Cleanup (2b01efa5) · Commits · 钟慕尧 / deepchem

deepchem/datasets/init.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -328,7 +328,8 @@ class Dataset(object):
		if not os.path.exists(select_dir):
		os.makedirs(select_dir)
		if not len(indices):
		return Dataset(data_dir=select_dir, metadata_row=[], verbosity=self.verbosity)
		return Dataset(
		data_dir=select_dir, metadata_row=[], verbosity=self.verbosity)
		indices = np.array(sorted(indices)).astype(int)
		count, indices_count = 0, 0
		metadata_rows = []

+4 −4

Original line number	Diff line number	Diff line
		@@ -90,7 +90,6 @@ class SingletaskToMultitask(Model):
		y_pred[:, ind] = task_model.predict_on_batch(X)
		else:
		raise ValueError("Invalid task_type")
		############################################### DEBUG
		return y_pred

		def predict_proba_on_batch(self, X, n_classes=2):
		@@ -101,7 +100,8 @@ class SingletaskToMultitask(Model):
		n_samples = X.shape[0]
		y_pred = np.zeros((n_samples, n_tasks, n_classes))
		for ind, task in enumerate(self.tasks):
		task_model = self.model_builder([task], {task: self.task_types[task]}, self.model_params,
		task_model = self.model_builder(
		[task], {task: self.task_types[task]}, self.model_params,
		self.task_model_dirs[task],
		verbosity=self.verbosity)
		task_model.reload()

+0 −13

Original line number	Diff line number	Diff line
		@@ -117,23 +117,10 @@ class RandomSplitter(Splitter):
		"""
		np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.)
		np.random.seed(seed)
		########################################################### DEBUG
		print("About to compute len!")
		########################################################### DEBUG
		num_datapoints = len(dataset)
		train_cutoff = int(frac_train * num_datapoints)
		########################################################### DEBUG
		print("Successfully computed len!")
		########################################################### DEBUG
		valid_cutoff = int((frac_train+frac_valid) * num_datapoints )
		########################################################### DEBUG
		print("num_datapoints, train_cutoff, valid_cutoff")
		print(num_datapoints, train_cutoff, valid_cutoff)
		########################################################### DEBUG
		shuffled = np.random.permutation(range(num_datapoints))
		########################################################### DEBUG
		print("Successfully computed shuffled.")
		########################################################### DEBUG
		return (shuffled[:train_cutoff], shuffled[train_cutoff:valid_cutoff],
		shuffled[valid_cutoff:])