def update_classifier(self, instance):
"""Update the classifier with the given instance.
Args:
instance (Instance): The new instance to be used to train the classifier.
"""
if instance.class_is_missing():
return
if self._root is None:
self._root = self.new_learning_node()
l = self._root.leaf_for_instance(instance, None, None)
actual_node = l.the_node
if actual_node is None:
actual_node = ActiveHNode()
l.parent_node.set_child(l.parent_branch, actual_node)
# ActiveHNode should be changed to a LearningNode interface if Naive Bayes nodes are used
if isinstance(actual_node, InactiveHNode):
actual_node.update_node(instance)
if isinstance(actual_node, ActiveHNode):
actual_node.update_node(instance)
total_weight = actual_node.total_weight()
if total_weight - actual_node.weight_seen_at_last_split_eval > self._grace_period:
self.try_split(actual_node, l.parent_node, l.parent_branch)
actual_node.weight_seen_at_last_split_eval = total_weight
def update_classifier(self, instance):
"""Update the classifier with the given instance.
Args:
instance (Instance): The new instance to be used to train the classifier.
"""
if instance.class_is_missing():
return
if self._root is None:
self._root = self.new_learning_node()
l = self._root.leaf_for_instance(instance, None, None)
actual_node = l.the_node
if actual_node is None:
actual_node = ActiveHNode()
l.parent_node.set_child(l.parent_branch, actual_node)
# ActiveHNode should be changed to a LearningNode interface if Naive Bayes nodes are used
if isinstance(actual_node, InactiveHNode):
actual_node.update_node(instance)
if isinstance(actual_node, ActiveHNode):
actual_node.update_node(instance)
total_weight = actual_node.total_weight()
if total_weight - actual_node.weight_seen_at_last_split_eval > self._grace_period:
self.try_split(actual_node, l.parent_node, l.parent_branch)
actual_node.weight_seen_at_last_split_eval = total_weight
def activate_node(self, to_activate, parent, parent_branch):
"""Allow supplied node to grow.
Args:
to_activate (InactiveHNode): The node to be activated.
parent (SplitNode): The parent of the node.
parent_branch (str): The branch leading from the parent to the node.
"""
leaf = ActiveHNode()
leaf.class_distribution = to_activate.class_distribution
if parent is None:
self._root = leaf
else:
parent.set_child(parent_branch, leaf)
self._active_leaf_count += 1
self._inactive_leaf_count -= 1
def new_learning_node(self):
"""Create a new learning node. Will always be an ActiveHNode while Naive Bayes
nodes are not implemented.
Returns:
ActiveHNode: The new learning node.
"""
# Leaf strategy should be handled here if/when the Naive Bayes approach is implemented
return ActiveHNode()
def activate_node(self, to_activate, parent, parent_branch):
"""Allow supplied node to grow.
Args:
to_activate (InactiveHNode): The node to be activated.
parent (SplitNode): The parent of the node.
parent_branch (str): The branch leading from the parent to the node.
"""
leaf = ActiveHNode()
leaf.class_distribution = to_activate.class_distribution
if parent is None:
self._root = leaf
else:
parent.set_child(parent_branch, leaf)
self._active_leaf_count += 1
self._inactive_leaf_count -= 1
