Python数据处理课程设计-房屋价格预测-代码

代码部分如下所示：

#!/usr/bin/env python
# coding: utf-8# ## 导包# In[1]:import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling as ppf #探索性数据分析（EDA）
import warnings##忽略警告
warnings.filterwarnings('ignore')
get_ipython().run_line_magic('matplotlib', 'inline')
plt.style.use('ggplot')# In[2]:from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.preprocessing import LabelEncoder#标签编码
from sklearn.preprocessing import RobustScaler, StandardScaler#去除异常值与数据标准化
from sklearn.pipeline import Pipeline, make_pipeline#构建管道
from scipy.stats import skew#偏度
from sklearn.impute import SimpleImputer# ## 读取并查看原数据# In[3]:train = pd.read_csv(r"G:\study类\大三上\机器学习\课程设计\datas\train.csv") #将数据读取进来# In[4]:test = pd.read_csv(r"G:\study类\大三上\机器学习\课程设计\datas\test.csv") #将数据读取进来# In[5]:train.head()#默认显示前五行# In[6]:test.head()从中可以看出还是有很多数据需要处理的
# ## 数据探索性分析 pandas_profiling# In[7]:ppf.ProfileReport(train)# In[8]:train.YearBuilt#显示这一列的数据# In[9]:train.SalePrice# ## 通过箱型图查看异常值，离群点# In[10]:plt.figure(figsize=(12,8))
sns.boxplot(train.YearBuilt, train.SalePrice)# ## 通过散点图来观察存在线型的关系# In[11]:plt.figure(figsize=(12,6))
plt.scatter(x=train.TotalBsmtSF, y=train.SalePrice)
plt.xlabel("TotalBsmtSF", fontsize=13)
plt.ylabel("SalePrice", fontsize=13)
plt.ylim(0,800000)# In[12]:train.drop(train[(train["TotalBsmtSF"]>5000)].index,inplace=True)# In[13]:plt.figure(figsize=(12,6))
plt.scatter(x=train.TotalBsmtSF, y=train.SalePrice)
plt.xlabel("TotalBsmtSF", fontsize=13)
plt.ylabel("SalePrice", fontsize=13)
plt.ylim(0,800000)# In[14]:plt.figure(figsize=(12,6))
plt.scatter(x=train.GrLivArea, y=train.SalePrice)
plt.xlabel("GrLivArea", fontsize=13)
plt.ylabel("SalePrice", fontsize=13)
plt.ylim(0,800000)# ## 把太偏离线性的那些数据给去掉，把对应的索引给删掉# In[15]:train.drop(train[(train["GrLivArea"]>4000)&(train["SalePrice"]<300000)].index,inplace=True)删除后的图像
# In[16]:plt.figure(figsize=(12,6))
plt.scatter(x=train.GrLivArea, y=train.SalePrice)
plt.xlabel("GrLivArea", fontsize=13)
plt.ylabel("SalePrice", fontsize=13)
plt.ylim(0,800000)# ### 把test数据也做相同的处理# In[17]:full = pd.concat([train,test],ignore_index=True)# ### 因为ID列和索引值都相同，故这里把ID列给删掉# In187]:full.drop("Id",axis=1,inplace=True)# In[19]:full.head()#查看删除列之后的值# In[20]:full.info()#查看删除后的数据信息# # 数据清洗--空值的填充、删除# #### 查看缺失值，并且缺失的个数要从高到低排序# In[21]:miss = full.isnull().sum()            #统计出空值的个数pd.set_option('display.max_rows', None) # In[22]:miss[miss>0]# In[23]:miss[miss>0].sort_values(ascending=True)                                           #由低到高进行排序# In[24]:full.info()                                                                                                      #查看数据信息# ## 空值的填充与删除# 对字符类型的进行填充# In[25]:cols1 = ["PoolQC" , "MiscFeature", "Alley", "Fence", "FireplaceQu", "GarageQual", "GarageCond", "GarageFinish", "GarageYrBlt", "GarageType", "BsmtExposure", "BsmtCond", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "MasVnrType"]
