utils.py

from rouge_score import rouge_scorer, scoring
from typing import Callable, Dict, Iterable, List, Tuple, Union

import re

from filelock import FileLock
try:
    import nltk

    NLTK_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
    NLTK_AVAILABLE = False

if NLTK_AVAILABLE:
    with FileLock(".lock") as lock:
        nltk.download("punkt", quiet=True)


def add_newline_to_end_of_each_sentence(x: str) -> str:
    """This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS."""
    re.sub("<n>", "", x)  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(x))



def freeze_encoder(model):
    for param in model.model.encoder.parameters():
      param.requires_grad = False


def freeze(model):
  for param in model.parameters():
    param.requires_grad = False

def unfreeze(model):
  for param in model.parameters():
    param.requires_grad = True


## ROUGE Utils
ROUGE_KEYS = ["rouge1", "rouge2", "rougeL", "rougeLsum"]


def extract_rouge_mid_statistics(dct):
    new_dict = {}
    for k1, v1 in dct.items():
        mid = v1.mid
        new_dict[k1] = {stat: round(getattr(mid, stat), 4) for stat in ["precision", "recall", "fmeasure"]}
    return new_dict


def calculate_rouge(
    pred_lns: List[str],
    tgt_lns: List[str],
    use_stemmer=True,
    rouge_keys=ROUGE_KEYS,
    return_precision_and_recall=False,
    bootstrap_aggregation=True,
    newline_sep=True,
) -> Dict:
    """Calculate rouge using rouge_scorer package.

    Args:
        pred_lns: list of summaries generated by model
        tgt_lns: list of groundtruth summaries (e.g. contents of val.target)
        use_stemmer:  Bool indicating whether Porter stemmer should be used to
        strip word suffixes to improve matching.
        rouge_keys:  which metrics to compute, defaults to rouge1, rouge2, rougeL, rougeLsum
        return_precision_and_recall: (False) whether to also return precision and recall.
        bootstrap_aggregation: whether to do the typical bootstrap resampling of scores. Defaults to True, if False
            this function returns a collections.defaultdict[metric: list of values for each observation for each subscore]``
        newline_sep:(default=True) whether to add newline between sentences. This is essential for calculation rougeL
        on multi sentence summaries (CNN/DM dataset).

    Returns:
         Dict[score: value] if aggregate else defaultdict(list) keyed by rouge_keys

    """
    scorer = rouge_scorer.RougeScorer(rouge_keys, use_stemmer=use_stemmer)
    aggregator = scoring.BootstrapAggregator()
    for pred, tgt in zip(tgt_lns, pred_lns):
        # rougeLsum expects "\n" separated sentences within a summary
        if newline_sep:
            pred = add_newline_to_end_of_each_sentence(pred)
            tgt = add_newline_to_end_of_each_sentence(tgt)
        scores = scorer.score(pred, tgt)
        aggregator.add_scores(scores)

    if bootstrap_aggregation:
        result = aggregator.aggregate()
        if return_precision_and_recall:
            return extract_rouge_mid_statistics(result)  # here we return dict
        else:
            return {k: round(v.mid.fmeasure * 100, 4) for k, v in result.items()}

    else:
        return aggregator._scores  # here we return defaultdict(list)