def test_fit(self):
a = EncodedBond()
a.fit(ALL_DATA)
self.assertEqual(
a._element_pairs,
set([('H', 'O'), ('O', 'O'), ('N', 'O'), ('C', 'O'), ('C', 'H'),
('H', 'N'), ('H', 'H'), ('C', 'C'), ('C', 'N'), ('N', 'N')]))
def test_small_to_large_transform(self):
a = EncodedBond()
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
9.207308e-001, # mean
1.062388e+000, # std
0., # min
5.023670e+000, # max
])
try:
m = a.transform([BIG])
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)
def test_large_to_small_transform(self):
a = EncodedBond()
a.fit([MID])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.014224, # mean
0.143824, # std
0., # min
2.392207, # max
])
try:
m = a.transform([METHANE])
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)
def test_add_unknown(self):
a = EncodedBond(add_unknown=True)
a.fit([METHANE])
# This is a cheap test to prevent needing all the values here
expected_results = numpy.array([
0.09105, # mean
0.231761, # std
0., # min
1.869012, # max
])
try:
m = a.transform([MID])
self.assertEqual(m.shape, (1, 300))
assert_close_statistics(m, expected_results)
except AssertionError as e:
self.fail(e)