文章目录
1 回归、分类概率融合
1）简单加权，结果直接融合
2）Stacking融合(回归)
2 分类模型融合
1）Voting投票机制
2）分类的Stacking\Blending融合
3)分类的Stacking融合(利用mlxtend)
3 一些其它方法
4 二手车数据使用加权融合
Step 1：加载函数工具包
Step 2：数据读取
Step 3：数据预处理+特征工程
Step 4：建模
1）模型封装
2）XGBoost的五折交叉回归验证实现
3）划分数据集，并用多种方法训练和预测
Step 5：模型融合
1）加权融合
2）Starking融合
Step 6：预测结果导出
5 总结
1 回归、分类概率融合
1）简单加权，结果直接融合
预测值
y_pre1 = [1.2, 3.2, 2.1, 6.2]
y_pre2 = [0.9, 3.1, 2.0, 5.9]
y_pre3 = [1.1, 2.9, 2.2, 6.0]
真实值
y_true = [1, 3, 2, 6]
import numpy as np
import pandas as pd
## 定义结果的加权平均函数，权重默认都是1/3
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_true, y_pre1))
print('Pred2 MAE:',metrics.mean_absolute_error(y_true, y_pre2))
print('Pred3 MAE:',metrics.mean_absolute_error(y_true, y_pre3))
根据加权计算MAE
w = [0.3,0.4,0.3] # 定义比重权值，权重的范围是什么，模型较多时，如何确定最优的权重
Weighted_pre = Weighted_method(y_pre1,y_pre2,y_pre3,w)
print('Weighted_pre MAE:',metrics.mean_absolute_error(y_true, Weighted_pre))
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Weighted_pre MAE: 0.05750000000000027
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可以发现加权结果相对于之前的结果是有提升的，这就是简单的加权平均。
还有一些其他的形式，比如mean平均，median平均
## 定义结果的mean平均函数，不需要权重
def Mean_method(test_pre1,test_pre2,test_pre3):
Mean_result = pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).mean(axis=1)
return Mean_result
Mean_pre = Mean_method(y_pre1,y_pre2,y_pre3)
print('Mean_pre MAE:',metrics.mean_absolute_error(y_true, Mean_pre))
## 定义结果的median平均函数，不需要权重
def Median_method(test_pre1,test_pre2,test_pre3):
Median_result = pd.concat([pd.Series(test_pre1),pd.Series(test_pre2),pd.Series(test_pre3)],axis=1).median(axis=1)
return Median_result
Median_pre = Median_method(y_pre1,y_pre2,y_pre3)
print('Median_pre MAE:',metrics.mean_absolute_error(y_true, Median_pre))
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Mean_pre MAE: 0.06666666666666693
Median_pre MAE: 0.07500000000000007
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效果没有加权平均好
2）Stacking融合(回归)
from sklearn import linear_model
## 定义stacking融合函数
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]
# 线性回归模型
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: 0.042134831460675204
可以发现模型结果相对于之前的加权融合有进一步的提升
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 分类模型融合
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
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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。
'''
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() # 鸢尾花数据集
x=iris.data
y=iris.target
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)
# xgboost分类模型
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')
# 随机森林分类模型
clf2 = RandomForestClassifier(n_estimators=50, max_depth=1, min_samples_split=4,
min_samples_leaf=63,oob_score=True)
# 支持向量机分类模型
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))
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Accuracy: 0.96 (+/- 0.02) [XGBBoosting]
Accuracy: 0.33 (+/- 0.00) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [SVM]
Accuracy: 0.91 (+/- 0.03) [Ensemble]
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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))
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Accuracy: 0.96 (+/- 0.02) [XGBBoosting]
Accuracy: 0.33 (+/- 0.00) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [SVM]
Accuracy: 0.96 (+/- 0.02) [Ensemble]
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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
from sklearn.model_selection import KFold,StratifiedKFold
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)))
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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
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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)))
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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
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- 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()
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3 一些其它方法
将特征放进模型中预测，并将预测结果变换并作为新的特征加入原有特征中再经过模型预测结果 （Stacking变化）
可以反复预测多次将结果加入最后的特征中
def Ensemble_add_feature(train,test,target,clfs):
# n_flods = 5
# skf = list(StratifiedKFold(y, n_folds=n_flods))
train_ = np.zeros((train.shape[0],len(clfs*2)))
test_ = np.zeros((test.shape[0],len(clfs*2)))
for j,clf in enumerate(clfs):
'''依次训练各个单模型'''
# print(j, clf)
'''使用第1个部分作为预测，第2部分来训练模型，获得其预测的输出作为第2部分的新特征。'''
# X_train, y_train, X_test, y_test = X[train], y[train], X[test], y[test]
clf.fit(train,target)
y_train = clf.predict(train)
y_test = clf.predict(test)
## 新特征生成
train_[:,j*2] = y_train**2
test_[:,j*2] = y_test**2
train_[:, j+1] = np.exp(y_train)
test_[:, j+1] = np.exp(y_test)
# print("val auc Score: %f" % r2_score(y_predict, dataset_d2[:, j]))
print('Method ',j)
train_ = pd.DataFrame(train_)
test_ = pd.DataFrame(test_)
return train_,test_
