以类方法定义Adaboost

import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import f1_score, accuracy_score

class Adaboost:
    """
    __init__(self, num_classifiers): 构造方法，用于初始化Adaboost对象。num_classifiers指定要使用的分类器数量。
    weighted_error(y_true, y_pred, weights): 静态方法，计算加权错误率。它接受真实标签y_true、预测标签y_pred和样本权重weights作为输入，并返回加权错误率。
    update_weights(weights, alpha, y_true, y_pred): 静态方法，根据分类器权重更新样本权重。它接受当前样本权重weights、分类器权重alpha、真实标签y_true和预测标签y_pred作为输入，并返回更新后的样本权重。
    fit(self, X, y): 训练Adaboost分类器。它接受训练数据X和标签y作为输入，并使用num_classifiers个决策树分类器进行训练
    """
    def __init__(self, num_classifiers):
        self.num_classifiers = num_classifiers
        self.classifiers = []
        self.classifier_weights = []

    @staticmethod
    def weighted_error(y_true, y_pred, weights):
        return np.sum(weights[y_true != y_pred])

    @staticmethod
    def update_weights(weights, alpha, y_true, y_pred):
        weights_new = weights * np.exp(alpha * (y_true != y_pred))
        return weights_new / np.sum(weights_new)

    def fit(self, X, y):
        # 初始化样本权重
        sample_weights = np.ones(len(X)) / len(X)

        for _ in range(self.num_classifiers):
            # 创建分类器并训练
            classifier = DecisionTreeClassifier(max_depth=1)
            classifier.fit(X, y, sample_weight=sample_weights)

            # 预测
            y_pred = classifier.predict(X)

            # 计算错误率和分类器权重
            error = self.weighted_error(y, y_pred, sample_weights)
            print(error)
            print('-'* 50)
            alpha = 0.5 * np.log((1 - error) / error)

            # 更新样本权重
            sample_weights = self.update_weights(sample_weights, alpha, y, y_pred)

            # 保存分类器和权重
            self.classifiers.append(classifier)
            self.classifier_weights.append(alpha)

    def predict(self, X):
        # 初始化预测结果
        y_pred = np.zeros(len(X))

        for classifier, alpha in zip(self.classifiers, self.classifier_weights):
            # 对每个分类器进行预测
            y_pred_single = classifier.predict(X)
            # 根据权重进行加权
            y_pred += alpha * y_pred_single

        # 使用符号函数将预测结果转换为类标签
        y_pred = np.sign(y_pred)
        print("预测y_pred:", y_pred)

        return y_pred

# 准备数据
df = pd.DataFrame([[0, 1],
                   [1, 1],
                   [2, 1],
                   [3, -1],
                   [4, -1],
                   [5, -1],
                   [6, 1],
                   [7, 1],
                   [8, 1],
                   [9, -1]])

# 提取数据信息
X = df.iloc[:, :-1]
Y = df.iloc[:, -1]

print(X)
print(Y)
# 实例化类对象
A = Adaboost(20)
A.fit(X, Y)
A.predict(X)

E:/XXXXX/机器学习课程/集成学习/Cls_adaboost.py
   0
0  0
1  1
2  2
3  3
4  4
5  5
6  6
7  7
8  8
9  9
0    1
1    1
2    1
3   -1
4   -1
5   -1
6    1
7    1
8    1
9   -1
Name: 1, dtype: int64
0.30000000000000004
--------------------------------------------------
0.34534632929202286
--------------------------------------------------
0.35008562397019116
--------------------------------------------------
0.28001073918680375
--------------------------------------------------
0.25381778971648017
--------------------------------------------------
0.3064962299371764
--------------------------------------------------
0.2816048333195814
--------------------------------------------------
0.2731345864165943
--------------------------------------------------
0.29156133793693806
--------------------------------------------------
0.2823934402541752
--------------------------------------------------
0.33466386078918664
--------------------------------------------------
0.24608157437902595
--------------------------------------------------
0.3502647590291356
--------------------------------------------------
0.25394656115341074
--------------------------------------------------
0.2731513767570225
--------------------------------------------------
0.3056825853100197
--------------------------------------------------
0.2720919041411729
--------------------------------------------------
0.34005542241591524
--------------------------------------------------
0.24744912020573245
--------------------------------------------------
0.3529066226801387
--------------------------------------------------
预测y_pred: [ 1.  1.  1. -1. -1. -1.  1.  1.  1. -1.]

Process finished with exit code 0