drop_eval.py

"""
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner
https://arxiv.org/abs/1903.00161
"""

import gzip
import json
import random
import re
import string
from typing import Any, Dict, List, Optional, Set, Tuple, Union

import blobfile as bf
import numpy as np
from scipy.optimize import linear_sum_assignment

from . import common
from .mmlu_eval import HTML_JINJA
from .types import Eval, EvalResult, SamplerBase, SingleEvalResult

"""
From here through _normalize_answer was originally copied from:
https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/
Then cleaned up and modified a bit.

The rest was originally copied from https://github.com/allenai/allennlp-reading-comprehension/blob/master/allennlp_rc
/eval/drop_eval.py
"""

ANSWER_PATTERN = r"(?i)Answer\s*:\s*([^\n]+)"


def _remove_articles(text: str) -> str:
    regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
    return re.sub(regex, " ", text)


def _white_space_fix(text: str) -> str:
    return " ".join(text.split())


EXCLUDE = set(string.punctuation)


def _remove_punc(text: str) -> str:
    if not _is_number(text):
        return "".join(ch for ch in text if ch not in EXCLUDE)
    else:
        return text


def _lower(text: str) -> str:
    return text.lower()


def _tokenize(text: str) -> List[str]:
    return re.split(" |-", text)


def _normalize_answer(text: str) -> str:
    """Lower text and remove punctuation, articles and extra whitespace."""

    parts = [
        _white_space_fix(_remove_articles(_normalize_number(_remove_punc(_lower(token)))))
        for token in _tokenize(text)
    ]
    parts = [part for part in parts if part.strip()]
    normalized = " ".join(parts).strip()
    return normalized


def _is_number(text: str) -> bool:
    try:
        float(text)
        return True
    except ValueError:
        return False


def _normalize_number(text: str) -> str:
    if _is_number(text):
        return str(float(text))
    else:
        return text


def _answer_to_bags(
    answer: Union[str, List[str], Tuple[str, ...]]
) -> Tuple[List[str], List[Set[str]]]:
    if isinstance(answer, (list, tuple)):
        raw_spans = answer
    else:
        raw_spans = [answer]
    normalized_spans: List[str] = []
    token_bags = []
    for raw_span in raw_spans:
        normalized_span = _normalize_answer(raw_span)
        normalized_spans.append(normalized_span)
        token_bags.append(set(normalized_span.split()))
    return normalized_spans, token_bags


def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> List[float]:
    """
    Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
    between them and gets maximum metric values over all the answers.
    """
    scores = np.zeros([len(gold), len(predicted)])
    for gold_index, gold_item in enumerate(gold):
        for pred_index, pred_item in enumerate(predicted):
            if _match_numbers_if_present(gold_item, pred_item):
                scores[gold_index, pred_index] = _compute_f1(pred_item, gold_item)
    row_ind, col_ind = linear_sum_assignment(-scores)

    max_scores = np.zeros([max(len(gold), len(predicted))])
    for row, column in zip(row_ind, col_ind):
        max_scores[row] = max(max_scores[row], scores[row, column])
    return max_scores


def _compute_f1(predicted_bag: Set[str], gold_bag: Set[str]) -> float:
    intersection = len(gold_bag.intersection(predicted_bag))
    if not predicted_bag:
        precision = 1.0
    else:
        precision = intersection / float(len(predicted_bag))
    if not gold_bag:
        recall = 1.0
    else:
        recall = intersection / float(len(gold_bag))
    f1 = (
        (2 * precision * recall) / (precision + recall)
        if not (precision == 0.0 and recall == 0.0)
        else 0.0
    ) * 100
    return f1


def _match_numbers_if_present(gold_bag: Set[str], predicted_bag: Set[str]) -> bool:
    gold_numbers = set()
    predicted_numbers = set()
    for word in gold_bag:
        if _is_number(word):
            gold_numbers.add(word)
    for word in predicted_bag:
        if _is_number(word):
            predicted_numbers.add(word)
    if (not gold_numbers) or gold_numbers.intersection(predicted_numbers):
        return True
    return False


def get_drop_metrics(
    predicted: Union[str, List[str], Tuple[str, ...]], gold: Union[str, List[str], Tuple[str, ...]]
) -> Tuple[float, float]:
    """
    Takes a predicted answer and a gold answer (that are both either a string or a list of
    strings), and returns exact match and the DROP F1 metric for the prediction.  If you are
    writing a script for evaluating objects in memory (say, the output of predictions during
    validation, or while training), this is the function you want to call, after using
    :func:`answer_json_to_strings` when reading the gold answer from the released data file.
    """
    predicted_bags = _answer_to_bags(predicted)
    gold_bags = _answer_to_bags(gold)

    if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
        exact_match = 1.0
    else:
        exact_match = 0.0

    f1_per_bag = _align_bags(predicted_bags[1], gold_bags[1])
    f1 = np.mean(f1_per_bag)
    f1 = round(f1, 2)
    return exact_match, f1


