Cleanup (6c30d262) · Commits · 钟慕尧 / deepchem

deepchem/datasets/init.py

+0 −7

Original line number	Diff line number	Diff line
		@@ -875,18 +875,11 @@ class DiskDataset(Dataset):
		def X(self):
		"""Get the X vector for this dataset as a single numpy array."""
		Xs = []
		##################################################### DEBUG
		#print("dataset.X computation.")
		one_dimensional = False
		##################################################### DEBUG
		for (X_b, _, _, _) in self.itershards():
		Xs.append(X_b)
		##################################################### DEBUG
		#print("X_b.shape")
		#print(X_b.shape)
		if len(X_b.shape) == 1:
		one_dimensional = True
		##################################################### DEBUG
		if not one_dimensional:
		return np.vstack(Xs)
		else:

deepchem/models/init.py

+0 −16

Original line number	Diff line number	Diff line
		@@ -158,11 +158,9 @@ class Model(object):
		for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(
		batch_size, deterministic=True):
		n_samples = len(X_batch)
		########################################################## DEBUG
		y_pred_batch = self.predict_on_batch(X_batch, pad_batch=pad_batches)
		# Discard any padded predictions
		y_pred_batch = y_pred_batch[:n_samples]
		########################################################## DEBUG
		y_pred_batch = np.reshape(y_pred_batch, (n_samples, n_tasks))
		y_pred_batch = undo_transforms(y_pred_batch, transformers)
		y_preds.append(y_pred_batch)
		@@ -171,10 +169,6 @@ class Model(object):
		# The iterbatches does padding with zero-weight examples on the last batch.
		# Remove padded examples.
		n_samples = len(dataset)
		################################################### DEBUG
		print("n_samples, y_pred.shape, (n_samples, n_tasks)")
		print(n_samples, y_pred.shape, (n_samples, n_tasks))
		################################################### DEBUG
		y_pred = np.reshape(y_pred, (n_samples, n_tasks))
		# Special case to handle singletasks.
		if n_tasks == 1:
		@@ -375,17 +369,7 @@ class Model(object):
		batch_size, deterministic=True):
		n_samples = len(X_batch)
		y_pred_batch = self.predict_proba_on_batch(X_batch, pad_batch=pad_batches)
		################################################################# DEBUG
		print("X_batch.shape")
		print(X_batch.shape)
		print("y_pred_batch.shape")
		print(y_pred_batch.shape)
		################################################################# DEBUG
		y_pred_batch = y_pred_batch[:n_samples]
		################################################################# DEBUG
		print("y_pred_batch.shape, (n_samples, n_tasks, n_classes)")
		print(y_pred_batch.shape, (n_samples, n_tasks, n_classes))
		################################################################# DEBUG
		y_pred_batch = np.reshape(y_pred_batch, (n_samples, n_tasks, n_classes))
		y_pred_batch = undo_transforms(y_pred_batch, transformers)
		y_preds.append(y_pred_batch)

deepchem/models/tensorflow_models/init.py

+0 −7

Original line number	Diff line number	Diff line
		@@ -556,13 +556,6 @@ class TensorflowClassifier(TensorflowGraphModel):
		'Unrecognized rank combination for output: %s ' %
		(batch_outputs.shape,))

		# TODO(rbharath): This is a bug! We're actually applying softmax twice.
		# I believe this is harmless since softmax of softmax doesn't change
		# properties, but I need to check this...
		# We apply softmax to predictions to get class probabilities.
		#outputs = softmax(np.squeeze(batch_outputs))
		#outputs = softmax(batch_outputs)

		# Note that softmax is already applied in construct_grpah
		outputs = batch_outputs

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