專案地址：https://github.com/datawhalechina/team-learning-data-mining/tree/master/FinancialRiskControl
比賽地址：https://tianchi.aliyun.com/competition/entrance/531830/introduction
對於多種調參完成的模型進行模型融合。
完成對於多種模型的融合，提交融合結果並打卡。
模型融合是比賽後期一個重要的環節，大體來說有如下的型別方式。
簡單加權融合:
stacking/blending:
boosting/bagging（在xgboost，Adaboost,GBDT中已經用到）:
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
## 資料讀取
Train_data = pd.read_csv('data/used_car_train_20200313.csv', sep=' ')
TestA_data = pd.read_csv('data/used_car_testA_20200313.csv', sep=' ')
print(Train_data.shape)
print(TestA_data.shape)
##(150000, 31)
##(50000, 30)
Train_data.head()
| SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 736 | 20040402 | 30.0 | 6 | 1.0 | 0.0 | 0.0 | 60 | 12.5 | ... | 0.235676 | 0.101988 | 0.129549 | 0.022816 | 0.097462 | -2.881803 | 2.804097 | -2.420821 | 0.795292 | 0.914762 |
| 1 | 1 | 2262 | 20030301 | 40.0 | 1 | 2.0 | 0.0 | 0.0 | 0 | 15.0 | ... | 0.264777 | 0.121004 | 0.135731 | 0.026597 | 0.020582 | -4.900482 | 2.096338 | -1.030483 | -1.722674 | 0.245522 |
| 2 | 2 | 14874 | 20040403 | 115.0 | 15 | 1.0 | 0.0 | 0.0 | 163 | 12.5 | ... | 0.251410 | 0.114912 | 0.165147 | 0.062173 | 0.027075 | -4.846749 | 1.803559 | 1.565330 | -0.832687 | -0.229963 |
| 3 | 3 | 71865 | 19960908 | 109.0 | 10 | 0.0 | 0.0 | 1.0 | 193 | 15.0 | ... | 0.274293 | 0.110300 | 0.121964 | 0.033395 | 0.000000 | -4.509599 | 1.285940 | -0.501868 | -2.438353 | -0.478699 |
| 4 | 4 | 111080 | 20120103 | 110.0 | 5 | 1.0 | 0.0 | 0.0 | 68 | 5.0 | ... | 0.228036 | 0.073205 | 0.091880 | 0.078819 | 0.121534 | -1.896240 | 0.910783 | 0.931110 | 2.834518 | 1.923482 |
5 rows × 31 columns
numerical_cols = Train_data.select_dtypes(exclude = 'object').columns
print(numerical_cols)
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
'gearbox', 'power', 'kilometer', 'regionCode', 'seller', 'offerType',
'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6',
'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13', 'v_14'],
dtype='object')
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)
X train shape: (150000, 26)
X test shape: (50000, 26)
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)
Sta of label:
_min 11
_max: 99999
_mean 5923.327333333334
_ptp 99988
_std 7501.973469876635
_var 56279605.942732885
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
## XGBoost的五折交叉迴歸驗證實現
## 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折交叉驗證方式
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))
Train mae: 600.0127885014529
Val mae 691.9976473362078
# 劃分資料集，並用多種方法訓練和預測
## 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)
Predict LR...
Predict Ridge...
Predict Lasso...
Predict GBDT...
{'learning_rate': 0.2}
# 一般比賽中效果最為顯著的兩種方法
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)
predict XGB...
predict lgb...
