Removing debugging cruft (2a38236e) · Commits · 钟慕尧 / deepchem

deepchem/datasets/tests/test_shuffle.py

+0 −19

Original line number	Diff line number	Diff line
		@@ -47,29 +47,10 @@ class TestShuffle(TestAPI):
		dataset.shuffle(iterations=5)
		X_new, y_new, w_new, new_ids = dataset.to_numpy()

		#print("X_orig.shape, y_orig.shape, w_orig.shape, orig_ids.shape")
		#print(X_orig.shape, y_orig.shape, w_orig.shape, orig_ids.shape)
		#print("X_new.shape, y_new.shape, w_new.shape, new_ids.shape")
		#print(X_new.shape, y_new.shape, w_new.shape, new_ids.shape)
		#print("len(dataset), orig_len")
		assert len(dataset) == orig_len
		# The shuffling should have switched up the ordering
		assert not np.array_equal(orig_ids, new_ids)
		# But all the same entries should still be present
		################################################## DEBUG
		#print("orig_ids")
		#print(orig_ids)
		#print("len(orig_ids)")
		#print(len(orig_ids))
		#print("new_ids")
		#print(new_ids)
		#print("len(new_ids)")
		#print(len(new_ids))
		#print("sorted(orig_ids)")
		#print(sorted(orig_ids))
		#print("sorted(new_ids)")
		#print(sorted(new_ids))
		################################################## DEBUG
		assert sorted(orig_ids) == sorted(new_ids)
		# All the data should have same shape
		assert X_orig.shape == X_new.shape

+0 −4

Original line number	Diff line number	Diff line
		@@ -138,10 +138,6 @@ class Model(object):
		y_preds = []
		batch_size = self.model_params["batch_size"]
		for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size):
		################################################ DEBUG
		#print("y_batch.shape, self.predict_on_batch(X_batch).shape")
		#print(y_batch.shape, self.predict_on_batch(X_batch).shape)
		################################################ DEBUG
		y_pred_batch = np.reshape(self.predict_on_batch(X_batch), y_batch.shape)
		y_pred_batch = undo_transforms(y_pred_batch, transformers)
		y_preds.append(y_pred_batch)

+0 −13

Original line number	Diff line number	Diff line
		@@ -104,18 +104,9 @@ class NormalizationTransformer(Transformer):
		self.X_means = X_means
		self.X_stds = X_stds
		self.y_means = y_means
		############################################### DEBUG
		# Control for pathological case with no variance.
		y_stds[y_stds == 0] = 1.
		############################################### DEBUG
		self.y_stds = y_stds
		########################################################## DEBUG
		#print("NormalizationTransformer()")
		#print("self.y_means, self.y_stds")
		#print(self.y_means, self.y_stds)
		#print("np.std(dataset.get_labels())")
		#print(np.std(dataset.get_labels()))
		########################################################## DEBUG

		def transform(self, dataset, parallel=False):
		super(NormalizationTransformer, self).transform(
		@@ -186,16 +177,12 @@ class LogTransformer(Transformer):
		row = df.iloc[i]
		if self.transform_X:
		X = load_from_disk(os.path.join(data_dir, row['X-transformed']))
		########################################################## DEBUG
		X = np.log(X+1)
		########################################################## DEBUG
		save_to_disk(X, os.path.join(data_dir, row['X-transformed']))

		if self.transform_y:
		y = load_from_disk(os.path.join(data_dir, row['y-transformed']))
		########################################################## DEBUG
		y = np.log(y+1)
		########################################################## DEBUG
		save_to_disk(y, os.path.join(data_dir, row['y-transformed']))

		def untransform(self, z):

+0 −14

Original line number	Diff line number	Diff line
		@@ -70,13 +70,6 @@ class Evaluator(object):
		"""
		y = self.dataset.get_labels()
		y = undo_transforms(y, self.transformers)
		############################################ DEBUG
		#print("self.dataset.get_labels().shape")
		#print(self.dataset.get_labels().shape)
		#print("Undoing transforms.")
		#print("y.shape")
		#print(y.shape)
		############################################ DEBUG
		w = self.dataset.get_weights()

		if not len(metrics):
		@@ -98,13 +91,6 @@ class Evaluator(object):
		# Compute multitask metrics
		for metric in metrics:
		multitask_scores[metric.name] = metric.compute_metric(y, y_pred, w)
		##################################################### DEBUG
		#print("compute_model_performance()")
		#print("y[w != 0], y_pred[w != 0]")
		#print(y[w != 0], y_pred[w != 0])
		#print("multitask_scores[metric.name]")
		#print(multitask_scores[metric.name])
		##################################################### DEBUG

		if stats_out is not None:
		log("Saving stats to %s" % stats_out, self.verbosity)