LightGBM大战XGBoost，谁将夺得桂冠？

git clone --recursive https://github.com/Microsoft/LightGBM
cd LightGBM
mkdir build
cd build
cmake -DCMAKE_GENERATOR_PLATFORM=x64 ..
cmake --build . --target ALL_BUILD --config Release

git clone --recursive https://github.com/Microsoft/LightGBM
cd LightGBM
mkdir build
cd build
cmake -G "MinGW Makefiles" ..
mingw32-make.exe -j

git clone --recursive https://github.com/Microsoft/LightGBM
cd LightGBM
mkdir build
cd build
cmake ..
make -j

brew install cmake
brew install gcc --without-multilib
git clone --recursive https://github.com/Microsoft/LightGBM
cd LightGBM
mkdir build 
cd build
cmake ..
make -j

http://archive.ics.uci.edu/ml/datasets/Adult。

#importing standard libraries import numpy as np 
import pandas as pd 
from pandas import Series, DataFrame 

#import lightgbm and xgboost import lightgbm as lgb 
import xgboost as xgb 

#loading our training dataset 'adult.csv' with name 'data' using pandas data=pd.read_csv('adult.csv',header=None) 

#Assigning names to the columns data.columns=['age','workclass','fnlwgt','education','education-num','marital_Status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country','Income'] 

#glimpse of the dataset data.head() 

# Label Encoding our target variable from sklearn.preprocessing import LabelEncoder,OneHotEncoder
l=LabelEncoder() 
l.fit(data.Income) 

l.classes_ 
data.Income=Series(l.transform(data.Income))  #label encoding our target variable data.Income.value_counts() 



#One Hot Encoding of the Categorical features one_hot_workclass=pd.get_dummies(data.workclass) 
one_hot_education=pd.get_dummies(data.education) 
one_hot_marital_Status=pd.get_dummies(data.marital_Status) 
one_hot_occupation=pd.get_dummies(data.occupation)
one_hot_relationship=pd.get_dummies(data.relationship) 
one_hot_race=pd.get_dummies(data.race) 
one_hot_sex=pd.get_dummies(data.sex) 
one_hot_native_country=pd.get_dummies(data.native_country) 

#removing categorical features data.drop(['workclass','education','marital_Status','occupation','relationship','race','sex','native_country'],axis=1,inplace=True) 



#Merging one hot encoded features with our dataset 'data' data=pd.concat([data,one_hot_workclass,one_hot_education,one_hot_marital_Status,one_hot_occupation,one_hot_relationship,one_hot_race,one_hot_sex,one_hot_native_country],axis=1) 

#removing dulpicate columns 
 _, i = np.unique(data.columns, return_index=True) 
data=data.iloc[:, i] 

#Here our target variable is 'Income' with values as 1 or 0.  #Separating our data into features dataset x and our target dataset y x=data.drop('Income',axis=1) 
y=data.Income 



#Imputing missing values in our target variable y.fillna(y.mode()[0],inplace=True) 

#Now splitting our dataset into test and train from sklearn.model_selection import train_test_split 
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3)

#The data is stored in a DMatrix object #label is used to define our outcome variabledtrain=xgb.DMatrix(x_train,label=y_train)
dtest=xgb.DMatrix(x_test)#setting parameters for xgboostparameters={'max_depth':7, 'eta':1, 'silent':1,'objective':'binary:logistic','eval_metric':'auc','learning_rate':.05}#training our model num_round=50from datetime import datetime 
start = datetime.now() 
xg=xgb.train(parameters,dtrain,num_round) 
stop = datetime.now()#Execution time of the model execution_time_xgb = stop-start 
print(execution_time_xgb)#datetime.timedelta( , , ) representation => (days , seconds , microseconds) #now predicting our model on test set ypred=xg.predict(dtest) 
print(ypred)#Converting probabilities into 1 or 0  for i in range(0,9769): 
    if ypred[i]>=.5:       # setting threshold to .5 
       ypred[i]=1 
    else: 
       ypred[i]=0  #calculating accuracy of our model from sklearn.metrics import accuracy_score 
accuracy_xgb = accuracy_score(y_test,ypred) 
print(accuracy_xgb)

train_data=lgb.Dataset(x_train,label=y_train)

setting parameters for lightgbm
param = {'num_leaves':150, 'objective':'binary','max_depth':7,'learning_rate':.05,'max_bin':200}
param['metric'] = ['auc', 'binary_logloss']#Here we have set max_depth in xgb and LightGBM to 7 to have a fair comparison between the two.#training our model using light gbmnum_round=50start=datetime.now()
lgbm=lgb.train(param,train_data,num_round)
stop=datetime.now()#Execution time of the modelexecution_time_lgbm = stop-start
print(execution_time_lgbm)#predicting on test setypred2=lgbm.predict(x_test)
print(ypred2[0:5])  # showing first 5 predictions#converting probabilities into 0 or 1for i in range(0,9769):    if ypred2[i]>=.5:       # setting threshold to .5
       ypred2[i]=1
    else:  
       ypred2[i]=0#calculating accuracyaccuracy_lgbm = accuracy_score(ypred2,y_test)
accuracy_lgbm
y_test.value_counts()from sklearn.metrics import roc_auc_score#calculating roc_auc_score for xgboostauc_xgb =  roc_auc_score(y_test,ypred)
print(auc_xgb)#calculating roc_auc_score for light gbm. auc_lgbm = roc_auc_score(y_test,ypred2)
auc_lgbm comparison_dict = {'accuracy score':(accuracy_lgbm,accuracy_xgb),'auc score':(auc_lgbm,auc_xgb),'execution time':(execution_time_lgbm,execution_time_xgb)}#Creating a dataframe ‘comparison_df’ for comparing the performance of Lightgbm and xgb. comparison_df = DataFrame(comparison_dict) 
comparison_df.index= ['LightGBM','xgboost'] 
print(comparison_df)