print('Sta inf of lgb:')
Sta_inf(subA_lgb)
Sta inf of lgb:
_min -183.5346885743444
_max: 87514.11713966732
_mean 5926.692984246488
_ptp 87697.65182824167
_std 7371.052328311919
_var 54332412.426712565
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]
## 測試驗證集準確度
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)
## 預測資料部分
subA = Weighted_method(subA_lgb,subA_xgb,subA_gbdt,w)
print('Sta inf:')
Sta_inf(subA)
## 生成提交檔案
sub = pd.DataFrame()
sub['SaleID'] = X_test.index
sub['price'] = subA
sub.to_csv('./sub_Weighted.csv',index=False)
MAE of Weighted of val: 722.4371711779407
Sta inf:
_min -748.3094464296096
_max: 86961.51700443946
_mean 5929.89705489961
_ptp 87709.82645086908
_std 7351.960778474033
_var 54051327.28822051
## 與簡單的LR（線性迴歸）進行對比
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 of lr: 2599.193170853424
## Stacking
## 第一層
train_lgb_pred = model_lgb.predict(x_train)
train_xgb_pred = model_xgb.predict(x_train)
train_gbdt_pred = model_gbdt.predict(x_train)
Stack_X_train = pd.DataFrame()
Stack_X_train['Method_1'] = train_lgb_pred
Stack_X_train['Method_2'] = train_xgb_pred
Stack_X_train['Method_3'] = train_gbdt_pred
Stack_X_val = pd.DataFrame()
Stack_X_val['Method_1'] = val_lgb
Stack_X_val['Method_2'] = val_xgb
Stack_X_val['Method_3'] = val_gbdt
Stack_X_test = pd.DataFrame()
Stack_X_test['Method_1'] = subA_lgb
Stack_X_test['Method_2'] = subA_xgb
Stack_X_test['Method_3'] = subA_gbdt
Stack_X_test.head()
| Method_1 | Method_2 | Method_3 | |
|---|---|---|---|
| 0 | 42063.070016 | 40670.992188 | 41630.731967 |
| 1 | 316.444941 | 297.938507 | 173.515799 |
| 2 | 7245.437440 | 7426.350098 | 7495.818904 |
| 3 | 11755.252587 | 11995.381836 | 11689.648844 |
| 4 | 539.252642 | 512.093628 | 549.944899 |
## level2-method
model_lr_Stacking = build_model_lr(Stack_X_train,y_train)
## 訓練集
train_pre_Stacking = model_lr_Stacking.predict(Stack_X_train)
print('MAE of Stacking-LR:',mean_absolute_error(y_train,train_pre_Stacking))
## 驗證集
val_pre_Stacking = model_lr_Stacking.predict(Stack_X_val)
print('MAE of Stacking-LR:',mean_absolute_error(y_val,val_pre_Stacking))
## 預測集
print('Predict Stacking-LR...')
subA_Stacking = model_lr_Stacking.predict(Stack_X_test)
MAE of Stacking-LR: 628.0883315330257
MAE of Stacking-LR: 711.5275218526992
Predict Stacking-LR...
subA_Stacking[subA_Stacking<10]=10 ## 去除過小的預測值
sub = pd.DataFrame()
sub['SaleID'] = X_test.index
sub['price'] = subA_Stacking
sub.to_csv('./sub_Stacking.csv',index=False)
print('Sta inf:')
Sta_inf(subA_Stacking)
Sta inf:
_min 10.0
_max: 87428.84906583659
_mean 5929.537761262649
_ptp 87418.84906583659
_std 7414.192031815497
_var 54970243.4846364
1）結果層面的融合，這種是最常見的融合方法，其可行的融合方法也有很多，比如根據結果的得分進行加權融合，還可以做Log，exp處理等。在做結果融合的時候，有一個很重要的條件是模型結果的得分要比較近似，然後結果的差異要比較大，這樣的結果融合往往有比較好的效果提升。
2）特徵層面的融合，這個層面其實感覺不叫融合，準確說可以叫分割，很多時候如果我們用同種模型訓練，可以把特徵進行切分給不同的模型，然後在後面進行模型或者結果融合有時也能產生比較好的效果。
3）模型層面的融合，模型層面的融合可能就涉及模型的堆疊和設計，比如加Staking層，部分模型的結果作為特徵輸入等，這些就需要多實驗和思考了，基於模型層面的融合最好不同模型型別要有一定的差異，用同種模型不同的引數的收益一般是比較小的。