yapf (18ea3f26) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorflow_models/lr.py

+1 −2

Original line number	Diff line number	Diff line
		@@ -243,7 +243,6 @@ class TensorflowLogisticRegression(TensorflowGraphModel):
		raise ValueError('Unrecognized rank combination for output: %s' %
		(batch_output.shape,))
		output.append(batch_output)
		outputs = np.array(
		from_one_hot(np.concatenate(output), axis=-1))
		outputs = np.array(from_one_hot(np.concatenate(output), axis=-1))

		return np.copy(outputs)

deepchem/models/tensorflow_models/robust_multitask.py

+8 −9

Original line number	Diff line number	Diff line
		@@ -164,10 +164,8 @@ class RobustMultitaskClassifier(TensorflowMultiTaskClassifier):
		task_layer,
		num_classes=2,
		weight_init=tf.truncated_normal(
		shape=[task_layer_size, 2],
		stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(
		value=bias_init_consts[-1], shape=[2])))
		shape=[task_layer_size, 2], stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(value=bias_init_consts[-1], shape=[2])))
		return (output, labels, weights)


		@@ -327,5 +325,6 @@ class RobustMultitaskRegressor(TensorflowMultiTaskRegressor):
		shape=[task_layer_size, 1],
		stddev=weight_init_stddevs[-1]),
		bias_init=tf.constant(
		value=bias_init_consts[-1], shape=[1])), axis=1))
		value=bias_init_consts[-1], shape=[1])),
		axis=1))
		return (output, labels, weights)

examples/benchmark_curve.py

+42 −22

Original line number	Diff line number	Diff line
		@@ -31,7 +31,6 @@ from deepchem.molnet.run_benchmark import load_dataset
		from deepchem.molnet.check_availability import CheckFeaturizer, CheckSplit
		from deepchem.molnet.preset_hyper_parameters import hps


		# Evaluate performances with different training set fraction
		frac_trains = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

		@@ -106,7 +105,8 @@ for dataset in datasets:
		featurizer = CheckFeaturizer[pair][0]
		n_features = CheckFeaturizer[pair][1]

		tasks, all_dataset, transformers = load_dataset(dataset, featurizer, split='index')
		tasks, all_dataset, transformers = load_dataset(
		dataset, featurizer, split='index')
		all_dataset = dc.data.DiskDataset.merge(all_dataset)
		for frac_train in frac_trains:
		splitters = {
		@@ -117,27 +117,47 @@ for dataset in datasets:
		}
		splitter = splitters[split]
		np.random.seed(seed)
		train, valid, test = splitter.train_valid_test_split(all_dataset,
		train, valid, test = splitter.train_valid_test_split(
		all_dataset,
		frac_train=frac_train,
		frac_valid=1 - frac_train,
		frac_test=0.)
		test = valid
		if mode == 'classification':
		train_score, valid_score, test_score = benchmark_classification(
		train, valid, test, tasks, transformers, n_features, metric,
		model, test=False, hyper_parameters=hyper_parameters, seed=seed)
		train,
		valid,
		test,
		tasks,
		transformers,
		n_features,
		metric,
		model,
		test=False,
		hyper_parameters=hyper_parameters,
		seed=seed)
		elif mode == 'regression':
		train_score, valid_score, test_score = benchmark_regression(
		train, valid, test, tasks, transformers, n_features, metric,
		model, test=False, hyper_parameters=hyper_parameters, seed=seed)
		with open(os.path.join(out_path, 'results_frac_train_curve.csv'), 'a') as f:
		train,
		valid,
		test,
		tasks,
		transformers,
		n_features,
		metric,
		model,
		test=False,
		hyper_parameters=hyper_parameters,
		seed=seed)
		with open(os.path.join(out_path, 'results_frac_train_curve.csv'),
		'a') as f:
		writer = csv.writer(f)
		model_name = list(train_score.keys())[0]
		for i in train_score[model_name]:
		output_line = [
		dataset, str(split), mode, model_name, i, 'train',
		dataset,
		str(split), mode, model_name, i, 'train',
		train_score[model_name][i], 'valid', valid_score[model_name][i]
		]
		output_line.extend([
		'frac_train', frac_train])
		output_line.extend(['frac_train', frac_train])
		writer.writerow(output_line)

devtools/jenkins/compare_results.py

+2 −2

File changed.

Contains only whitespace changes.

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