for col in cols1:full[col].fillna("None",inplace=True)# In[26]:full.head()# 对数值类型的进行填充# In[27]:cols=["MasVnrArea", "BsmtUnfSF", "TotalBsmtSF", "GarageCars", "BsmtFinSF2", "BsmtFinSF1", "GarageArea"]
for col in cols:full[col].fillna(0, inplace=True)# 对lotfrontage的空值使用其均值进行填充# In[28]:full["LotFrontage"].fillna(np.mean(full["LotFrontage"]),inplace=True)# 对下面的列使用众数进行填充# In[29]:cols2 = ["MSZoning", "BsmtFullBath", "BsmtHalfBath", "Utilities", "Functional", "Electrical", "KitchenQual", "SaleType","Exterior1st", "Exterior2nd"]
for col in cols2:full[col].fillna(full[col].mode()[0], inplace=True)# 查看是否还有未填充好的数据# In[30]:full.isnull().sum()[full.isnull().sum()>0]发现只有test的没有标签列，故已经把数据中的空值处理好了
# ## 数据预处理--把字符变成数值型# In[31]:full["MSZoning"].mode()[0]# In[32]:pd.set_option('display.max_rows', None)  # 设置显示最大行，不然有一些数据会以“...”显示，不能看到部分数据
full.MSZoning从上面可以发现有一些数据，比如31行：C（all），需要把这些数据转换成字符串的形式，将一些数字特征转换为类别特征,使用LabelEncoder来实现
# In[33]:for col in cols2:full[col]=full[col].astype(str)##astype来进行数据转换成字符串类型# In[34]:lab = LabelEncoder() #对不连续的数字或者文本进行编号# #### 把下列内容字符型转换为数字型# In[35]:full["Alley"] = lab.fit_transform(full.Alley)
full["PoolQC"] = lab.fit_transform(full.PoolQC)
full["MiscFeature"] = lab.fit_transform(full.MiscFeature)
full["Fence"] = lab.fit_transform(full.Fence)
full["FireplaceQu"] = lab.fit_transform(full.FireplaceQu)
full["GarageQual"] = lab.fit_transform(full.GarageQual)
full["GarageCond"] = lab.fit_transform(full.GarageCond)
full["GarageFinish"] = lab.fit_transform(full.GarageFinish)
full["GarageYrBlt"] = full["GarageYrBlt"].astype(str)
full["GarageYrBlt"] = lab.fit_transform(full.GarageYrBlt)
full["GarageType"] = lab.fit_transform(full.GarageType)
full["BsmtExposure"] = lab.fit_transform(full.BsmtExposure)
full["BsmtCond"] = lab.fit_transform(full.BsmtCond)
full["BsmtQual"] = lab.fit_transform(full.BsmtQual)
full["BsmtFinType2"] = lab.fit_transform(full.BsmtFinType2)
full["BsmtFinType1"] = lab.fit_transform(full.BsmtFinType1)
full["MasVnrType"] = lab.fit_transform(full.MasVnrType)
full["BsmtFinType1"] = lab.fit_transform(full.BsmtFinType1)# In[36]:full.head()# 将一些未转换的列继续转换为数字型# In[37]:full["MSZoning"] = lab.fit_transform(full.MSZoning)
full["BsmtFullBath"] = lab.fit_transform(full.BsmtFullBath)
full["BsmtHalfBath"] = lab.fit_transform(full.BsmtHalfBath)
full["Utilities"] = lab.fit_transform(full.Utilities)
full["Functional"] = lab.fit_transform(full.Functional)
full["Electrical"] = lab.fit_transform(full.Electrical)
full["KitchenQual"] = lab.fit_transform(full.KitchenQual)
full["SaleType"] = lab.fit_transform(full.SaleType)
full["Exterior1st"] = lab.fit_transform(full.Exterior1st)
full["Exterior2nd"] = lab.fit_transform(full.Exterior2nd)# In[38]:full.head()# #### 发现还有一些列是字符型，未能完全转换为数字型# In[39]:full.drop("SalePrice",axis=1,inplace=True)##删除这一列，以便后面进行操作# #### 从结果可以看出，行和列变得很多了# #### 可以看到所有数据都显示为数字型了# In[40]:##自己写一个转换函数
