Updated BACE notebook (a8c65e9e) · Commits · 钟慕尧 / deepchem

deepchem/models/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -22,7 +22,7 @@ class Model(object):
		registered_model_classes = {}
		non_sklearn_models = ["SingleTaskDNN", "MultiTaskDNN", "DockingDNN"]
		def __init__(self, task_types, model_params, model_instance=None,
		initialize_raw_model=True, verbosity="low"):
		initialize_raw_model=True, verbosity="low", **kwargs):
		self.model_class = model_instance.__class__
		self.task_types = task_types
		self.model_params = model_params

+6 −4

Original line number	Diff line number	Diff line
		@@ -59,9 +59,10 @@ class MultiTaskDNN(KerasModel):
		Model for multitask MLP in keras.
		"""
		def __init__(self, task_types, model_params,
		initialize_raw_model=True):
		initialize_raw_model=True, verbosity="low"):
		super(MultiTaskDNN, self).__init__(task_types, model_params,
		initialize_raw_model=initialize_raw_model)
		initialize_raw_model=initialize_raw_model,
		verbosity=verbosity)
		if initialize_raw_model:
		sorted_tasks = sorted(task_types.keys())
		(n_inputs,) = model_params["data_shape"]
		@@ -176,9 +177,10 @@ class SingleTaskDNN(MultiTaskDNN):
		"""
		Abstract base class for different ML models.
		"""
		def __init__(self, task_types, model_params, initialize_raw_model=True):
		def __init__(self, task_types, model_params, initialize_raw_model=True, verbosity="low"):
		super(SingleTaskDNN, self).__init__(task_types, model_params,
		initialize_raw_model=initialize_raw_model)
		initialize_raw_model=initialize_raw_model,
		verbosity=verbosity)

		Model.register_model_type(SingleTaskDNN)

+0 −21

Original line number	Diff line number	Diff line
		@@ -20,27 +20,6 @@ __author__ = "Bharath Ramsundar"
		__copyright__ = "Copyright 2015, Stanford University"
		__license__ = "LGPL"

		#def undo_normalization(y, y_means, y_stds):
		# """Undo the applied normalization transform."""
		# return y * y_stds + y_means

		#def undo_transform(y, y_means, y_stds, output_transforms):
		# """Undo transforms on y_pred, W_pred."""
		# if not isinstance(output_transforms, list):
		# output_transforms = [output_transforms]
		# if (output_transforms == [""]
		# or output_transforms == ['']
		# or output_transforms == []):
		# return y
		# elif output_transforms == ["log"]:
		# return np.exp(y)
		# elif output_transforms == ["normalize"]:
		# return undo_normalization(y, y_means, y_stds)
		# elif output_transforms == ["log", "normalize"]:
		# return np.exp(undo_normalization(y, y_means, y_stds))
		# else:
		# raise ValueError("Unsupported output transforms %s." % str(output_transforms))

		def undo_transforms(y, transformers):
		"""Undoes all transformations applied."""
		# Note that transformers have to be undone in reversed order

+309 −44

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