATA) == expected).all())
def test_wide_depths(self):
a = Autocorrelation(depths=[-1, 1, 4, 10, 100], properties=['I'])
a.fit(ALL_DATA)
expected = numpy.array([
[0, 8, 0, 0, 0],
[0, 8, 0, 0, 0],
[0, 104, 216, 166, 0]
])
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_default_depths(self):
a = Autocorrelation(properties=['I'])
a.fit(ALL_DATA)
expected = numpy.array([
[5, 8, 12, 0],
[9, 8, 2, 0],
[49, 104, 156, 190]
])
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_property_function(self):
a = Autocorrelation(
depths=[1], properties=[lambda data: [2 for x in data.numbers]])
a.fit(ALL_DATA)
expected = numpy.array([
[32],
[32],
[416],
])
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_get_labels(self):
a = Autocorrelation(depths=[1, 2], properties=['I', 'EN'])
X = a.fit_transform([METHANE])
labels = a.get_labels()
self.assertEqual(X.shape[1], len(labels))
expected = (
'EN_1', 'EN_2',
'I_1', 'I_2',
)
self.assertEqual(labels, expected)
def test_properties(self):
a = Autocorrelation(depths=[0])
a.fit(ALL_DATA)
expected = numpy.array([[20., 25.8625, 5., 2.1625, 40.],
[10., 74.8594, 9., 4.4571, 331.],
[260., 328.7049, 49., 28.1326, 1416.]])
try:
m = a.transform(ALL_DATA)
numpy.testing.assert_array_almost_equal(m, expected)
except AssertionError as e:
self.fail(e)
def test_fit_atom_separated(self):
a = Autocorrelation(depths=[0, 1], properties=['I', 'Z'])
a.fit([METHANE2])
self.assertTrue((a.transform([METHANE2]) == numpy.array([[5, 0, 40,
0]])).all())
def test_depths_properties(self):
a = Autocorrelation(depths=[1, 2], properties=['I', 'Z'])
a.fit([METHANE])
self.assertTrue(
(a.transform([METHANE]) == numpy.array([[8, 12, 48, 12]])).all())
