写在前面：最近因为手头上的事情比较多，关于模型融合并没有太深入的研究，这里仅介绍下模型融合大致的内容。
1 模型融合目标
- 对于多种调参完成的模型进行模型融合。
- 完成对于多种模型的融合，提交融合结果并打卡。
2 内容介绍
2.1 stacking（参考链接:https://blog.csdn.net/maqunfi/article/details/82220115）
Stacking的思想是一种有层次的融合模型，比如我们将用不同特征训练出来的三个GBDT模型进行融合时，我们会将三个GBDT作为基层模型，在其上在训练一个次学习器（通常为线性模型LR）,用于组织利用基学习器的答案，也就是将基层模型的答案作为输入，让次学习器学习组织给基层模型的答案分配权重。
假设我们有三个基模型M1,M2,M3和一个元模型M4, 有训练集train和测试集test，我们进行下面的操作(为了便于理解，我这里还给出了代码的形式)：
用训练集train训练基模型M1(M1.fit(train))， 然后分别在train和test上做预测， 得到P1(M1.predict(train))和T1(M1.predict(test)
用训练集train训练基模型M2(M2.fit(train))， 然后分别在train和test上做预测， 得到P2(M2.predict(train))和T2(M2.predict(test)
用训练集train训练基模型M3(M3.fit(train))， 然后分别在train和test上做预测， 得到P3(M3.predict(train))和T3(M3.predict(test)
Stacking本质上就是这么直接的思路，但是直接这样有时对于如果训练集和测试集分布不那么一致的情况下是有一点问题的，其问题在于用初始模型训练的标签再利用真实标签进行再训练，毫无疑问会导致一定的模型过拟合训练集，这样或许模型在测试集上的泛化能力或者说效果会有一定的下降，因此现在的问题变成了如何降低再训练的过拟合性，这里我们一般有两种方法。
- 次级模型尽量选择简单的线性模型
- 利用K折交叉验证
2.2 代码示例
（1）简单加权平均，结果直接融合
## 生成一些简单的样本数据，test_prei 代表第i个模型的预测值
test_pre1 = [1.2, 3.2, 2.1, 6.2]
test_pre2 = [0.9, 3.1, 2.0, 5.9]
test_pre3 = [1.1, 2.9, 2.2, 6.0]
# y_test_true 代表第模型的真实值
y_test_true = [1, 3, 2, 6]
import numpy as np
import pandas as pd
## 定义结果的加权平均函数
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):
Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)
return Weighted_result
from sklearn import metrics
# 各模型的预测结果计算MAE
print('Pred1 MAE:',metrics.mean_absolute_error(y_test_true, test_pre1))
print('Pred2 MAE:',metrics.mean_absolute_error(y_test_true, test_pre2))
print('Pred3 MAE:',metrics.mean_absolute_error(y_test_true, test_pre3))
预测的MAE结果：
Pred1 MAE: 0.175
Pred2 MAE: 0.075
Pred3 MAE: 0.1
如果带有权重计算：
## 根据加权计算MAE
w = [0.3,0.4,0.3] # 定义比重权值
Weighted_pre = Weighted_method(test_pre1,test_pre2,test_pre3,w)
print('Weighted_pre MAE:',metrics.mean_absolute_error(y_test_true, Weighted_pre))
Weighted_pre MAE: 0.0575
（2） stacking融合(回归)：
from sklearn import linear_model
def Stacking_method(train_reg1,train_reg2,train_reg3,y_train_true,test_pre1,test_pre2,test_pre3,model_L2= linear_model.LinearRegression()):
model_L2.fit(pd.concat([pd.Series(train_reg1),pd.Series(train_reg2),pd.Series(train_reg3)],axis=1).values,y_train_true)
Stacking_result = model_L2.predict(pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).values)
return Stacking_result
## 生成一些简单的样本数据，test_prei 代表第i个模型的预测值
train_reg1 = [3.2, 8.2, 9.1, 5.2]
train_reg2 = [2.9, 8.1, 9.0, 4.9]
train_reg3 = [3.1, 7.9, 9.2, 5.0]
# y_test_true 代表第模型的真实值
y_train_true = [3, 8, 9, 5]
test_pre1 = [1.2, 3.2, 2.1, 6.2]
test_pre2 = [0.9, 3.1, 2.0, 5.9]
test_pre3 = [1.1, 2.9, 2.2, 6.0]
# y_test_true 代表第模型的真实值
y_test_true = [1, 3, 2, 6]
model_L2= linear_model.LinearRegression()
Stacking_pre = Stacking_method(train_reg1,train_reg2,train_reg3,y_train_true,
test_pre1,test_pre2,test_pre3,model_L2)
print('Stacking_pre MAE:',metrics.mean_absolute_error(y_test_true, Stacking_pre))
Stacking_pre MAE: 0.0421348314607
可以发现模型结果相对于之前有进一步的提升，这是我们需要注意的一点是，对于第二层Stacking的模型不宜选取的过于复杂，这样会导致模型在训练集上过拟合，从而使得在测试集上并不能达到很好的效果。
2.2 分类模型融合：
对于分类，同样可以使用融合方法，比如简单投票，Stacking...