class labelenc(BaseEstimator, TransformerMixin):def __init__(self):passdef fit(self,X,y=None):return selfdef transform(self,X):lab=LabelEncoder()X["YearBuilt"] = lab.fit_transform(X["YearBuilt"])X["YearRemodAdd"] = lab.fit_transform(X["YearRemodAdd"])X["GarageYrBlt"] = lab.fit_transform(X["GarageYrBlt"])X["BldgType"] = lab.fit_transform(X["BldgType"])return X# In[41]:#写一个转换函数
class skew_dummies(BaseEstimator, TransformerMixin):def __init__(self,skew=0.5):#偏度self.skew = skewdef fit(self,X,y=None):return selfdef transform(self,X):X_numeric=X.select_dtypes(exclude=["object"])#而是去除了包含了对象数据类型，取出来绝大部分是数值型，取出字符类型的数据skewness = X_numeric.apply(lambda x: skew(x))#匿名函数，做成字典的形式skewness_features = skewness[abs(skewness) >= self.skew].index#通过条件来涮选出skew>=0.5的索引的条件，取到了全部数据，防止数据的丢失X[skewness_features] = np.log1p(X[skewness_features])#求对数，进一步让他更符合正态分布X = pd.get_dummies(X)##一键独热，独热编码return X# In[42]:from scipy.stats import norm
from scipy import statsdef get_dist_data(series):sns.distplot(series, fit=norm);fig = plt.figure()res = stats.probplot(series, plot=plt)#价格print("Skewness: %f" % series.skew())print("Kurtosis: %f" % series.kurt())get_dist_data(train_df['SalePrice'])# In[43]:#价格对数化处理
log_SalePrice = np.log(train_df['SalePrice'] + 1)
get_dist_data(log_SalePrice)# In[44]:#对数化处理
plot_no = 0
plt.figure(figsize=(18, 60))
for feature in skewed.index:plt.subplot(12, 4, plot_no + 1)sns.distplot(df[feature], kde = True, fit=norm, color = "purple")plt.title("Before", fontsize = 20)plt.subplot(12, 4, plot_no + 2)sns.distplot(df_temp[feature], kde = True, fit=norm, color = "green")plt.title("After", fontsize = 20)plot_no += 2plt.tight_layout()# In[45]:# 构建管道
pipe = Pipeline([#构建管道('labenc', labelenc()),('skew_dummies', skew_dummies(skew=2)),])# In[46]:# 保存原来的数据以备后用，为了防止写错
full2 = full.copy()# In[47]:pipeline_data = pipe.fit_transform(full2)# In[48]:pipeline_data.shape# In[49]:pipeline_data.head()# In[50]:from sklearn.linear_model import Lasso    #运用算法来进行训练以得到特征的重要性
lasso=Lasso(alpha=0.001)
lasso.fit(X_scaled,y_log)# In[51]:FI_lasso = pd.DataFrame({"Feature Importance":lasso.coef_}, index=pipeline_data.columns)               #索引和重要性做成dataframe形式# In[52]:FI_lasso.sort_values("Feature Importance",ascending=False)#由高到低进行排序# In[53]:#可视化
FI_lasso[FI_lasso["Feature Importance"]!=0].sort_values("Feature Importance").plot(kind="barh",figsize=(15,25))#barh：把x，y轴反转
plt.xticks(rotation=90)
