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My XGB boilerplate
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# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/ | |
#Import libraries: | |
import pandas as pd | |
import numpy as np | |
import xgboost as xgb | |
from xgboost.sklearn import XGBClassifier | |
from sklearn import cross_validation, metrics #Additional scklearn functions | |
from sklearn.grid_search import GridSearchCV #Perforing grid search | |
import matplotlib.pylab as plt | |
%matplotlib inline | |
from matplotlib.pylab import rcParams | |
def modelfit_w_test(alg, | |
dtrain, | |
dtest, | |
predictors, | |
useTrainCV=True, | |
cv_folds=5, | |
early_stopping_rounds=50): | |
if useTrainCV: | |
xgb_param = alg.get_xgb_params() | |
xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values) | |
xgtest = xgb.DMatrix(dtest[predictors].values) | |
cvresult = xgb.cv(xgb_param, | |
xgtrain, | |
num_boost_round=alg.get_params()['n_estimators'], | |
nfold=cv_folds, | |
metrics='auc', | |
early_stopping_rounds=early_stopping_rounds, | |
verbose_eval = False) | |
alg.set_params(n_estimators=cvresult.shape[0]) | |
#Fit the algorithm on the data | |
alg.fit(dtrain[predictors], dtrain[target],eval_metric='auc') | |
#Predict training set: | |
dtrain_predictions = alg.predict(dtrain[predictors]) | |
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1] | |
#Print model report: | |
print ("\nModel Report") | |
print ("Accuracy : %.4g" % metrics.accuracy_score(dtrain[target].values, dtrain_predictions)) | |
print ("Precision : %.4g" % metrics.precision_score(dtrain[target].values, dtrain_predictions)) | |
print ("Recall : %.4g" % metrics.recall_score(dtrain[target].values, dtrain_predictions)) | |
print ("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain[target], dtrain_predprob)) | |
print ("\n") | |
# Predict on testing data: | |
dtest_predictions = alg.predict(dtest[predictors]) | |
dtest_predprob = alg.predict_proba(dtest[predictors])[:,1] | |
# results = test_results.merge(dtest[['ID','predprob']], on='ID') | |
print ("Accuracy : %.4g" % metrics.accuracy_score(dtest[target].values, dtest_predictions)) | |
print ("Precision : %.4g" % metrics.precision_score(dtest[target].values, dtest_predictions)) | |
print ("Recall : %.4g" % metrics.recall_score(dtest[target].values, dtest_predictions)) | |
print ('AUC Score (Test): %f' % metrics.roc_auc_score(dtest[target], dtest_predprob)) | |
xgb.plot_importance(alg, max_num_features=100, height=0.8) | |
return alg | |
def modelfit(alg, | |
dtrain, | |
predictors, | |
useTrainCV=True, | |
cv_folds=5, | |
early_stopping_rounds=50): | |
if useTrainCV: | |
xgb_param = alg.get_xgb_params() | |
xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values) | |
cvresult = xgb.cv( | |
xgb_param, | |
xgtrain, | |
num_boost_round=alg.get_params()['n_estimators'], | |
nfold=cv_folds, | |
metrics='auc', | |
early_stopping_rounds=early_stopping_rounds, | |
verbose_eval = False | |
) | |
alg.set_params(n_estimators=cvresult.shape[0]) | |
#Fit the algorithm on the data | |
alg.fit(dtrain[predictors], dtrain[target],eval_metric='auc') | |
#Predict training set: | |
dtrain_predictions = alg.predict(dtrain[predictors]) | |
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1] | |
#Print model report: | |
print ("\nModel Report") | |
print ("Accuracy : %.4g" % metrics.accuracy_score(dtrain[target].values, dtrain_predictions)) | |
print ("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain[target], dtrain_predprob)) | |
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False) | |
feat_imp.plot(kind='bar', title='Feature Importances') | |
plt.ylabel('Feature Importance Score') | |
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from sklearn.grid_search import GridSearchCV #Perforing grid search | |
# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/ | |
param_test1 = { | |
'max_depth':range(3,10,2), | |
'min_child_weight':range(1,6,2) | |
} | |
gsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate=0.1, | |
n_estimators=140, | |
max_depth=5, | |
min_child_weight=1, | |
gamma=0, | |
subsample=0.8, | |
colsample_bytree=0.8, | |
objective= 'binary:logistic', | |
nthread=4, | |
scale_pos_weight=1, | |
seed=27), | |
param_grid = param_test1, | |
scoring='roc_auc', | |
n_jobs=4, | |
iid=False, | |
cv=5) | |
gsearch1.fit(train[predictors],train[target]) | |
gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_ |
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predictors = list(set(photo_meta_data + ae_score) ) | |
# predictors = list(set(predictors) - set(['name_popularity', 'surname_popularity' 'name', 'surname'])) | |
train_ind = list(annotation_df[(annotation_df.view_date < '2017-12-15')&(annotation_df.followings_count_y>1)].index) | |
val_ind = list(annotation_df[(annotation_df.view_date > '2017-12-15')&(annotation_df.followings_count_y>1)].index) | |
target = 'has_interaction' | |
rcParams['figure.figsize'] = 12, 4 | |
xgb1 = XGBClassifier( | |
learning_rate =0.1, | |
n_estimators=100, | |
max_depth=5, | |
min_child_weight=1, | |
gamma=0, | |
subsample=0.8, | |
colsample_bytree=0.8, | |
objective= 'binary:logistic', | |
nthread=7, | |
scale_pos_weight=1, | |
silent = False, | |
seed=27) | |
alg = modelfit_w_test(xgb1, | |
annotation_df.filter(items=train_ind,axis=0), | |
annotation_df.filter(items=val_ind,axis=0), | |
predictors) |
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