Repository: apple/ml-mkqa
Branch: main
Commit: 651b8cc85c40
Files: 18
Total size: 4.1 MB

Directory structure:
gitextract_i684binf/

├── .gitignore
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── mkqa_eval.py
├── mkqa_eval_all_languages.py
├── mkqa_eval_util.py
├── requirements.txt
├── sample_predictions/
│   ├── en.jsonl
│   ├── es.jsonl
│   ├── results/
│   │   ├── en_results/
│   │   │   └── metrics.json
│   │   ├── es_results/
│   │   │   └── metrics.json
│   │   ├── metrics.json
│   │   └── zh_cn_results/
│   │       └── metrics.json
│   └── zh_cn.jsonl
└── tests/
    ├── test_mkqa_eval.py
    └── testdata/
        └── en_prediction.jsonl

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FILE CONTENTS
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FILE: .gitignore
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FILE: CODE_OF_CONDUCT.md
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# Code of Conduct

## Our Pledge

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# Contribution Guide

Thanks for your interest in contributing. This project was released to accompany a research paper for purposes of reproducability, and beyond its publication there are limited plans for future development of the repository.

While we welcome new pull requests and issues please note that our response may be limited. Forks and out-of-tree improvements are strongly encouraged.

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We ask that all community members read and observe our [Code of Conduct](CODE_OF_CONDUCT.md).

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================================================
FILE: README.md
================================================
# MKQA: Multilingual Knowledge Questions & Answers

[**Tasks**](#task-description) | [**Dataset**](#dataset) | [**Leaderboard**](#leaderboard) | [**Evaluation**](#evaluation) |
[**Paper**](https://arxiv.org/abs/2007.15207) |
[**Citation**](#citation) | [**License**](#license)

We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs 
aligned across 26 typologically diverse languages (260k question-answer pairs in total).
The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of
languages. Please refer to our paper for details, [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://arxiv.org/abs/2007.15207)


## Task Description
Given a question <code>q<sup>l</sup></code> in language `l`, the task is to produce a prediction <code>p<sup>l</sup></code> in {No Answer, Yes, No, Text Answer}, 
where a Text Answer is a span of tokens in the corresponding language. 
<code>p<sup>l</sup></code> can be obtained by any method, extracted from a document, generated, or derived from a knowledge graph.
Wherever possible, textual answers are accompanied by Wikidata QIDs, for entity linking and evaluating knowledge graph approaches. 
These QIDs also enable automatic translations for most answers into any Wikipedia language through the Wikidata knowledge graph.


## Dataset
MKQA contains 10,000 queries sampled from the [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions).  

For each query we collect new passage-independent answers. 
These queries and answers are then human translated into 25 Non-English languages.
MKQA data can be downloaded from [here](dataset/mkqa.jsonl.gz).

Each example in the dataset contains the unique Natural Questions `example_id`, the original English `query`, and then `queries` and `answers` in 26 languages.

```
{
 'example_id': 563260143484355911,
 'queries': {
  'en': "who sings i hear you knocking but you can't come in",
  'ru': "кто поет i hear you knocking but you can't come in",
  'ja': '「 I hear you knocking」は誰が歌っていますか',
  'zh_cn': "《i hear you knocking but you can't come in》是谁演唱的",
  ...
 },
 'query': "who sings i hear you knocking but you can't come in",
 'answers': {'en': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'Dave Edmunds',
    'aliases': []}],
  'ru': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'Эдмундс, Дэйв',
    'aliases': ['Эдмундс', 'Дэйв Эдмундс', 'Эдмундс Дэйв', 'Dave Edmunds']}],
  'ja': [{'type': 'entity',
    'entity': 'Q545186',
    'text': 'デイヴ・エドモンズ',
    'aliases': ['デーブ・エドモンズ', 'デイブ・エドモンズ']}],
  'zh_cn': [{'type': 'entity', 'text': '戴维·埃德蒙兹 ', 'entity': 'Q545186'}],
  ...
  },
}
```
Each answer is labelled with an answer type. The breakdown is:

| Answer Type | Occurrence |
|---------------|---------------|
| `entity`               | `4221`             |
| `long_answer`          | `1815`             |
| `unanswerable`         | `1427`             |
| `date`                 | `1174`             |
| `number`               | `485`              |
| `number_with_unit`     | `394`              |
| `short_phrase`         | `346`              |
| `binary`               | `138`              |
  
For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers. 
All the supported languages are:  

| Language code | Language name |
|---------------|---------------|
| `ar`     | `Arabic`                    |
| `da`     | `Danish`                    |
| `de`     | `German`                    |
| `en`     | `English`                   |
| `es`     | `Spanish`                   |
| `fi`     | `Finnish`                   |
| `fr`     | `French`                    |
| `he`     | `Hebrew`                    |
| `hu`     | `Hungarian`                 |
| `it`     | `Italian`                   |
| `ja`     | `Japanese`                  |
| `ko`     | `Korean`                    |
| `km`     | `Khmer`                    |
| `ms`     | `Malay`                     |
| `nl`     | `Dutch`                     |
| `no`     | `Norwegian`                 |
| `pl`     | `Polish`                    |
| `pt`     | `Portuguese`                |
| `ru`     | `Russian`                   |
| `sv`     | `Swedish`                   |
| `th`     | `Thai`                      |
| `tr`     | `Turkish`                   |
| `vi`     | `Vietnamese`                |
| `zh_cn`     | `Chinese (Simplified)`   |
| `zh_hk`     | `Chinese (Hong kong)`    |
| `zh_tw`     | `Chinese (Traditional)`  |


## Leaderboard
| Model name | Best Overall F1 | Best Answerable F1 | Best Unnswerable F1 | Link to paper |
|---------------|---------------|---------------|---------------|---------------|
| (Baseline) XLM-R Large Translate Train   |46.0        | 27.6        |84.5        |https://arxiv.org/pdf/1911.02116.pdf        |

> Submit a pull request to this repository to add yourself to the MKQA leaderboard. Scores are ordered by Best Overall F1.

