请给出C4.5算法的代码，并实现可视化

C4.5算法是一种决策树算法，它通过对数据集进行划分，构建决策树，并对新数据进行分类。以下是Python实现的C4.5算法代码：

import pandas as pd
import numpy as np
import math

class C45:
    def __init__(self, eps=1e-4):
        self.eps = eps
        self.tree = {}

    def calc_entropy(self, y):
        entropy = 0
        for i in np.unique(y):
            p = len(y[y == i]) / len(y)
            entropy += -p * math.log(p, 2)
        return entropy

    def calc_cond_entropy(self, y, x):
        cond_entropy = 0
        for i in np.unique(x):
            p = len(x[x == i]) / len(x)
            cond_entropy += p * self.calc_entropy(y[x == i])
        return cond_entropy

    def calc_info_gain_ratio(self, y, x):
        info_gain = self.calc_entropy(y) - self.calc_cond_entropy(y, x)
        split_info = -sum([(len(x[x == i]) / len(x)) * math.log((len(x[x == i]) / len(x)), 2) for i in np.unique(x)])
        if split_info == 0:
            return 0
        return info_gain / split_info

    def fit(self, X, y):
        self.tree = self._fit(X, y, list(range(X.shape[1])), {})

    def _fit(self, X, y, features, tree):
        if len(np.unique(y)) == 1:
            return np.unique(y)[0]
        if len(features) == 0:
            return np.bincount(y).argmax()
        max_gain_ratio = -1
        best_feature = None
        for feature in features:
            gain_ratio = self.calc_info_gain_ratio(y, X[:, feature])
            if gain_ratio > max_gain_ratio:
                max_gain_ratio = gain_ratio
                best_feature = feature
        if max_gain_ratio < self.eps:
            return np.bincount(y).argmax()
        sub_features = [f for f in features if f != best_feature]
        tree = {best_feature: {}}
        for i in np.unique(X[:, best_feature]):
            X_sub = X[X[:, best_feature] == i]
            y_sub = y[X[:, best_feature] == i]
            tree[best_feature][i] = self._fit(X_sub, y_sub, sub_features, {})
        return tree

    def predict(self, X):
        return np.array([self._predict(x, self.tree) for x in X])

    def _predict(self, x, tree):
        if isinstance(tree, dict):
            return self._predict(x, tree[x.argmax()])
        else:
            return tree

def plot_tree(tree, feature_names, class_names, filename):
    import pydotplus
    from IPython.display import Image
    from sklearn.externals.six import StringIO
    dot_data = StringIO()
    dot_data.write('digraph Tree {\n')
    dot_data.write('node [shape=box] ;\n')
    def traverse(node, parent, i):
        for k, v in node.items():
            if isinstance(v, dict):
                i += 1
                dot_data.write(f'{parent} -> {i} [label="{feature_names[k]}"] ;\n')
                dot_data.write(f'{i} [label="{feature_names[k]}"] ;\n')
                i = traverse(v, i, i)
            else:
                dot_data.write(f'{parent} -> {i+1} [label="{class_names[v]}"] ;\n')
                dot_data.write(f'{i+1} [label="{class_names[v]}"] ;\n')
                i += 1
        return i
    traverse(tree, 0, 0)
    dot_data.write('}\n')
    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
    graph.write_png(filename)
    return Image(graph.create_png())

代码中包含了一个可视化函数`plot_tree`，它可以将决策树可视化，方便我们理解决策树的构建过程。使用方法如下：

model = C45()
model.fit(X_train, y_train)
plot_tree(model.tree, feature_names, class_names, 'tree.png')

其中，`X_train`是训练集特征矩阵，`y_train`是训练集标签向量，`feature_names`是特征名称列表，`class_names`是类别名称列表，`tree.png`是保存决策树可视化结果的文件名。