adaboost模型python实现

以下是使用Python实现Adaboost模型的基本步骤：

1. 导入必要的库

import numpy as np
from sklearn.tree import DecisionTreeClassifier

2. 定义Adaboost分类器类

class Adaboost:
    def __init__(self, n_estimators=50, learning_rate=1.0):
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.estimators = []
        self.weights = []

3. 定义训练方法

    def fit(self, X, y):
        n_samples, n_features = X.shape
        # 初始化权重
        weights = np.full(n_samples, (1 / n_samples))
        for i in range(self.n_estimators):
            # 创建决策树分类器
            tree = DecisionTreeClassifier(max_depth=1)
            # 使用加权数据拟合分类器
            tree.fit(X, y, sample_weight=weights)
            # 预测数据
            predictions = tree.predict(X)
            # 计算错误率
            error = np.sum(weights * (predictions != y))
            # 计算分类器权重
            alpha = self.learning_rate * (np.log((1 - error) / error))
            # 更新权重
            weights *= np.exp(alpha * (predictions != y))
            # 标准化权重
            weights /= np.sum(weights)
            # 保存分类器和权重
            self.estimators.append(tree)
            self.weights.append(alpha)

4. 定义预测方法

    def predict(self, X):
        # 计算每个分类器的预测值
        predictions = np.array([estimator.predict(X) for estimator in self.estimators]).T
        # 计算权重加权的预测值
        weighted_predictions = np.dot(predictions, self.weights)
        # 返回预测结果
        return np.where(weighted_predictions > 0, 1, -1)

完整代码如下：

import numpy as np
from sklearn.tree import DecisionTreeClassifier

class Adaboost:
    def __init__(self, n_estimators=50, learning_rate=1.0):
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.estimators = []
        self.weights = []

    def fit(self, X, y):
        n_samples, n_features = X.shape
        # 初始化权重
        weights = np.full(n_samples, (1 / n_samples))
        for i in range(self.n_estimators):
            # 创建决策树分类器
            tree = DecisionTreeClassifier(max_depth=1)
            # 使用加权数据拟合分类器
            tree.fit(X, y, sample_weight=weights)
            # 预测数据
            predictions = tree.predict(X)
            # 计算错误率
            error = np.sum(weights * (predictions != y))
            # 计算分类器权重
            alpha = self.learning_rate * (np.log((1 - error) / error))
            # 更新权重
            weights *= np.exp(alpha * (predictions != y))
            # 标准化权重
            weights /= np.sum(weights)
            # 保存分类器和权重
            self.estimators.append(tree)
            self.weights.append(alpha)

    def predict(self, X):
        # 计算每个分类器的预测值
        predictions = np.array([estimator.predict(X) for estimator in self.estimators]).T
        # 计算权重加权的预测值
        weighted_predictions = np.dot(predictions, self.weights)
        # 返回预测结果
        return np.where(weighted_predictions > 0, 1, -1)

使用方法如下：

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
data = load_breast_cancer()
X = data.data
y = data.target
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建Adaboost分类器
clf = Adaboost(n_estimators=100, learning_rate=1.0)
# 训练模型
clf.fit(X_train, y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)