吴恩达机器学习课后习题ex2

ex2:Logistic_regression

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt 
import scipy.optimize as opt #优化算法
from sklearn.metrics import classification_report
from sklearn import linear_model

Scipy优化算法：scipy.optimize.fmin_tnc()/minimize()

参见：https://www.cnblogs.com/tongtong123/p/10634716.html

sklearn.metrics.classification_report

参见：https://blog.csdn.net/kancy110/article/details/74937469

# 获取原始数据
def raw_data(path):
    data=pd.read_csv(path,names=['exam1','exam2','admit'])
    return data

#绘制初始数据
#需要将两种判断结果区别颜色绘制
def draw_data(data):
    accept = data[data.admit==1]
    refuse = data[data.admit == 0]
    #accept=data[data['admit'].isin([1])]
    #refuse=data[data['admit'].isin([0])]
    plt.scatter(accept.exam1,accept.exam2,c='g',label = 'admit')
    plt.scatter(refuse.exam1,refuse.exam2,c='r',label = 'not admit')
    plt.title('admission')
    plt.xlabel('score1')
    plt.ylabel('score2')
    return plt

pandas中isin()函数及其逆函数使用

https://blog.csdn.net/weixin_44056331/article/details/89461816

#sigmoid函数
def sigmoid(z):
    return 1/(1+np.exp(-z))#exp（-z）:e的-z次方

#代价函数
def cost_function(theta, x, y):
    m = x.shape[0]
    j = (y.dot(np.log(sigmoid(x.dot(theta))))+(1-y).dot(np.log(1-sigmoid(x.dot(theta)))))/(-m)
    return j

#计算偏导数即可，后面会用scipy.optimize中方法求最优解
def gradient_descent(theta, x, y):
    return ((sigmoid(x.dot(theta))-y).T).dot(x)

#预测函数
def predict(theta, x):
    h = sigmoid(x.dot(theta))
    return [1 if x>= 0.5 else 0 for x in h]

#决策边界
def boundary(theta, data):
    x1 = np.arange(20,100,0.01)  #x1范围
    x2 = (theta[0]+theta[1]*x1)/-theta[2] #直线方程为:theta[0] + theta[1]*x1 + theta[2]*x2 = 0
    plt = draw_data(data) 
    plt.plot(x1, x2)
    plt.show()

def main():
    data = raw_data('ex2data1.txt')
    plt = draw_data(data) 
    plt.title('original')
    plt.show()
    data.insert(0,'Ones',1)
    #x1=data['exam1']
    #x2=data['exam2']
    #x=np.c_[np.ones(x1.shape[0]),x1,x2]
    #和ex1同样的数据处理步骤
    cols = data.shape[1]
    x = data.iloc[:,:cols-1]
    y=data.admit#data['admit']都可以
    # 不要将theta初始化为1，初始化为1的话，h(x)会过大，sigmoid近似返回1，log(1-h(x))无意义
    theta=np.zeros(x.shape[1])
    # print(cost_function(theta,x,y))    # 0.6931471805599453
    # theta=opt.fmin_tnc(func=cost_function,x0=theta,fprime=gradient_descent,args=(x,y))
    theta = opt.minimize(fun=cost_function,x0=theta,args=(x,y),method='tnc',jac=gradient_descent)
    theta=theta.x#x是opt.minimize返回的最优解数组
    # print(cost_function(theta,x,y))
    print(classification_report(predict(theta,x),y))
    model=linear_model.LogisticRegression()
    model.fit(x,y)
    print(model.score(x,y))
    boundary(theta,data)

Scipy优化算法：scipy.optimize.fmin_tnc()/minimize()

参见：https://www.cnblogs.com/tongtong123/p/10634716.html

main()

             precision    recall  f1-score   support

          0       0.85      0.87      0.86        39
          1       0.92      0.90      0.91        61

avg / total       0.89      0.89      0.89       100

0.91

参考学习博客：
https://blog.csdn.net/zy1337602899/article/details/84777396