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

def fit(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