def fit_tree(self, X, y, depth=0):
"""Fit a decision tree with recursive splitting on nodes.
Args:
X (ndarray): training set without class labels.
y (ndarray) : class labels for training set.
depth (int) : starting depth of decision tree.
Returns:
tree (Node): root node of learned decision tree.
"""
# Get number of training observations in current node with each class label 0 and 1.
class_distribution = [np.sum(y == i) for i in range(self.n_classes)]
# Instantiate node to grow the decision tree.
tree = Node(n=y.size,
class_distribution=class_distribution,
gini_index=_gini(y, self.n_classes))
# Perform recursive splitting to max depth.
if depth < self.max_depth:
gini_index, split_index = self.get_split(X, y)
# Get indices for data and class labels to go to the left child, send the rest to the right child.
if split_index is not None:
index_left = (X[:, split_index] == 1)
X_left, y_left = X[index_left], y[index_left]
X_right, y_right = X[~index_left], y[~index_left]
tree.gini_index = gini_index
tree.feature_index = split_index
depth += 1
tree.left = self.fit_tree(X_left, y_left, depth=depth)
tree.right = self.fit_tree(X_right, y_right, depth=depth)
return tree
def fit_tree(self, X, y, weights, depth=0):
"""Fit a decision tree with recursive splitting on nodes, takes additional weight argument for AdaBoost.
Args:
X (ndarray): training set without class labels.
y (ndarray): class labels for training set.
weights (ndarray): weights for each training instance.
depth (int): starting depth of decision tree.
Returns:
tree (Node): root node of learned decision tree.
"""
# Get sum of weights from each class for the class distribution in current node.
D = weights
class_weights = [np.sum(D * (y == i)) for i in range(self.n_classes)]
# Instantiate node to grow the decision tree.
tree = Node(n=y.size,
class_distribution=class_weights,
gini_index=_gini(y, self.n_classes, weights=D))
# Perform recursive splitting to max depth.
if depth < self.max_depth:
gini_index, split_index = self.get_split(X, y, weights=D)
# Get indices for data, class labels, and weights to go to the left child, send the rest to the right child.
if split_index is not None:
index_left = (X[:, split_index] == 1)
X_left, y_left, D_left = X[index_left], y[index_left], D[
index_left]
X_right, y_right, D_right = X[~index_left], y[~index_left], D[
~index_left]
tree.gini_index = gini_index
tree.feature_index = split_index
depth += 1
tree.left = self.fit_tree(X_left,
y_left,
weights=D_left,
depth=depth)
tree.right = self.fit_tree(X_right,
y_right,
weights=D_right,
depth=depth)
return tree
