model

model.py

# A parameter grid for XGBoost
parameters = {    
    'n_estimators': [100,500,1000],
    'learning_rate': [0.1, 0.01, 0.05]
}

xgb = XGBClassifier(objective='binary:logistic',
                    silent=True, nthread=4)

xg_grid = GridSearchCV(xgb, param_grid=parameters, n_jobs=-1, verbose=1,scoring='f1_macro',cv=3,return_train_score=True)
xg_grid.fit(X_train,y_train)

from sklearn.metrics import f1_score
best_est=xg_grid.best_params_['n_estimators']
best_rate=xg_grid.best_params_['learning_rate']
xgb_model = XGBClassifier(max_depth=10,n_estimators=best_est,learning_rate=best_rate,objective='binary:logistic',silent=True, nthread=4,njobs=-1).fit(X_train, y_train) 
xgb_prediction = xgb_model.predict(X_test)
print('training score:', f1_score(y_train,xgb_model.predict(X_train), average='macro'))
print('testing score:', f1_score(y_test,xgb_prediction, average='macro'))

parameters = {    
    'n_estimators': [100,500,1000],
    'learning_rate': [0.1, 0.01, 0.05]
}

xgb = XGBClassifier(objective='binary:logistic',
                    silent=True, nthread=4)

xg_grid = GridSearchCV(xgb, param_grid=parameters, n_jobs=-1, verbose=1,scoring='roc_auc',cv=3,return_train_score=True)
xg_grid.fit(X_train,y_train)

def pred_func(model,data):
  list1=[]
  list1=model.predict_proba(data)[:,1]
  return list1
  
best_est=xg_grid.best_params_['n_estimators']
best_rate=xg_grid.best_params_['learning_rate']
xg1= XGBClassifier(learning_rate=best_rate,n_estimators=best_est,objective='binary:logistic',
                    silent=True, nthread=4)

xg1.fit(X_train, y_train)

y_train_pred = pred_func(xg1,X_train)
y_test_pred = pred_func(xg1,X_test)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.title("AUC")
plt.grid()
plt.show()