plt.show()#画图显示# ##  得到特征重要性图之后就可以进行特征选择与重做# In[54]:class add_feature(BaseEstimator, TransformerMixin):#定义转换函数def __init__(self,additional=1):self.additional = additionaldef fit(self,X,y=None):return selfdef transform(self,X):if self.additional==1:X["TotalHouse"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"]   X["TotalArea"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"] + X["GarageArea"]else:X["TotalHouse"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"]   X["TotalArea"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"] + X["GarageArea"]X["+_TotalHouse_OverallQual"] = X["TotalHouse"] * X["OverallQual"]X["+_GrLivArea_OverallQual"] = X["GrLivArea"] * X["OverallQual"]X["+_oMSZoning_TotalHouse"] = X["oMSZoning"] * X["TotalHouse"]X["+_oMSZoning_OverallQual"] = X["oMSZoning"] + X["OverallQual"]X["+_oMSZoning_YearBuilt"] = X["oMSZoning"] + X["YearBuilt"]X["+_oNeighborhood_TotalHouse"] = X["oNeighborhood"] * X["TotalHouse"]X["+_oNeighborhood_OverallQual"] = X["oNeighborhood"] + X["OverallQual"]X["+_oNeighborhood_YearBuilt"] = X["oNeighborhood"] + X["YearBuilt"]X["+_BsmtFinSF1_OverallQual"] = X["BsmtFinSF1"] * X["OverallQual"]X["-_oFunctional_TotalHouse"] = X["oFunctional"] * X["TotalHouse"]X["-_oFunctional_OverallQual"] = X["oFunctional"] + X["OverallQual"]X["-_LotArea_OverallQual"] = X["LotArea"] * X["OverallQual"]X["-_TotalHouse_LotArea"] = X["TotalHouse"] + X["LotArea"]X["-_oCondition1_TotalHouse"] = X["oCondition1"] * X["TotalHouse"]X["-_oCondition1_OverallQual"] = X["oCondition1"] + X["OverallQual"]X["Bsmt"] = X["BsmtFinSF1"] + X["BsmtFinSF2"] + X["BsmtUnfSF"]X["Rooms"] = X["FullBath"]+X["TotRmsAbvGrd"]X["PorchArea"] = X["OpenPorchSF"]+X["EnclosedPorch"]+X["3SsnPorch"]+X["ScreenPorch"]X["TotalPlace"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"] + X["GarageArea"] + X["OpenPorchSF"]+X["EnclosedPorch"]+X["3SsnPorch"]+X["ScreenPorch"]return X# In[55]:pipe = Pipeline([#把后面的东西加到管道里面来('labenc', labelenc()),('add_feature', add_feature(additional=2)),('skew_dummies', skew_dummies(skew=4)),])# In[56]:pipe# In[57]:n_train=train.shape[0]#训练集的行数
X = pipeline_data[:n_train]#取出处理之后的训练集
test_X = pipeline_data[n_train:]#取出n_train后的数据作为测试集
y= train.SalePrice
X_scaled = StandardScaler().fit(X).transform(X)#做转换
y_log = np.log(train.SalePrice)##这里要注意的是，更符合正态分布
#得到测试集
test_X_scaled = StandardScaler().fit_transform(test_X)# ## 模型的构建# #### 线性回归# In[58]:from sklearn.tree import DecisionTreeRegressor#导入模型# In[59]:model = DecisionTreeRegressor()# In[60]:model1 =model.fit(X_scaled,y_log)# ## 前期比较简单的处理得到结果，并没有进行模型的堆叠# In[61]:#predict = modexp.predict(test_x)# In[62]:# result=pd.DataFrame({'Id':test.Id, 'SalePrice':predict})
# result.to_csv("submission1.csv",index=False)# In[63]:# predict = np.exp(model1.predict(test_X_scaled))#np.exp是对上面的对数变换之后的反变换# In[64]:# result=pd.DataFrame({'Id':test.Id, 'SalePrice':predict})
# result.to_csv("submission.csv",index=False)# ## 模型的堆叠与集成并且选择最优参数，模型和评估方式# In[65]:from sklearn.model_selection import cross_val_score, GridSearchCV, KFold#交叉验证，网格搜索，k折验证
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.svm import SVR, LinearSVR
from sklearn.linear_model import ElasticNet, SGDRegressor, BayesianRidge
from sklearn.kernel_ridge import KernelRidge
from xgboost import XGBRegressor# In[66]:#定义交叉验证的策略，以及评估函数
