:ok_hand: fix for review (c032aaac) · Commits · 钟慕尧 / deepchem

deepchem/hyper/gaussian_process.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -9,7 +9,6 @@ from typing import Dict, List, Optional, Tuple, Union
		from deepchem.data import Dataset
		from deepchem.trans import Transformer
		from deepchem.metrics import Metric
		from deepchem.utils.evaluate import Evaluator
		from deepchem.hyper.base_classes import HyperparamOpt
		from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename

		@@ -285,8 +284,8 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		except NotImplementedError:
		pass

		evaluator = Evaluator(model, valid_dataset, output_transformers)
		multitask_scores = evaluator.compute_model_performance([metric])
		multitask_scores = model.evaluate(valid_dataset, [metric],
		output_transformers)
		score = multitask_scores[metric.name]

		if log_file:

deepchem/hyper/grid_search.py

+5 −10

Original line number	Diff line number	Diff line
		@@ -10,12 +10,11 @@ import collections
		import logging
		from functools import reduce
		from operator import mul
		from typing import cast, Dict, List, Optional
		from typing import Dict, List, Optional

		from deepchem.data import Dataset
		from deepchem.trans import Transformer
		from deepchem.metrics import Metric
		from deepchem.utils.evaluate import Evaluator
		from deepchem.hyper.base_classes import HyperparamOpt
		from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename

		@@ -152,10 +151,8 @@ class GridHyperparamOpt(HyperparamOpt):
		except NotImplementedError:
		pass

		evaluator = Evaluator(model, valid_dataset, output_transformers)
		multitask_scores = evaluator.compute_model_performance([metric])
		# NOTE: this casting is workaround. This line doesn't effect anything to the runtime
		multitask_scores = cast(Dict[str, float], multitask_scores)
		multitask_scores = model.evaluate(valid_dataset, [metric],
		output_transformers)
		valid_score = multitask_scores[metric.name]
		hp_str = _convert_hyperparam_dict_to_filename(hyper_params)
		all_scores[hp_str] = valid_score
		@@ -179,10 +176,8 @@ class GridHyperparamOpt(HyperparamOpt):
		# arbitrarily return last model
		best_model, best_hyperparams = model, hyperparameter_tuple
		return best_model, best_hyperparams, all_scores
		train_evaluator = Evaluator(best_model, train_dataset, output_transformers)
		multitask_scores = train_evaluator.compute_model_performance([metric])
		# NOTE: this casting is workaround. This line doesn't effect anything to the runtime
		multitask_scores = cast(Dict[str, float], multitask_scores)
		multitask_scores = best_model.evaluate(train_dataset, [metric],
		output_transformers)
		train_score = multitask_scores[metric.name]
		logger.info("Best hyperparameters: %s" % str(best_hyperparams))
		logger.info("train_score: %f" % train_score)

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