def test_transform_depth3(self):
# This is to test loop backs in the breadth-first search
a = Shell(depth=3)
a.fit(ALL_DATA)
expected_results = numpy.array([
[[0, 0, 0, 0], [0, 3, 0, 0], [0, 3, 0, 0], [0, 3, 0, 0],
[0, 3, 0, 0]],
[[0, 0, 0, 1], [0, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]],
[[2, 2, 0, 0], [1, 1, 1, 1], [2, 2, 0, 0], [2, 1, 1, 0],
[4, 1, 0, 0], [3, 0, 0, 1], [2, 0, 0, 0], [2, 0, 0, 0],
[1, 0, 1, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 1, 0, 0],
[2, 1, 2, 1], [3, 0, 0, 1], [3, 1, 1, 0], [3, 0, 0, 2],
[3, 0, 0, 1], [2, 0, 0, 0], [1, 0, 1, 0], [1, 0, 0, 0],
[2, 3, 0, 0], [1, 0, 0, 0], [0, 2, 0, 1], [0, 2, 0, 1],
[0, 2, 0, 1], [4, 0, 1, 0], [4, 0, 0, 1], [3, 0, 1, 1],
[5, 0, 0, 0], [4, 0, 0, 0], [2, 1, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 2, 0, 0],
[2, 1, 2, 1], [4, 0, 0, 0], [2, 2, 1, 0], [3, 0, 0, 0],
[2, 1, 1, 0], [2, 0, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0],
[1, 1, 0, 0], [1, 1, 0, 0], [2, 1, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0]]
])
self.assertTrue((a.transform(ALL_DATA) == expected_results).all())
def test_transform_depth3(self):
# This is to test loop backs in the breadth-first search
a = Shell(depth=3)
a.fit(ALL_DATA)
expected_results = numpy.array([
[[0, 0, 0, 0], [0, 3, 0, 0], [0, 3, 0, 0], [0, 3, 0, 0],
[0, 3, 0, 0]],
[[0, 0, 0, 1], [0, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]],
[[2, 2, 0, 0], [1, 1, 1, 1], [2, 2, 0, 0], [2, 1, 1, 0],
[4, 1, 0, 0], [3, 0, 0, 1], [2, 0, 0, 0], [2, 0, 0, 0],
[1, 0, 1, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 1, 0, 0],
[2, 1, 2, 1], [3, 0, 0, 1], [3, 1, 1, 0], [3, 0, 0, 2],
[3, 0, 0, 1], [2, 0, 0, 0], [1, 0, 1, 0], [1, 0, 0, 0],
[2, 3, 0, 0], [1, 0, 0, 0], [0, 2, 0, 1], [0, 2, 0, 1],
[0, 2, 0, 1], [4, 0, 1, 0], [4, 0, 0, 1], [3, 0, 1, 1],
[5, 0, 0, 0], [4, 0, 0, 0], [2, 1, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 2, 0, 0],
[2, 1, 2, 1], [4, 0, 0, 0], [2, 2, 1, 0], [3, 0, 0, 0],
[2, 1, 1, 0], [2, 0, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0],
[1, 1, 0, 0], [1, 1, 0, 0], [2, 1, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0]]
])
self.assertTrue((a.transform(ALL_DATA) == expected_results).all())
def test_fit_features(self):
trans = Shell(input_type="filename")
feats = trans.fit_transform(ALL)
a = AtomKernel()
values = list(zip(feats, ALL_NUMS))
a.fit(values)
self.assertEqual(ALL_NUMS, list(a._numbers))
self.assertEqual(list(ALL_FEATURES), list(a._features))
def test_fit_use_coordination(self):
a = Shell(depth=1, use_coordination=True)
a.fit(ALL_DATA)
self.assertEqual(
a._elements,
set([
'H0', 'H1', 'O2', 'C4', 'N1', 'C3', 'C2', 'N2', 'N3', 'C1',
'O1', 'O0'
]))
def test_fit_transform_features(self):
trans = Shell(input_type="filename")
feats = trans.fit_transform(ALL)
a = AtomKernel()
values = list(zip(feats, ALL_NUMS))
res = a.fit_transform(values)
try:
numpy.testing.assert_array_almost_equal(RBF_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_add_unknown(self):
a = Shell(add_unknown=True)
a.fit([METHANE])
temp = []
for mol in BASE_SHELL:
inner = []
for atom in mol:
inner.append(atom[:2] + [atom[2] + atom[3]])
temp.append(inner)
expected = numpy.array(temp)
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_add_unknown(self):
a = Shell(add_unknown=True)
a.fit([METHANE])
temp = []
for mol in BASE_SHELL:
inner = []
for atom in mol:
inner.append(atom[:2] + [atom[2] + atom[3]])
temp.append(inner)
