lr_train.py

# coding:UTF-8
'''
Date:20160901
@author: zhaozhiyong
'''
import numpy as np

def load_data(file_name):
    '''导入训练数据
    input:  file_name(string)训练数据的位置
    output: feature_data(mat)特征
            label_data(mat)标签
    '''
    f = open(file_name)  # 打开文件
    feature_data = []
    label_data = []
    for line in f.readlines():
        feature_tmp = []
        lable_tmp = []
        lines = line.strip().split("\t")
        feature_tmp.append(1)  # 偏置项
        for i in xrange(len(lines) - 1):
            feature_tmp.append(float(lines[i]))
        lable_tmp.append(float(lines[-1]))
        
        feature_data.append(feature_tmp)
        label_data.append(lable_tmp)
    f.close()  # 关闭文件
    return np.mat(feature_data), np.mat(label_data)

def sig(x):
    '''Sigmoid函数
    input:  x(mat):feature * w
    output: sigmoid(x)(mat):Sigmoid值
    '''
    return 1.0 / (1 + np.exp(-x))

def lr_train_bgd(feature, label, maxCycle, alpha):
    '''利用梯度下降法训练LR模型
    input:  feature(mat)特征
            label(mat)标签
            maxCycle(int)最大迭代次数
            alpha(float)学习率
    output: w(mat):权重
    '''
    n = np.shape(feature)[1]  # 特征个数
    w = np.mat(np.ones((n, 1)))  # 初始化权重
    i = 0
    while i <= maxCycle:  # 在最大迭代次数的范围内
        i += 1  # 当前的迭代次数
        h = sig(feature * w)  # 计算Sigmoid值
        err = label - h
        if i % 100 == 0:
            print "\t---------iter=" + str(i) + \
            " , train error rate= " + str(error_rate(h, label))
        w = w + alpha * feature.T * err  # 权重修正
    return w

def error_rate(h, label):
    '''计算当前的损失函数值
    input:  h(mat):预测值
            label(mat):实际值
    output: err/m(float):错误率
    '''
    m = np.shape(h)[0]
    
    sum_err = 0.0
    for i in xrange(m):
        if h[i, 0] > 0 and (1 - h[i, 0]) > 0:
            sum_err -= (label[i,0] * np.log(h[i,0]) + \
                        (1-label[i,0]) * np.log(1-h[i,0]))
        else:
            sum_err -= 0
    return sum_err / m

def save_model(file_name, w):
    '''保存最终的模型
    input:  file_name(string):模型保存的文件名
            w(mat):LR模型的权重
    '''
    m = np.shape(w)[0]
    f_w = open(file_name, "w")
    w_array = []
    for i in xrange(m):
        w_array.append(str(w[i, 0]))
    f_w.write("\t".join(w_array))
    f_w.close()           

if __name__ == "__main__":
    # 1、导入训练数据
    print "---------- 1.load data ------------"
    feature, label = load_data("data.txt")
    # 2、训练LR模型
    print "---------- 2.training ------------"
    w = lr_train_bgd(feature, label, 1000, 0.01)
    # 3、保存最终的模型
    print "---------- 3.save model ------------"
    save_model("weights", w)