def epic100_unseen_tail_eval()

in notebooks/utils.py [0:0]

• 50 lines of code
• 2 McCabe index (conditional complexity)

def epic100_unseen_tail_eval(probs, dataset):
    """
    probs: contains 3 elements: predictions for verb, noun and action
    """
    # based on https://github.com/fpv-iplab/rulstm/blob/d44612e4c351ff668f149e2f9bc870f1e000f113/RULSTM/main.py#L379
    unseen_participants_ids = pd.read_csv(osp.join(
        dataset.rulstm_annotation_dir,
        'validation_unseen_participants_ids.csv'),
                                          names=['id'],
                                          squeeze=True)
    tail_verbs_ids = pd.read_csv(osp.join(dataset.rulstm_annotation_dir,
                                          'validation_tail_verbs_ids.csv'),
                                 names=['id'],
                                 squeeze=True)
    tail_nouns_ids = pd.read_csv(osp.join(dataset.rulstm_annotation_dir,
                                          'validation_tail_nouns_ids.csv'),
                                 names=['id'],
                                 squeeze=True)
    tail_actions_ids = pd.read_csv(osp.join(dataset.rulstm_annotation_dir,
                                            'validation_tail_actions_ids.csv'),
                                   names=['id'],
                                   squeeze=True)
    # Now based on https://github.com/fpv-iplab/rulstm/blob/d44612e4c351ff668f149e2f9bc870f1e000f113/RULSTM/main.py#L495
    unseen_bool_idx = dataset.df.narration_id.isin(
        unseen_participants_ids).values
    tail_verbs_bool_idx = dataset.df.narration_id.isin(tail_verbs_ids).values
    tail_nouns_bool_idx = dataset.df.narration_id.isin(tail_nouns_ids).values
    tail_actions_bool_idx = dataset.df.narration_id.isin(
        tail_actions_ids).values
    # For tail
    _, _, vrec5_tail, _, _ = compute_accuracy(
        probs[0][tail_verbs_bool_idx],
        dataset.df.verb_class.values[tail_verbs_bool_idx])
    _, _, nrec5_tail, _, _ = compute_accuracy(
        probs[1][tail_nouns_bool_idx],
        dataset.df.noun_class.values[tail_nouns_bool_idx])
    _, _, arec5_tail, _, _ = compute_accuracy(
        probs[2][tail_actions_bool_idx],
        dataset.df.action_class.values[tail_actions_bool_idx])
    # for unseen
    _, _, vrec5_unseen, _, _ = compute_accuracy(
        probs[0][unseen_bool_idx],
        dataset.df.verb_class.values[unseen_bool_idx])
    _, _, nrec5_unseen, _, _ = compute_accuracy(
        probs[1][unseen_bool_idx],
        dataset.df.noun_class.values[unseen_bool_idx])
    _, _, arec5_unseen, _, _ = compute_accuracy(
        probs[2][unseen_bool_idx],
        dataset.df.action_class.values[unseen_bool_idx])
    return dict(
        vrec5_tail=vrec5_tail,
        nrec5_tail=nrec5_tail,
        arec5_tail=arec5_tail,
        vrec5_unseen=vrec5_unseen,
        nrec5_unseen=nrec5_unseen,
        arec5_unseen=arec5_unseen,
    )