def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est, **self.params, **hyperparams)
model.fit(X=X_trn, y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False)
score = model.evals_result()['validation_0'][self.metric][model.best_iteration] * self.loss_sign
return {'loss': score, 'status': STATUS_OK, 'model': model}
def objective(hyperparams):
model = XGBModel(n_estimators=self.n_est,
**self.params,
**hyperparams)
model.fit(
X=X_trn,
y=y_trn,
eval_set=[(X_val, y_val)],
eval_metric=self.metric,
early_stopping_rounds=self.n_stop,
verbose=False,
)
score = (model.evals_result()["validation_0"][self.metric][
model.best_iteration] * self.loss_sign)
return {"loss": score, "status": STATUS_OK, "model": model}