split currently works, but fails valid split. decomposing to avoid repeating code

    """

        raise NotImplementedError

=

class StratifiedSplitter(Splitter):

    """

  Class for doing stratified splits -- where data is too sparse to do regular splits

            array = array[perm]

        return array_list

    def __generate_required_hits(self, w, frac_train):

    def __generate_required_hits(self, w, frac_split):

        required_hits = (w != 0).sum(0)#returns list of per column sum of non zero elements

        for colHits in required_hits:

          colHits = int(frac_train * colHits)

          colHits = int(frac_split * colHits)

        return required_hits

    def __generate_required_index(self, w, required_hit_list):

        colIndex = 0

        index_hits = []

        #loop through each column and obtain index required to splice out for frac train hits

        #loop through each column and obtain index required to splice out for required fraction of hits

        for col in w.T:

            num_hit = 0

            num_required = required_hit_list[colIndex]

                    break

            colIndex += 1

        return index_hits

    def __split(self, X, y, w, ids, index_list):

    def train_valid_test_split(self, dataset, train_dir,

                               valid_dir, test_dir, frac_train=.8,

        required_hits_list = self.__generate_required_hits(w, frac_train)

        index_list = self.__generate_required_index(w, required_hits_list)

        X_train = X_test = X

        y_train = y_test = y

        w_train = w_test = np.zeros(w.shape)

        y_test = y_test[rowsToKeepTest]

        ids_test = ids_test[rowsToKeepTest]

        #mix and match test_set and valid_set to ensure 50/50 data

        valid_percentage = frac_valid/(frac_valid + frac_test)

        X_test, y_test, w_test, ids_test = self.__randomize_arrays([X_test, y_test, w_test, ids_test])

        valid_required_hits_list = self.__generate_required_hits(w_test, valid_percentage)

        valid_index_list = self.__generate_required_index(w_test, valid_required_hits_list)

        # make valid split - 50/50 split of test

        X_test, X_valid = np.array_split(X_test, 2)

        y_test, y_valid = np.array_split(y_test, 2)

                        print("valid_data failed")

                    elif datasetIndex == 2:

                        print("test_data failed")

                    assert NaN_count != totalRows

                    assert np.any(col)

            if datasetIndex == 0:

                print("train_data passed")

            elif datasetIndex == 1: