Update KerasModel and SKlearnModel for fit_transformers (bbe6b67f) · Commits · 钟慕尧 / deepchem

deepchem/models/keras_models/fcnet.py

+4 −2

Original line number	Diff line number	Diff line
		@@ -19,9 +19,10 @@ class MultiTaskDNN(KerasModel):
		"""
		Model for multitask MLP in keras.
		"""
		def __init__(self, task_types, model_params,
		def __init__(self, task_types, model_params, fit_transformers=None,
		initialize_raw_model=True, verbosity="low"):
		super(MultiTaskDNN, self).__init__(task_types, model_params,
		fit_transformers=fit_transformers,
		initialize_raw_model=initialize_raw_model,
		verbosity=verbosity)
		if initialize_raw_model:
		@@ -136,7 +137,8 @@ class SingleTaskDNN(MultiTaskDNN):
		"""
		Abstract base class for different ML models.
		"""
		def __init__(self, task_types, model_params, initialize_raw_model=True, verbosity="low"):
		def __init__(self, task_types, model_params, fit_transformers=None, initialize_raw_model=True, verbosity="low"):
		super(SingleTaskDNN, self).__init__(task_types, model_params,
		fit_transformers=fit_transformers,
		initialize_raw_model=initialize_raw_model,
		verbosity=verbosity)

+4 −2

Original line number	Diff line number	Diff line
		@@ -18,14 +18,16 @@ class SklearnModel(Model):
		"""
		Abstract base class for different ML models.
		"""
		def __init__(self, task_types, model_params,
		def __init__(self, task_types, model_params, fit_transformers,
		model_instance=None,
		initialize_raw_model=True):
		super(SklearnModel, self).__init__(
		task_types, model_params, initialize_raw_model)
		task_types, model_params, fit_transformers=fit_transformers,
		initialize_raw_model=initialize_raw_model)
		self.task_types = task_types
		self.model_params = model_params
		self.raw_model = model_instance
		self.fit_transformers = fit_transformers

		# TODO(rbharath): This does not work with very large datasets! sklearn does
		# support partial_fit, but only for some models. Might make sense to make