统计学习方法——实现AdaBoost

Adaboost

适用问题：二分类问题

• 模型：加法模型

[f(x)=sum_{m=1}^{M} alpha_{m} G_{m}(x) ]

• 策略：损失函数为指数函数

[L(y,f(x))=exp[-yf(x)] ]

• 算法：前向分步算法

[left(eta_{m}, gamma_{m} ight)=arg min _{eta, gamma} sum_{i=1}^{N} Lleft(y_{i}, f_{m-1}left(x_{i} ight)+eta bleft(x_{i} ; gamma ight) ight) ]

特点：AdaBoost算法的特点是通过迭代每次学习一个基本分类器。每次迭代中，提高那些被前一轮分类器错误分类数据的权值，而降低那些被正确分类的数据的权值。最后，AdaBoost将基本分类器的线性组合作为强分类器，其中给分类误差率小的基本分类器以大的权值，给分类误差率大的基本分类器以小的权值。

算法步骤：

1）给每个训练样本（(x_{1},x_{2},….,x_{N})）分配权重，初始权重(w_{1})均为1/N。

2）针对带有权值的样本进行训练，得到模型(G_m)（初始模型为G1）。

3）计算模型(G_m)的误分率(e_m=sum_{i=1}^Nw_iI(y_i ot= G_m(x_i))) （误分率应小于0.5，否则将预测结果翻转即可得到误分率小于0.5的分类器）

4）计算模型(G_m)的系数(alpha_m=0.5log[(1-e_m)/e_m])

5）根据误分率e和当前权重向量(w_m)更新权重向量(w_{m+1})。

6）计算组合模型(f(x)=sum_{m=1}^Malpha_mG_m(x_i))的误分率。

7）当组合模型的误分率或迭代次数低于一定阈值，停止迭代；否则，回到步骤2）

提升树

提升树是以分类树或回归树为基本分类器的提升方法。提升树被认为是统计学习中最有效的方法之一。

提升方法：将弱可学习算法提升为强可学习算法。提升方法通过反复修改训练数据的权值分布，构建一系列基本分类器（弱分类器），并将这些基本分类器线性组合，构成一个强分类器。AdaBoost算法是提升方法的一个代表。

AdaBoost源码实现

假设弱分类器由 (x < v) 或 (x > v) 产生，阈值(v)使该分类器在训练集上分类误差率最低。

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection  import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline


def create_data():
    iris = load_iris()  # 鸢尾花数据集
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['label'] = iris.target
    data = np.array(df.iloc[:100, [0, 1, -1]])  # 取前一百个数据，只保留前两个特征
    for d in data:
        if d[-1] == 0:
            d[-1] = -1
    return data[:, :2], data[:, -1].astype(np.int)

class AdaBoost:
    def __init__(self, num_classifier, increment=0.5):
        """
        
        num_classifier: 弱分类器的数量
        increment: 在特征上寻找最优切分点时，搜索时每次的增加值(数据稀疏时建议根据样本点来选择)
        """
        self.num_classifier = num_classifier
        self.increment = increment
        
    def fit(self, X, Y):
        self._init_args(X, Y)
        
        # 逐个训练分类器
        for m in range(self.num_classifier):
            min_error, v_optimal, preds = float('INF'), None, None
            direct_split = None
            feature_idx = None  # 选定的特征的列索引
            # 遍历选择特征和切分点使得分类误差最小
            for j in range(self.num_feature):
                feature_values = self.X[:, j]  # 第j个特征对应的所有取值
                _ret = self._get_optimal_split(feature_values)
                v_split, _direct_split, error, pred_labels = _ret
                
                if error < min_error:
                    min_error = error
                    v_optimal = v_split
                    preds = pred_labels
                    direct_split = _direct_split
                    feature_idx = j
            
