逻辑回归模型

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
path='data'+os.sep+'Logireg_data.txt'
pdData=pd.read_csv(path,header=None,names=['Exam1','Exam2','Admitted'])
pdData.head()
print(pdData.head())
print(pdData.shape)
positive=pdData[pdData['Admitted']==1]#定义正
nagative=pdData[pdData['Admitted']==0]#定义负
fig,ax=plt.subplots(figsize=(10,5))
ax.scatter(positive['Exam1'],positive['Exam2'],s=30,c='b',marker='o',label='Admitted')
ax.scatter(nagative['Exam1'],nagative['Exam2'],s=30,c='r',marker='x',label='not Admitted')
ax.legend()
ax.set_xlabel('Exam 1 score')
ax.set_ylabel('Exam 2 score')
plt.show()#画图
##实现算法 the logistics regression 目标建立一个分类器 设置阈值来判断录取结果
##sigmoid 函数
def sigmoid(z):
    return 1/(1+np.exp(-z))
#画图
nums=np.arange(-10,10,step=1)
fig,ax=plt.subplots(figsize=(12,4))
ax.plot(nums,sigmoid(nums),'r')#画图定义
plt.show()
#按照理论实现预测函数
def model(X,theta):
    return sigmoid(np.dot(X,theta.T))
 
pdData.insert(0,'ones',1)#插入一列
orig_data=pdData.as_matrix()
cols=orig_data.shape[1]
X=orig_data[:,0:cols-1]
y=orig_data[:,cols-1:cols]
theta=np.zeros([1,3])
print(X[:5])
print(X.shape,y.shape,theta.shape)
##损失函数
def cost(X,y,theta):
    left=np.multiply(-y,np.log(model(X,theta)))
    right=np.multiply(1-y,np.log(1-model(X,theta)))
    return np.sum(left-right)/(len(X))
print(cost(X,y,theta))
 
#计算梯度
def gradient(X, y, theta):
    grad = np.zeros(theta.shape)
    error = (model(X, theta) - y).ravel()
    for j in range(len(theta.ravel())):  # for each parmeter
        term = np.multiply(error, X[:, j])
        grad[0, j] = np.sum(term) / len(X)
 
    return grad
##比较3种不同梯度下降方法
STOP_ITER=0
STOP_COST=1
STOP_GRAD=2
 
def stopCriterion(type,value,threshold):
    if type==STOP_ITER: return value>threshold
    elif type==STOP_COST: return abs(value[-1]-value[-2])<threshold
    elif type==STOP_GRAD: return np.linalg.norm(value)<threshold
 
import numpy.random
#打乱数据洗牌
def shuffledata(data):
    np.random.shuffle(data)
    cols=data.shape[1]
    X=data[:,0:cols-1]
    y=data[:,cols-1:]
    return X,y
 
 
import time
 
 
def descent(data, theta, batchSize, stopType, thresh, alpha):
    # 梯度下降求解
 
    init_time = time.time()
    i = 0  # 迭代次数
    k = 0  # batch
    X, y = shuffledata(data)
    grad = np.zeros(theta.shape)  # 计算的梯度
    costs = [cost(X, y, theta)]  # 损失值
 
    while True:
        grad = gradient(X[k:k + batchSize], y[k:k + batchSize], theta)
        k += batchSize  # 取batch数量个数据
        if k >= n:
            k = 0
            X, y = shuffledata(data)  # 重新洗牌
        theta = theta - alpha * grad  # 参数更新
        costs.append(cost(X, y, theta))  # 计算新的损失
        i += 1
 
        if stopType == STOP_ITER:
            value = i
        elif stopType == STOP_COST:
            value = costs
        elif stopType == STOP_GRAD:
            value = grad
        if stopCriterion(stopType, value, thresh): break
 
    return theta, i - 1, costs, grad, time.time() - init_time
#选择梯度下降
def runExpe(data, theta, batchSize, stopType, thresh, alpha):
    #import pdb; pdb.set_trace();
    theta, iter, costs, grad, dur = descent(data, theta, batchSize, stopType, thresh, alpha)
    name = "Original" if (data[:,1]>2).sum() > 1 else "Scaled"
    name += " data - learning rate: {} - ".format(alpha)
    if batchSize==n: strDescType = "Gradient"
    elif batchSize==1:  strDescType = "Stochastic"
    else: strDescType = "Mini-batch ({})".format(batchSize)
    name += strDescType + " descent - Stop: "
    if stopType == STOP_ITER: strStop = "{} iterations".format(thresh)
    elif stopType == STOP_COST: strStop = "costs change < {}".format(thresh)
    else: strStop = "gradient norm < {}".format(thresh)
    name += strStop
    print ("***{}\nTheta: {} - Iter: {} - Last cost: {:03.2f} - Duration: {:03.2f}s".format(
        name, theta, iter, costs[-1], dur))
    fig, ax = plt.subplots(figsize=(12,4))
    ax.plot(np.arange(len(costs)), costs, 'r')
    ax.set_xlabel('Iterations')
    ax.set_ylabel('Cost')
    ax.set_title(name.upper() + ' - Error vs. Iteration')
    return theta
n= 100
runExpe(orig_data,theta,n,STOP_ITER,thresh=5000,alpha=0.000001)
plt.show()
runExpe(orig_data,theta,n,STOP_GRAD,thresh=0.05,alpha=0.001)
plt.show()
runExpe(orig_data,theta,n,STOP_COST,thresh=0.000001,alpha=0.001)
plt.show()
#对比
runExpe(orig_data, theta, 1, STOP_ITER, thresh=5000, alpha=0.001)
plt.show()
runExpe(orig_data, theta, 1, STOP_ITER, thresh=15000, alpha=0.000002)
plt.show()
runExpe(orig_data, theta, 16, STOP_ITER, thresh=15000, alpha=0.001)
plt.show()
##对数据进行标准化 将数据按其属性(按列进行)减去其均值，然后除以其方差。
#最后得到的结果是，对每个属性/每列来说所有数据都聚集在0附近，方差值为1
 
from sklearn import preprocessing as pp
 
scaled_data = orig_data.copy()
scaled_data[:, 1:3] = pp.scale(orig_data[:, 1:3])
 
runExpe(scaled_data, theta, n, STOP_ITER, thresh=5000, alpha=0.001)
#设定阈值
def predict(X, theta):
    return [1 if x >= 0.5 else 0 for x in model(X, theta)]
 
# if __name__=='__main__':
 
scaled_X = scaled_data[:, :3]
y = scaled_data[:, 3]
predictions = predict(scaled_X, theta)
correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]
accuracy = (sum(map(int, correct)) % len(correct))
print ('accuracy = {0}%'.format(accuracy))