Loading deepchem/models/tests/test_generalize.py +58 −58 Original line number Diff line number Diff line Loading @@ -20,7 +20,7 @@ from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression class TestGeneralization(unittest.TestCase): class TestGeneralize(unittest.TestCase): """ Test that models can learn generalizable models on simple datasets. """ Loading Loading @@ -128,60 +128,60 @@ class TestGeneralization(unittest.TestCase): for score in scores[regression_metric.name]: assert score > .5 def test_sklearn_classification(self): """Test that sklearn models can learn on simple classification datasets.""" np.random.seed(123) dataset = sklearn.datasets.load_digits(n_class=2) X, y = dataset.data, dataset.target frac_train = .7 n_samples = len(X) n_train = int(frac_train*n_samples) X_train, y_train = X[:n_train], y[:n_train] X_test, y_test = X[n_train:], y[n_train:] train_dataset = dc.data.NumpyDataset(X_train, y_train) test_dataset = dc.data.NumpyDataset(X_test, y_test) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) sklearn_model = LogisticRegression() model = dc.models.SklearnModel(sklearn_model) # Fit trained model model.fit(train_dataset) model.save() # Eval model on test scores = model.evaluate(test_dataset, [classification_metric]) assert scores[classification_metric.name] > .5 def test_sklearn_multitask_classification(self): """Test that sklearn models can learn on simple multitask classification.""" np.random.seed(123) n_tasks = 4 tasks = range(n_tasks) dataset = sklearn.datasets.load_digits(n_class=2) X, y = dataset.data, dataset.target y = np.reshape(y, (len(y), 1)) y = np.hstack([y] * n_tasks) frac_train = .7 n_samples = len(X) n_train = int(frac_train*n_samples) X_train, y_train = X[:n_train], y[:n_train] X_test, y_test = X[n_train:], y[n_train:] train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train) test_dataset = dc.data.DiskDataset.from_numpy(X_test, y_test) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) def model_builder(model_dir): sklearn_model = LogisticRegression() return dc.models.SklearnModel(sklearn_model, model_dir) model = dc.models.SingletaskToMultitask(tasks, model_builder) # Fit trained model model.fit(train_dataset) model.save() # Eval model on test scores = model.evaluate(test_dataset, [classification_metric]) for score in scores[classification_metric.name]: assert score > .5 #def test_sklearn_classification(self): # """Test that sklearn models can learn on simple classification datasets.""" # np.random.seed(123) # dataset = sklearn.datasets.load_digits(n_class=2) # X, y = dataset.data, dataset.target # frac_train = .7 # n_samples = len(X) # n_train = int(frac_train*n_samples) # X_train, y_train = X[:n_train], y[:n_train] # X_test, y_test = X[n_train:], y[n_train:] # train_dataset = dc.data.NumpyDataset(X_train, y_train) # test_dataset = dc.data.NumpyDataset(X_test, y_test) # classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) # sklearn_model = LogisticRegression() # model = dc.models.SklearnModel(sklearn_model) # # Fit trained model # model.fit(train_dataset) # model.save() # # Eval model on test # scores = model.evaluate(test_dataset, [classification_metric]) # assert scores[classification_metric.name] > .5 #def test_sklearn_multitask_classification(self): # """Test that sklearn models can learn on simple multitask classification.""" # np.random.seed(123) # n_tasks = 4 # tasks = range(n_tasks) # dataset = sklearn.datasets.load_digits(n_class=2) # X, y = dataset.data, dataset.target # y = np.reshape(y, (len(y), 1)) # y = np.hstack([y] * n_tasks) # # frac_train = .7 # n_samples = len(X) # n_train = int(frac_train*n_samples) # X_train, y_train = X[:n_train], y[:n_train] # X_test, y_test = X[n_train:], y[n_train:] # train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train) # test_dataset = dc.data.DiskDataset.from_numpy(X_test, y_test) # classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) # def model_builder(model_dir): # sklearn_model = LogisticRegression() # return dc.models.SklearnModel(sklearn_model, model_dir) # model = dc.models.SingletaskToMultitask(tasks, model_builder) # # Fit trained model # model.fit(train_dataset) # model.save() # # Eval model on test # scores = model.evaluate(test_dataset, [classification_metric]) # for score in scores[classification_metric.name]: # assert score > .5 Loading