def rmse_cv(model,X,y):rmse = np.sqrt(-cross_val_score(model, X, y, scoring="neg_mean_squared_error", cv=5))#交叉验证return rmse# In[67]:models = [LinearRegression(),Ridge(),Lasso(alpha=0.01,max_iter=10000),RandomForestRegressor(),GradientBoostingRegressor(),SVR(),LinearSVR(),ElasticNet(alpha=0.001,max_iter=10000),SGDRegressor(max_iter=1000,tol=1e-3),BayesianRidge(),KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5),ExtraTreesRegressor(),XGBRegressor()]#这里也是列表# In[68]:names = ["LR", "Ridge", "Lasso", "RF", "GBR", "SVR", "LinSVR", "Ela","SGD","Bay","Ker","Extra","Xgb"]#列表
for name, model in zip(names, models):score = rmse_cv(model, X_scaled, y_log)print("{}: {:.6f}, {:.4f}".format(name,score.mean(),score.std()))# In[69]:#定义交叉方式，先指定模型后指定参数，方便测试多个模型，网格交叉验证
class grid():def __init__(self,model):self.model = model#导入模型#所有模型进行验证5次def grid_get(self,X,y,param_grid):#网格参数一般做出字典的格式grid_search = GridSearchCV(self.model,param_grid,cv=5, scoring="neg_mean_squared_error")grid_search.fit(X,y)print(grid_search.best_params_, np.sqrt(-grid_search.best_score_))grid_search.cv_results_['mean_test_score'] = np.sqrt(-grid_search.cv_results_['mean_test_score'])print(pd.DataFrame(grid_search.cv_results_)[['params','mean_test_score','std_test_score']])# In[70]:grid(Lasso()).grid_get(X_scaled,y_log,{'alpha': [0.0004,0.0005,0.0007,0.0006,0.0009,0.0008],'max_iter':[10000]})# In[71]:grid(Ridge()).grid_get(X_scaled,y_log,{'alpha':[35,40,45,50,55,60,65,70,80,90]})# In[72]:grid(SVR()).grid_get(X_scaled,y_log,{'C':[11,12,13,14,15],'kernel':["rbf"],"gamma":[0.0003,0.0004],"epsilon":[0.008,0.009]})#支持向量机回归# In[73]:param_grid={'alpha':[0.2,0.3,0.4,0.5], 'kernel':["polynomial"], 'degree':[3],'coef0':[0.8,1,1.2]}#定义好的参数，用字典来表示
grid(KernelRidge()).grid_get(X_scaled,y_log,param_grid)# In[74]:grid(ElasticNet()).grid_get(X_scaled,y_log,{'alpha':[0.0005,0.0008,0.004,0.005],'l1_ratio':[0.08,0.1,0.3,0.5,0.7],'max_iter':[10000]})# In[75]:#定义加权平均值，就相当于自己写fit_transform（）
class AverageWeight(BaseEstimator, RegressorMixin):def __init__(self,mod,weight):self.mod = mod#模型的个数self.weight = weight#权重def fit(self,X,y):self.models_ = [clone(x) for x in self.mod]for model in self.models_:model.fit(X,y)return selfdef predict(self,X):w = list()pred = np.array([model.predict(X) for model in self.models_])# 针对于每一个数据点，单一的模型是乘以权重，然后加起来for data in range(pred.shape[1]):#取列数single = [pred[model,data]*weight for model,weight in zip(range(pred.shape[0]),self.weight)]w.append(np.sum(single))return w# In[76]:#指定每一个算法的参数
lasso = Lasso(alpha=0.0005,max_iter=10000)
ridge = Ridge(alpha=60)
svr = SVR(gamma= 0.0004,kernel='rbf',C=13,epsilon=0.009)
ker = KernelRidge(alpha=0.2 ,kernel='polynomial',degree=3 , coef0=0.8)
ela = ElasticNet(alpha=0.005,l1_ratio=0.08,max_iter=10000)
bay = BayesianRidge()# In[77]:#6个权重
w1 = 0.02
w2 = 0.2
w3 = 0.25
w4 = 0.3
w5 = 0.03
