def fit(self, X_train, y_train, X_valid, y_valid, seed=42, label=None):
self.label = label if label else 'LGB'
self.models = []
for bag in range(self.meta_bags):
self.params['seed'] = seed * (bag + 1)
model = XGBClassifier(**self.params)
model.fit(X_train,
y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_metric=self.feval,
early_stopping_rounds=self.early_stop,
verbose=False)
model.xgb_eval = model.evals_result_['validation_1']['gini']
model.best_round = np.argsort(model.xgb_eval)[::-1][0]
model.importances = model.feature_importances_
self.models.append(model)
print('[%s Bag %d] Found %.6f @ %d with %d estimators' %
(self.label, bag, model.xgb_eval[model.best_round],
model.best_round, self.params.get('n_estimators', 100)))
return self
