# -*- coding: utf-8 -*-"""Created on Sat Oct 20 14:03:05 2018@author: 12958"""import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns# 忽略警告import warningswarnings.filterwarnings('ignore')# 读取训练集和测试集train = pd.read_csv('train.csv')train_len = len(train)test = pd.read_csv('test.csv')#print(train.head())#print(test.head())# 查看训练集的房价分布，左图是原始房价分布，右图是将房价对数化之后的分布all_data = pd.concat([train, test], axis = 0, ignore_index= True)all_data.drop(labels = ["SalePrice"],axis = 1, inplace = True)fig = plt.figure(figsize=(12,5))ax1 = fig.add_subplot(121)ax2 = fig.add_subplot(122)g1 = sns.distplot(train['SalePrice'],hist = True,label='skewness:{:.2f}'.format(train['SalePrice'].skew()),ax = ax1)g1.legend()g1.set(xlabel = 'Price')g2 = sns.distplot(np.log1p(train['SalePrice']),hist = True,label='skewness:{:.2f}'.format(np.log1p(train['SalePrice']).skew()),ax=ax2)g2.legend()g2.set(xlabel = 'log(Price+1)')plt.show()# 由于房价是有偏度的,将房价对数化train['SalePrice'] = np.log1p(train['SalePrice']) # 将有偏的数值特征对数化num_features_list = list(all_data.dtypes[all_data.dtypes != "object"].index)for i in num_features_list:    if all_data[i].dropna().skew() > 0.75:        all_data[i] = np.log1p(all_data[i])# 将类别数值转化为虚拟变量all_data = pd.get_dummies(all_data)# 查看缺失值print(all_data.isnull().sum())# 将缺失值用该列的均值填充all_data = all_data.fillna(all_data.mean())# 将测试集和训练集分开X_train = all_data[:train_len]X_test = all_data[train_len:]Y_train = train['SalePrice']from sklearn.linear_model import Ridge, LassoCVfrom sklearn.model_selection import cross_val_score# 定义交叉验证,用均方根误差来评价模型的拟合程度def rmse_cv(model):    rmse = np.sqrt(-cross_val_score(model, X_train, Y_train, scoring = 'neg_mean_squared_error', cv=5))    return rmse# Ridge模型model_ridge = Ridge()alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75]cv_ridge = [rmse_cv(Ridge(alpha = a)).mean() for a in alphas]cv_ridge = pd.Series(cv_ridge, index = alphas)cv_ridge# 交叉验证可视化fig = plt.figure(figsize=(8,5))cv_ridge.plot(title = 'Cross Validation Score with Model Ridge')plt.xlabel("alpha")plt.ylabel("rmse")plt.show()# 当alpha为10时,均方根误差最小cv_ridge.min()# lasso模型,均方根误差的均值更小,因此最终选择lasso模型model_lasso = LassoCV(alphas = [1, 0.1, 0.001, 0.0005]).fit(X_train, Y_train)rmse_cv(model_lasso).mean()# 查看模型系数, lasso模型能选择特征,将不重要的特征系数设置为0coef = pd.Series(model_lasso.coef_, index = X_train.columns)print("Lasso picked {} variables and eliminated the other {} variables".format(sum(coef != 0), sum(coef==0)))# 查看重要的特征, GrLivArea地上面积是最重要的正相关特征imp_coef = pd.concat([coef.sort_values().head(10),coef.sort_values().tail(10)])fig = plt.figure(figsize=(6,8))imp_coef.plot(kind = "barh")plt.title("Coefficients in the Lasso Model")plt.show()# 查看残差est = pd.DataFrame({
   "est":model_lasso.predict(X_train), "true":Y_train})plt.rcParams["figure.figsize"] = [6,6]est["resi"] = est["true"] - est["est"]est.plot(x = "est", y = "resi",kind = "scatter")plt.show()# xgboost模型import xgboost as xgbdtrain = xgb.DMatrix(X_train, label = Y_train)dtest = xgb.DMatrix(X_test)# 交叉验证params = {
   "max_depth":2, "eta":0.1}cv_xgb = xgb.cv(params, dtrain,  num_boost_round=500, early_stopping_rounds=100)cv_xgb.loc[30:,["test-rmse-mean", "train-rmse-mean"]].plot()plt.show()# 训练模型model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1) model_xgb.fit(X_train, Y_train)'''XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,       max_depth=2, min_child_weight=1, missing=None, n_estimators=360,       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,       silent=True, subsample=1)'''# 查看两种模型的预测结果, 将结果指数化lasso_preds = np.expm1(model_lasso.predict(X_test))xgb_preds = np.expm1(model_xgb.predict(X_test))predictions = pd.DataFrame({
   "xgb":xgb_preds, "lasso":lasso_preds})predictions.plot(x = "xgb", y = "lasso", kind = "scatter")plt.show()# 最终结果采用两种模型预测的加权平均值,提交结果preds = 0.7*lasso_preds + 0.3*xgb_predsresult = pd.DataFrame({
   "id":test.Id, "SalePrice":preds})result.to_csv('result.csv', index = False)

需要实验数据的请留言哦