sklearn实现多分类逻辑回归

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2018/4/4 19:22
# @Author  : HJH
# @Site    : 
# @File    : mul_logistics.py
# @Software: PyCharm


import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from numpy import *
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pickle


#从文件中加载数据：特征X，标签label
def loadDataSet():
    digits=load_iris()
    X_train = digits.data[:-20,:]
    y_train = digits.target[:-20]
    # print(y_train.shape)
    X_test = digits.data[-20:,:]
    y_test =digits.target[-20:]
    return X_train,y_train,X_test,y_test

def plot():
    fig = plt.figure()
    data = load_iris()
    x_index = 0
    y_index = 1
    colors = ['blue', 'red','green']
    # plt.subplot(211)
    for label, color in zip(range(len(data.target_names)), colors):
        plt.scatter(data.data[data.target == label, x_index],
                    data.data[data.target == label, y_index],
                    label=data.target_names[label],
                    c=color)
    plt.xlabel(data.feature_names[x_index])
    plt.ylabel(data.feature_names[y_index])
    plt.legend(loc='upper left')

    plt.show()

def main():
    X_train, y_train, X_test, y_test=loadDataSet()
    lr_model=LogisticRegression()
    lr_model.fit(X_train,y_train)
    y_pred=lr_model.predict(X_test)
    print(accuracy_score(y_test,y_pred))
    with open('./log.pkl','wb') as f:
        pickle.dump(lr_model,f)

    # digits = load_iris()
    # with open('./log.pkl','rb') as f:
    #     model=pickle.load(f)
    # random_index=np.random.randint(0,100,5)
    # random_samples=digits.data[random_index,:]
    # random_targets=digits.target[random_index]
    # random_pred=model.predict(random_samples)
    # print(random_pred)
    # print(random_targets)


if __name__=='__main__':
    main()
    plot()