Cleanup (6b55bb5e) · Commits · 钟慕尧 / deepchem

deepchem/datasets/init.py

+0 −27

Original line number	Diff line number	Diff line
		@@ -383,35 +383,17 @@ class Dataset(object):
		y_nonzero = np.reshape(y_task[w_task != 0], (num_datapoints, 1))
		w_nonzero = np.reshape(w_task[w_task != 0], (num_datapoints, 1))
		ids_nonzero = ids[w_task != 0]
		########################################################## DEBUG
		print("X_nonzero.shape, y_nonzero.shape, w_nonzero.shape, ids_nonzero.shape")
		print(X_nonzero.shape, y_nonzero.shape, w_nonzero.shape, ids_nonzero.shape)
		########################################################## DEBUG

		task_metadata_rows[task].append(
		Dataset.write_data_to_disk(
		task_dirs[task_num], basename, [task],
		X_nonzero, y_nonzero, w_nonzero, ids_nonzero))
		########################################################## DEBUG
		if shard_num >= 0:
		break
		########################################################## DEBUG

		task_datasets = [
		Dataset(data_dir=task_dirs[task_num],
		metadata_rows=task_metadata_rows[task],
		verbosity=self.verbosity)
		for (task_num, task) in enumerate(tasks)]
		########################################################## DEBUG
		#for task_num, (task, task_dataset) in enumerate(zip(tasks, task_datasets)):
		# print("Task %s" % task)
		# print("task_dirs[task_num]")
		# print(task_dirs[task_num])
		# print("task_metadata_rows[task]")
		# print(task_metadata_rows[task])
		# print("task_dataset.get_shape()")
		# print(task_dataset.get_shape())
		########################################################## DEBUG
		return task_datasets

		def to_numpy(self):
		@@ -475,11 +457,6 @@ class Dataset(object):
		y_shape = np.array((0,) + (0,))
		w_shape = np.array((0,) + (0,))
		ids_shape = np.array((0,))
		############################################## DEBUG
		#print("dataset.get_shape()")
		#print("X_shape, y_shape, w_shape, ids_shape")
		#print(X_shape, y_shape, w_shape, ids_shape)
		############################################## DEBUG
		for shard_num, (X, y, w, ids) in enumerate(self.itershards()):
		if shard_num == 0:
		X_shape += np.array(X.shape)
		@@ -491,10 +468,6 @@ class Dataset(object):
		y_shape[0] += np.array(y.shape)[0]
		w_shape[0] += np.array(w.shape)[0]
		ids_shape[0] += np.array(ids.shape)[0]
		############################################## DEBUG
		#print("X_shape, y_shape, w_shape, ids_shape")
		#print(X_shape, y_shape, w_shape, ids_shape)
		############################################## DEBUG
		return tuple(X_shape), tuple(y_shape), tuple(w_shape), tuple(ids_shape)

		def get_label_means(self):

deepchem/models/multitask.py

+0 −10

Original line number	Diff line number	Diff line
		@@ -9,7 +9,6 @@ import os
		import numpy as np
		from deepchem.utils.save import log
		from deepchem.models import Model
		# DEBUG
		import sklearn

		class SingletaskToMultitask(Model):
		@@ -75,16 +74,7 @@ class SingletaskToMultitask(Model):
		self.task_model_dirs[task],
		verbosity=self.verbosity)
		if y_task.size > 0:
		############################################################## DEBUG
		print("ind, task")
		print(ind, task)
		print("X_task.shape, y_task.shape, w_task.shape, ids_task.shape")
		print(X_task.shape, y_task.shape, w_task.shape, ids_task.shape)
		print("type(X_task), type(y_task)")
		print(type(X_task), type(y_task))
		############################################################## DEBUG
		task_model.raw_model.fit(X_task, np.ravel(y_task))
		############################################################## DEBUG
		else:
		print("No labels for task %s" % task)
		print("Fitting on dummy dataset.")

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