1）Voting投票机制：
Voting即投票机制，分为软投票和硬投票两种，其原理采用少数服从多数的思想。
from sklearn.datasets import make_blobs
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons
from sklearn.metrics import accuracy_score,roc_auc_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
'''
硬投票：对多个模型直接进行投票，不区分模型结果的相对重要度，最终投票数最多的类为最终被预测的类。
'''
iris = datasets.load_iris()
x=iris.data
y=iris.target
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)
clf1 = XGBClassifier(learning_rate=0.1, n_estimators=150, max_depth=3, min_child_weight=2, subsample=0.7,
colsample_bytree=0.6, objective='binary:logistic')
clf2 = RandomForestClassifier(n_estimators=50, max_depth=1, min_samples_split=4,
min_samples_leaf=63,oob_score=True)
clf3 = SVC(C=0.1)
# 硬投票
eclf = VotingClassifier(estimators=[('xgb', clf1), ('rf', clf2), ('svc', clf3)], voting='hard')
for clf, label in zip([clf1, clf2, clf3, eclf], ['XGBBoosting', 'Random Forest', 'SVM', 'Ensemble']):
scores = cross_val_score(clf, x, y, cv=5, scoring='accuracy')
print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))
硬投票的结果：
Accuracy: 0.97 (+/- 0.02) [XGBBoosting]
Accuracy: 0.33 (+/- 0.00) [Random Forest]
Accuracy: 0.95 (+/- 0.03) [SVM]
Accuracy: 0.94 (+/- 0.04) [Ensemble]
'''
软投票：和硬投票原理相同，增加了设置权重的功能，可以为不同模型设置不同权重，进而区别模型不同的重要度。
'''
x=iris.data
y=iris.target
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)
clf1 = XGBClassifier(learning_rate=0.1, n_estimators=150, max_depth=3, min_child_weight=2, subsample=0.8,
colsample_bytree=0.8, objective='binary:logistic')
clf2 = RandomForestClassifier(n_estimators=50, max_depth=1, min_samples_split=4,
min_samples_leaf=63,oob_score=True)
clf3 = SVC(C=0.1, probability=True)
# 软投票
eclf = VotingClassifier(estimators=[('xgb', clf1), ('rf', clf2), ('svc', clf3)], voting='soft', weights=[2, 1, 1])
clf1.fit(x_train, y_train)
for clf, label in zip([clf1, clf2, clf3, eclf], ['XGBBoosting', 'Random Forest', 'SVM', 'Ensemble']):
scores = cross_val_score(clf, x, y, cv=5, scoring='accuracy')
print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))
软投票的结果为：
Accuracy: 0.96 (+/- 0.02) [XGBBoosting]
Accuracy: 0.33 (+/- 0.00) [Random Forest]
Accuracy: 0.95 (+/- 0.03) [SVM]
Accuracy: 0.96 (+/- 0.02) [Ensemble]
2）分类的Stacking\Blending融合：
stacking是一种分层模型集成框架。
以两层为例，第一层由多个基学习器组成，其输入为原始训练集，第二层的模型则是以第一层基学习器的输出作为训练集进行再训练，从而得到完整的stacking模型, stacking两层模型都使用了全部的训练数据。''' 5-Fold Stacking ''' from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import ExtraTreesClassifier,GradientBoostingClassifier import pandas as pd #创建训练的数据集 data_0 = iris.data data = data_0[:100,:] target_0 = iris.target target = target_0[:100] #模型融合中使用到的各个单模型 clfs = [LogisticRegression(solver='lbfgs'), RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'), ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='gini'), ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'), GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=5)] #切分一部分数据作为测试集 X, X_predict, y, y_predict = train_test_split(data, target, test_size=0.3, random_state=2020) dataset_blend_train = np.zeros((X.shape[0], len(clfs))) dataset_blend_test = np.zeros((X_predict.shape[0], len(clfs))) #5折stacking n_splits = 5 skf = StratifiedKFold(n_splits) skf = skf.split(X, y) for j, clf in enumerate(clfs): #依次训练各个单模型 dataset_blend_test_j = np.zeros((X_predict.shape[0], 5)) for i, (train, test) in enumerate(skf): #5-Fold交叉训练，使用第i个部分作为预测，剩余的部分来训练模型，获得其预测的输出作为第i部分的新特征。 X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test] clf.fit(X_train, y_train) y_submission = clf.predict_proba(X_test)[:, 1] dataset_blend_train[test, j] = y_submission dataset_blend_test_j[:, i] = clf.predict_proba(X_predict)[:, 1] #对于测试集，直接用这k个模型的预测值均值作为新的特征。 dataset_blend_test[:, j] = dataset_blend_test_j.mean(1) print("val auc Score: %f" % roc_auc_score(y_predict, dataset_blend_test[:, j])) clf = LogisticRegression(solver='lbfgs') clf.fit(dataset_blend_train, y) y_submission = clf.predict_proba(dataset_blend_test)[:, 1] print("Val auc Score of Stacking: %f" % (roc_auc_score(y_predict, y_submission)))
预测结果为：
val auc Score: 1.000000
val auc Score: 0.500000
val auc Score: 0.500000
val auc Score: 0.500000
val auc Score: 0.500000
Val auc Score of Stacking: 1.000000
Blending，其实和Stacking是一种类似的多层模型融合的形式
其主要思路是把原始的训练集先分成两部分，比如70%的数据作为新的训练集，剩下30%的数据作为测试集。
在第一层，我们在这70%的数据上训练多个模型，然后去预测那30%数据的label，同时也预测test集的label。
在第二层，我们就直接用这30%数据在第一层预测的结果做为新特征继续训练，然后用test集第一层预测的label做特征，用第二层训练的模型做进一步预测
其优点在于：
- 1.比stacking简单（因为不用进行k次的交叉验证来获得stacker feature）
- 2.避开了一个信息泄露问题：generlizers和stacker使用了不一样的数据集
缺点在于：
- 1.使用了很少的数据（第二阶段的blender只使用training set10%的量）
- 2.blender可能会过拟合
- 3.stacking使用多次的交叉验证会比较稳健 '''
'''
Blending
'''
#创建训练的数据集
#创建训练的数据集
data_0 = iris.data
data = data_0[:100,:]
target_0 = iris.target
target = target_0[:100]
#模型融合中使用到的各个单模型
clfs = [LogisticRegression(solver='lbfgs'),
RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),
#ExtraTreesClassifier(n_estimators=5, n_jobs=-1, criterion='entropy'),
GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=5)]
#切分一部分数据作为测试集
X, X_predict, y, y_predict = train_test_split(data, target, test_size=0.3, random_state=2020)
#切分训练数据集为d1,d2两部分
X_d1, X_d2, y_d1, y_d2 = train_test_split(X, y, test_size=0.5, random_state=2020)
dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))
dataset_d2 = np.zeros((X_predict.shape[0], len(clfs)))
for j, clf in enumerate(clfs):
#依次训练各个单模型
clf.fit(X_d1, y_d1)
y_submission = clf.predict_proba(X_d2)[:, 1]
dataset_d1[:, j] = y_submission
#对于测试集，直接用这k个模型的预测值作为新的特征。
dataset_d2[:, j] = clf.predict_proba(X_predict)[:, 1]
print("val auc Score: %f" % roc_auc_score(y_predict, dataset_d2[:, j]))
#融合使用的模型
clf = GradientBoostingClassifier(learning_rate=0.02, subsample=0.5, max_depth=6, n_estimators=30)
clf.fit(dataset_d1, y_d2)
y_submission = clf.predict_proba(dataset_d2)[:, 1]
print("Val auc Score of Blending: %f" % (roc_auc_score(y_predict, y_submission)))
准确率得分：
val auc Score: 1.000000
