def METHOD_NAME(self, X, y=None):
    """Fit FeatureSetSelector for feature selection
    Parameters
    ----------
    X: array-like of shape (n_samples, n_features)
        The training input samples.
    y: array-like, shape (n_samples,)
        The target values (integers that correspond to classes in classification, real numbers in regression).
    Returns
    -------
    self: object
        Returns a copy of the estimator
    """
    subset_df = pd.read_csv(self.subset_list, header=0, index_col=0)
    if isinstance(self.sel_subset, int):
        self.sel_subset_name = subset_df.index[self.sel_subset]
    elif isinstance(self.sel_subset, str):
        self.sel_subset_name = self.sel_subset
    else: # list or tuple
        self.sel_subset_name = []
        for s in self.sel_subset:
            if isinstance(s, int):
                self.sel_subset_name.append(subset_df.index[s])
            else:
                self.sel_subset_name.append(s)
    sel_features = subset_df.loc[self.sel_subset_name, 'Features']
    if not isinstance(sel_features, str):
        sel_features = ";".join(sel_features.tolist())
    sel_uniq_features = set(sel_features.split(';'))
    if isinstance(X, pd.DataFrame): # use columns' names
        self.feature_names = list(X.columns.values)
        self.feat_list = sorted(list(set(sel_uniq_features).intersection(set(self.feature_names))))
        self.feat_list_idx = [list(X.columns).index(feat_name) for feat_name in self.feat_list]
    elif isinstance(X, np.ndarray): # use index
        self.feature_names = list(range(X.shape[1]))
        sel_uniq_features = [int(val) for val in sel_uniq_features]
        self.feat_list = sorted(list(set(sel_uniq_features).intersection(set(self.feature_names))))
        self.feat_list_idx = self.feat_list
    if not len(self.feat_list):
        raise ValueError('No feature is found on the subset list!')
    return self