Merge pull request #175 from rbharath/extra_learning_tests (26a0f5e4) · Commits · 钟慕尧 / deepchem

deepchem/datasets/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -397,7 +397,7 @@ def _df_to_numpy(df, feature_types, tasks):
		# perform common train/test split across all tasks
		n_samples = df.shape[0]
		n_tasks = len(tasks)
		y = df[tasks].values
		y = np.array([df[task].values for task in tasks])
		y = np.reshape(y, (n_samples, n_tasks))
		w = np.ones((n_samples, n_tasks))
		missing = np.zeros_like(y).astype(int)

+0 −0

File moved.

+9 −2

Original line number	Diff line number	Diff line
		@@ -78,6 +78,13 @@ class Model(object):
		"""
		return self.raw_model

		def reload(self):
		"""
		Reload trained model from disk.
		"""
		raise NotImplementedError(
		"Each model is responsible for its own reload method.")

		@staticmethod
		def get_model_filename(model_dir):
		"""
		@@ -128,7 +135,7 @@ class Model(object):

		return X, y, w

		def predict(self, dataset, transformers):
		def predict(self, dataset, transformers=[]):
		"""
		Uses self to make predictions on provided Dataset object.

		@@ -150,7 +157,7 @@ class Model(object):
		y_pred = np.reshape(y_pred, (n_samples, n_tasks))
		return y_pred

		def predict_proba(self, dataset, transformers, n_classes=2):
		def predict_proba(self, dataset, transformers=[], n_classes=2):
		"""
		TODO: Do transformers even make sense here?

+2 −2

Original line number	Diff line number	Diff line
		@@ -39,11 +39,11 @@ class KerasModel(Model):
		file_obj.write(json_string)
		model.save_weights(h5_filename, overwrite=True)

		def load(self, model_dir):
		def reload(self):
		"""
		Load keras multitask DNN from disk.
		"""
		filename = Model.get_model_filename(model_dir)
		filename = Model.get_model_filename(self.model_dir)
		filename, _ = os.path.splitext(filename)

		json_filename = "%s.%s" % (filename, "json")

+1 −1

Original line number	Diff line number	Diff line
		@@ -73,6 +73,6 @@ class SklearnModel(Model):
		super(SklearnModel, self).save()
		save_to_disk(self.raw_model, self.get_model_filename(self.model_dir))

		def load(self):
		def reload(self):
		"""Loads sklearn model from joblib file on disk."""
		self.raw_model = load_from_disk(Model.get_model_filename(self.model_dir))