val auc Score: 1.000000
val auc Score: 1.000000
val auc Score: 1.000000
val auc Score: 1.000000
Val auc Score of Blending: 1.000000
3）分类的Stacking融合(利用mlxtend)：
!pip install mlxtend
import warnings
warnings.filterwarnings('ignore')
import itertools
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingClassifier
from sklearn.model_selection import cross_val_score
from mlxtend.plotting import plot_learning_curves
from mlxtend.plotting import plot_decision_regions
# 以python自带的鸢尾花数据集为例
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target
clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],
meta_classifier=lr)
label = ['KNN', 'Random Forest', 'Naive Bayes', 'Stacking Classifier']
clf_list = [clf1, clf2, clf3, sclf]
fig = plt.figure(figsize=(10,8))
gs = gridspec.GridSpec(2, 2)
grid = itertools.product([0,1],repeat=2)
clf_cv_mean = []
clf_cv_std = []
for clf, label, grd in zip(clf_list, label, grid):
scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')
print("Accuracy: %.2f (+/- %.2f) [%s]" %(scores.mean(), scores.std(), label))
clf_cv_mean.append(scores.mean())
clf_cv_std.append(scores.std())
clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf)
plt.title(label)
plt.show()
可以发现 基模型 用 'KNN', 'Random Forest', 'Naive Bayes' 然后再这基础上 次级模型加一个 'LogisticRegression'，模型测试效果有着很好的提升。
3 本赛题示例
（1）EDA分析+特征工程
import pandas as pd
import numpy as np
import warnings
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
warnings.filterwarnings('ignore')
%matplotlib inline
import itertools
import matplotlib.gridspec as gridspec
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
# from mlxtend.classifier import StackingClassifier
from sklearn.model_selection import cross_val_score, train_test_split
# from mlxtend.plotting import plot_learning_curves
# from mlxtend.plotting import plot_decision_regions
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn import preprocessing
from sklearn.svm import SVR
from sklearn.decomposition import PCA,FastICA,FactorAnalysis,SparsePCA
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import GridSearchCV,cross_val_score
from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
## 数据读取
Train_data = pd.read_csv('datalab/231784/used_car_train_20200313.csv', sep=' ')
TestA_data = pd.read_csv('datalab/231784/used_car_testA_20200313.csv', sep=' ')
print(Train_data.shape)
print(TestA_data.shape)
numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)
feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','price']]
X_data = Train_data[feature_cols]
Y_data = Train_data['price']
X_test = TestA_data[feature_cols]
print('X train shape:',X_data.shape)
print('X test shape:',X_test.shape)
def Sta_inf(data):
print('_min',np.min(data))
print('_max:',np.max(data))
print('_mean',np.mean(data))
print('_ptp',np.ptp(data))
print('_std',np.std(data))
print('_var',np.var(data))
print('Sta of label:')
Sta_inf(Y_data)
X_data = X_data.fillna(-1)
X_test = X_test.fillna(-1)
