yapf (6536f886) · Commits · 钟慕尧 / deepchem

deepchem/hyper/init.py

+14 −9

Original line number	Diff line number	Diff line
		@@ -12,6 +12,7 @@ from operator import mul
		from deepchem.utils.evaluate import Evaluator
		from deepchem.utils.save import log


		class HyperparamOpt(object):
		"""
		Provides simple hyperparameter search capabilities.
		@@ -23,8 +24,13 @@ class HyperparamOpt(object):

		# TODO(rbharath): This function is complicated and monolithic. Is there a nice
		# way to refactor this?
		def hyperparam_search(self, params_dict, train_dataset, valid_dataset,
		output_transformers, metric, use_max=True,
		def hyperparam_search(self,
		params_dict,
		train_dataset,
		valid_dataset,
		output_transformers,
		metric,
		use_max=True,
		logdir=None):
		"""Perform hyperparams search according to params_dict.

		@@ -49,10 +55,10 @@ class HyperparamOpt(object):
		best_hyperparams = None
		best_model, best_model_dir = None, None
		all_scores = {}
		for ind, hyperparameter_tuple in enumerate(itertools.product(*hyperparam_vals)):
		for ind, hyperparameter_tuple in enumerate(
		itertools.product(*hyperparam_vals)):
		model_params = {}
		log("Fitting model %d/%d" % (ind+1, number_combinations),
		self.verbose)
		log("Fitting model %d/%d" % (ind + 1, number_combinations), self.verbose)
		for hyperparam, hyperparam_val in zip(hyperparams, hyperparameter_tuple):
		model_params[hyperparam] = hyperparam_val
		log("hyperparameters: %s" % str(model_params), self.verbose)
		@@ -92,8 +98,8 @@ class HyperparamOpt(object):
		shutil.rmtree(model_dir)

		log("Model %d/%d, Metric %s, Validation set %s: %f" %
		(ind+1, number_combinations, metric.name, ind, valid_score),
		self.verbose)
		(ind + 1, number_combinations, metric.name, ind,
		valid_score), self.verbose)
		log("\tbest_validation_score so far: %f" % best_validation_score,
		self.verbose)
		if best_model is None:
		@@ -107,8 +113,7 @@ class HyperparamOpt(object):
		multitask_scores = train_evaluator.compute_model_performance(
		[metric], train_csv_out.name, train_stats_out.name)
		train_score = multitask_scores[metric.name]
		log("Best hyperparameters: %s" % str(best_hyperparams),
		self.verbose)
		log("Best hyperparameters: %s" % str(best_hyperparams), self.verbose)
		log("train_score: %f" % train_score, self.verbose)
		log("validation_score: %f" % best_validation_score, self.verbose)
		return best_model, best_hyperparams, all_scores

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