Loading deepchem/hyper/tests/test_grid_hyperparam_opt.py 0 → 100644 +152 −0 Original line number Diff line number Diff line """ Tests for Grid hyperparam optimization. """ import os import unittest import tempfile import shutil import numpy as np import tensorflow as tf import deepchem as dc import sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor class TestGridHyperparamOpt(unittest.TestCase): """ Test grid hyperparameter optimization API. """ def test_singletask_sklearn_rf_ECFP_regression_hyperparam_opt(self): """Test of hyperparam_opt with singletask RF ECFP regression API.""" featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [ dc.trans.NormalizationTransformer( transform_y=True, dataset=train_dataset) ] for dataset in [train_dataset, test_dataset]: for transformer in transformers: dataset = transformer.transform(dataset) params_dict = {"n_estimators": [10, 100]} metric = dc.metrics.Metric(dc.metrics.r2_score) def rf_model_builder(model_params, model_dir): sklearn_model = RandomForestRegressor(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) optimizer = dc.hyper.GridHyperparamOpt(rf_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) def test_singletask_to_multitask_sklearn_hyperparam_opt(self): """Test of hyperparam_opt with singletask_to_multitask.""" tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = "multitask_example.csv" n_features = 10 n_tasks = len(tasks) # Define train dataset n_train = 100 X_train = np.random.rand(n_train, n_features) y_train = np.random.randint(2, size=(n_train, n_tasks)) w_train = np.ones_like(y_train) ids_train = ["C"] * n_train train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train, w_train, ids_train, tasks) # Define validation dataset n_valid = 10 X_valid = np.random.rand(n_valid, n_features) y_valid = np.random.randint(2, size=(n_valid, n_tasks)) w_valid = np.ones_like(y_valid) ids_valid = ["C"] * n_valid valid_dataset = dc.data.DiskDataset.from_numpy(X_valid, y_valid, w_valid, ids_valid, tasks) transformers = [] classification_metric = dc.metrics.Metric( dc.metrics.matthews_corrcoef, np.mean, mode="classification") params_dict = {"n_estimators": [1, 10]} def multitask_model_builder(model_params, model_dir): def model_builder(model_dir): sklearn_model = RandomForestClassifier(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) return dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) optimizer = dc.hyper.GridHyperparamOpt(multitask_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, classification_metric, logdir=None) def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self): """Straightforward test of Tensorflow multitask deepchem classification API.""" task_type = "classification" current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] n_features = 1024 featurizer = dc.feat.CircularFingerprint(size=n_features) loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [] metric = dc.metrics.Metric( dc.metrics.roc_auc_score, np.mean, mode="classification") params_dict = {"layer_sizes": [(10,), (100,)]} def model_builder(model_params, model_dir): return dc.models.MultitaskClassifier( len(tasks), n_features, model_dir=model_dir, **model_params) optimizer = dc.hyper.GridHyperparamOpt(model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) deepchem/hyper/tests/test_hyperparam_opt.py +2 −146 Original line number Diff line number Diff line """ Tests for hyperparam optimization. """ import os import unittest import tempfile import shutil import numpy as np import tensorflow as tf import deepchem as dc import sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor import deepchem as dc class TestHyperparamOpt(unittest.TestCase): """ Loading @@ -32,140 +25,3 @@ class TestHyperparamOpt(unittest.TestCase): initialized = False assert not initialized class TestGridHyperparamOpt(unittest.TestCase): """ Test grid hyperparameter optimization API. """ def test_singletask_sklearn_rf_ECFP_regression_hyperparam_opt(self): """Test of hyperparam_opt with singletask RF ECFP regression API.""" featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [ dc.trans.NormalizationTransformer( transform_y=True, dataset=train_dataset) ] for dataset in [train_dataset, test_dataset]: for transformer in transformers: dataset = transformer.transform(dataset) params_dict = {"n_estimators": [10, 100]} metric = dc.metrics.Metric(dc.metrics.r2_score) def rf_model_builder(model_params, model_dir): sklearn_model = RandomForestRegressor(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) optimizer = dc.hyper.GridHyperparamOpt(rf_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) def