def test_transform(self):
a = EncodedAngle()
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.116708, # mean
0.450738, # std
0., # min
3.043729, # max
])
try:
m = a.fit_transform([METHANE])
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)
def test_small_to_large_transform(self):
a = EncodedAngle()
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.018603, # mean
0.130329, # std
0., # min
1.568823, # max
])
try:
m = a.transform([MID])
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)
def test_add_unknown(self):
a = EncodedAngle(add_unknown=True)
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.117057, # mean
0.510819, # std
0., # min
6.343512, # max
])
try:
m = a.transform([MID])
self.assertEqual(m.shape, (1, 200))
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)
def test_fit(self):
a = EncodedAngle()
a.fit(ALL_DATA)
expected = set([('C', 'N', 'C'), ('C', 'C', 'C'), ('H', 'H', 'H'),
('H', 'O', 'O'), ('O', 'N', 'O'), ('H', 'N', 'N'),
('C', 'H', 'H'), ('C', 'O', 'H'), ('C', 'H', 'C'),
('N', 'C', 'N'), ('O', 'O', 'O'), ('H', 'O', 'N'),
('H', 'N', 'O'), ('O', 'H', 'O'), ('H', 'H', 'N'),
('C', 'C', 'N'), ('H', 'N', 'H'), ('C', 'H', 'N'),
('H', 'C', 'O'), ('N', 'O', 'O'), ('N', 'N', 'N'),
('C', 'C', 'H'), ('C', 'O', 'O'), ('C', 'N', 'N'),
('H', 'O', 'H'), ('H', 'H', 'O'), ('C', 'C', 'O'),
('N', 'H', 'N'), ('C', 'H', 'O'), ('O', 'C', 'O'),
('H', 'C', 'N'), ('C', 'O', 'C'), ('N', 'O', 'N'),
('N', 'N', 'O'), ('C', 'N', 'O'), ('C', 'O', 'N'),
('H', 'C', 'H'), ('C', 'N', 'H'), ('N', 'H', 'O'),
('N', 'C', 'O')])
self.assertEqual(a._groups, expected)