Loading deepchem/data/datasets.py +5 −6 Original line number Diff line number Diff line Loading @@ -1051,7 +1051,6 @@ class DiskDataset(Dataset): Location on disk of an existing `DiskDataset`. """ self.data_dir = data_dir self.legacy_metadata = legacy_metadata logger.info("Loading dataset from disk.") self.tasks, self.metadata_df = self.load_metadata() Loading Loading @@ -1114,7 +1113,7 @@ class DiskDataset(Dataset): DiskDataset._save_metadata(tasks, metadata_df, data_dir) time2 = time.time() logger.info("TIMING: dataset construction took %0.3f s" % (time2 - time1)) return DiskDataset(data_dir, legacy_metadata) return DiskDataset(data_dir) def load_metadata(self) -> Tuple[List[str], pd.DataFrame]: """Helper method that loads metadata from disk.""" Loading Loading @@ -2193,23 +2192,23 @@ class DiskDataset(Dataset): for shard_num in range(n_rows): row = self.metadata_df.iloc[shard_num] if row['X_shape'] is not None: shard_X_shape = make_tuple(row['X_shape']) shard_X_shape = make_tuple(str(row['X_shape'])) else: shard_X_shape = tuple() if n_tasks > 0: if row['y_shape'] is not None: shard_y_shape = make_tuple(row['y_shape']) shard_y_shape = make_tuple(str(row['y_shape'])) else: shard_y_shape = tuple() if row['w_shape'] is not None: shard_w_shape = make_tuple(row['w_shape']) shard_w_shape = make_tuple(str(row['w_shape'])) else: shard_w_shape = tuple() else: shard_y_shape = tuple() shard_w_shape = tuple() if row['ids_shape'] is not None: shard_ids_shape = make_tuple(row['ids_shape']) shard_ids_shape = make_tuple(str(row['ids_shape'])) else: shard_ids_shape = tuple() if shard_num == 0: Loading deepchem/models/tests/test_generalize.py +59 −57 Original line number Diff line number Diff line Loading @@ -122,63 +122,65 @@ def test_sklearn_multitask_regression(): assert score > .5 #def test_sklearn_classification(): # """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(): # """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(): """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(): """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]) assert scores['roc_auc_score'] > 0.5 def test_xgboost_regression(): Loading Loading
deepchem/data/datasets.py +5 −6 Original line number Diff line number Diff line Loading @@ -1051,7 +1051,6 @@ class DiskDataset(Dataset): Location on disk of an existing `DiskDataset`. """ self.data_dir = data_dir self.legacy_metadata = legacy_metadata logger.info("Loading dataset from disk.") self.tasks, self.metadata_df = self.load_metadata() Loading Loading @@ -1114,7 +1113,7 @@ class DiskDataset(Dataset): DiskDataset._save_metadata(tasks, metadata_df, data_dir) time2 = time.time() logger.info("TIMING: dataset construction took %0.3f s" % (time2 - time1)) return DiskDataset(data_dir, legacy_metadata) return DiskDataset(data_dir) def load_metadata(self) -> Tuple[List[str], pd.DataFrame]: """Helper method that loads metadata from disk.""" Loading Loading @@ -2193,23 +2192,23 @@ class DiskDataset(Dataset): for shard_num in range(n_rows): row = self.metadata_df.iloc[shard_num] if row['X_shape'] is not None: shard_X_shape = make_tuple(row['X_shape']) shard_X_shape = make_tuple(str(row['X_shape'])) else: shard_X_shape = tuple() if n_tasks > 0: if row['y_shape'] is not None: shard_y_shape = make_tuple(row['y_shape']) shard_y_shape = make_tuple(str(row['y_shape'])) else: shard_y_shape = tuple() if row['w_shape'] is not None: shard_w_shape = make_tuple(row['w_shape']) shard_w_shape = make_tuple(str(row['w_shape'])) else: shard_w_shape = tuple() else: shard_y_shape = tuple() shard_w_shape = tuple() if row['ids_shape'] is not None: shard_ids_shape = make_tuple(row['ids_shape']) shard_ids_shape = make_tuple(str(row['ids_shape'])) else: shard_ids_shape = tuple() if shard_num == 0: Loading
deepchem/models/tests/test_generalize.py +59 −57 Original line number Diff line number Diff line Loading @@ -122,63 +122,65 @@ def test_sklearn_multitask_regression(): assert score > .5 #def test_sklearn_classification(): # """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(): # """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(): """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(): """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]) assert scores['roc_auc_score'] > 0.5 def test_xgboost_regression(): Loading
