def test_sdf_file_parser_target_index(sdf_file, mols):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
result = parser.parse(sdf_file,
return_smiles=True,
target_index=[0, 2],
return_is_successful=True)
dataset = result['dataset']
smiles = result['smiles']
assert len(dataset) == 2
is_successful = result['is_successful']
assert numpy.alltrue(is_successful)
assert len(is_successful) == 2
# As we want test SDFFileParser, we assume
# NFPPreprocessor works as documented.
expect = preprocessor.get_input_features(mols[0])
check_input_features(dataset[0], expect)
expect = preprocessor.get_input_features(mols[2])
check_input_features(dataset[1], expect)
# check smiles array
assert type(smiles) == numpy.ndarray
assert smiles.ndim == 1
assert len(smiles) == len(dataset)
assert smiles[0] == 'CN=C=O'
assert smiles[1] == 'CC1=CC2CC(CC1)O2'
def test_sdf_file_parser(sdf_file, mols):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
dataset = parser.parse(sdf_file)
assert len(dataset) == 2
# As we want test SDFFileParser, we assume
# NFPPreprocessor works as documented.
expect = preprocessor.get_input_features(mols[0])
check_input_features(dataset[0], expect)
expect = preprocessor.get_input_features(mols[1])
check_input_features(dataset[1], expect)
def test_sdf_file_parser_not_return_smiles(sdf_file, mols):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
result = parser.parse(sdf_file, return_smiles=False)
dataset = result['dataset']
smiles = result['smiles']
assert len(dataset) == 3
assert smiles is None
# As we want test SDFFileParser, we assume
# NFPPreprocessor works as documented.
for i in range(3):
expect = preprocessor.get_input_features(mols[i])
check_input_features(dataset[i], expect)
def test_sdf_file_parser_return_is_successful(sdf_file_long, mols):
"""test `labels` option and retain_smiles=True."""
preprocessor = NFPPreprocessor(max_atoms=10)
parser = SDFFileParser(preprocessor)
result = parser.parse(sdf_file_long,
return_smiles=True,
return_is_successful=True)
dataset = result['dataset']
# smiles = result['smiles']
assert len(dataset) == 3
is_successful = result['is_successful']
assert len(is_successful) == 5
assert numpy.alltrue(is_successful[[1, 3, 4]])
assert numpy.alltrue(~is_successful[[0, 2]])
# We assume NFPPreprocessor works as documented.
for i in range(3):
expect = preprocessor.get_input_features(mols[i])
check_input_features(dataset[i], expect)
def test_sdf_file_parser_retain_smiles(sdf_file, mols):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
dataset = parser.parse(sdf_file, retain_smiles=True)
smiles = parser.get_smiles()
assert len(dataset) == 2
# As we want test SDFFileParser, we assume
# NFPPreprocessor works as documented.
expect = preprocessor.get_input_features(mols[0])
check_input_features(dataset[0], expect)
expect = preprocessor.get_input_features(mols[1])
check_input_features(dataset[1], expect)
# check smiles array
assert type(smiles) == numpy.ndarray
assert smiles.ndim == 1
assert len(smiles) == len(dataset)
assert smiles[0] == 'CN=C=O'
assert smiles[1] == 'Cc1ccccc1'
def test_sdf_file_parser_return_smiles(sdf_file, mols):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
result = parser.parse(sdf_file, return_smiles=True)
dataset = result['dataset']
smiles = result['smiles']
assert len(dataset) == 3
# As we want test SDFFileParser, we assume
# NFPPreprocessor works as documented.
for i in range(3):
expect = preprocessor.get_input_features(mols[i])
check_input_features(dataset[i], expect)
# check smiles array
assert type(smiles) == numpy.ndarray
assert smiles.ndim == 1
assert len(smiles) == len(dataset)
assert smiles[0] == 'CN=C=O'
assert smiles[1] == 'Cc1ccccc1'
assert smiles[2] == 'CC1=CC2CC(CC1)O2'
def test_nfp_preprocessor_with_tox21():
preprocessor = NFPPreprocessor()
dataset = SDFFileParser(preprocessor, postprocess_label=None).parse(
get_tox21_filepath('train'))
index = numpy.random.choice(len(dataset), None)
atoms, adjs = dataset[index]
assert atoms.ndim == 1 # (atom, )
assert atoms.dtype == numpy.int32
# (atom from, atom to)
assert adjs.ndim == 2
assert adjs.dtype == numpy.float32
def test_gat_preprocessor():
preprocessor = RelGATPreprocessor()
def postprocess_label(label_list):
# Set -1 to the place where the label is not found,
# this corresponds to not calculate loss with `sigmoid_cross_entropy`
return [-1 if label is None else label for label in label_list]
dataset = SDFFileParser(preprocessor, postprocess_label=postprocess_label
).parse(get_tox21_filepath('train'))["dataset"]
index = numpy.random.choice(len(dataset), None)
atoms, adjs = dataset[index]
assert atoms.ndim == 1 # (atom, )
assert atoms.dtype == numpy.int32
# (edge_type, atom from, atom to)
assert adjs.ndim == 3
assert adjs.dtype == numpy.float32
def test_sdf_file_parser_extract_total_num(sdf_file):
preprocessor = NFPPreprocessor()
parser = SDFFileParser(preprocessor)
num = parser.extract_total_num(sdf_file)
assert num == 3