基于线性回归实现波士顿房价预测.zip

from sklearn.datasets import load_boston import matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import train_test_split from sklearn import linear_model dataset = load_boston() x_data = dataset.data y_data = dataset.target name_data = dataset.feature_names for i in range(13): # 13是name_data的维数，你可以在上一步中将name_data打印出来查看 plt.subplot(7, 2, i+1) # 7行2列第i+1个图 plt.scatter(x_data[:,i], y_data, s = 10) # 横纵坐标和点的大小 plt.title(name_data[i]) plt.show() print(name_data[i], np.corrcoef(x_data[:,i], y_data)) i_ = [] for i in range(len(y_data)): if y_data[i] == 50: i_.append(i) # 存储房价等于50 的异常值下标 x_data = np.delete(x_data, [i], axis = 0) # 删除样本异常值数据 y_data = np.delete(y_data, [i], axis = 0) # 删除标签异常值 me_data = dataset.feature_names j_ = [] for i in range(13): if x_data[i] == 'RM' or 'PTRATIO' or 'LSTAT': continue j_.append(i) # 存储其他次要特征下标 x_data = np.delete(x_data, j_, axis = 1) # 在总特征中删除次要特征 print(np.shape(y_data)) print(np.shape(x_data)) X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, random_state = 0, test_size = 0.20) ''' print(len(X_train)) print(len(X_test)) print(len(y_train)) print(len(y_test)) ''' from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() X_train = min_max_scaler.fit_transform(X_train) X_test = min_max_scaler.fit_transform(X_test) #标签归一化的目的是什么呢，实验证明，归一化之后结果好了0.1左右 y_train = min_max_scaler.fit_transform(y_train.reshape(-1,1)) #转化为任意行一列 y_test = min_max_scaler.fit_transform(y_test.reshape(-1,1)) #转化为一列 lr = linear_model.LinearRegression(fit_intercept=True, normalize=False) lr.fit(X_train, y_train) lr_y_predict = lr.predict(X_test) from sklearn.metrics import r2_score score_lr = r2_score(y_test,lr_y_predict) from sklearn.linear_model import RidgeCV rr = RidgeCV(alphas=np.array([.1, .2, .3, .4])) rr.fit(X_train,y_train) rr_y_predict = rr.predict(X_test) score_rr = r2_score(y_test,rr_y_predict) score_rr lassr = linear_model.Lasso(alpha=.0001) lassr.fit(X_train,y_train) lassr_y_predict=lassr.predict(X_test) score_lassr = r2_score(y_test,lassr_y_predict) print(score_lassr) from sklearn.svm import SVR svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1) #高斯核 svr_lin = SVR(kernel='linear', C=100, gamma='auto') #线性核 svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1, coef0=1) #径向基核函数 svr_rbf_y_predict=svr_rbf.fit(X_train, y_train).predict(X_test) score_svr_rbf = r2_score(y_test,svr_rbf_y_predict) svr_lin_y_predict=svr_lin.fit(X_train, y_train).predict(X_test) score_svr_lin = r2_score(y_test,svr_lin_y_predict) svr_poly_y_predict=svr_poly.fit(X_train, y_train).predict(X_test) score_svr_poly = r2_score(y_test,svr_poly_y_predict) #绘制真实值和预测值对比图 def draw_infer_result(groud_truths,infer_results): title='Boston' plt.title(title, fontsize=24) x = np.arange(-0.2,2) y = x plt.plot(x, y) plt.xlabel('ground truth', fontsize=14) plt.ylabel('infer result', fontsize=14) plt.scatter(groud_truths, infer_results,color='green',label='training cost') plt.grid() plt.show() draw_infer_result(y_test,lr_y_predict) draw_infer_result(y_test,rr_y_predict) draw_infer_result(y_test,lassr_y_predict) draw_infer_result(y_test,svr_rbf_y_predict) draw_infer_result(y_test,svr_lin_y_predict) draw_infer_result(y_test,svr_poly_y_predict) print("score of lr:",score_lr) print("score of rr:",score_rr) print("score of lassr:",score_lassr) print("score of svr_rbf:",score_svr_rbf) print("score of svr_lin:",score_svr_lin) print("score of svr_poly:",score_svr_poly)