python3 learn to use api
Linear regression, and random parameter regression
git: https://github.com/linyi0604/MachineLearning
1 from sklearn.datasets import load_boston 2 from sklearn.cross_validation import train_test_split 3 from sklearn.preprocessing import StandardScaler 4 from sklearn.linear_model import LinearRegression, SGDRegressor 5 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error 6 import numpy as np 7 8 # 1 准备数据 9 # 读取波士顿地区房价信息 10 boston =load_boston() 11 #View the data description 12 # print(boston.DESCR) # A total of 506 pieces of housing price information in the Boston area, each with 13 numerical feature descriptions and the target house price 13 #Check the difference between the data 14 # print("Maximum house price: ", np.max(boston.target)) # 50 15 # print("Minimum house price:",np.min(boston.target)) # 5 16 # print("Average house price:", np.mean(boston. target)) # 22.532806324110677 17 18 x = boston.data 19 y = boston.target 20 21 # 2 split training data and test data 22 # randomly sample 25% as test 75% as training 23 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33 ) 24 25 26 # 3 Normalize training data and test data 27 ss_x = StandardScaler() 28 x_train = ss_x.fit_transform(x_train) 29 x_test = ss_x.transform(x_test) 30 31 ss_y = StandardScaler() 32 y_train = ss_y.fit_transform(y_train.reshape(-1, 1 )) 33 y_test = ss_y.transform(y_test.reshape(-1, 1 )) 34 35 # 4 Use two linear regression models for training and prediction 36 #Initialize LinearRegression linear regression model 37lr = LinearRegression () 38 #training 39 lr.fit (x_train, y_train) 40 #predict and save the prediction result 41 lr_y_predict = lr.predict(x_test) 42 43 #initialize SGDRRegressor stochastic gradient regression model 44 sgdr = SGDRegressor () 45 #training 46 sgdr.fit(x_train, y_train) 47 #Predict and save the prediction result 48 sgdr_y_predict = sgdr.predict(x_test) 49 50 # 5 Model evaluation 51 #Evaluate the Linear model 52 lr_score = lr.score(x_test, y_test) 53 print ( " The default evaluation value for Linear is: " , lr_score) 54 lr_R_squared = r2_score(y_test, lr_y_predict) 55 print ( " The R_squared value for Linear is: " , lr_R_squared) 56 lr_mse = mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_predict)) 57 print ( " Linear mean square error is: " , lr_mse) 58 lr_mae = mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(lr_y_predict)) 59 print ( " Linear mean absolute error for: ", lr_mae) 60 61 #Evaluate the SGD model 62 sgdr_score = sgdr.score(x_test, y_test) 63 print ( " The default evaluation value of SGD is: " , sgdr_score) 64 sgdr_R_squared = r2_score(y_test, sgdr_y_predict) 65 print ( " SGD The R_squared value is: " , sgdr_R_squared) 66 sgdr_mse = mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict)) 67 print ( " The mean squared error of SGD is: " , sgdr_mse) 68 sgdr_mae =mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict)) 69 print ( " The mean absolute error of SGD is: " , sgdr_mae) 70 71 ''' 72 Linear's default evaluation value: 0.6763403830998702 73 Linear's R_squared value is: 0.6763403830998701 74 Linear mean square error: 25.09698569206773 75 Linear mean absolute error is: 3.5261239963985433 76 77 default evaluation value of SGD: .659795654161198 78 SGD values of R_squared: .659795654161198 79 SGD mean square error: 26.379885392159494 80 SGD The mean absolute error is: 3.5094445431026413 81 '''