Loading deepchem/utils/test/test_vector_api.pydeleted 100644 → 0 +0 −209 Original line number Diff line number Diff line """ Integration tests for singletask vector feature models. """ __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "LGPL" import os import unittest import tempfile import shutil from deepchem.featurizers.featurize import DataFeaturizer from deepchem.featurizers.featurize import FeaturizedSamples from deepchem.utils.dataset import Dataset from deepchem.utils.evaluate import Evaluator from deepchem.models import Model # We need to import models so they can be created by model_builder import deepchem.models.deep import deepchem.models.standard import deepchem.models.deep3d class TestSingletaskVectorAPI(unittest.TestCase): """ Test top-level API for singletask vector models." """ def setUp(self): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samplesdir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() self.model_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samplesdir) shutil.rmtree(self.train_dir) shutil.rmtree(self.test_dir) shutil.rmtree(self.model_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) def test_singletask_rf_ECFP_regression_API(self): """Test of singletask RF ECFP regression API.""" splittype = "scaffold" feature_types = ["ECFP"] input_transforms = [] output_transforms = ["normalize"] task_type = "regression" model_params = {"batch_size": 5} model_name = "rf_regressor" input_file = "example.csv" tasks = ["log-solubility"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, tasks=tasks) def test_singletask_rf_RDKIT_descriptor_regression_API(self): """Test of singletask RF RDKIT-descriptor regression API.""" splittype = "scaffold" feature_types = ["RDKIT-descriptors"] input_transforms = ["normalize", "truncate"] output_transforms = ["normalize"] task_type = "regression" model_params = {"batch_size": 5} model_name = "rf_regressor" input_file = "example.csv" tasks = ["log-solubility"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, tasks=tasks) def test_singletask_mlp_NNScore_regression_API(self): """Test of singletask MLP NNScore regression API.""" splittype = "scaffold" feature_types = ["NNScore"] input_transforms = ["normalize", "truncate"] output_transforms = ["normalize"] task_type = "regression" 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 = "singletask_deep_regressor" protein_pdb_field = "protein_pdb" ligand_pdb_field = "ligand_pdb" input_file = "nnscore_example.pkl.gz" tasks = ["label"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, protein_pdb_field=protein_pdb_field, ligand_pdb_field=ligand_pdb_field, tasks=tasks) class TestMultitaskVectorAPI(unittest.TestCase): """ Test top-level API for singletask vector models." """ def setUp(self): current_dir = os.path.dirname(os.path.abspath(__file__)) self.input_file = os.path.join(current_dir, "multitask_example.csv") self.tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16"] self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samplesdir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() self.model_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samplesdir) shutil.rmtree(self.train_dir) shutil.rmtree(self.test_dir) shutil.rmtree(self.model_dir) 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) Loading
deepchem/utils/test/test_vector_api.pydeleted 100644 → 0 +0 −209 Original line number Diff line number Diff line """ Integration tests for singletask vector feature models. """ __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "LGPL" import os import unittest import tempfile import shutil from deepchem.featurizers.featurize import DataFeaturizer from deepchem.featurizers.featurize import FeaturizedSamples from deepchem.utils.dataset import Dataset from deepchem.utils.evaluate import Evaluator from deepchem.models import Model # We need to import models so they can be created by model_builder import deepchem.models.deep import deepchem.models.standard import deepchem.models.deep3d class TestSingletaskVectorAPI(unittest.TestCase): """ Test top-level API for singletask vector models." """ def setUp(self): self.current_dir = os.path.dirname(os.path.abspath(__file__)) self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samplesdir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() self.model_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samplesdir) shutil.rmtree(self.train_dir) shutil.rmtree(self.test_dir) shutil.rmtree(self.model_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) def test_singletask_rf_ECFP_regression_API(self): """Test of singletask RF ECFP regression API.""" splittype = "scaffold" feature_types = ["ECFP"] input_transforms = [] output_transforms = ["normalize"] task_type = "regression" model_params = {"batch_size": 5} model_name = "rf_regressor" input_file = "example.csv" tasks = ["log-solubility"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, tasks=tasks) def test_singletask_rf_RDKIT_descriptor_regression_API(self): """Test of singletask RF RDKIT-descriptor regression API.""" splittype = "scaffold" feature_types = ["RDKIT-descriptors"] input_transforms = ["normalize", "truncate"] output_transforms = ["normalize"] task_type = "regression" model_params = {"batch_size": 5} model_name = "rf_regressor" input_file = "example.csv" tasks = ["log-solubility"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, tasks=tasks) def test_singletask_mlp_NNScore_regression_API(self): """Test of singletask MLP NNScore regression API.""" splittype = "scaffold" feature_types = ["NNScore"] input_transforms = ["normalize", "truncate"] output_transforms = ["normalize"] task_type = "regression" 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 = "singletask_deep_regressor" protein_pdb_field = "protein_pdb" ligand_pdb_field = "ligand_pdb" input_file = "nnscore_example.pkl.gz" tasks = ["label"] self._create_model(splittype, feature_types, input_transforms, output_transforms, task_type, model_params, model_name, input_file=input_file, protein_pdb_field=protein_pdb_field, ligand_pdb_field=ligand_pdb_field, tasks=tasks) class TestMultitaskVectorAPI(unittest.TestCase): """ Test top-level API for singletask vector models." """ def setUp(self): current_dir = os.path.dirname(os.path.abspath(__file__)) self.input_file = os.path.join(current_dir, "multitask_example.csv") self.tasks = ["task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16"] self.smiles_field = "smiles" self.feature_dir = tempfile.mkdtemp() self.samplesdir = tempfile.mkdtemp() self.train_dir = tempfile.mkdtemp() self.test_dir = tempfile.mkdtemp() self.model_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.feature_dir) shutil.rmtree(self.samplesdir) shutil.rmtree(self.train_dir) shutil.rmtree(self.test_dir) shutil.rmtree(self.model_dir) 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)
