main1（波士顿房价预测）.zip

# 波士顿房价预测 # 防止警告出现 import warnings warnings.filterwarnings("ignore") # 引入数据基础包 import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import pandas as pd import scipy.stats as st import seaborn as sns ## 设置属性防止画面出现乱码 mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False # 引入机器学习，预处理，模型选择，评估指标 from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.metrics import r2_score # 引入本次试验数据集 from sklearn.datasets import load_boston # 引入算法 from sklearn.linear_model import RidgeCV, LassoCV, LinearRegression, ElasticNet # 对比SVC，是svm的回归形式 from sklearn.svm import SVR # 集成算法 from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from xgboost import XGBRegressor # 载入房价数据集 boston = load_boston() # x是特征，y是标签 x = boston.data y = boston.target # 查看相关属性 print('特征的列名') print(boston.feature_names) print("样本数据量:%d, 特征个数:%d" % x.shape) print("target样本数据量:%d" % y.shape[0]) # 转化为dataframe的形式 x = pd.DataFrame(boston.data, columns=boston.feature_names) x.head() # 对标签的分布进行可视化 sns.distplot(tuple(y), kde=False, fit=st.norm) # 数据分割 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=28) # 数据集标准化 ss = StandardScaler() x_train = ss.fit_transform(x_train) x_test = ss.transform(x_test) x_train[0:100] # 模型的名字 names = ['LinerRegression', 'Ridge', 'Random Forrest', 'GBDT', 'Support Vector Regression', 'ElasticNet', 'XgBoost'] # 定义模型 models = [LinearRegression(), RidgeCV(alphas=(0.001,0.1,1),cv=3), # 这里的cv指的是交叉验证的思想 LassoCV(alphas=(0.001,0.1,1),cv=5), RandomForestRegressor(n_estimators=10), GradientBoostingRegressor(n_estimators=30), SVR(), ElasticNet(alpha=0.001,max_iter=10000), XGBRegressor()] # 输出所有回归模型的R2评分 # 定义R2评分的函数 def R2(model,x_train,x_test,y_train,y_test): model_fittesd = model.fit(x_train,y_train) y_pred = model_fittesd.predict(x_test) score = r2_score(y_test,y_pred) return score # 遍历所有模型进行评分 for name,model in zip(names,models): score = R2(model,x_train,x_test,y_train,y_test) print("{}:{:.6f},{:.4f}".format(name,score.mean(),score.std())) # 模型构建 ''' 'kernel': 核函数 'C':SVR的正则化因子, 'gamma':'rbf','poly'and'sigmoid'核函数的系数，影响模型性能 ''' parameters={ 'kernel':['linear','rbf'], 'C':[0.1,0.5,0.9,1.5], 'gamma':[0.001,0.01,0.1,1] } # 使用网格搜索，以及交叉验证 model = GridSearchCV(SVR(),param_grid=parameters,cv=3) model.fit(x_train,y_train) ## 获取最优参数 print("最优参数列表:",model.best_params_) print("最优模型:",model.best_estimator_) print("最优R2值:",model.best_score_) ## 可视化 ln_x_test = range(len(x_test)) y_predict = model.predict(x_test) # 设置画布 plt.figure(figsize=(16,8),facecolor='W') # 用红线画图 plt.plot(ln_x_test,y_test,'r-',lw=2,label=u'真实值') # 用绿线画图 plt.plot(ln_x_test,y_predict,'g-',lw = 3,label=u'SVR算法估值，$R^2$=%.3f' % (model.best_score_)) # 图形显示 plt.legend(loc = 'upper left') plt.grid(True) plt.title(u"波士顿房价价格预测(SVM)") plt.xlim(0, 101) plt.show()