1.获取数据
对于想深入了解线性回归的童鞋，这里给出一个完整的例子，详细学完这个例子，对用scikit-learn来运行线性回归，评估模型不会有什么问题了。
获取数据，定义问题 这里我们用UCI大学公开的机器学习数据来跑线性回归。
数据的介绍在这： http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant
数据的下载地址在这： http://archive.ics.uci.edu/ml/machine-learning-databases/00294/
里面是一个循环发电场的数据，共有9568个样本数据，每个数据有5列，分别是:AT（温度）, V（压力）, AP（湿度）, RH（压强）, PE（输出电力)。 我们的问题是得到一个线性的关系，对应PE是样本输出，而AT/V/AP/RH这4个是样本特征， 机器学习的目的就是得到一个线性回归模型，即:
PE=θ0+θ1∗AT+θ2∗V+θ3∗AP+θ4∗RHPE=θ0+θ1∗AT+θ2∗V+θ3∗AP+θ4∗RH 而需要学习的，就是θ0,θ1,θ2,θ3,θ4θ0,θ1,θ2,θ3,θ4这5个参数。
#coding:utf-8 import numpy as np from sklearn.utils import shuffle import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet from sklearn import metrics diabetes = np.loadtxt("F:\data\CCPP\Folds5x2_pp.csv",delimiter = ',',dtype=float,skiprows=1) print(diabetes) data = diabetes[:,0:4] # print(data) target = diabetes[:,4] # print(target) # 打乱数据 x, y = shuffle(data, target, random_state=13) print(x) print(y) offset = int(x.shape[0] * 0.9) print("offset:%s" % offset) x_train, y_train = x[:offset], y[:offset] print("x_train.shape[1]:%s"%x_train.shape[1]) print("x_train.shape[0]:%s"%x_train.shape[0]) print("y_train.shape[0]:%s"%y_train.shape[0]) x_test, y_test = x[offset:], y[offset:] y_train = y_train.reshape(-1, 1) y_test = y_test.reshape(-1, 1) #建立模型 model= LinearRegression() # model = Ridge(alpha=10) # model = Lasso() # model = ElasticNet() #开始训练 model.fit(x_train,y_train) #测试 predict = model.predict(x_test) print('predict:',predict) print('y_test:',y_test) num=0 for i in range(len(y_test)): if abs(predict[i] - y_test[i]) <5 : num +=1 # print("num:",num) print("预测率：%s"%(num/len(y_test))) print ("MSE:",metrics.mean_squared_error(y_test, predict)) print ("RMSE:",np.sqrt(metrics.mean_squared_error(y_test, predict))) #结果显示 # w=[] print("coefficients:",model.coef_) w = model.coef_[0] print("intercept:",model.intercept_) b = model.intercept_ # f = b + w[0]*x_train[:,0] +w[1]*x_train[:,1]+ # f = b + w*x_train # print(f)运算结果如下： diabetes
[[ 8.34 40.77 1010.84 90.01 480.48] [ 23.64 58.49 1011.4 74.2 445.75] [ 29.74 56.9 1007.15 41.91 438.76] ..., [ 15.99 43.34 1014.2 78.66 465.96] [ 17.65 59.87 1018.58 94.65 450.93] [ 23.68 51.3 1011.86 71.24 451.67]]x_train
[[ 28.77 58.33 1013.73 36.6 ] [ 13.42 41.74 1020.96 61.8 ] [ 6.17 39.33 1012.57 93.32] ..., [ 5.17 39.33 1009.68 94.19] [ 10.25 41.46 1018.67 84.41] [ 18.32 45. 1022.67 46.38]]y_train
[ 448.06 473.45 491.54 ..., 485.46 479.28 471.43]offset:8611 x_train.shape[1]:4 x_train.shape[0]:8611 y_train.shape[0]:8611
预测率：0.7439916405433646 MSE: 18.7396222877 RMSE: 4.32892853807 coefficients: [[-1.97273028 -0.23312582 0.06450914 -0.15567916]] intercept: [ 451.86222609]
方法2： #coding:utf-8 import numpy as np from sklearn.utils import shuffle import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet from sklearn import metrics import pandas as pd from sklearn.cross_validation import train_test_split
data = pd.read_csv("F:\data\CCPP\Folds5x2_pp.csv") x=data[['AT','V','AP','RH']] # print(x.head()) y=data[['PE']] print(y.head()) x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=1) print(x_train.shape) print(y_train.shape) print(x_test.shape) #建立模型 model= LinearRegression() # model = Ridge(alpha=10) # model = Lasso() # model = ElasticNet() #开始训练 model.fit(x_train,y_train) #测试 predict = model.predict(x_test) y_test=np.array(y_test)#将DataFrame转换为数组array print('y_test:',type(y_test)) print('y_test:',y_test) num=0 for i in range(len(y_test)): if abs(predict[i] - y_test[i]) <5 : num +=1 # print("num:",num) print("预测率：%s"%(num/len(y_test))) print ("MSE:",metrics.mean_squared_error(y_test, predict)) print ("RMSE:",np.sqrt(metrics.mean_squared_error(y_test, predict))) #结果显示 print("coefficients:",model.coef_) w = model.coef_[0] print("intercept:",model.intercept_) b = model.intercept_预测率：0.7328595317725752 MSE: 20.0804012021 RMSE: 4.48111606657 coefficients: [[-1.97376045 -0.23229086 0.0693515 -0.15806957]] intercept: [ 447.06297099]
注意：#将DataFrame转换为数组array的方法，参考如下文档： https://blog.csdn.net/weixin_42263508/article/details/90487483
本算法参考了如下文档： https://blog.csdn.net/lai19941994/article/details/81748660
