基尼系数实现决策树
基尼指数
Gini
(
D
)
=
1
−
∑
k
=
1
K
(
∣
C
k
∣
∣
D
∣
)
2
\operatorname{Gini}(D)=1-\sum_{k=1}^{K}\left(\frac{\left|C_{k}\right|}{|D|}\right)^{2}
Gini(D)=1−k=1∑K(∣D∣∣Ck∣)2
特征
A
A
A条件下集合
D
D
D的基尼指数：
Gini
(
D
,
A
)
=
∣
D
1
∣
∣
D
∣
Gini
(
D
1
)
+
∣
D
2
∣
∣
D
∣
Gini
(
D
2
)
\operatorname{Gini}(D, A)=\frac{\left|D_{1}\right|}{|D|} \operatorname{Gini}\left(D_{1}\right)+\frac{\left|D_{2}\right|}{|D|} \operatorname{Gini}\left(D_{2}\right)
Gini(D,A)=∣D∣∣D1∣Gini(D1)+∣D∣∣D2∣Gini(D2)
import numpy as np
def calculate_gini(labels):
# 计算标签的基尼系数
_, counts = np.unique(labels, return_counts=True)
probabilities = counts / len(labels)
gini = 1 - np.sum(probabilities ** 2)
return gini
def calculate_gini_index(data, labels, feature_index, threshold):
# 根据给定的特征和阈值划分数据
left_mask = data[:, feature_index] <= threshold
right_mask = data[:, feature_index] > threshold
left_labels = labels[left_mask]
right_labels = labels[right_mask]
# 计算左右子集的基尼系数
left_gini = calculate_gini(left_labels)
right_gini = calculate_gini(right_labels)
# 计算基尼指数
total_gini = calculate_gini(labels)
left_weight = len(left_labels) / len(labels)
right_weight = len(right_labels) / len(labels)
gini_index = (left_weight * left_gini) + (right_weight * right_gini)
return gini_index
def find_best_split(data, labels):
num_features = data.shape[1]
best_gini_index = float('inf')
best_feature_index = -1
best_threshold = None
for feature_index in range(num_features):
feature_values = data[:, feature_index]
unique_values = np.unique(feature_values)
for threshold in unique_values:
gini_index = calculate_gini_index(data, labels, feature_index, threshold)
if gini_index < best_gini_index:
best_gini_index = gini_index
best_feature_index = feature_index
best_threshold = threshold
return best_feature_index, best_threshold
def create_decision_tree(data, labels):
# 基本情况：如果所有标签都相同，则返回一个叶节点，其中包含该标签
if len(np.unique(labels)) == 1:
return {'label': labels[0]}
# 找到最佳的划分特征
best_feature_index, best_threshold = find_best_split(data, labels)
# 创建一个新的内部节点，其中包含最佳特征和阈值
node = {
'feature_index': best_feature_index,
'threshold': best_threshold,
'left': None,
'right': None
}
# 根据最佳特征和阈值划分数据
left_mask = data[:, best_feature_index] <= best_threshold
right_mask = data[:, best_feature_index] > best_threshold
left_data = data[left_mask]
left_labels = labels[left_mask]
right_data = data[right_mask]
right_labels = labels[right_mask]
# 递归创建左右子树
node['left'] = create_decision_tree(left_data, left_labels)
node['right'] = create_decision_tree(right_data, right_labels)
return node
def predict(node, sample):
if 'label' in node:
return node['label']
feature_value = sample[node['feature_index']]
if feature_value <= node['threshold']:
return predict(node['left'], sample)
else:
return predict(node['right'], sample)
# 示例数据集
data = np.array([
[1, 2, 0],
[1, 2, 1],
[1, 3, 1],
[2, 3, 1],
[2, 3, 0],
[2, 2, 0],
[1, 1, 0],
[1, 1, 1],
[2, 1, 1],
[1, 3, 0]
])
labels = np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1])
# 创建决策树
decision_tree = create_decision_tree(data, labels)
# 测试数据
test_data = np.array([
[1, 2, 0],
[2, 1, 1],
[1, 3, 1],
[2, 3, 0]
])
# 预测结果
for sample in test_data:
prediction = predict(decision_tree, sample)
print(f"样本: {sample}, 预测标签: {prediction}")
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