def _deactivate_learning_node(self, to_deactivate: ActiveLearningNode,
parent: SplitNode, parent_branch: int):
"""Deactivate a learning node.
Parameters
----------
to_deactivate: ActiveLearningNode
The node to deactivate.
parent: SplitNode
The node's parent.
parent_branch: int
Parent node's branch index.
"""
if self.leaf_prediction == _TARGET_MEAN:
new_leaf = InactiveLearningNodeForRegression(
to_deactivate.get_observed_class_distribution())
else:
new_leaf = InactiveLearningNodePerceptron(
to_deactivate.get_observed_class_distribution(),
to_deactivate.perceptron_weight)
if parent is None:
self._tree_root = new_leaf
else:
parent.set_child(parent_branch, new_leaf)
self._active_leaf_node_cnt -= 1
self._inactive_leaf_node_cnt += 1
def _new_learning_node(self,
initial_class_observations=None,
parent_node=None,
is_active_node=True):
"""Create a new learning node. The type of learning node depends on the tree
configuration."""
if initial_class_observations is None:
initial_class_observations = {}
# Generate a random seed for the new learning node
random_state = self._random_state.randint(0, 4294967295, dtype='u8')
if is_active_node:
if self.leaf_prediction == self._TARGET_MEAN:
return RandomLearningNodeForRegression(
initial_class_observations,
max_features=self.max_features,
random_state=random_state)
elif self.leaf_prediction == self._PERCEPTRON:
return RandomLearningNodePerceptron(
initial_class_observations,
max_features=self.max_features,
parent_node=parent_node,
random_state=random_state)
else:
if self.leaf_prediction == self._TARGET_MEAN:
return InactiveLearningNodeForRegression(
initial_class_observations)
elif self.leaf_prediction == self._PERCEPTRON:
return InactiveLearningNodePerceptron(
initial_class_observations,
parent_node,
random_state=random_state)
def _new_learning_node(self,
initial_class_observations=None,
parent_node=None,
is_active_node=True):
"""Create a new learning node. The type of learning node depends on the tree
configuration."""
if initial_class_observations is None:
initial_class_observations = {}
if is_active_node:
if self.leaf_prediction == self._TARGET_MEAN:
return ActiveLearningNodeForRegression(
initial_class_observations)
elif self.leaf_prediction == self._PERCEPTRON:
return ActiveLearningNodePerceptron(
initial_class_observations,
parent_node,
random_state=self.random_state)
else:
if self.leaf_prediction == self._TARGET_MEAN:
return InactiveLearningNodeForRegression(
initial_class_observations)
elif self.leaf_prediction == self._PERCEPTRON:
return InactiveLearningNodePerceptron(
initial_class_observations,
parent_node,
random_state=self.random_state)
def _deactivate_learning_node(self, to_deactivate: ActiveLearningNode,
parent: SplitNode, parent_branch: int):
"""Deactivate a learning node.
Parameters
----------
to_deactivate: ActiveLearningNode
The node to deactivate.
parent: SplitNode
The node's parent.
parent_branch: int
Parent node's branch index.
"""
if self.leaf_prediction == _TARGET_MEAN:
new_leaf = InactiveLearningNodeForRegression(
to_deactivate.get_observed_class_distribution())
elif self.leaf_prediction == _PERCEPTRON:
new_leaf = InactiveLearningNodePerceptronMultiTarget(
to_deactivate.get_observed_class_distribution(),
to_deactivate.perceptron_weight, to_deactivate.random_state)
elif self.leaf_prediction == _ADAPTIVE:
new_leaf = InactiveLearningNodeAdaptiveMultiTarget(
to_deactivate.get_observed_class_distribution(),
to_deactivate.perceptron_weight, to_deactivate.random_state)
new_leaf.fMAE_M = to_deactivate.fMAE_M
new_leaf.fMAE_P = to_deactivate.fMAE_P
if parent is None:
self._tree_root = new_leaf
else:
parent.set_child(parent_branch, new_leaf)
self._active_leaf_node_cnt -= 1
self._inactive_leaf_node_cnt += 1
def _new_learning_node(self, initial_class_observations=None, parent_node=None,
is_active_node=True):
"""Create a new learning node. The type of learning node depends on
the tree configuration.
"""
if initial_class_observations is None:
initial_class_observations = {}
if is_active_node:
if self.leaf_prediction == self._TARGET_MEAN:
return ActiveLearningNodeForRegressionMultiTarget(
initial_class_observations
)
elif self.leaf_prediction == self._PERCEPTRON:
return ActiveLearningNodePerceptronMultiTarget(
initial_class_observations,
parent_node,
random_state=self.random_state
)
elif self.leaf_prediction == self._ADAPTIVE:
new_node = ActiveLearningNodeAdaptiveMultiTarget(
initial_class_observations,
parent_node,
random_state=self.random_state
)
# Resets faded errors
new_node.fMAE_M = np.zeros(self._n_targets, dtype=np.float64)
new_node.fMAE_P = np.zeros(self._n_targets, dtype=np.float64)
return new_node
else:
if self.leaf_prediction == self._TARGET_MEAN:
return InactiveLearningNodeForRegression(
initial_class_observations
)
elif self.leaf_prediction == self._PERCEPTRON:
return InactiveLearningNodePerceptronMultiTarget(
initial_class_observations,
parent_node,
random_state=parent_node.random_state
)
elif self.leaf_prediction == self._ADAPTIVE:
new_node = InactiveLearningNodeAdaptiveMultiTarget(
initial_class_observations,
parent_node,
random_state=parent_node.random_state
)
new_node.fMAE_M = parent_node.fMAE_M
new_node.fMAE_P = parent_node.fMAE_P
return new_node