:bug: fix inconsisten api (f4315a1b) · Commits · 钟慕尧 / deepchem

deepchem/hyper/base_classes.py

+3 −3

Original line number	Diff line number	Diff line
		@@ -78,8 +78,8 @@ class HyperparamOpt(object):
		params_dict: Dict,
		train_dataset: Dataset,
		valid_dataset: Dataset,
		output_transformers: List[Transformer],
		metric: Metric,
		output_transformers: List[Transformer] = [],
		use_max: bool = True,
		logdir: Optional[str] = None,
		**kwargs) -> Tuple[Model, Dict, Dict]:
		@@ -103,13 +103,13 @@ class HyperparamOpt(object):
		dataset used for training
		valid_dataset: Dataset
		dataset used for validation(optimization on valid scores)
		metric: Metric
		metric used for evaluation
		output_transformers: list[Transformer]
		Transformers for evaluation. This argument is needed since
		`train_dataset` and `valid_dataset` may have been transformed
		for learning and need the transform to be inverted before
		the metric can be evaluated on a model.
		metric: Metric
		metric used for evaluation
		use_max: bool, optional
		If True, return the model with the highest score. Else return
		model with the minimum score.

+3 −3

Original line number	Diff line number	Diff line
		@@ -131,8 +131,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		params_dict: Dict,
		train_dataset: Dataset,
		valid_dataset: Dataset,
		output_transformers: List[Transformer],
		metric: Metric,
		output_transformers: List[Transformer] = [],
		use_max: bool = True,
		logdir: Optional[str] = None,
		max_iter: int = 20,
		@@ -154,13 +154,13 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		dataset used for training
		valid_dataset: Dataset
		dataset used for validation(optimization on valid scores)
		metric: Metric
		metric used for evaluation
		output_transformers: list[Transformer]
		Transformers for evaluation. This argument is needed since
		`train_dataset` and `valid_dataset` may have been transformed
		for learning and need the transform to be inverted before
		the metric can be evaluated on a model.
		metric: Metric
		metric used for evaluation
		use_max: bool, (default True)
		Specifies whether to maximize or minimize `metric`.
		maximization(True) or minimization(False)

+3 −3

Original line number	Diff line number	Diff line
		@@ -65,8 +65,8 @@ class GridHyperparamOpt(HyperparamOpt):
		params_dict: Dict,
		train_dataset: Dataset,
		valid_dataset: Dataset,
		output_transformers: List[Transformer],
		metric: Metric,
		output_transformers: List[Transformer] = [],
		use_max: bool = True,
		logdir: Optional[str] = None,
		**kwargs,
		@@ -85,13 +85,13 @@ class GridHyperparamOpt(HyperparamOpt):
		dataset used for training
		valid_dataset: Dataset
		dataset used for validation(optimization on valid scores)
		metric: Metric
		metric used for evaluation
		output_transformers: list[Transformer]
		Transformers for evaluation. This argument is needed since
		`train_dataset` and `valid_dataset` may have been transformed
		for learning and need the transform to be inverted before
		the metric can be evaluated on a model.
		metric: Metric
		metric used for evaluation
		use_max: bool, optional
		If True, return the model with the highest score. Else return
		model with the minimum score.

+5 −10

Original line number	Diff line number	Diff line
		@@ -42,12 +42,7 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict,
		self.train_dataset,
		self.valid_dataset,
		transformers,
		metric,
		max_iter=2)
		params_dict, self.train_dataset, self.valid_dataset, metric, max_iter=2)

		valid_score = best_model.evaluate(self.valid_dataset, [metric],
		transformers)
		@@ -66,8 +61,8 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		params_dict,
		self.train_dataset,
		self.valid_dataset,
		transformers,
		metric,
		transformers,
		use_max=False,
		max_iter=2)

		@@ -87,8 +82,8 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		params_dict,
		self.train_dataset,
		self.valid_dataset,
		transformers,
		metric,
		transformers,
		logdir=tmpdirname,
		max_iter=2)
		valid_score = best_model.evaluate(self.valid_dataset, [metric],
		@@ -122,8 +117,8 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		params_dict,
		train_dataset,
		valid_dataset,
		transformers,
		metric,
		transformers,
		max_iter=1,
		use_max=False)

		@@ -162,8 +157,8 @@ class TestGaussianHyperparamOpt(unittest.TestCase):
		params_dict,
		train_dataset,
		valid_dataset,
		transformers,
		metric,
		transformers,
		max_iter=2,
		logdir=tmpdirname,
		search_range=search_range,

+6 −6

Original line number	Diff line number	Diff line
		@@ -36,8 +36,8 @@ class TestGridHyperparamOpt(unittest.TestCase):
		metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

		best_model, best_hyperparams, all_results = optimizer.hyperparam_search(
		params_dict, self.train_dataset, self.valid_dataset, transformers,
		metric)
		params_dict, self.train_dataset, self.valid_dataset, metric,
		transformers)
		valid_score = best_model.evaluate(self.valid_dataset, [metric],
		transformers)

		@@ -55,8 +55,8 @@ class TestGridHyperparamOpt(unittest.TestCase):
		params_dict,
		self.train_dataset,
		self.valid_dataset,
		transformers,
		metric,
		transformers,
		use_max=False)
		valid_score = best_model.evaluate(self.valid_dataset, [metric],
		transformers)
		@@ -75,8 +75,8 @@ class TestGridHyperparamOpt(unittest.TestCase):
		params_dict,
		self.train_dataset,
		self.valid_dataset,
		transformers,
		metric,
		transformers,
		logdir=tmpdirname)
		valid_score = best_model.evaluate(self.valid_dataset, [metric],
		transformers)
		@@ -108,8 +108,8 @@ class TestGridHyperparamOpt(unittest.TestCase):
		params_dict,
		train_dataset,
		valid_dataset,
		transformers,
		metric,
		transformers,
		use_max=False)

		valid_score = best_model.evaluate(valid_dataset, [metric])
		@@ -145,8 +145,8 @@ class TestGridHyperparamOpt(unittest.TestCase):
		params_dict,
		train_dataset,
		valid_dataset,
		transformers,
		metric,
		transformers,
		logdir=tmpdirname,
		use_max=False)
		valid_score = best_model.evaluate(valid_dataset, [metric])