imports and yapf (6729f20d) · Commits · 钟慕尧 / deepchem

deepchem/trans/tests/test_transformers.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -5,7 +5,7 @@ from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		from molnet import load_delaney
		from deepchem.molnet import load_delaney
		from trans.transformers import FeaturizationTransformer

		__author__ = "Bharath Ramsundar"

+7 −5

Original line number	Diff line number	Diff line
		@@ -736,8 +736,8 @@ class IRVTransformer():
		print('start similarity calculation')
		time1 = time.time()
		similarity = IRVTransformer.matrix_mul(X_target, np.transpose(self.X)) / (
		n_features - IRVTransformer.matrix_mul(1 - X_target,
		np.transpose(1 - self.X)))
		n_features -
		IRVTransformer.matrix_mul(1 - X_target, np.transpose(1 - self.X)))
		time2 = time.time()
		print('similarity calculation takes %i s' % (time2 - time1))
		for i in range(self.n_tasks):
		@@ -784,8 +784,8 @@ class IRVTransformer():
		X_trans = []
		for count in range(X_length // 5000 + 1):
		X_trans.append(
		self.X_transform(dataset.X[count * 5000:min((count + 1) * 5000,
		X_length), :]))
		self.X_transform(
		dataset.X[count * 5000:min((count + 1) * 5000, X_length), :]))
		X_trans = np.concatenate(X_trans, axis=0)
		return NumpyDataset(X_trans, dataset.y, dataset.w, ids=None)

		@@ -993,7 +993,9 @@ class ANITransformer(Transformer):
		end = min((start + 1) * batch_size, X.shape[0])
		X_batch = X[(start * batch_size):end]
		output = self.sess.run(
		[self.outputs], feed_dict={self.inputs: X_batch})[0]
		[self.outputs], feed_dict={
		self.inputs: X_batch
		})[0]
		X_out.append(output)
		num_transformed = num_transformed + X_batch.shape[0]
		print('%i samples transformed' % num_transformed)