Merge pull request #1406 from peastman/arraysize (eb928b7d) · Commits · 钟慕尧 / deepchem

deepchem/data/datasets.py

+33 −25

Original line number	Diff line number	Diff line
		@@ -94,11 +94,15 @@ def pad_batch(batch_size, X_b, y_b, w_b, ids_b):

		if y_b is None:
		y_out = None
		elif len(y_b.shape) < 2:
		y_out = np.zeros(batch_size, dtype=y_b.dtype)
		else:
		y_out = np.zeros((batch_size, y_b.shape[1]), dtype=y_b.dtype)

		if w_b is None:
		w_out = None
		elif len(w_b.shape) < 2:
		w_out = np.zeros(batch_size, dtype=w_b.dtype)
		else:
		w_out = np.zeros((batch_size, w_b.shape[1]), dtype=w_b.dtype)

		@@ -343,6 +347,8 @@ class NumpyDataset(Dataset):

		def get_task_names(self):
		"""Get the names of the tasks associated with this dataset."""
		if len(self._y.shape) < 2:
		return np.array([0])
		return np.arange(self._y.shape[1])

		@property
		@@ -956,20 +962,16 @@ class DiskDataset(Dataset):
		data_dir=None,
		verbose=True):
		"""Creates a DiskDataset object from specified Numpy arrays."""
		# if data_dir is None:
		# data_dir = tempfile.mkdtemp()
		n_samples = len(X)
		# The -1 indicates that y will be reshaped to have length -1
		if n_samples > 0:
		y = np.reshape(y, (n_samples, -1))
		if w is not None:
		w = np.reshape(w, (n_samples, -1))
		if ids is None:
		ids = np.arange(n_samples)
		if w is None:
		w = np.ones_like(y)
		if tasks is None:
		if len(y.shape) > 1:
		n_tasks = y.shape[1]
		else:
		n_tasks = 1
		tasks = np.arange(n_tasks)
		# raw_data = (X, y, w, ids)
		return DiskDataset.create_dataset(
		@@ -1205,8 +1207,8 @@ class DiskDataset(Dataset):
		if indices_count + num_shard_elts >= len(indices):
		break
		# Need to offset indices to fit within shard_size
		shard_inds = indices[indices_count:
		indices_count + num_shard_elts] - count
		shard_inds = indices[indices_count:indices_count +
		num_shard_elts] - count
		X_sel = X[shard_inds]
		# Handle the case of datasets with y/w missing
		if y is not None:
		@@ -1257,17 +1259,29 @@ class DiskDataset(Dataset):
		def y(self):
		"""Get the y vector for this dataset as a single numpy array."""
		ys = []
		one_dimensional = False
		for (_, y_b, _, _) in self.itershards():
		ys.append(y_b)
		if len(y_b.shape) == 1:
		one_dimensional = True
		if not one_dimensional:
		return np.vstack(ys)
		else:
		return np.concatenate(ys)

		@property
		def w(self):
		"""Get the weight vector for this dataset as a single numpy array."""
		ws = []
		one_dimensional = False
		for (_, _, w_b, _) in self.itershards():
		ws.append(np.array(w_b))
		if len(w_b.shape) == 1:
		one_dimensional = True
		if not one_dimensional:
		return np.vstack(ws)
		else:
		return np.concatenate(ws)

		def __len__(self):
		"""
		@@ -1282,22 +1296,16 @@ class DiskDataset(Dataset):
		def get_shape(self):
		"""Finds shape of dataset."""
		n_tasks = len(self.get_task_names())
		X_shape = np.array((0,) + (0,) * len(self.get_data_shape()))
		ids_shape = np.array((0,))
		for shard_num, (X, y, w, ids) in enumerate(self.itershards()):
		if shard_num == 0:
		X_shape = np.array(X.shape)
		if n_tasks > 0:
		y_shape = np.array((0,) + (0,))
		w_shape = np.array((0,) + (0,))
		y_shape = np.array(y.shape)
		w_shape = np.array(w.shape)
		else:
		y_shape = tuple()
		w_shape = tuple()

		for shard_num, (X, y, w, ids) in enumerate(self.itershards()):
		if shard_num == 0:
		X_shape += np.array(X.shape)
		if n_tasks > 0:
		y_shape += np.array(y.shape)
		w_shape += np.array(w.shape)
		ids_shape += np.array(ids.shape)
		ids_shape = np.array(ids.shape)
		else:
		X_shape[0] += np.array(X.shape)[0]
		if n_tasks > 0:

deepchem/metrics/init.py

+5 −2

Original line number	Diff line number	Diff line
		@@ -56,7 +56,9 @@ def _ensure_class_labels(y):

		def roc_auc_score(y, y_pred):
		"""Area under the receiver operating characteristic curve."""
		return sklearn.metrics.roc_auc_score(_ensure_one_hot(y), y_pred)
		if y.shape != y_pred.shape:
		y = _ensure_one_hot(y)
		return sklearn.metrics.roc_auc_score(y, y_pred)


		def accuracy_score(y, y_pred):
		@@ -106,6 +108,7 @@ def pixel_error(y, y_pred):

		def prc_auc_score(y, y_pred):
		"""Compute area under precision-recall curve"""
		if y.shape != y_pred.shape:
		y = _ensure_one_hot(y)
		assert y_pred.shape == y.shape
		assert y_pred.shape[1] == 2

deepchem/splits/splitters.py

+4 −0

Original line number	Diff line number	Diff line
		@@ -338,6 +338,10 @@ class RandomStratifiedSplitter(Splitter):
		return dataset_1, dataset_2
		X, y, w, ids = randomize_arrays((dataset.X, dataset.y, dataset.w,
		dataset.ids))
		if len(y.shape) == 1:
		y = np.expand_dims(y, 1)
		if len(w.shape) == 1:
		w = np.expand_dims(w, 1)
		split_indices = self.get_task_split_indices(y, w, frac_split)

		# Create weight matrices fpor two haves.

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