Merge pull request #115 from joegomes/master (b2a32795) · Commits · 钟慕尧 / deepchem

deepchem/hyperparameters/init.py

+10 −5

Original line number	Diff line number	Diff line
		@@ -19,7 +19,7 @@ class HyperparamOpt(object):
		self.task_types = task_types

		def hyperparam_search(self, params_dict, train_dataset, valid_dataset,
		output_transformers, metric, verbosity=None):
		output_transformers, metric, use_max=True, verbosity=None):
		"""Perform hyperparams search according to params_dict.

		Each key to hyperparams_dict is a model_param. The values should be a list
		@@ -34,7 +34,10 @@ class HyperparamOpt(object):

		valid_csv_out = tempfile.NamedTemporaryFile()
		valid_stats_out = tempfile.NamedTemporaryFile()
		if use_max:
		best_validation_score = -np.inf
		else:
		best_validation_score = np.inf
		best_hyperparams = None
		best_model, best_model_dir = None, None
		all_scores = {}
		@@ -52,12 +55,10 @@ class HyperparamOpt(object):
		df, score = evaluator.compute_model_performance(
		valid_csv_out, valid_stats_out)
		valid_score = score.iloc[0][metric]
		print("Model %d/%d, Metric %s, Validation set %s: %f" %
		(ind, number_combinations, metric, ind, valid_score))
		print("\tbest_validation_score so far: %f" % best_validation_score)
		all_scores[hyperparameter_tuple] = valid_score

		if valid_score > best_validation_score:
		if (use_max and valid_score > best_validation_score) or (
		not use_max and valid_score < best_validation_score):
		best_validation_score = valid_score
		best_hyperparams = hyperparameter_tuple
		if best_model_dir is not None:
		@@ -67,6 +68,10 @@ class HyperparamOpt(object):
		else:
		shutil.rmtree(model_dir)

		print("Model %d/%d, Metric %s, Validation set %s: %f" %
		(ind, number_combinations, metric, ind, valid_score))
		print("\tbest_validation_score so far: %f" % best_validation_score)

		train_csv_out = tempfile.NamedTemporaryFile()
		train_stats_out = tempfile.NamedTemporaryFile()
		train_evaluator = Evaluator(best_model, train_dataset, output_transformers)

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