<gh_stars>1-10
""" Evaluation script for NarrativeQA dataset. """

import nltk

try:
    nltk.data.find("tokenizers/punkt")
except LookupError:
    nltk.download("punkt")
    nltk.download("wordnet")

import rouge
from nltk.translate.bleu_score import sentence_bleu
from nltk.tokenize import word_tokenize
from nltk.translate.meteor_score import meteor_score
import copy

rouge_l_evaluator = rouge.Rouge(
    metrics=["rouge-l"],
    max_n=4,
    limit_length=True,
    length_limit=100,
    length_limit_type="words",
    apply_avg=True,
    apply_best=True,
    alpha=0.5,
    weight_factor=1.2,
    stemming=True,
)


def bleu_1(p, g):
    return sentence_bleu(g, p, weights=(1, 0, 0, 0))


def bleu_4(p, g):
    return sentence_bleu(g, p, weights=(0, 0, 0, 1))


def meteor(p, g):
    return meteor_score([x.split() for x in g], p.split())


def rouge_l(p, g):
    return rouge_l_evaluator.get_scores(p, g)


def metric_max_over_ground_truths(metric_fn, prediction, ground_truths, tokenize=False):
    scores_for_ground_truths = []
    for ground_truth in ground_truths:
        if tokenize:
            score = metric_fn(word_tokenize(prediction), [word_tokenize(ground_truth)])
        else:
            score = metric_fn(prediction, [ground_truth])
        scores_for_ground_truths.append(score)
    if isinstance(score, dict) and "rouge-l" in score:
        max_score = copy.deepcopy(score)
        max_score["rouge-l"]["f"] = round(
            max([score["rouge-l"]["f"] for score in scores_for_ground_truths]), 2
        )
        max_score["rouge-l"]["p"] = round(
            max([score["rouge-l"]["p"] for score in scores_for_ground_truths]), 2
        )
        max_score["rouge-l"]["r"] = round(
            max([score["rouge-l"]["r"] for score in scores_for_ground_truths]), 2
        )
        return max_score
    else:
        return round(max(scores_for_ground_truths), 2)


def get_metric_score(prediction, ground_truths):
    bleu_1_score = metric_max_over_ground_truths(bleu_1, prediction, ground_truths, tokenize=True)
    bleu_4_score = metric_max_over_ground_truths(bleu_4, prediction, ground_truths, tokenize=True)
    meteor_score = metric_max_over_ground_truths(meteor, prediction, ground_truths, tokenize=False)
    rouge_l_score = metric_max_over_ground_truths(
        rouge_l, prediction, ground_truths, tokenize=False
    )

    return (
        bleu_1_score,
        bleu_4_score,
        meteor_score,
        rouge_l_score["rouge-l"]["f"],
        rouge_l_score["rouge-l"]["p"],
        rouge_l_score["rouge-l"]["r"],
    )

""" Evaluation script for NarrativeQA dataset. """

import nltk

try:
    nltk.data.find("tokenizers/punkt")
except LookupError:
    nltk.download("punkt")
    nltk.download("wordnet")

import rouge
from nltk.translate.bleu_score import sentence_bleu
from nltk.tokenize import word_tokenize
from nltk.translate.meteor_score import meteor_score
import copy

rouge_l_evaluator = rouge.Rouge(
    metrics=["rouge-l"],
    max_n=4,
    limit_length=True,
    length_limit=100,
    length_limit_type="words",
    apply_avg=True,
    apply_best=True,
    alpha=0.5,
    weight_factor=1.2,
    stemming=True,
)


def bleu_1(p, g):
    return sentence_bleu(g, p, weights=(1, 0, 0, 0))


def bleu_4(p, g):
    return sentence_bleu(g, p, weights=(0, 0, 0, 1))


def meteor(p, g):
    return meteor_score([x.split() for x in g], p.split())


def rouge_l(p, g):
    return rouge_l_evaluator.get_scores(p, g)


def metric_max_over_ground_truths(metric_fn, prediction, ground_truths, tokenize=False):
    scores_for_ground_truths = []
    for ground_truth in ground_truths:
        if tokenize:
            score = metric_fn(word_tokenize(prediction), [word_tokenize(ground_truth)])
        else:
            score = metric_fn(prediction, [ground_truth])
        scores_for_ground_truths.append(score)
    if isinstance(score, dict) and "rouge-l" in score:
        max_score = copy.deepcopy(score)
        max_score["rouge-l"]["f"] = round(
            max([score["rouge-l"]["f"] for score in scores_for_ground_truths]), 2
        )
        max_score["rouge-l"]["p"] = round(
            max([score["rouge-l"]["p"] for score in scores_for_ground_truths]), 2
        )
        max_score["rouge-l"]["r"] = round(
            max([score["rouge-l"]["r"] for score in scores_for_ground_truths]), 2
        )
        return max_score
    else:
        return round(max(scores_for_ground_truths), 2)


def get_metric_score(prediction, ground_truths):
    bleu_1_score = metric_max_over_ground_truths(bleu_1, prediction, ground_truths, tokenize=True)
    bleu_4_score = metric_max_over_ground_truths(bleu_4, prediction, ground_truths, tokenize=True)
    meteor_score = metric_max_over_ground_truths(meteor, prediction, ground_truths, tokenize=False)
    rouge_l_score = metric_max_over_ground_truths(
        rouge_l, prediction, ground_truths, tokenize=False
    )

    return (
        bleu_1_score,
        bleu_4_score,
        meteor_score,
        rouge_l_score["rouge-l"]["f"],
        rouge_l_score["rouge-l"]["p"],
        rouge_l_score["rouge-l"]["r"],
    )
Evaluation script for NarrativeQA dataset.