李航《统计学习方法》——第二章感知机模型

川长思鸟来 2022-05-30 10:26 289阅读 0赞

由于网上资料很多，这里就不再对算法原理进行推导，仅给出博主用Python实现的代码，供大家参考

**适用问题**：二类分类  
  
**实验数据**：由于是二分类器，所以将MINST数据集[train.csv][]的label列进行了一些微调，label等于0的继续等于0，label大于0改为1。这样就将十分类的数据改为二分类的数据。获取地址[train\_binary.csv][train_binary.csv]  
  
**实现代码**：

# encoding=utf-8
    
    import pandas as pd
    import random
    import time
    
    from sklearn.cross_validation import train_test_split
    from sklearn.metrics import accuracy_score
    
    
    class Perceptron(object):
    
        def __init__(self):
            self.learning_step = 0.001  # 学习率
            self.max_iteration = 5000  # 分类正确上界，当分类正确的次数超过上界时，认为已训练好，退出训练
    
        def train(self, features, labels):        
    
            # 初始化w,b为0,b在最后一位
            self.w = [0.0] * (len(features[0]) + 1)
    
            correct_count = 0  # 分类正确的次数
    
            while correct_count < self.max_iteration:
    
                # 随机选取数据(xi,yi)
                index = random.randint(0, len(labels) - 1) 
                x = list(features[index])
                x.append(1.0)  # 加上1是为了与b相乘
                y = 2 * labels[index] - 1  # label为1转化为正实例点+1，label为0转化为负实例点-1
    
                # 计算w*xi+b
                wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
    
                # 如果yi(w*xi+b) > 0 则分类正确的次数加1
                if wx * y > 0:
                    correct_count += 1
                    continue
    
                # 如果yi(w*xi+b) <= 0 则更新w(最后一位实际上b)的值
                for i in range(len(self.w)):
                    self.w[i] += self.learning_step * (y * x[i])
    
        def predict_(self, x):
            wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
            return int(wx > 0)  # w*xi+b>0则返回返回1,否则返回0
    
        def predict(self, features):
            labels = []
            for feature in features:
                x = list(feature)
                x.append(1)
                labels.append(self.predict_(x))
            return labels
    
    
    if __name__ == '__main__':
    
        print("Start read data")
    
        time_1 = time.time()
    
        raw_data = pd.read_csv('../data/train_binary.csv', header=0)  # 读取csv数据，并将第一行视为表头，返回DataFrame类型
        data = raw_data.values
    
        features = data[::, 1::]
        labels = data[::, 0]
    
        # 避免过拟合，采用交叉验证，随机选取33%数据作为测试集，剩余为训练集
        train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=0)
    
        time_2 = time.time()
        print('read data cost %f seconds' % (time_2 - time_1))
    
        print('Start training')
        p = Perceptron()
        p.train(train_features, train_labels)
    
        time_3 = time.time()
        print('training cost %f seconds' % (time_3 - time_2))
    
        print('Start predicting')
        test_predict = p.predict(test_features)
        time_4 = time.time()
        print('predicting cost %f seconds' % (time_4 - time_3))
    
        score = accuracy_score(test_labels, test_predict)
    print("The accruacy score is %f" % score)

代码亦可从我的[github][]上获取  
  
**运行结果**：  
  
![perceptron_result.png][]  
  
  
**实现代码(用sklearn实现)**：

# encoding=utf-8
    
    import pandas as pd
    import time
    
    from sklearn.cross_validation import train_test_split
    from sklearn.metrics import accuracy_score
    
    from sklearn.linear_model import Perceptron
    
    
    
    if __name__ == '__main__':
    
        print("Start read data...")
        time_1 = time.time()
    
        raw_data = pd.read_csv('../data/train_binary.csv', header=0)  # 读取csv数据，并将第一行视为表头，返回DataFrame类型
        data = raw_data.values
    
        features = data[::, 1::]
        labels = data[::, 0]
    
        # 随机选取33%数据作为测试集，剩余为训练集
        train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=0)
    
        time_2 = time.time()
        print('read data cost %f seconds' % (time_2 - time_1))
    
        print('Start training...')
        clf = Perceptron(alpha=0.0001,max_iter=2000) # 设置步长及最大迭代次数
        clf.fit(train_features,train_labels)
        time_3 = time.time()
        print('training cost %f seconds' % (time_3 - time_2))
    
        print('Start predicting...')
        test_predict = clf.predict(test_features)
        time_4 = time.time()
        print('predicting cost %f seconds' % (time_4 - time_3))
    
        score = accuracy_score(test_labels, test_predict)
    print("The accruacy score is %f" % score)

代码亦可从我的[github][github 1]上获取  
  
**运行结果**：  
  
![perceptron_sklearn_result.png][]

[train.csv]: https://github.com/fuqiuai/lihang_algorithms/blob/master/data/train.csv
[train_binary.csv]: https://github.com/fuqiuai/lihang_algorithms/blob/master/data/train_binary.csv
[github]: https://github.com/fuqiuai/lihang_algorithms/blob/master/perceptron/perceptron.py
[perceptron_result.png]: /images/20220530/925d925457a0419b8559763e6922bb39.png
[github 1]: https://github.com/fuqiuai/lihang_algorithms/blob/master/perceptron/perceptron_sklearn.py
[perceptron_sklearn_result.png]: /images/20220530/5f5d7a5a1d2a49d59e87fa03554371a1.png