python决策树鸢尾花id3算法代码

以下是使用Python实现决策树鸢尾花ID3算法的示例代码：

import pandas as pd
import numpy as np

# 定义节点的类
class Node:
    def __init__(self, feature=None, label=None, sub_nodes=None):
        self.feature = feature  # 当前节点的特征
        self.label = label  # 当前节点的标签
        self.sub_nodes = sub_nodes  # 当前节点的子节点

# 定义决策树的类
class DecisionTree:
    def __init__(self, epsilon=0.1):
        self.epsilon = epsilon  # 定义划分阈值

    # 计算信息熵
    def calc_entropy(self, data):
        labels = data[:, -1]
        label_count = np.unique(labels, return_counts=True)[1]
        probs = label_count / len(labels)
        entropy = np.sum(-probs * np.log2(probs))
        return entropy

    # 计算条件熵
    def calc_condition_entropy(self, data, feature_idx):
        feature_values = data[:, feature_idx]
        unique_values = np.unique(feature_values)
        entropy = 0
        for value in unique_values:
            sub_data = data[feature_values == value]
            sub_entropy = self.calc_entropy(sub_data)
            entropy += (len(sub_data) / len(data)) * sub_entropy
        return entropy

    # 选择最优划分特征
    def choose_best_feature(self, data):
        feature_count = data.shape[1] - 1
        max_info_gain = 0
        best_feature_idx = 0
        base_entropy = self.calc_entropy(data)
        for i in range(feature_count):
            condition_entropy = self.calc_condition_entropy(data, i)
            info_gain = base_entropy - condition_entropy
            if info_gain > max_info_gain:
                max_info_gain = info_gain
                best_feature_idx = i
        return best_feature_idx

    # 构建决策树
    def build_tree(self, data):
        labels = data[:, -1]
        if len(np.unique(labels)) == 1:
            return Node(label=labels[0])
        if data.shape[1] == 1:
            return Node(label=np.argmax(np.bincount(labels)))
        best_feature_idx = self.choose_best_feature(data)
        best_feature = data[:, best_feature_idx]
        root = Node(feature=best_feature_idx)
        unique_values = np.unique(best_feature)
        sub_nodes = []
        for value in unique_values:
            sub_data = data[best_feature == value]
            sub_node = self.build_tree(sub_data)
            sub_nodes.append(sub_node)
        root.sub_nodes = sub_nodes
        return root

    # 预测单个样本的类别
    def predict_sample(self, root, sample):
        while root.sub_nodes:
            feature_idx = root.feature
            feature_value = sample[feature_idx]
            sub_node = root.sub_nodes[int(feature_value)]
            root = sub_node
        return root.label

    # 预测测试集的类别
    def predict(self, root, test_data):
        predictions = []
        for sample in test_data:
            prediction = self.predict_sample(root, sample)
            predictions.append(prediction)
        return np.array(predictions)

    # 计算准确率
    def accuracy(self, y_true, y_pred):
        return np.sum(y_true == y_pred) / len(y_true)

# 读取数据集
data = pd.read_csv('iris.csv').values
np.random.shuffle(data)
train_data = data[:120]
test_data = data[120:]

# 构建决策树并预测测试集
dt = DecisionTree()
root = dt.build_tree(train_data)
y_true = test_data[:, -1]
y_pred = dt.predict(root, test_data[:, :-1])
print('Accuracy:', dt.accuracy(y_true, y_pred))

说明：

- 该代码使用了鸢尾花数据集，数据集文件名为`iris.csv`，可以自行更改为其他数据集。
- 在`DecisionTree`类的构造函数中，定义了划分阈值`epsilon`，默认值为`0.1`。
- `Node`类表示决策树的节点，包含特征、标签和子节点三个属性。
- `DecisionTree`类中的`calc_entropy`方法计算信息熵，`calc_condition_entropy`方法计算条件熵，`choose_best_feature`方法选择最优划分特征，`build_tree`方法递归构建决策树，`predict_sample`方法预测单个样本的类别，`predict`方法预测测试集的类别，`accuracy`方法计算准确率。
- 最后输出测试集的准确率。