Merge pull request #1660 from VIGS25/molnet-addons-chemception (369d9b51) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/chemnet_models.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -280,7 +280,7 @@ class ChemCeption(KerasModel):
		avg_pooling_out = GlobalAveragePooling2D()(inceptionC_out)

		if self.mode == "classification":
		logits = Dense(self.n_tasks * self.n_classes)(rnn_embeddings)
		logits = Dense(self.n_tasks * self.n_classes)(avg_pooling_out)
		logits = Reshape((self.n_tasks, self.n_classes))(logits)
		if self.n_classes == 2:
		output = Activation(activation='sigmoid')(logits)

deepchem/molnet/load_function/bace_datasets.py

+22 −4

Original line number	Diff line number	Diff line
		@@ -90,11 +90,20 @@ def load_bace_regression(featurizer='ECFP',
		splitters = {
		'index': deepchem.splits.IndexSplitter(),
		'random': deepchem.splits.RandomSplitter(),
		'scaffold': deepchem.splits.ScaffoldSplitter()
		'scaffold': deepchem.splits.ScaffoldSplitter(),
		'stratified': deepchem.splits.SingletaskStratifiedSplitter()
		}
		splitter = splitters[split]
		logger.info("About to split data using {} splitter".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)

		transformers = [
		deepchem.trans.NormalizationTransformer(
		@@ -182,12 +191,21 @@ def load_bace_classification(featurizer='ECFP',
		splitters = {
		'index': deepchem.splits.IndexSplitter(),
		'random': deepchem.splits.RandomSplitter(),
		'scaffold': deepchem.splits.ScaffoldSplitter()
		'scaffold': deepchem.splits.ScaffoldSplitter(),
		'stratified': deepchem.splits.RandomStratifiedSplitter()
		}

		splitter = splitters[split]
		logger.info("About to split data using {} splitter".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)

		transformers = [
		deepchem.trans.BalancingTransformer(transform_w=True, dataset=train)

deepchem/molnet/load_function/bbbc_datasets.py

+20 −4

Original line number	Diff line number	Diff line
		@@ -43,7 +43,7 @@ def load_bbbc001(split='index',
		save_dir = DEFAULT_DIR

		if reload:
		save_folder = os.path.join(save_dir, "bbbc001-featurized/" + str(split))
		save_folder = os.path.join(save_dir, "bbbc001-featurized", str(split))
		loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
		save_folder)
		if loaded:
		@@ -86,7 +86,15 @@ def load_bbbc001(split='index',
		splitter = splitters[split]

		logger.info("About to split dataset with {} splitter.".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)
		transformers = []
		all_dataset = (train, valid, test)
		if reload:
		@@ -117,7 +125,7 @@ def load_bbbc002(split='index',
		save_dir = DEFAULT_DIR

		if reload:
		save_folder = os.path.join(save_dir, "bbbc002-featurized/" + str(split))
		save_folder = os.path.join(save_dir, "bbbc002-featurized", str(split))
		loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
		save_folder)
		if loaded:
		@@ -161,7 +169,15 @@ def load_bbbc002(split='index',
		splitter = splitters[split]

		logger.info("About to split dataset with {} splitter.".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)
		all_dataset = (train, valid, test)
		transformers = []
		if reload:

deepchem/molnet/load_function/bbbp_datasets.py

+9 −1

Original line number	Diff line number	Diff line
		@@ -83,7 +83,15 @@ def load_bbbp(featurizer='ECFP',
		}
		splitter = splitters[split]
		logger.info("About to split data with {} splitter.".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)

		# Initialize transformers
		transformers = [

deepchem/molnet/load_function/cell_counting_datasets.py

+10 −3

Original line number	Diff line number	Diff line
		@@ -36,8 +36,7 @@ def load_cell_counting(split=None,
		# For now images are loaded directly by ImageLoader
		featurizer = ""
		if reload:
		save_folder = os.path.join(save_dir,
		"cell_counting-featurized/" + str(split))
		save_folder = os.path.join(save_dir, "cell_counting-featurized", str(split))
		loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
		save_folder)
		if loaded:
		@@ -64,7 +63,15 @@ def load_cell_counting(split=None,
		splitter = splitters[split]

		logger.info("About to split dataset with {} splitter.".format(split))
		train, valid, test = splitter.train_valid_test_split(dataset)
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)
		transformers = []
		all_dataset = (train, valid, test)
		if reload:

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