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from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
data_0 = iris.data
data = data_0[:100,:]
target_0 = iris.target
target = target_0[:100]
x_train,x_test,y_train,y_test=train_test_split(data,target,test_size=0.3)
x_train = pd.DataFrame(x_train) ; x_test = pd.DataFrame(x_test)
#模型融合中使用到的各个单模型
clfs = [LogisticRegression(),
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)]
New_train,New_test = Ensemble_add_feature(x_train,x_test,y_train,clfs)
clf = LogisticRegression()
# clf = GradientBoostingClassifier(learning_rate=0.02, subsample=0.5, max_depth=6, n_estimators=30)
clf.fit(New_train, y_train)
y_emb = clf.predict_proba(New_test)[:, 1]
print("Val auc Score of stacking: %f" % (roc_auc_score(y_test, y_emb)))
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Method 0
Method 1
Method 2
Method 3
Method 4
Val auc Score of stacking: 1.000000
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4 二手车数据使用加权融合
Step 1：加载函数工具包
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
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Step 2：数据读取
## 数据读取
Train_data = pd.read_csv('used_car_train_20200313.csv', sep=' ')
TestA_data = pd.read_csv('used_car_testA_20200313.csv', sep=' ')
print(Train_data.shape)
print(TestA_data.shape)
Train_data.head()
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Step 3：数据预处理+特征工程
# 提取非object类型特征
numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)
# 提取不包括'SaleID','name','regDate','price'的特征
feature_cols = [col for col in numerical_cols if col not in ['SaleID','name','regDate','price']]
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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)
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# 统计信息
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)
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# 缺失值处理
X_data = X_data.fillna(-1)
X_test = X_test.fillna(-1)
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Step 4：建模
1）模型封装
# 封装模型
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
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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))
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Train mae: 594.8909340395033
Val mae 693.382067947197
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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)
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Predict LR...
Predict Ridge...
Predict Lasso...
Predict GBDT...
{'learning_rate': 0.2}
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一般比赛中效果最为显著的两种方法：xgb、lgb
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)
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predict XGB...
predict lgb...
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print('Sta inf of lgb:')
Sta_inf(subA_lgb)
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Sta inf of lgb:
_min -128.66598755757244
_max: 90667.81617866123
_mean 5923.553229672178
_ptp 90796.4821662188
_std 7370.180653683828
_var 54319562.867935374
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Step 5：模型融合
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)
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MAE of Weighted of val: 729.9470926081082
Sta inf:
_min -178.120687289992
_max: 88223.09829815029
_mean 5924.958183854553
_ptp 88401.21898544028
_std 7347.443407903759
_var 53984924.63234841
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## 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)
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MAE of lr: 2590.2247870177375
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2）Starking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
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## 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)
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MAE of Stacking-LR: 628.6682626656338
MAE of Stacking-LR: 720.1885140128251
Predict Stacking-LR...
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Step 6：预测结果导出
subA_Stacking[subA_Stacking<10]=10 ## 去除过小的预测值
sub = pd.DataFrame()
sub['SaleID'] = TestA_data.SaleID
sub['price'] = subA_Stacking
sub.to_csv('./sub_Stacking.csv',index=False)
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print('Sta inf:')
Sta_inf(subA_Stacking)
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Sta inf:
_min 10.0
_max: 89933.95494785428
_mean 5925.287312558363
_ptp 89923.95494785428
_std 7407.64521868686
_var 54873207.685934305
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5 总结
比赛的融合这个问题，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。
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版权声明：本文为CSDN博主「sliceoflife」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/sliceoflife/article/details/105287601
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