cv_model.py

def cv_model(X, y, features, n_fold=5, random_state=45245, params=None):
    """Evaluate a score by cross validation.
    
    Parameters
    ----------
    
    X : pandas.DataFrame
        The data to fit.
        
    y : pandas.DataFrame or pandas.Series
        The scalar coupling constants as target variables.
        
    features : list
        The list of features to use during training.
        
    n_fold : int, default `5`
        The number of fold to use in cross validation.
    
    random_state : int, default `45245`
        The seed for the KFold split
        
    params : dict, default `None`
        The parameter dictionary for XGBRegressor
        
    Returns
    -------
    
    results_mean : list
        List of the scores for each coupling type, averaged over all folds.
        
    results_details : list
        List of all the scores as a list of lists.
    """

    X = X[features]

    folds = KFold(n_splits=n_fold, shuffle=True, random_state=random_state)
    model = XGBRegressor(**params)
    results_mean = []
    results_details = []

    for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):
        X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]
        y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]
        model.fit(X_train, y_train)
        y_pred = model.predict(X_valid)
        scores = group_mean_log_mae(y_pred, y_valid, X_valid["type"])
        results_mean.append(scores[0])
        results_details.append(list(scores[1]))

    print(
        "After {}-fold CV: Mean: ".format(n_fold),
        np.mean(results_mean),
        "Std.:",
        np.std(results_mean),
    )
    return results_mean, results_details