def build_model_lr(x_train,y_train):
reg_model = linear_model.LinearRegression()
reg_model.fit(x_train,y_train)
return reg_model
def build_model_ridge(x_train,y_train):
reg_model = linear_model.Ridge(alpha=0.8)#alphas=range(1,100,5)
reg_model.fit(x_train,y_train)
return reg_model
def build_model_lasso(x_train,y_train):
reg_model = linear_model.LassoCV()
reg_model.fit(x_train,y_train)
return reg_model
def build_model_gbdt(x_train,y_train):
estimator =GradientBoostingRegressor(loss='ls',subsample= 0.85,max_depth= 5,n_estimators = 100)
param_grid = {
'learning_rate': [0.05,0.08,0.1,0.2],
}
gbdt = GridSearchCV(estimator, param_grid,cv=3)
gbdt.fit(x_train,y_train)
print(gbdt.best_params_)
# print(gbdt.best_estimator_ )
return gbdt
def build_model_xgb(x_train,y_train):
model = xgb.XGBRegressor(n_estimators=120, learning_rate=0.08, gamma=0, subsample=0.8,\
colsample_bytree=0.9, max_depth=5) #, objective ='reg:squarederror'
model.fit(x_train, y_train)
return model
def build_model_lgb(x_train,y_train):
estimator = lgb.LGBMRegressor(num_leaves=63,n_estimators = 100)
param_grid = {
'learning_rate': [0.01, 0.05, 0.1],
}
gbm = GridSearchCV(estimator, param_grid)
gbm.fit(x_train, y_train)
return gbm
（2）XGBoost的五折交叉回归验证实现
## xgb
xgr = xgb.XGBRegressor(n_estimators=120, learning_rate=0.1, subsample=0.8,\
colsample_bytree=0.9, max_depth=7) # ,objective ='reg:squarederror'
scores_train = []
scores = []
## 5折交叉验证方式
sk=StratifiedKFold(n_splits=5,shuffle=True,random_state=0)
for train_ind,val_ind in sk.split(X_data,Y_data):
train_x=X_data.iloc[train_ind].values
train_y=Y_data.iloc[train_ind]
val_x=X_data.iloc[val_ind].values
val_y=Y_data.iloc[val_ind]
xgr.fit(train_x,train_y)
pred_train_xgb=xgr.predict(train_x)
pred_xgb=xgr.predict(val_x)
score_train = mean_absolute_error(train_y,pred_train_xgb)
scores_train.append(score_train)
score = mean_absolute_error(val_y,pred_xgb)
scores.append(score)
print('Train mae:',np.mean(score_train))
print('Val mae',np.mean(scores))
训练接和验证集的结果为：
Train mae: 558.212360169
Val mae 693.120168439
（3）划分数据集，并用多种方法训练和预测
## Split data with val
x_train,x_val,y_train,y_val = train_test_split(X_data,Y_data,test_size=0.3)
## Train and Predict
print('Predict LR...')
model_lr = build_model_lr(x_train,y_train)
val_lr = model_lr.predict(x_val)
subA_lr = model_lr.predict(X_test)
print('Predict Ridge...')
model_ridge = build_model_ridge(x_train,y_train)
val_ridge = model_ridge.predict(x_val)
subA_ridge = model_ridge.predict(X_test)
print('Predict Lasso...')
model_lasso = build_model_lasso(x_train,y_train)
val_lasso = model_lasso.predict(x_val)
subA_lasso = model_lasso.predict(X_test)
print('Predict GBDT...')
model_gbdt = build_model_gbdt(x_train,y_train)
val_gbdt = model_gbdt.predict(x_val)
subA_gbdt = model_gbdt.predict(X_test)
4 一般比赛中效果最为显著的两种方法
print('predict XGB...')
model_xgb = build_model_xgb(x_train,y_train)
val_xgb = model_xgb.predict(x_val)
subA_xgb = model_xgb.predict(X_test)
print('predict lgb...')
