Fixing some leftover changes in test_hyperparam_opt (b2344183) · Commits · 钟慕尧 / deepchem

deepchem/hyper/tests/test_hyperparam_opt.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -52,7 +52,7 @@ class TestHyperparamOptAPI(unittest.TestCase):
		sklearn_model = RandomForestRegressor(**model_params)
		return dc.models.SklearnModel(sklearn_model, model_dir)

		optimizer = dc.hyperparameters.HyperparamOpt(rf_model_builder)
		optimizer = dc.hyper.HyperparamOpt(rf_model_builder)
		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, train_dataset, valid_dataset, transformers,
		metric, logdir=None)
		@@ -95,7 +95,7 @@ class TestHyperparamOptAPI(unittest.TestCase):
		return dc.models.SklearnModel(sklearn_model, model_dir)
		return dc.models.SingletaskToMultitask(tasks, model_builder, model_dir)

		optimizer = dc.hyperparameters.HyperparamOpt(multitask_model_builder)
		optimizer = dc.hyper.HyperparamOpt(multitask_model_builder)
		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, train_dataset, valid_dataset, transformers,
		classification_metric, logdir=None)
		@@ -130,7 +130,7 @@ class TestHyperparamOptAPI(unittest.TestCase):
		keras_model = dc.models.MultiTaskDNN(
		len(tasks), n_features, task_type, dropout=0., **model_params)
		return dc.models.KerasModel(keras_model, model_dir)
		optimizer = dc.hyperparameters.HyperparamOpt(model_builder)
		optimizer = dc.hyper.HyperparamOpt(model_builder)
		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, train_dataset, valid_dataset, transformers,
		metric, logdir=None)
		@@ -168,7 +168,7 @@ class TestHyperparamOptAPI(unittest.TestCase):
		tensorflow_model = dc.models.TensorflowMultiTaskClassifier(
		len(tasks), n_features, model_dir, **model_params)
		return dc.models.TensorflowModel(tensorflow_model)
		optimizer = dc.hyperparameters.HyperparamOpt(model_builder)
		optimizer = dc.hyper.HyperparamOpt(model_builder)
		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, train_dataset, valid_dataset, transformers, metric,
		logdir=None)

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