Example of using the XGBoost CV with custom function (Python)

Hello XGBoost team,

I am trying to use the XGBoost CV module with Optuna for parameter optimization with the following code:

``` python
def kappa_scorer(y_true, y_pred):
"""Kappa scorer for the XGBoost model."""
return "kappa", cohen_kappa_score(y_true, y_pred)

def objective_xgboost(trial, study_name, X_train, y_train, label_to_idx, exp_type: str):
"""Objective function for the XGBoost classifier with final eval metric as Kappa."""
params = {
"verbosity": 0,
"objective": "multi:softmax",
"num_class": len(label_to_idx),
"feval": kappa_scorer,
"n_estimators": 1000,
"eta": trial.suggest_float("learning_rate", 1e-2, 0.1, log=True),
"max_depth": trial.suggest_int("max_depth", 2, 10),
"colsample_bytree": trial.suggest_float(
"colsample_bytree", 0.1, 0.7
), # Percentage of features used per tree.
"disable_default_eval_metric": 1,
}

# Training data
y_train = y_train.map(label_to_idx)

dtrain = xgb.DMatrix(X_train, label=y_train)

# Optimization of kappa scoe
pruning_callback = optuna.integration.XGBoostPruningCallback(trial, "test-kappa")
xgboost_model = xgb.cv(
params, dtrain, callbacks=[pruning_callback], seed=SEED, nfold=5
)

# Save model
trial_path = _generate_dirs(exp_type)
save_xgboost_trial(
trial=trial, model=xgboost_model, study_name=study_name, trial_dir=trial_path
)

mean_kappa = xgboost_model["test-kappa-mean"].values[-1] # Optimized for kappa

return mean_kappa
```
However, I am not able to extract the metric results from the trained model and stable upon an error: `KeyError: 'test-kappa'`. Is there any pointer to what I am doing wrong?

Thank you.

Start by reproducing the example at the xgb.cv entry point with the shown DMatrix, custom scorer, and XGBoostPruningCallback. Trace the evaluation metric names returned by cv and used by the callback; done means the reported KeyError is explained and the supported metric access pattern or documentation example is identified.

Written by the indexing model from the issue text.