expected = numpy.array(temp)
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_transform_use_coordination(self):
a = Shell(depth=1, use_coordination=True)
a.fit([MID])
expected_results = numpy.array([[[1, 0, 0, 0, 0,
0], [0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0,
1], [0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0,
1], [0, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 0,
0], [0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0]]])
self.assertTrue((a.transform([MID]) == expected_results).all())
def test_transform_use_coordination(self):
a = Shell(depth=1, use_coordination=True)
a.fit([MID])
expected_results = numpy.array([
[[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0]]
])
self.assertTrue((a.transform([MID]) == expected_results).all())
def test_fit_transform_transformer(self):
trans = Shell(input_type="filename")
a = AtomKernel(transformer=trans)
res = a.fit_transform(ALL)
try:
numpy.testing.assert_array_almost_equal(RBF_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_laplace_kernel(self):
# Set depth=2 so the comparison is not trivial
trans = Shell(input_type="filename", depth=2)
a = AtomKernel(transformer=trans, kernel="laplace", gamma=1.)
a.fit(ALL)
res = a.transform(ALL)
try:
numpy.testing.assert_array_almost_equal(LAPLACE_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_same_element(self):
# Set depth=2 so the comparison is not trivial
trans = Shell(input_type="filename", depth=2)
# Set gamma=1 to make the differences more noticeable
a = AtomKernel(transformer=trans, same_element=False, gamma=1.)
res = a.fit_transform(ALL)
expected = numpy.array([[17.00000033, 14.58016505],
[14.58016505, 32.76067832]])
try:
numpy.testing.assert_array_almost_equal(expected, res)
except AssertionError as e:
self.fail(e)
def test_custom_kernel(self):
# Set depth=2 so the comparison is not trivial
trans = Shell(input_type="filename", depth=2)
# Simple linear kernel
a = AtomKernel(transformer=trans,
kernel=lambda x, y: numpy.dot(x, numpy.transpose(y)))
a.fit(ALL)
res = a.transform(ALL)
try:
numpy.testing.assert_array_almost_equal(LINEAR_KERNEL, res)
except AssertionError as e:
self.fail(e)
def test_transform_depth2(self):
a = Shell(depth=2)
a.fit(ALL_DATA)
expected_results = numpy.array([[[0, 4, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0]],
[[1, 0, 0, 0], [1, 0, 0, 1],
[1, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 1], [0, 0, 0, 1],
[0, 0, 0, 0], [0, 1, 0, 0],
[0, 1, 0, 0]],
[[1, 0, 1, 1], [2, 1, 0, 0],
[2, 1, 0, 0], [2, 1, 0, 0],
[2, 0, 1, 0], [2, 0, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0],
[1, 1, 0, 0], [0, 0, 0, 1],
[1, 0, 0, 0], [1, 0, 1, 1],
[3, 0, 0, 0], [2, 1, 0, 0],
[2, 0, 0, 1], [2, 0, 1, 0],
[2, 0, 0, 0], [1, 0, 0, 0],
[1, 1, 0, 0], [0, 0, 0, 1],
[2, 0, 0, 0], [0, 3, 0, 1],
[1, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [2, 0, 0, 1],
[3, 0, 0, 0], [3, 0, 0, 0],
[2, 0, 0, 1], [2, 0, 0, 0],
[2, 0, 0, 0], [1, 1, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 1, 0], [1, 0, 2, 0],
[3, 0, 0, 0], [2, 1, 0, 0],
[3, 0, 0, 0], [1, 1, 1, 0],
[2, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [1, 2, 0, 0],
[0, 0, 1, 0], [0, 0, 1, 0],
[2, 0, 0, 0], [1, 1, 0, 0],
[1, 0, 0, 0]]])
self.assertTrue((a.transform(ALL_DATA) == expected_results).all())
def test_transform_depth2(self):
a = Shell(depth=2)
a.fit(ALL_DATA)
expected_results = numpy.array([
[[0, 4, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0]],
[[1, 0, 0, 0], [1, 0, 0, 1], [1, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0], [0, 1, 0, 0],
[0, 1, 0, 0]],
[[1, 0, 1, 1], [2, 1, 0, 0], [2, 1, 0, 0], [2, 1, 0, 0],
[2, 0, 1, 0], [2, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0],
[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 1, 1],
[3, 0, 0, 0], [2, 1, 0, 0], [2, 0, 0, 1], [2, 0, 1, 0],
[2, 0, 0, 0], [1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 1],
[2, 0, 0, 0], [0, 3, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0],
[1, 0, 0, 0], [2, 0, 0, 1], [3, 0, 0, 0], [3, 0, 0, 0],
[2, 0, 0, 1], [2, 0, 0, 0], [2, 0, 0, 0], [1, 1, 0, 0],
[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 1, 0], [1, 0, 2, 0],
[3, 0, 0, 0], [2, 1, 0, 0], [3, 0, 0, 0], [1, 1, 1, 0],
[2, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0],
[0, 0, 1, 0], [0, 0, 1, 0], [2, 0, 0, 0], [1, 1, 0, 0],
[1, 0, 0, 0]]
])
self.assertTrue((a.transform(ALL_DATA) == expected_results).all())
def test_transform(self):
a = Shell()
a.fit(ALL_DATA)
self.assertTrue((a.transform(ALL_DATA) == BASE_SHELL).all())
def test_fit(self):
a = Shell(depth=1)
a.fit(ALL_DATA)
self.assertEqual(a._elements, ('C', 'H', 'N', 'O'))
def test_get_labels_unknown(self):
a = Shell(add_unknown=True)
a.fit(ALL_DATA)
expected = ('C', 'H', 'N', 'O', UNKNOWN)
self.assertEqual(a.get_labels(), expected)
def test_get_labels(self):
a = Shell()
a.fit(ALL_DATA)
expected = ('C', 'H', 'N', 'O')
self.assertEqual(a.get_labels(), expected)
def test_fit_transformer(self):
trans = Shell(input_type="filename")
a = AtomKernel(transformer=trans)
a.fit(ALL)
self.assertEqual(ALL_NUMS, [x.tolist() for x in a._numbers])
self.assertEqual(list(ALL_FEATURES), list(a._features))
def test_transform_before_fit(self):
a = Shell()
with self.assertRaises(ValueError):
a.transform(ALL_DATA)
def test_get_labels(self):
a = Shell()
a.fit(ALL_DATA)
expected = ('C', 'H', 'N', 'O')
self.assertEqual(a.get_labels(), expected)
def test_small_to_large_transform(self):
a = Shell()
a.fit([METHANE])
expected = numpy.array(
[numpy.array(x)[:, :2].tolist() for x in BASE_SHELL])
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_transform(self):
a = Shell()
a.fit(ALL_DATA)
self.assertTrue((a.transform(ALL_DATA) == BASE_SHELL).all())
def test_small_to_large_transform(self):
a = Shell()
a.fit([METHANE])
expected = numpy.array([numpy.array(x)[:, :2].tolist()
for x in BASE_SHELL])
self.assertTrue((a.transform(ALL_DATA) == expected).all())
def test_fit_use_coordination(self):
a = Shell(depth=1, use_coordination=True)
a.fit(ALL_DATA)
self.assertEqual(a._elements, ('C1', 'C2', 'C3', 'C4', 'H0', 'H1',
'N1', 'N2', 'N3', 'O0', 'O1', 'O2'))
def test_fit(self):
a = Shell(depth=1)
a.fit(ALL_DATA)
self.assertEqual(a._elements, ('C', 'H', 'N', 'O'))
def test_large_to_small_transform(self):
a = Shell()
a.fit([BIG])
self.assertTrue((a.transform(ALL_DATA) == BASE_SHELL).all())
def test_invalid_kernel(self):
with self.assertRaises(ValueError):
trans = Shell(input_type="filename")
a = AtomKernel(kernel=1, transformer=trans)
a.fit_transform(ALL)
def test_large_to_small_transform(self):
a = Shell()
a.fit([BIG])
self.assertTrue((a.transform(ALL_DATA) == BASE_SHELL).all())
def test_get_labels_unknown(self):
a = Shell(add_unknown=True)
a.fit(ALL_DATA)
expected = ('C', 'H', 'N', 'O', 'UNKNOWN')
self.assertEqual(a.get_labels(), expected)
def test_transform_before_fit(self):
a = Shell()
with self.assertRaises(ValueError):
a.transform(ALL_DATA)
