def compute_predictions_y_known(y, n2i, model, dataGen):
    """
    Computes predictions for a model and generator, NOT aggregating scores for long contigs.

    Inputs: 
        n2i: dictionary with contig_name -> list of idx corresponding to that contig.
    Output:
        scores:
        pred: scores for individual contigs
        y: corresponding true labels
    """
    score_val = model.predict_generator(dataGen)
    score_val = score_val.flatten()
    scores = {}
    for k in n2i:
        inf = n2i[k][0]
        sup = n2i[k][-1] + 1
        if k[0] not in scores:
            scores[k[0]] = {}
        assert k[1] not in scores[k[0]]
        if sup > len(score_val):
            continue
        assert (y[inf:sup] == y[inf]).all()
        scores[k[0]][k[1]] = {'y': int(y[inf]), 'pred': score_val[inf:sup]}
    return scores
