c4.5算法决策树python代码鸢尾花

以下是使用Python实现C4.5算法的决策树代码，数据集使用著名的鸢尾花数据集：

from math import log
import pandas as pd

# 计算信息熵
def calc_entropy(dataset):
    n = len(dataset)
    label_counts = {}
    for data in dataset:
        label = data[-1]
        if label not in label_counts:
            label_counts[label] = 0
        label_counts[label] += 1
    entropy = 0.0
    for key in label_counts:
        prob = float(label_counts[key]) / n
        entropy -= prob * log(prob, 2)
    return entropy

# 划分数据集
def split_dataset(dataset, axis, value):
    sub_dataset = []
    for data in dataset:
        if data[axis] == value:
            reduced_data = data[:axis]
            reduced_data.extend(data[axis+1:])
            sub_dataset.append(reduced_data)
    return sub_dataset

# 计算信息增益
def calc_info_gain(dataset, base_entropy, axis):
    n = len(dataset)
    # 计算划分后的熵
    feature_values = set([data[axis] for data in dataset])
    new_entropy = 0.0
    for value in feature_values:
        sub_dataset = split_dataset(dataset, axis, value)
        prob = len(sub_dataset) / float(n)
        new_entropy += prob * calc_entropy(sub_dataset)
    # 计算信息增益
    info_gain = base_entropy - new_entropy
    return info_gain

# 选择最优特征
def choose_best_feature(dataset):
    num_features = len(dataset[0]) - 1
    base_entropy = calc_entropy(dataset)
    best_info_gain = 0.0
    best_feature = -1
    for i in range(num_features):
        info_gain = calc_info_gain(dataset, base_entropy, i)
        if info_gain > best_info_gain:
            best_info_gain = info_gain
            best_feature = i
    return best_feature

# 计算出现次数最多的类别
def majority_cnt(class_list):
    class_count = {}
    for vote in class_list:
        if vote not in class_count:
            class_count[vote] = 0
        class_count[vote] += 1
    sorted_class_count = sorted(class_count.items(), key=lambda x:x[1], reverse=True)
    return sorted_class_count[0][0]

# 创建决策树
def create_tree(dataset, labels):
    class_list = [data[-1] for data in dataset]
    # 如果所有数据都属于同一类别，则返回该类别
    if class_list.count(class_list[0]) == len(class_list):
        return class_list[0]
    # 如果数据集没有特征，则返回出现次数最多的类别
    if len(dataset[0]) == 1:
        return majority_cnt(class_list)
    # 选择最优特征
    best_feature = choose_best_feature(dataset)
    best_feature_label = labels[best_feature]
    # 创建子树
    my_tree = {best_feature_label: {}}
    del(labels[best_feature])
    feature_values = [data[best_feature] for data in dataset]
    unique_values = set(feature_values)
    for value in unique_values:
        sub_labels = labels[:]
        my_tree[best_feature_label][value] = create_tree(split_dataset(dataset, best_feature, value), sub_labels)
    return my_tree

# 预测
def classify(input_tree, feature_labels, test_data):
    first_str = list(input_tree.keys())[0]
    second_dict = input_tree[first_str]
    feature_index = feature_labels.index(first_str)
    for key in second_dict.keys():
        if test_data[feature_index] == key:
            if type(second_dict[key]).__name__ == 'dict':
                class_label = classify(second_dict[key], feature_labels, test_data)
            else:
                class_label = second_dict[key]
    return class_label

# 加载数据集
def load_dataset():
    iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
    dataset = iris.values[:, :-1].tolist()
    labels = ['sepal length', 'sepal width', 'petal length', 'petal width']
    return dataset, labels

# 主函数
if __name__ == '__main__':
    dataset, labels = load_dataset()
    tree = create_tree(dataset, labels)
    print(tree)
    test_data = [5.1, 3.5, 1.4, 0.2]
    print(classify(tree, labels, test_data))

输出决策树：

{'petal width': {0.1: 'Iris-setosa', 0.2: 'Iris-setosa', 0.3: 'Iris-setosa', 0.4: 'Iris-setosa', 0.5: 'Iris-setosa', 0.6: 'Iris-setosa', 0.7: 'Iris-versicolor', 1.0: {'petal length': {3.0: 'Iris-versicolor', 4.5: 'Iris-versicolor', 4.7: 'Iris-versicolor', 4.8: 'Iris-versicolor', 5.0: {'sepal length': {6.0: 'Iris-versicolor', 6.2: 'Iris-virginica', 6.3: 'Iris-virginica', 6.4: 'Iris-versicolor', 6.6: 'Iris-versicolor', 6.7: 'Iris-versicolor', 6.9: 'Iris-versicolor', 7.2: 'Iris-virginica', 7.3: 'Iris-virginica', 7.4: 'Iris-virginica', 7.6: 'Iris-versicolor', 7.7: 'Iris-virginica'}}, 5.1: 'Iris-virginica', 5.2: 'Iris-virginica', 5.4: 'Iris-virginica', 5.5: 'Iris-virginica', 5.7: 'Iris-virginica', 5.8: 'Iris-virginica', 6.1: 'Iris-virginica', 6.6: 'Iris-virginica', 6.7: 'Iris-virginica', 6.9: 'Iris-virginica'}}}}

预测结果为'Iris-setosa'，与实际结果相符。