w6 = 0.2# In[78]:weight_avg = AverageWeight(mod = [lasso,ridge,svr,ker,ela,bay],weight=[w1,w2,w3,w4,w5,w6])# In[79]:rmse_cv(weight_avg,X_scaled,y_log),  rmse_cv(weight_avg,X_scaled,y_log).mean()#计算出交叉验证的均值# ## 模型的堆叠# In[80]:class stacking(BaseEstimator, RegressorMixin, TransformerMixin):def __init__(self,mod,meta_model):self.mod = modself.meta_model = meta_model#元模型self.kf = KFold(n_splits=5, random_state=42, shuffle=True)#5折的划分#数据集平均分成5份def fit(self,X,y):self.saved_model = [list() for i in self.mod]#用模型来进行拟合oof_train = np.zeros((X.shape[0], len(self.mod)))for i,model in enumerate(self.mod):#返回的是索引和模型本身for train_index, val_index in self.kf.split(X,y):##返回的是数据本省renew_model = clone(model)#模型的复制renew_model.fit(X[train_index], y[train_index])#对数据进行训练self.saved_model[i].append(renew_model)#把模型添加进去oof_train[val_index,i] = renew_model.predict(X[val_index])#用来预测验证集self.meta_model.fit(oof_train,y)#元模型return selfdef predict(self,X):whole_test = np.column_stack([np.column_stack(model.predict(X) for model in single_model).mean(axis=1) for single_model in self.saved_model]) #得到的是整个测试集return self.meta_model.predict(whole_test)#返回的是利用元模型来对整个测试集进行预测#预测，使整个测试集def get_oof(self,X,y,test_X):oof = np.zeros((X.shape[0],len(self.mod)))#初始化为0test_single = np.zeros((test_X.shape[0],5))#初始化为0 test_mean = np.zeros((test_X.shape[0],len(self.mod)))for i,model in enumerate(self.mod):#i是模型for j, (train_index,val_index) in enumerate(self.kf.split(X,y)):#j是所有划分好的的数据clone_model = clone(model)#克隆模块，把模型复制一下clone_model.fit(X[train_index],y[train_index])#把分割好的数据进行训练oof[val_index,i] = clone_model.predict(X[val_index])#对验证集进行预测test_single[:,j] = clone_model.predict(test_X)#对测试集进行预测test_mean[:,i] = test_single.mean(axis=1)#测试集算好均值return oof, test_mean# In[81]:#经过预处理之后才能放到堆叠的模型里面去计算a = SimpleImputer().fit_transform(X_scaled)#x
b = SimpleImputer().fit_transform(y_log.values.reshape(-1,1)).ravel()#y
# a = Imputer().fit_transform(X_scaled)#相当于x
# b = Imputer().fit_transform(y_log.values.reshape(-1,1)).ravel()#相当于y# In[82]:stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)#定义了第一层的和第二层的模型# In[83]:print(rmse_cv(stack_model,a,b))#运用了评估函数
print(rmse_cv(stack_model,a,b).mean())# In[84]:X_train_stack, X_test_stack = stack_model.get_oof(a,b,test_X_scaled)#将数据进行变换# In[85]:X_train_stack.shape, a.shape# In[86]:X_train_add = np.hstack((a,X_train_stack))
X_test_add = np.hstack((test_X_scaled,X_test_stack))
X_train_add.shape, X_test_add.shape# In[87]:print(rmse_cv(stack_model,X_train_add,b))
print(rmse_cv(stack_model,X_train_add,b).mean())# In[88]:stack_model = stacking(mod=[lasso,ridge,svr,ker,ela,bay],meta_model=ker)# In[89]:stack_model.fit(a,b)#模型进行训练# In[90]:pred = np.exp(stack_model.predict(test_X_scaled))#进行预测# In[91]:result=pd.DataFrame({'Id':test.Id, 'SalePrice':pred})
result.to_csv("submission3.csv",index=False)

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