def featurize_input(
input_file,
feature_dir,
user_specified_features,
tasks,
smiles_field,
split_field,
id_field,
threshold,
protein_pdb_field,
ligand_pdb_field,
ligand_mol2_field,
):
"""Featurizes raw input data."""
featurizer = DataFeaturizer(
tasks=tasks,
smiles_field=smiles_field,
split_field=split_field,
id_field=id_field,
threshold=threshold,
protein_pdb_field=protein_pdb_field,
ligand_pdb_field=ligand_pdb_field,
ligand_mol2_field=ligand_mol2_field,
user_specified_features=user_specified_features,
verbose=True,
)
out = os.path.join(feature_dir, "%s.joblib" % (os.path.splitext(os.path.basename(input_file))[0]))
featurizer.featurize(input_file, FeaturizedSamples.feature_types, out)
def test_API(self):
"""Straightforward test of multitask deepchem classification API."""
splittype = "scaffold"
feature_types = ["ECFP"]
output_transforms = []
input_transforms = []
task_type = "classification"
# TODO(rbharath): There should be some automatic check to ensure that all
# required model_params are specified.
model_params = {"nb_hidden": 10, "activation": "relu",
"dropout": .5, "learning_rate": .01,
"momentum": .9, "nesterov": False,
"decay": 1e-4, "batch_size": 5,
"nb_epoch": 2}
model_name = "multitask_deep_classifier"
# Featurize input
featurizer = DataFeaturizer(tasks=self.tasks,
smiles_field=self.smiles_field,
verbose=True)
feature_files = featurizer.featurize(self.input_file, feature_types, self.feature_dir)
# Transform data into arrays for ML
samples = FeaturizedSamples(self.samplesdir, feature_files,
reload_data=False)
# Split into train/test
train_samples, test_samples = samples.train_test_split(
splittype, self.train_dir, self.test_dir)
train_dataset = Dataset(self.train_dir, train_samples, feature_types)
test_dataset = Dataset(self.test_dir, test_samples, feature_types)
# Transforming train/test data
train_dataset.transform(input_transforms, output_transforms)
test_dataset.transform(input_transforms, output_transforms)
# Fit model
task_types = {task: task_type for task in self.tasks}
model_params["data_shape"] = train_dataset.get_data_shape()
model = Model.model_builder(model_name, task_types, model_params)
model.fit(train_dataset)
model.save(self.model_dir)
# Eval model on train
evaluator = Evaluator(model, test_dataset, verbose=True)
with tempfile.NamedTemporaryFile() as test_csv_out:
with tempfile.NamedTemporaryFile() as test_stats_out:
evaluator.compute_model_performance(test_csv_out, test_stats_out)
def featurize_input(input_file, feature_dir, user_specified_features, tasks,
smiles_field, split_field, id_field, threshold, protein_pdb_field,
ligand_pdb_field, ligand_mol2_field):
"""Featurizes raw input data."""
featurizer = DataFeaturizer(tasks=tasks,
smiles_field=smiles_field,
split_field=split_field,
id_field=id_field,
threshold=threshold,
protein_pdb_field=protein_pdb_field,
ligand_pdb_field=ligand_pdb_field,
ligand_mol2_field=ligand_mol2_field,
user_specified_features=user_specified_features,
verbose=True)
featurizer.featurize(input_file, FeaturizedSamples.feature_types, feature_dir)
def _create_model(self, splittype, feature_types, input_transforms,
output_transforms, task_type, model_params, model_name,
input_file, tasks, protein_pdb_field=None, ligand_pdb_field=None):
"""Helper method to create model for test."""
# Featurize input
input_file = os.path.join(self.current_dir, input_file)
featurizer = DataFeaturizer(tasks=tasks,
smiles_field=self.smiles_field,
protein_pdb_field=protein_pdb_field,
ligand_pdb_field=ligand_pdb_field,
verbose=True)
feature_files = featurizer.featurize(input_file, feature_types, self.feature_dir)
# Transform data into arrays for ML
samples = FeaturizedSamples(self.samplesdir, feature_files,
reload_data=False)
# Split into train/test
train_samples, test_samples = samples.train_test_split(
splittype, self.train_dir, self.test_dir)
train_dataset = Dataset(self.train_dir, train_samples, feature_types)
test_dataset = Dataset(self.test_dir, test_samples, feature_types)
# Transforming train/test data
train_dataset.transform(input_transforms, output_transforms)
test_dataset.transform(input_transforms, output_transforms)
# Fit model
task_types = {task: task_type for task in tasks}
model_params["data_shape"] = train_dataset.get_data_shape()
model = Model.model_builder(model_name, task_types, model_params)
model.fit(train_dataset)
model.save(self.model_dir)
# Eval model on train
evaluator = Evaluator(model, test_dataset, verbose=True)
with tempfile.NamedTemporaryFile() as test_csv_out:
with tempfile.NamedTemporaryFile() as test_stats_out:
_, _ = evaluator.compute_model_performance(
test_csv_out, test_stats_out)