def answer_json_to_strings(answer: Dict[str, Any]) -> Tuple[Tuple[str, ...], str]:
    """
    Takes an answer JSON blob from the DROP data release and converts it into strings used for
    evaluation.
    """
    if "number" in answer and answer["number"]:
        return tuple([str(answer["number"])]), "number"
    elif "spans" in answer and answer["spans"]:
        return tuple(answer["spans"]), "span" if len(answer["spans"]) == 1 else "spans"
    elif "date" in answer:
        return (
            tuple(
                [
                    "{0} {1} {2}".format(
                        answer["date"]["day"], answer["date"]["month"], answer["date"]["year"]
                    ).strip()
                ]
            ),
            "date",
        )
    else:
        raise ValueError(
            f"Answer type not found, should be one of number, spans or date at: {json.dumps(answer)}"
        )


def answer_json_to_string(answer_json):
    return json.dumps(answer_json_to_strings(answer_json))


def normalize(s: str) -> str:
    """Lower text and remove punctuation, articles and extra whitespace."""
    s = s.lower()
    exclude = set(string.punctuation)
    s = "".join(char for char in s if char not in exclude)
    s = re.sub(r"\b(a|an|the)\b", " ", s)
    s = " ".join(s.split())
    return s


def fuzzy_match(s1: str, s2: str) -> bool:
    s1 = normalize(s1)
    s2 = normalize(s2)

    if s1 == "" or s2 == "":
        return s1 == s2

    return s1 in s2 or s2 in s1


def drop_metric(sample: str, reference: list[str]) -> Tuple[float, float]:
    em_scores = []
    f1_scores = []
    for answer in reference:
        if answer.strip() != "":
            em, f1 = get_drop_metrics(sample, answer)
            em_scores.append(em)
            f1_scores.append(f1)
    return (max(em_scores), max(f1_scores))


class DropEval(Eval):
    def __init__(self, num_examples: int | None = None, train_samples_per_prompt: int = 3):
        self.seed = 42
        self._num_examples = num_examples
        self._train_samples_per_prompt = train_samples_per_prompt
        self.train_jsonl = (
            "https://openaipublic.blob.core.windows.net/simple-evals/drop_v0_train.jsonl.gz"
        )
        self.test_jsonl = (
            "https://openaipublic.blob.core.windows.net/simple-evals/drop_v0_dev.jsonl.gz"
        )
        with gzip.GzipFile(fileobj=bf.BlobFile(self.train_jsonl, "rb"), mode="rb") as f:
            self.train_samples = list(map(json.loads, f.readlines()))
        with gzip.GzipFile(fileobj=bf.BlobFile(self.test_jsonl, "rb"), mode="rb") as f:
            self.test_samples = list(map(json.loads, f.readlines()))
            if self._num_examples:
                self.test_samples = random.Random(self.seed).sample(
                    self.test_samples, self._num_examples
                )

    def __call__(self, sampler: SamplerBase) -> EvalResult:
        rng = random.Random(self.seed)

        def fn(example: dict[str, str]):
            stuffing = rng.sample(self.train_samples, self._train_samples_per_prompt)

            # prompt = """TASK: Read the provided passage, then identify the correct answer to questions below."""
            prompt = """You will be asked to read a passage and answer a question. Some examples of passages and Q&A are provided below."""
            prompt += "\n\n# Examples"
            samples = stuffing + [example]
            for i, sample in enumerate(samples):
                is_test = i == len(stuffing)
                prompt += "\n# Your Task\n" if is_test else ""
                prompt += f"""
---
{sample["context"]} """

                a = sample["completion"]
                correct_answers = sample["ref_text"].split("|")

                if not is_test:
                    prompt += a + "\n"
                else:
                    prompt += """\n
Think step by step, then write a line of the form "Answer: $ANSWER" at the end of your response.
                    """
                    prompt_messages = [dict(content=prompt, role="user")]
                    response_text = sampler(prompt_messages)
                    match = re.search(ANSWER_PATTERN, response_text)
                    extracted_answer = match.group(1) if match else response_text
                    em_score, f1_score = drop_metric(extracted_answer, correct_answers)
                    matches = [
                        fuzzy_match(extracted_answer, correct_answer)
                        for correct_answer in correct_answers
                    ]
                    extracted_answers = [
                        extracted_answer for i in range(len(correct_answers)) if matches[i]
                    ]
                    score = True in matches
                    html = common.jinja_env.from_string(HTML_JINJA).render(
                        prompt_messages=prompt_messages,
                        next_message=dict(content=extracted_answer, role="assistant"),
                        score=score,
                        correct_answer=correct_answers,
                        extracted_answer=extracted_answers,
                    )
                    convo = prompt_messages + [dict(content=extracted_answer, role="assistant")]
                    return SingleEvalResult(
                        html=html,
                        score=score,
                        convo=convo,
                        metrics={"em_score": em_score, "f1_score": f1_score},
                    )

        results = common.map_with_progress(fn, self.test_samples)
        return common.aggregate_results(results)