model_lgb = build_model_lgb(x_train,y_train)
val_lgb = model_lgb.predict(x_val)
subA_lgb = model_lgb.predict(X_test)
（1）加权融合
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):
Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)
return Weighted_result
## Init the Weight
w = [0.3,0.4,0.3]
## 测试验证集准确度
val_pre = Weighted_method(val_lgb,val_xgb,val_gbdt,w)
MAE_Weighted = mean_absolute_error(y_val,val_pre)
print('MAE of Weighted of val:',MAE_Weighted)
## 预测数据部分
subA = Weighted_method(subA_lgb,subA_xgb,subA_gbdt,w)
print('Sta inf:')
Sta_inf(subA)
## 生成提交文件
sub = pd.DataFrame()
sub['SaleID'] = X_test.index
sub['price'] = subA
sub.to_csv('./sub_Weighted.csv',index=False)
## 与简单的LR（线性回归）进行对比
val_lr_pred = model_lr.predict(x_val)
MAE_lr = mean_absolute_error(y_val,val_lr_pred)
print('MAE of lr:',MAE_lr)
MAE结果为：
MAE of lr: 2597.45638384
（2） Stacking融合
## Starking
## 第一层
train_lgb_pred = model_lgb.predict(x_train)
train_xgb_pred = model_xgb.predict(x_train)
train_gbdt_pred = model_gbdt.predict(x_train)
Strak_X_train = pd.DataFrame()
Strak_X_train['Method_1'] = train_lgb_pred
Strak_X_train['Method_2'] = train_xgb_pred
Strak_X_train['Method_3'] = train_gbdt_pred
Strak_X_val = pd.DataFrame()
Strak_X_val['Method_1'] = val_lgb
Strak_X_val['Method_2'] = val_xgb
Strak_X_val['Method_3'] = val_gbdt
Strak_X_test = pd.DataFrame()
Strak_X_test['Method_1'] = subA_lgb
Strak_X_test['Method_2'] = subA_xgb
Strak_X_test['Method_3'] = subA_gbdt
## level2-method
model_lr_Stacking = build_model_lr(Strak_X_train,y_train)
## 训练集
train_pre_Stacking = model_lr_Stacking.predict(Strak_X_train)
print('MAE of Stacking-LR:',mean_absolute_error(y_train,train_pre_Stacking))
## 验证集
val_pre_Stacking = model_lr_Stacking.predict(Strak_X_val)
print('MAE of Stacking-LR:',mean_absolute_error(y_val,val_pre_Stacking))
## 预测集
print('Predict Stacking-LR...')
subA_Stacking = model_lr_Stacking.predict(Strak_X_test)
subA_Stacking[subA_Stacking<10]=10 ## 去除过小的预测值
sub = pd.DataFrame()
sub['SaleID'] = X_test.index
sub['price'] = subA_Stacking
sub.to_csv('./sub_Stacking.csv',index=False)
MAE结果为：
MAE of Stacking-LR: 628.399441036
MAE of Stacking-LR: 707.673951794
5 经验总结
比赛的融合这个问题，个人的看法来说其实涉及多个层面，也是提分和提升模型鲁棒性的一种重要方法：
1）结果层面的融合，这种是最常见的融合方法，其可行的融合方法也有很多，比如根据结果的得分进行加权融合，还可以做Log，exp处理等。在做结果融合的时候，有一个很重要的条件是模型结果的得分要比较近似，然后结果的差异要比较大，这样的结果融合往往有比较好的效果提升。
2）特征层面的融合，这个层面其实感觉不叫融合，准确说可以叫分割，很多时候如果我们用同种模型训练，可以把特征进行切分给不同的模型，然后在后面进行模型或者结果融合有时也能产生比较好的效果。
3）模型层面的融合，模型层面的融合可能就涉及模型的堆叠和设计，比如加Staking层，部分模型的结果作为特征输入等，这些就需要多实验和思考了，基于模型层面的融合最好不同模型类型要有一定的差异，用同种模型不同的参数的收益一般是比较小的。
参考：
1. https://tianchi.aliyun.com/competition/entrance/231784/forum
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