test_singletask_to_multitask_sklearn_hyperparam_opt(self): """Test of hyperparam_opt with singletask_to_multitask.""" tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = "multitask_example.csv" n_features = 10 n_tasks = len(tasks) # Define train dataset n_train = 100 X_train = np.random.rand(n_train, n_features) y_train = np.random.randint(2, size=(n_train, n_tasks)) w_train = np.ones_like(y_train) ids_train = ["C"] * n_train train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train, w_train, ids_train, tasks) # Define validation dataset n_valid = 10 X_valid = np.random.rand(n_valid, n_features) y_valid = np.random.randint(2, size=(n_valid, n_tasks)) w_valid = np.ones_like(y_valid) ids_valid = ["C"] * n_valid valid_dataset = dc.data.DiskDataset.from_numpy(X_valid, y_valid, w_valid, ids_valid, tasks) transformers = [] classification_metric = dc.metrics.Metric( dc.metrics.matthews_corrcoef, np.mean, mode="classification") params_dict = {"n_estimators": [1, 10]} def multitask_model_builder(model_params, model_dir): def model_builder(model_dir): sklearn_model = RandomForestClassifier(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) return dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) optimizer = dc.hyper.GridHyperparamOpt(multitask_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, classification_metric, logdir=None) def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self): """Straightforward test of Tensorflow multitask deepchem classification API.""" task_type = "classification" current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] n_features = 1024 featurizer = dc.feat.CircularFingerprint(size=n_features) loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [] metric = dc.metrics.Metric( dc.metrics.roc_auc_score, np.mean, mode="classification") params_dict = {"layer_sizes": [(10,), (100,)]} def model_builder(model_params, model_dir): return dc.models.MultitaskClassifier( len(tasks), n_features, model_dir=model_dir, **model_params) optimizer = dc.hyper.GridHyperparamOpt(model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) Loading
deepchem/hyper/tests/test_grid_hyperparam_opt.py 0 → 100644 +152 −0 Original line number Diff line number Diff line """ Tests for Grid hyperparam optimization. """ import os import unittest import tempfile import shutil import numpy as np import tensorflow as tf import deepchem as dc import sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor class TestGridHyperparamOpt(unittest.TestCase): """ Test grid hyperparameter optimization API. """ def test_singletask_sklearn_rf_ECFP_regression_hyperparam_opt(self): """Test of hyperparam_opt with singletask RF ECFP regression API.""" featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [ dc.trans.NormalizationTransformer( transform_y=True, dataset=train_dataset) ] for dataset in [train_dataset, test_dataset]: for transformer in transformers: dataset = transformer.transform(dataset) params_dict = {"n_estimators": [10, 100]} metric = dc.metrics.Metric(dc.metrics.r2_score) def rf_model_builder(model_params, model_dir): sklearn_model = RandomForestRegressor(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) optimizer = dc.hyper.GridHyperparamOpt(rf_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) def test_singletask_to_multitask_sklearn_hyperparam_opt(self): """Test of hyperparam_opt with singletask_to_multitask.""" tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = "multitask_example.csv" n_features = 10 n_tasks = len(tasks) # Define train dataset n_train = 100 X_train = np.random.rand(n_train, n_features) y_train = np.random.randint(2, size=(n_train, n_tasks)) w_train = np.ones_like(y_train) ids_train = ["C"] * n_train train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train, w_train, ids_train, tasks) # Define validation dataset n_valid = 10 X_valid = np.random.rand(n_valid, n_features) y_valid = np.random.randint(2, size=(n_valid, n_tasks)) w_valid = np.ones_like(y_valid) ids_valid = ["C"] * n_valid valid_dataset = dc.data.DiskDataset.from_numpy(X_valid, y_valid, w_valid, ids_valid, tasks) transformers = [] classification_metric = dc.metrics.Metric( dc.metrics.matthews_corrcoef, np.mean, mode="classification") params_dict = {"n_estimators": [1, 10]} def multitask_model_builder(model_params, model_dir): def model_builder(model_dir): sklearn_model = RandomForestClassifier(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) return dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) optimizer = dc.hyper.GridHyperparamOpt(multitask_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, classification_metric, logdir=None) def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self): """Straightforward test of Tensorflow multitask deepchem classification API.""" task_type = "classification" current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] n_features = 1024 featurizer = dc.feat.CircularFingerprint(size=n_features) loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [] metric = dc.metrics.Metric( dc.metrics.roc_auc_score, np.mean, mode="classification") params_dict = {"layer_sizes": [(10,), (100,)]} def model_builder(model_params, model_dir): return dc.models.MultitaskClassifier( len(tasks), n_features, model_dir=model_dir, **model_params) optimizer = dc.hyper.GridHyperparamOpt(model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None)
deepchem/hyper/tests/test_hyperparam_opt.py +2 −146 Original line number Diff line number Diff line """ Tests for hyperparam optimization. """ import os import unittest import tempfile import shutil import numpy as np import tensorflow as tf import deepchem as dc import sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor import deepchem as dc class TestHyperparamOpt(unittest.TestCase): """ Loading @@ -32,140 +25,3 @@ class TestHyperparamOpt(unittest.TestCase): initialized = False assert not initialized class TestGridHyperparamOpt(unittest.TestCase): """ Test grid hyperparameter optimization API. """ def test_singletask_sklearn_rf_ECFP_regression_hyperparam_opt(self): """Test of hyperparam_opt with singletask RF ECFP regression API.""" featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [ dc.trans.NormalizationTransformer( transform_y=True, dataset=train_dataset) ] for dataset in [train_dataset, test_dataset]: for transformer in transformers: dataset = transformer.transform(dataset) params_dict = {"n_estimators": [10, 100]} metric = dc.metrics.Metric(dc.metrics.r2_score) def rf_model_builder(model_params, model_dir): sklearn_model = RandomForestRegressor(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) optimizer = dc.hyper.GridHyperparamOpt(rf_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None) def test_singletask_to_multitask_sklearn_hyperparam_opt(self): """Test of hyperparam_opt with singletask_to_multitask.""" tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = "multitask_example.csv" n_features = 10 n_tasks = len(tasks) # Define train dataset n_train = 100 X_train = np.random.rand(n_train, n_features) y_train = np.random.randint(2, size=(n_train, n_tasks)) w_train = np.ones_like(y_train) ids_train = ["C"] * n_train train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train, w_train, ids_train, tasks) # Define validation dataset n_valid = 10 X_valid = np.random.rand(n_valid, n_features) y_valid = np.random.randint(2, size=(n_valid, n_tasks)) w_valid = np.ones_like(y_valid) ids_valid = ["C"] * n_valid valid_dataset = dc.data.DiskDataset.from_numpy(X_valid, y_valid, w_valid, ids_valid, tasks) transformers = [] classification_metric = dc.metrics.Metric( dc.metrics.matthews_corrcoef, np.mean, mode="classification") params_dict = {"n_estimators": [1, 10]} def multitask_model_builder(model_params, model_dir): def model_builder(model_dir): sklearn_model = RandomForestClassifier(**model_params) return dc.models.SklearnModel(sklearn_model, model_dir) return dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) optimizer = dc.hyper.GridHyperparamOpt(multitask_model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, classification_metric, logdir=None) def test_multitask_tf_mlp_ECFP_classification_hyperparam_opt(self): """Straightforward test of Tensorflow multitask deepchem classification API.""" task_type = "classification" current_dir = os.path.dirname(os.path.abspath(__file__)) input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] n_features = 1024 featurizer = dc.feat.CircularFingerprint(size=n_features) loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(input_file) splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( dataset) transformers = [] metric = dc.metrics.Metric( dc.metrics.roc_auc_score, np.mean, mode="classification") params_dict = {"layer_sizes": [(10,), (100,)]} def model_builder(model_params, model_dir): return dc.models.MultitaskClassifier( len(tasks), n_features, model_dir=model_dir, **model_params) optimizer = dc.hyper.GridHyperparamOpt(model_builder) best_model, best_hyperparams, all_results = optimizer.hyperparam_search( params_dict, train_dataset, valid_dataset, transformers, metric, logdir=None)
