属性有性别成绩班委就业情况其中类标号属性为就业情况基于c4.5算法使用python实现算法实例50个训练样本并分别计算信息量再用10个测试样本进行测试给出完整代码

好的，以下是基于C4.5算法的Python实现代码：

import pandas as pd
import numpy as np
import math

# 读取数据集
data = pd.read_csv("dataset.csv")

# 划分训练集和测试集
train_data = data.sample(n=50, random_state=1)  # 随机抽取50个样本作为训练集
test_data = data.drop(train_data.index)  # 剩下的样本作为测试集

# 定义节点类
class Node:
    def __init__(self):
        self.children = {}
        self.feature = None
        self.label = None

# 计算信息熵
def entropy(data):
    labels = data.iloc[:, -1].value_counts()
    ent = 0
    for label in labels:
        p = label / len(data)
        ent -= p * math.log(p, 2)
    return ent

# 计算信息增益
def gain(data, feature):
    values = data[feature].unique()
    ent = entropy(data)
    for value in values:
        sub_data = data[data[feature] == value]
        ent -= len(sub_data) / len(data) * entropy(sub_data)
    return ent

# 计算信息增益率
def gain_ratio(data, feature):
    return gain(data, feature) / entropy(data)

# 选择最优特征
def choose_feature(data):
    features = data.columns[:-1]
    best_feature = None
    max_gain_ratio = 0
    for feature in features:
        gr = gain_ratio(data, feature)
        if gr > max_gain_ratio:
            max_gain_ratio = gr
            best_feature = feature
    return best_feature

# 构建决策树
def build_tree(data):
    node = Node()
    # 如果数据集中所有样本都属于同一类别，则返回该类别
    if len(data.iloc[:, -1].unique()) == 1:
        node.label = data.iloc[0, -1]
        return node
    # 如果数据集中只有一个特征，则返回样本中类别最多的类别
    if len(data.columns[:-1]) == 1:
        node.label = data.iloc[:, -1].value_counts().index[0]
        return node
    # 选择最优特征
    best_feature = choose_feature(data)
    node.feature = best_feature
    # 根据最优特征划分子节点
    values = data[best_feature].unique()
    for value in values:
        sub_data = data[data[best_feature] == value].drop(best_feature, axis=1)
        if len(sub_data) == 0:
            node.children[value] = Node()
            node.children[value].label = data.iloc[:, -1].value_counts().index[0]
        else:
            node.children[value] = build_tree(sub_data)
    return node

# 预测
def predict(node, sample):
    if node.label is not None:
        return node.label
    feature = node.feature
    value = sample[feature]
    if value not in node.children:
        return node.label
    child_node = node.children[value]
    return predict(child_node, sample)

# 计算准确率
def accuracy(test_data, root):
    correct = 0
    for i in range(len(test_data)):
        sample = test_data.iloc[i, :-1]
        label = test_data.iloc[i, -1]
        if predict(root, sample) == label:
            correct += 1
    return correct / len(test_data)

# 构建决策树
root = build_tree(train_data)

# 测试模型
acc = accuracy(test_data, root)
print("Accuracy:", acc)

请注意，这里的数据集需要根据实际情况进行处理，确保数据集中每个样本都有正确的类标号。