Category features support

[UTimeStrange]

Only dataframe object is supported for the "fit" method

xgbse_model = XGBSEKaplanTree(PARAMS_TREE)
# X = xgb.DMatrix(X, enable_categorical=True)
xgbse_model.fit(X, y)

but enable_categorical is not set "True" in the source code of xgbse
it gives error :
"ValueError: DataFrame.dtypes for data must be int, float, bool or category. When
categorical type is supplied, DMatrix parameter enable_categorical must
be set to True"

def build_xgb_cox_dmatrix(X, T, E):
    """Builds a XGB DMatrix using specified Data Frame of features (X)
        arrays of times (T) and censors/events (E).

    Args:
        X ([pd.DataFrame, np.array]): Data Frame to be converted to XGBDMatrix format.
        T ([np.array, pd.Series]): Array of times.
        E ([np.array, pd.Series]): Array of censors(False) / events(True).

    Returns:
        (DMatrix): A XGB DMatrix is returned including features and target.
    """

    target = np.where(E, T, -T)

    return xgb.DMatrix(X, label=target)

The last line here does not set "enable_categorical = True". Category features are not supported? Or I just need to change the code here myself.

[dhuang-apex]

I would be huge if we can get categorical feature support please

[frank1010111]

It's a little more complicated because there are other instances of xgb.DMatrix being called by the estimators. I'm working on finding and changing them for a pull request, but for now I would recommend using pandas.get_dummies to convert categorical columns to one-hot encoding.