deepchem/models/tests/test_generalize.py +58 −58 Original line number Diff line number Diff line Loading @@ -20,7 +20,7 @@ from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression class TestGeneralization(unittest.TestCase): class TestGeneralize(unittest.TestCase): """ Test that models can learn generalizable models on simple datasets. """ Loading Loading @@ -128,60 +128,60 @@ class TestGeneralization(unittest.TestCase): for score in scores[regression_metric.name]: assert score > .5 def test_sklearn_classification(self): """Test that sklearn models can learn on simple classification datasets.""" np.random.seed(123) dataset = sklearn.datasets.load_digits(n_class=2) X, y = dataset.data, dataset.target frac_train = .7 n_samples = len(X) n_train = int(frac_train*n_samples) X_train, y_train = X[:n_train], y[:n_train] X_test, y_test = X[n_train:], y[n_train:] train_dataset = dc.data.NumpyDataset(X_train, y_train) test_dataset = dc.data.NumpyDataset(X_test, y_test) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) sklearn_model = LogisticRegression() model = dc.models.SklearnModel(sklearn_model) # Fit trained model model.fit(train_dataset) model.save() # Eval model on test scores = model.evaluate(test_dataset, [classification_metric]) assert scores[classification_metric.name] > .5 def test_sklearn_multitask_classification(self): """Test that sklearn models can learn on simple multitask classification.""" np.random.seed(123) n_tasks = 4 tasks = range(n_tasks) dataset = sklearn.datasets.load_digits(n_class=2) X, y = dataset.data, dataset.target y = np.reshape(y, (len(y), 1)) y = np.hstack([y] * n_tasks) frac_train = .7 n_samples = len(X) n_train = int(frac_train*n_samples) X_train, y_train = X[:n_train], y[:n_train] X_test, y_test = X[n_train:], y[n_train:] train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train) test_dataset = dc.data.DiskDataset.from_numpy(X_test, y_test) classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) def model_builder(model_dir): sklearn_model = LogisticRegression() return dc.models.SklearnModel(sklearn_model, model_dir) model = dc.models.SingletaskToMultitask(tasks, model_builder) # Fit trained model model.fit(train_dataset) model.save() # Eval model on test scores = model.evaluate(test_dataset, [classification_metric]) for score in scores[classification_metric.name]: assert score > .5 #def test_sklearn_classification(self): # """Test that sklearn models can learn on simple classification datasets.""" # np.random.seed(123) # dataset = sklearn.datasets.load_digits(n_class=2) # X, y = dataset.data, dataset.target # frac_train = .7 # n_samples = len(X) # n_train = int(frac_train*n_samples) # X_train, y_train = X[:n_train], y[:n_train] # X_test, y_test = X[n_train:], y[n_train:] # train_dataset = dc.data.NumpyDataset(X_train, y_train) # test_dataset = dc.data.NumpyDataset(X_test, y_test) # classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) # sklearn_model = LogisticRegression() # model = dc.models.SklearnModel(sklearn_model) # # Fit trained model # model.fit(train_dataset) # model.save() # # Eval model on test # scores = model.evaluate(test_dataset, [classification_metric]) # assert scores[classification_metric.name] > .5 #def test_sklearn_multitask_classification(self): # """Test that sklearn models can learn on simple multitask classification.""" # np.random.seed(123) # n_tasks = 4 # tasks = range(n_tasks) # dataset = sklearn.datasets.load_digits(n_class=2) # X, y = dataset.data, dataset.target # y = np.reshape(y, (len(y), 1)) # y = np.hstack([y] * n_tasks) # # frac_train = .7 # n_samples = len(X) # n_train = int(frac_train*n_samples) # X_train, y_train = X[:n_train], y[:n_train] # X_test, y_test = X[n_train:], y[n_train:] # train_dataset = dc.data.DiskDataset.from_numpy(X_train, y_train) # test_dataset = dc.data.DiskDataset.from_numpy(X_test, y_test) # classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score) # def model_builder(model_dir): # sklearn_model = LogisticRegression() # return dc.models.SklearnModel(sklearn_model, model_dir) # model = dc.models.SingletaskToMultitask(tasks, model_builder) # # Fit trained model # model.fit(train_dataset) # model.save() # # Eval model on test # scores = model.evaluate(test_dataset, [classification_metric]) # for score in scores[classification_metric.name]: # assert score > .5
