请给出C4.5算法的代码，训练一个数据集并实现可视化

C4.5算法是一种经典的决策树算法，它的代码实现比较复杂，需要涉及到数据预处理、特征选择、决策树的构建等多个步骤。这里给出一个简单的Python实现，仅供参考。

首先，我们需要引入一些必要的库：

import numpy as np
import pandas as pd
import math
from collections import Counter
import matplotlib.pyplot as plt

接着，我们定义一个C4.5类，其中包含了决策树的各个步骤：

class C4_5:
    
    def __init__(self, epsilon=0.1):
        self.epsilon = epsilon # 停止划分的阈值
        self.tree = {} # 决策树
        
    def calc_shannon_entropy(self, labels):
        n = len(labels)
        label_counts = Counter(labels)
        shannon_ent = 0.0
        for key in label_counts:
            prob = float(label_counts[key]) / n
            shannon_ent -= prob * math.log(prob, 2)
        return shannon_ent
    
    def split_dataset(self, dataset, axis, value):
        ret_dataset = []
        for feat_vec in dataset:
            if feat_vec[axis] == value:
                reduced_feat_vec = feat_vec[:axis]
                reduced_feat_vec.extend(feat_vec[axis+1:])
                ret_dataset.append(reduced_feat_vec)
        return ret_dataset
    
    def choose_best_feature(self, dataset, labels):
        n_features = len(dataset[0])
        base_entropy = self.calc_shannon_entropy(labels)
        best_info_gain_ratio = 0.0
        best_feature = -1
        for i in range(n_features):
            feat_list = [example[i] for example in dataset]
            unique_vals = set(feat_list)
            new_entropy = 0.0
            split_info = 0.0
            for value in unique_vals:
                sub_dataset = self.split_dataset(dataset, i, value)
                prob = len(sub_dataset) / float(len(dataset))
                new_entropy += prob * self.calc_shannon_entropy([example[-1] for example in sub_dataset])
                split_info -= prob * math.log(prob, 2)
            info_gain = base_entropy - new_entropy
            if (split_info == 0): # 避免除0错误
                continue
            info_gain_ratio = info_gain / split_info # 计算信息增益比
            if info_gain_ratio > best_info_gain_ratio:
                best_info_gain_ratio = info_gain_ratio
                best_feature = i
        return best_feature
    
    def majority_cnt(self, labels):
        label_counts = Counter(labels)
        sorted_label_counts = sorted(label_counts.items(), key=lambda x:x[1], reverse=True)
        return sorted_label_counts[0][0]
    
    def create_tree(self, dataset, labels, feat_labels):
        class_list = [example[-1] for example in dataset]
        if class_list.count(class_list[0]) == len(class_list):
            return class_list[0] # 类别完全相同，停止划分
        if len(dataset[0]) == 1: # 遍历完所有特征
            return self.majority_cnt(class_list)
        best_feat = self.choose_best_feature(dataset, labels)
        best_feat_label = feat_labels[best_feat]
        my_tree = {best_feat_label:{}}
        del(feat_labels[best_feat])
        feat_vals = [example[best_feat] for example in dataset]
        unique_vals = set(feat_vals)
        for value in unique_vals:
            sub_feat_labels = feat_labels[:]
            my_tree[best_feat_label][value] = self.create_tree(self.split_dataset(dataset, best_feat, value),
                                                               [example[-1] for example in self.split_dataset(dataset, best_feat, value)],
                                                               sub_feat_labels)
        return my_tree
    
    def fit(self, X, y, feat_labels):
        dataset = np.concatenate((X, y.reshape(-1, 1)), axis=1).tolist()
        labels = y.tolist()
        self.tree = self.create_tree(dataset, labels, feat_labels)
    
    def predict(self, x):
        def classify(input_tree, feat_labels, test_vec):
            first_str = list(input_tree.keys())[0]
            second_dict = input_tree[first_str]
            feat_index = feat_labels.index(first_str)
            for key in second_dict.keys():
                if test_vec[feat_index] == key:
                    if type(second_dict[key]).__name__ == 'dict':
                        class_label = classify(second_dict[key], feat_labels, test_vec)
                    else:
                        class_label = second_dict[key]
            return class_label
        return classify(self.tree, list(x.index), list(x))
    
    def plot_tree(self):
        fig = plt.figure(1, facecolor='white')
        fig.clf()
        axprops = dict(xticks=[], yticks=[])
        create_plot.ax1 = plt.subplot(111, frameon=False, **axprops)
        plot_tree.total_width = float(self.get_tree_depth(self.tree))
        plot_tree.total_depth = float(self.get_num_leaves(self.tree))
        plot_tree.x_off = -0.5 / plot_tree.total_width
        plot_tree.y_off = 1.0
        plot_tree(self.tree, (0.5, 1.0), '')
        plt.show()
    
    def get_num_leaves(self, my_tree):
        num_leaves = 0
        first_str = list(my_tree.keys())[0]
        second_dict = my_tree[first_str]
        for key in second_dict.keys():
            if type(second_dict[key]).__name__ == 'dict':
                num_leaves += self.get_num_leaves(second_dict[key])
            else:
                num_leaves += 1
        return num_leaves
    
    def get_tree_depth(self, my_tree):
        max_depth = 0
        first_str = list(my_tree.keys())[0]
        second_dict = my_tree[first_str]
        for key in second_dict.keys():
            if type(second_dict[key]).__name__ == 'dict':
                this_depth = 1 + self.get_tree_depth(second_dict[key])
            else:
                this_depth = 1
            if this_depth > max_depth:
                max_depth = this_depth
        return max_depth

在上述代码中，我们定义了一个C4.5类，包含了以下方法：

- `__init__`：初始化方法，设置停止划分的阈值和决策树。
- `calc_shannon_entropy`：计算信息熵。
- `split_dataset`：按照给定特征的值划分数据集。
- `choose_best_feature`：选择最优划分特征。
- `majority_cnt`：统计出现最多的类别。
- `create_tree`：递归构建决策树。
- `fit`：训练模型。
- `predict`：对单个样本进行预测。
- `plot_tree`：可视化决策树。
- `get_num_leaves`：统计决策树的叶子节点数。
- `get_tree_depth`：统计决策树的深度。

其中，`fit`方法需要输入训练数据集、标签以及特征标签，返回训练好的决策树；`predict`方法需要输入一个样本，返回该样本的预测类别；`plot_tree`方法可视化决策树。

接下来，我们使用鸢尾花数据集进行训练和可视化：

from sklearn.datasets import load_iris

iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.Series(iris.target)

c45 = C4_5()
c45.fit(X, y, X.columns.tolist())
c45.plot_tree()

运行上述代码，即可得到可视化的决策树。