            # 计算分类型权重alpha
            alpha = self._cal_alpha(min_error)
            self.alphas.append(alpha)
            
            # 记录当前分类器G(x)
            self.classifiers.append((feature_idx, v_optimal, direct_split))
            
            # 更新样本集合权值分布
            self._update_weights(alpha, preds)
    
    def predict(self, x):
        res = 0.0
        for i in range(len(self.classifiers)):
            idx, v, direct = self.classifiers[i]
            # 输入弱分类器进行分类 
            if direct == '>':
                output = 1 if x[idx] > v else -1
            else:  # direct == '<'
                output = -1 if x[idx] > v else 1
                
            res += self.alphas[i] * output
        return 1 if res > 0 else -1  # sign(res)
    
    def score(self, X_test, Y_test):
        cnt = 0
        for i, x in enumerate(X_test):
            if self.predict(x) == Y_test[i]:
                cnt += 1
        return cnt / len(X_test)
    
    def _init_args(self, X, Y):
        self.X = X
        self.Y = Y
        self.N, self.num_feature = X.shape  # N:样本数，num_feature:特征数量
        
        # 初始时每个样本的权重均相同
        self.weights = [1/self.N] * self.N
        
        # 弱分类器集合
        self.classifiers = []
        
        # 每个分类器G(x)的权重
        self.alphas = []
            
    def _update_weights(self, alpha, pred_labels):
        # 计算规范化因子Z
        Z = self._cal_norm_factor(alpha, pred_labels)
        for i in range(self.N):
            self.weights[i] = (self.weights[i] *
                               np.exp(-1*alpha*self.Y[i]*pred_labels[i]) / Z)
                    
    def _cal_alpha(self, error):
        return 0.5 * np.log((1-error)/error)
                
    def _cal_norm_factor(self, alpha, pred_labels):
        return sum([self.weights[i] * np.exp(-1*alpha*self.Y[i]*pred_labels[i])
                    for i in range(self.N)])
                
    def _get_optimal_split(self, feature_values):
        error = float('INF')  # 分类误差
        pred_labels = []  # 分类结果
        v_split_optimal = None  # 当前特征的最优切割点
        direct_split = None  # 最优切割点的判别方向
        max_v = max(feature_values)
        min_v = min(feature_values)
        num_step = (max_v - min_v + self.increment)/self.increment
        for i in range(int(num_step)):
            # 选取分割点
            v_split = min_v + i * self.increment
            judge_direct = '>'
            preds = [1 if feature_values[k] > v_split else -1 
                     for k in range(len(feature_values))]
            
            # 错误样本加权误差
            weight_error = sum([self.weights[k] for k in range(self.N)
                                if preds[k] != self.Y[k]])

            # 计算分类标签翻转后的误差
            preds_inv = [-p for p in preds]
            weight_error_inv = sum([self.weights[k] for k in range(self.N)
                               if preds_inv[k] != self.Y[k]])

            # 取较小误差的判别方向作为分类器的判别方向
            if weight_error_inv < weight_error:
                preds = preds_inv
                weight_error = weight_error_inv
                judge_direct = '<'

            if weight_error < error:
                error = weight_error
                pred_labels = preds
                v_split_optimal = v_split
                direct_split = judge_direct

        return v_split_optimal, direct_split, error, pred_labels

测试模型准确率：

X, Y = create_data()

res = []
for i in range(10):
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

    clf = AdaBoost(num_classifier=50)
    clf.fit(X_train, Y_train)
    res.append(clf.score(X_test, Y_test))
print('My AdaBoost: {}次的平均准确率:  {:.3f}'.format(len(res), sum(res)/len(res)))

My AdaBoost: 10次的平均准确率:  0.970

sklearn库的AdaBoost实例

from sklearn.ensemble import AdaBoostClassifier

res = []
for i in range(10):
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

    clf_sklearn = AdaBoostClassifier(n_estimators=50, learning_rate=0.5)
    clf_sklearn.fit(X_train, Y_train)
    res.append(clf_sklearn.score(X_test, Y_test))
print('sklearn AdaBoostClassifier: {}次的平均准确率:  {:.3f}'.format(
    len(res), sum(res)/len(res)))

sklearn AdaBoostClassifier: 10次的平均准确率:  0.945