more

        X, y, w, ids = sparse_dataset.to_numpy()

        ############################################ DEBUG

        print("y[11]")

        print(y[11])

        print("w[11]")

        print(w[11])

        ############################################ DEBUG

        """

        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

        """

        frac_train = 0.5

        cutoff = int(frac_train * w.shape[0])

        w = w[:cutoff, :]

        sparse_flag = False

        colIndex = 0

        for i in range(3):

            print("columns")

            print(w.T[:, i])

        print(w)

        print(w.T)

        for col in w.T:

            if not np.any(col): #check to see if any columns are all zero

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