Loading deepchem/splits/tests/__init__.py +26 −62 Original line number Diff line number Diff line Loading @@ -25,90 +25,54 @@ class TestSplitAPI(unittest.TestCase): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.test_data_dir = os.path.join(self.current_dir, "../../models/tests") self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samples_dir = tempfile.mkdtemp() self.data_dir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.valid_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samples_dir) shutil.rmtree(self.data_dir) shutil.rmtree(self.train_dir) shutil.rmtree(self.valid_dir) shutil.rmtree(self.test_dir) def _gen_samples(self, compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks, protein_pdb_field=None, ligand_pdb_field=None, user_specified_features=None, split_field=None, shard_size=100): # Featurize input featurizers = compound_featurizers + complex_featurizers input_file = os.path.join(self.test_data_dir, input_file) def load_solubility_data(self): """Loads solubility data from example.csv""" featurizers = [CircularFingerprint(size=1024)] tasks = ["log-solubility"] task_type = "regression" input_file = os.path.join(self.test_data_dir, "example.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, protein_pdb_field=protein_pdb_field, ligand_pdb_field=ligand_pdb_field, compound_featurizers=compound_featurizers, complex_featurizers=complex_featurizers, user_specified_features=user_specified_features, split_field=split_field, featurizers=featurizers, verbosity="low") samples = featurizer.featurize(input_file, self.feature_dir, self.samples_dir, shard_size=shard_size) return samples return featurizer.featurize(input_file, self.data_dir) def _load_solubility_samples(self): """Loads solubility data from example.csv""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] input_transformer_classes = [] output_transformer_classes = [] tasks = ["log-solubility"] task_type = "regression" task_types = {task: task_type for task in tasks} input_file = "example.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) def _load_classification_samples(self): def load_classification_data(self): """Loads classification data from example.csv""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] input_transformer_classes = [] output_transformer_classes = [] featurizers = [CircularFingerprint(size=1024)] tasks = ["outcome"] task_type = "classification" task_types = {task: task_type for task in tasks} input_file = "example_classification.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) input_file = os.path.join(self.test_data_dir, "example_classification.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, featurizers=featurizers, verbosity="low") return featurizer.featurize(input_file, self.data_dir) def _load_multitask_samples(self): def load_multitask_data(self): """Load example multitask data.""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] output_transformer_classes = [] input_transformer_classes = [] featurizers = [CircularFingerprint(size=1024)] tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16"] task_types = {task: "classification" for task in tasks} input_file = "multitask_example.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) input_file = os.path.join(self.test_data_dir, "multitask_example.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, featurizers=featurizers, verbosity="low") return featurizer.featurize(input_file, self.data_dir) Loading
deepchem/splits/tests/__init__.py +26 −62 Original line number Diff line number Diff line Loading @@ -25,90 +25,54 @@ class TestSplitAPI(unittest.TestCase): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.test_data_dir = os.path.join(self.current_dir, "../../models/tests") self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samples_dir = tempfile.mkdtemp() self.data_dir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.valid_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samples_dir) shutil.rmtree(self.data_dir) shutil.rmtree(self.train_dir) shutil.rmtree(self.valid_dir) shutil.rmtree(self.test_dir) def _gen_samples(self, compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks, protein_pdb_field=None, ligand_pdb_field=None, user_specified_features=None, split_field=None, shard_size=100): # Featurize input featurizers = compound_featurizers + complex_featurizers input_file = os.path.join(self.test_data_dir, input_file) def load_solubility_data(self): """Loads solubility data from example.csv""" featurizers = [CircularFingerprint(size=1024)] tasks = ["log-solubility"] task_type = "regression" input_file = os.path.join(self.test_data_dir, "example.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, protein_pdb_field=protein_pdb_field, ligand_pdb_field=ligand_pdb_field, compound_featurizers=compound_featurizers, complex_featurizers=complex_featurizers, user_specified_features=user_specified_features, split_field=split_field, featurizers=featurizers, verbosity="low") samples = featurizer.featurize(input_file, self.feature_dir, self.samples_dir, shard_size=shard_size) return samples return featurizer.featurize(input_file, self.data_dir) def _load_solubility_samples(self): """Loads solubility data from example.csv""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] input_transformer_classes = [] output_transformer_classes = [] tasks = ["log-solubility"] task_type = "regression" task_types = {task: task_type for task in tasks} input_file = "example.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) def _load_classification_samples(self): def load_classification_data(self): """Loads classification data from example.csv""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] input_transformer_classes = [] output_transformer_classes = [] featurizers = [CircularFingerprint(size=1024)] tasks = ["outcome"] task_type = "classification" task_types = {task: task_type for task in tasks} input_file = "example_classification.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) input_file = os.path.join(self.test_data_dir, "example_classification.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, featurizers=featurizers, verbosity="low") return featurizer.featurize(input_file, self.data_dir) def _load_multitask_samples(self): def load_multitask_data(self): """Load example multitask data.""" compound_featurizers = [CircularFingerprint(size=1024)] complex_featurizers = [] output_transformer_classes = [] input_transformer_classes = [] featurizers = [CircularFingerprint(size=1024)] tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16"] task_types = {task: "classification" for task in tasks} input_file = "multitask_example.csv" return self._gen_samples( compound_featurizers, complex_featurizers, input_transformer_classes, output_transformer_classes, input_file, tasks) input_file = os.path.join(self.test_data_dir, "multitask_example.csv") featurizer = DataFeaturizer( tasks=tasks, smiles_field=self.smiles_field, featurizers=featurizers, verbosity="low") return featurizer.featurize(input_file, self.data_dir)
