def build_XB(X_train,y_train,X_cal,y_cal,X_test):
cal_prob,test_prob = 0,0
for i in range(3):
print("--Building and Training model %s" % i)
seed = randrange(1,10000)
model = XGBoostClassifier(n_iter=1000,
max_features=0.3,
max_depth=8,
min_child_weight=10,
gamma=0.0093,random_state=seed,
learning_rate=0.05,
l2_weight=0.1,
max_samples=0.77,
)
model = CalibratedClassifierCV(base_estimator=model,method='isotonic',cv=3).fit(X_train,y_train)
print("Model %s training complete." % i)
test_prob += model.predict_proba(X_test)
test_prob = test_prob/3.
return(cal_prob,test_prob)
def build_XB1(X_train,y_train,X_cal,y_cal,X_test):
cal_prob,test_prob = 0,0
for i in range(5):
print("--Building and Training model %s" % i)
seed = randrange(1,10000)
model = XGBoostClassifier(n_iter=500,
max_features=0.3,
max_depth=7,
min_child_weight=10,
gamma=0.0093,random_state=seed,
learning_rate=0.2,
l2_weight=0.1,
max_samples=0.9
)
model = CalibratedClassifierCV(base_estimator=model,method='isotonic',cv=5).fit(X_train,y_train)
print("Model %s training complete." % i)
cal_prob += model.predict_proba(X_cal)
test_prob += model.predict_proba(X_test)
cal_prob = cal_prob/5.
test_prob = test_prob/5.
print("Average Model Loss: %0.4f" % of.logloss_mc(y_cal,cal_prob))
return(cal_prob,test_prob)