Numpy 实现基尼指数算法的决策树

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基尼系数实现决策树

基尼指数

$\operatorname{Gini}(D)=1-\sum_{k=1}^{K}\left(\frac{\left|C_{k}\right|}{|D|}\right)^{2}$

特征 $A$ 条件下集合 $D$ 的基尼指数：

$\operatorname{Gini}(D, A)=\frac{\left|D_{1}\right|}{|D|} \operatorname{Gini}\left(D_{1}\right)+\frac{\left|D_{2}\right|}{|D|} \operatorname{Gini}\left(D_{2}\right)$

import numpy as npdef 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 ginidef 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] <= thresholdright_mask = data[:, feature_index] > thresholdleft_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_indexdef find_best_split(data, labels):num_features = data.shape[1]best_gini_index = float('inf')best_feature_index = -1best_threshold = Nonefor 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_indexbest_feature_index = feature_indexbest_threshold = thresholdreturn best_feature_index, best_thresholddef 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_thresholdright_mask = data[:, best_feature_index] > best_thresholdleft_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 nodedef 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}")