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cart.py
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import numpy as np
from sklearn.base import RegressorMixin, ClassifierMixin
from .mobtree import MoBTreeRegressor, MoBTreeClassifier
__all__ = ["CARTRegressor", "CARTClassifier"]
class CARTRegressor(MoBTreeRegressor, RegressorMixin):
def __init__(self, max_depth=3, min_samples_leaf=1, min_impurity_decrease=0,
split_features=None, feature_names=None, random_state=0):
super(CARTRegressor, self).__init__(max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
min_impurity_decrease=min_impurity_decrease,
split_features=split_features,
feature_names=feature_names,
random_state=random_state)
def build_root(self):
root_impurity = self.y.var()
return root_impurity
def build_leaf(self, sample_indice):
best_estimator = None
predict_func = lambda x: np.mean(self.y[sample_indice])
best_impurity = self.y[sample_indice].var()
return predict_func, best_estimator, best_impurity
def node_split(self, sample_indice):
node_x = self.x[sample_indice]
node_y = self.y[sample_indice]
n_samples, n_features = node_x.shape
best_impurity = np.inf
best_feature = None
best_threshold = None
best_left_indice = None
best_right_indice = None
for feature_indice in self.split_features:
current_feature = node_x[:, feature_indice]
sortted_indice = np.argsort(current_feature)
sortted_feature = current_feature[sortted_indice]
feature_range = sortted_feature[-1] - sortted_feature[0]
if feature_range < self.EPSILON:
continue
sum_left = 0
sum_total = np.sum(node_y)
sq_sum_total = np.sum(node_y ** 2)
for i, _ in enumerate(sortted_indice):
sum_left += node_y[sortted_indice[i]]
if ((i + 1) < self.min_samples_leaf) or ((n_samples - i - 1) < self.min_samples_leaf):
continue
n_left = i + 1
n_right = n_samples - i - 1
current_impurity = (sq_sum_total / n_samples - (sum_left / n_left) ** 2 * n_left / n_samples -
((sum_total - sum_left) / n_right) ** 2 * n_right / n_samples)
if current_impurity < best_impurity:
best_position = i + 1
best_feature = feature_indice
best_impurity = current_impurity
best_threshold = (sortted_feature[i] + sortted_feature[i + 1]) / 2
sortted_indice = np.argsort(node_x[:, best_feature])
best_left_indice = sample_indice[sortted_indice[:best_position]]
best_right_indice = sample_indice[sortted_indice[best_position:]]
best_left_impurity = node_y[sortted_indice[:best_position]].var()
best_right_impurity = node_y[sortted_indice[best_position:]].var()
node = {"feature": best_feature, "threshold": best_threshold, "left": best_left_indice, "right": best_right_indice,
"impurity": best_impurity, "left_impurity": best_left_impurity, "right_impurity": best_right_impurity}
return node
class CARTClassifier(MoBTreeClassifier, ClassifierMixin):
def __init__(self, max_depth=3, min_samples_leaf=1, min_impurity_decrease=0,
split_features=None, feature_names=None, random_state=0):
super(CARTClassifier, self).__init__(max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
min_impurity_decrease=min_impurity_decrease,
split_features=split_features,
feature_names=feature_names,
random_state=random_state)
def build_root(self):
p = self.y.mean()
root_impurity = - p * np.log2(p) - (1 - p) * np.log2((1 - p)) if (p > 0) and (p < 1) else 0
return root_impurity
def build_leaf(self, sample_indice):
best_estimator = None
predict_func = lambda x: np.ones(x.shape[0]) * self.y[sample_indice].mean()
best_impurity = self.get_loss(self.y[sample_indice], predict_func(self.x[sample_indice]))
return predict_func, best_estimator, best_impurity
def node_split(self, sample_indice):
node_x = self.x[sample_indice]
node_y = self.y[sample_indice]
n_samples, n_features = node_x.shape
best_feature = None
best_position = None
best_threshold = None
best_left_indice = None
best_right_indice = None
best_impurity = np.inf
best_left_impurity = np.inf
best_right_impurity = np.inf
for feature_indice in self.split_features:
current_feature = node_x[:, feature_indice]
sortted_indice = np.argsort(current_feature)
sortted_feature = current_feature[sortted_indice]
feature_range = sortted_feature[-1] - sortted_feature[0]
if feature_range < self.EPSILON:
continue
sum_left = 0
sum_total = np.sum(node_y)
for i, _ in enumerate(sortted_indice):
sum_left += node_y[sortted_indice[i]]
if ((i + 1) < self.min_samples_leaf) or ((n_samples - i - 1) < self.min_samples_leaf):
continue
n_left = i + 1
n_right = n_samples - i - 1
left_impurity = 0
right_impurity = 0
pleft = sum_left / n_left
pright = (sum_total - sum_left) / n_right
if (pleft > 0) and (pleft < 1):
left_impurity = (- pleft * np.log2(pleft) - (1 - pleft) * np.log2((1 - pleft)))
if (pright > 0) and (pright < 1):
right_impurity = (- pright * np.log2(pright) - (1 - pright) * np.log2((1 - pright)))
current_impurity = (n_left / n_samples * left_impurity + n_right / n_samples * right_impurity)
if current_impurity < best_impurity:
best_position = i + 1
best_feature = feature_indice
best_impurity = current_impurity
best_threshold = (sortted_feature[i] + sortted_feature[i + 1]) / 2
if best_position is not None:
sortted_indice = np.argsort(node_x[:, best_feature])
best_left_indice = sample_indice[sortted_indice[:best_position]]
best_right_indice = sample_indice[sortted_indice[best_position:]]
pleft = node_y[sortted_indice[:best_position]].mean()
pright = node_y[sortted_indice[best_position:]].mean()
best_left_impurity = - pleft * np.log2(pleft) - (1 - pleft) * np.log2((1 - pleft)) if (pleft > 0) and (pleft < 1) else 0
best_right_impurity = - pright * np.log2(pright) - (1 - pright) * np.log2((1 - pright)) if (pright > 0) and (pright < 1) else 0
node = {"feature": best_feature, "threshold": best_threshold, "left": best_left_indice, "right": best_right_indice,
"impurity": best_impurity, "left_impurity": best_left_impurity, "right_impurity": best_right_impurity}
return node