trying to fix test

        y = numpyArrayList[1]

        w = numpyArrayList[2]

        ids = numpyArrayList[3]

        print("this is y!")

        print(y)

        """

        frac_train identifies percentage of datapoints that need to be present in split -- so 80% training data may actually be 90% of data (but 80% of actual datapoints, not NaN, will be present in split)

        """

        sparse_dataset = self.load_sparse_multitask_dataset()

        sparse_np_list = sparse_dataset.to_numpy()

        sparse_np = sparse_np_list[1]

        """

        sparsity is determined by number of w weights that are 0 for a given task

        structure of w np array is such that each row corresponds to a sample -- e.g., analyze third column for third

        sparse task

        """

        sparse_np = sparse_np_list[2]

        #debugging

        print(sparse_np[:, 3])

        frac_train = 0.5

        cutoff = int(math.floor(frac_train * len(sparse_np)))

        sparse_np = sparse_np[:cutoff, :]

        #debugging

        print(sparse_np[:, 3])

        sparse_df = pd.DataFrame(data = sparse_np)

        #debugging

        print(sparse_df.iloc[:, 3])

        total_rows = len(sparse_df.index)

        sparse_flag = False

        # for col in sparse_df:

        #     column = sparse_df[col]

        #     NaN_count = column.isnull().sum()

        #     if NaN_count == total_rows:

        #         print("good -- one column doesn't have results")

        #         sparse_flag = True

        #         assert NaN_count == total_rows

        #         break

        # if not sparse_flag:

        #     print("dataset isn't sparse")

        #     assert sparse_flag is True

        # else:

        #     print("dataset is sparse")

        for col in sparse_df:

            column = sparse_df[col] #get series representation

            zero_count = column.value_counts()[0]

            if zero_count == total_rows:

                print("good -- one column doesn't have results")

                sparse_flag = True

                assert NaN_count == total_rows

                break

        if not sparse_flag:

            print("dataset isn't sparse")

            assert sparse_flag is True

        else:

            print("dataset is sparse")

        stratified_splitter = StratifiedSplitter()

        train_data, valid_data, test_data = \