李航《統計學習方法》讀書記錄

李航《統計學習方法》第六章課後習題

6.2邏輯斯谛回歸模型的梯度下降算法

import time
import math
import random

import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score


class LogisticRegression(object):

def __init__(self):
    self.learning_step = 0.00001
    self.max_iteration = 5000

def predict_(self,x):
    wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
    exp_wx = math.exp(wx)

    predict1 = exp_wx / (1 + exp_wx)
    predict0 = 1 / (1 + exp_wx)

    if predict1 > predict0:
        return 1
    else:
        return 0


def train(self,features, labels):
    self.w = [0.0] * (len(features[0]) + 1)

    correct_count = 0
    time = 0

    while time < self.max_iteration:
        index = random.randint(0, len(labels) - 1)
        x = list(features[index])
        x.append(1.0)
        y = labels[index]

        if y == self.predict_(x):
            correct_count += 1
            if correct_count > self.max_iteration:
                break
            continue

        # print 'iterater times %d' % time
        time += 1
        correct_count = 0

        wx = sum([self.w[i] * x[i] for i in range(len(self.w))])
        exp_wx = math.exp(wx)


        for i in range(len(self.w)):
            self.w[i] =self.w[i] - self.learning_step * (-y * x[i] + float(x[i] * exp_wx) / float(1 + exp_wx))  #這個位置總感覺不好 應該還可以更好


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('YOUDATA',header=0)
data = raw_data.values

imgs = data[0::,1::]
labels = data[::,0]


# 選取 2/3 資料作為訓練集， 1/3 資料作為測試集
train_features, test_features, train_labels, test_labels = train_test_split(imgs, labels, test_size=0.33, random_state=23323)

time_2 = time.time()
print ('read data cost ',time_2 - time_1,' second','\n')

print ('Start training')
lr = LogisticRegression()
lr.train(train_features, train_labels)

time_3 = time.time()
print ('training cost ',time_3 - time_2,' second','\n')

print ('Start predicting')
test_predict = lr.predict(test_features)
time_4 = time.time()
print ('predicting cost ',time_4 - time_3,' second','\n')

score = accuracy_score(test_labels,test_predict)
print ("The accruacy socre is ", score)