## Evaluation
The official evaluation scripts provide two ways to evaluate performance on the MKQA dataset

The evaluation script expects a json lines (jsonl) prediction file with a specific format:
```
{
  "example_id": -7449157003522518870,
  "prediction": "Hafþór Júlíus `` Thor '' Björnsson",
  "binary_answer": null,
  "no_answer_prob": 0.23618
}
...
```

### Evaluate performance for single language 
To evaluate prediscion for a single language, use `mkqa_eval.py` script and indicate prediction language
```
python mkqa_eval.py --annotation_file ./dataset/mkqa.jsonl.gz \
 --predictions_file ./sample_predictions/en.jsonl \
 --language en \
 (--out_dir <optional output directory for saving metrics and pr curves>, --verbose) 
```

### Evaluate performances for all languages
To evaluate predictions for all languages, use the following script. Save the prediction file for each language in the same directory and name each prediction file by its language code, such as `en.jsonl`
```
python mkqa_eval_all_languages.py --annotation_file ./dataset/mkqa.jsonl.gz \
 --predictions_dir ./sample_predictions \
 (--out_dir <optional output directory for saving metrics and pr curves>, --verbose)
``` 

To get our the zero shot multilingual bert baseline, use the provided prediction jsonl files [here][./sample_predictions] 
Sample output:

```
+---------------+-----------+-----------+----------------------+----------------------+------------------------+---------------------+
| language      |   best_em |   best_f1 |   best_answerable_em |   best_answerable_f1 |   best_unanswerable_em |   best_f1_threshold |
|---------------+-----------+-----------+----------------------+----------------------+------------------------+---------------------|
| en            |     45.39 |     51.97 |                26.65 |                36.38 |                  84.45 |               -5.95 |
| es            |     39.73 |     43.83 |                16.69 |                22.76 |                  87.75 |               -2.52 |
| zh_cn         |     32.42 |     32.43 |                 0    |                 0.01 |                  100   |              -10.53 |
| Macro Average |     39.18 |     42.74 |                14.45 |                19.72 |                  90.73 |               -6.33 |
+---------------+-----------+-----------+----------------------+----------------------+------------------------+---------------------+
```  
> Here it computes the macro average over the languages for which prediction files were supplied for.
The official macro-average requires all 26 prediction files to be included.

To run tests for the evaluation scripts
```
pytest ./tests/test_mkqa_eval.py
```


## Citation
Please cite the following if you found MKQA, our [paper](https://arxiv.org/abs/2007.15207), or these resources useful.
```
@misc{mkqa,
    title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
    author = {Shayne Longpre and Yi Lu and Joachim Daiber},
    year = {2020},
    URL = {https://arxiv.org/pdf/2007.15207.pdf}
}
```

## License
The code in this repository is licensed according to the [LICENSE](LICENSE) file.


The Multilingual Knowledge Questions and Answers dataset is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/  


## Contact Us
To contact us feel free to email the authors in the paper or create an issue in this repository.


================================================
FILE: mkqa_eval.py
================================================
import argparse
import collections
import json
import logging
import os
import sys
from gzip import GzipFile
from typing import Dict, Optional, Any

import numpy as np

import mkqa_eval_util as eval_util

MKQA_LANGUAGES = [
    "ar",
    "da",
    "de",
    "en",
    "es",
    "fi",
    "fr",
    "he",
    "hu",
    "it",
    "ja",
    "km",
    "ko",
    "ms",
    "nl",
    "no",
    "pl",
    "pt",
    "ru",
    "sv",
    "th",
    "tr",
    "vi",
    "zh_cn",
    "zh_hk",
    "zh_tw",
]

# A data structure for storing annotations
MKQAAnnotation = collections.namedtuple(
    "MKQAAnnotation",
    [
        "example_id",  # The unique ID for each example.
        "types",  # All answer types selected by inter-grader agreement
        "answers",  # All valid answer strings
    ],
)

# A data structure for storing predictions
MKQAPrediction = collections.namedtuple(
    "MKQAPrediction",
    [
        "example_id",  # The unique ID for each example.
        "prediction",  # The predicted answer text ("" serves as No Answer)
        "binary_answer",  # Is answer {"yes", "no"} (case insensitive), or `None` (indicating neither)
        "no_answer_prob",
        # (Optional) Score/probability that the answer is No Answer. Used to select the best threshold that maximizes F1.
    ],
)


def parse_args():
    parser = argparse.ArgumentParser("Official evaluation script for single language MKQA.")
    parser.add_argument(
        "-a",
        "--annotation_file",
        metavar="mkqa.jsonl.gz",
        required=True,
        help="Input annotations MKQA JSON Lines gzip file.",
    )
    parser.add_argument(
        "-p",
        "--predictions_file",
        metavar="preds.json",
        required=True,
        help="Model predictions json line file",
    )
    parser.add_argument(
        "-l",
        "--language",
        required=True,
        choices=MKQA_LANGUAGES,
        help=f"Language code. Accepts any of: {MKQA_LANGUAGES}",
    )
    parser.add_argument(
        "-o",
        "--out-dir",
        metavar="results/",
        help="Write accuracy metrics to file (default is stdout).",
    )
    parser.add_argument("-v", "--verbose", action="store_true")

    parser.add_argument("--print_metrics", "-m", action="store_true", default=True)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)
    return parser.parse_args()


def read_annotations(gold_path: str) -> Dict[str, Any]:
    """Read mkqa gold annotation for all languages

    Args:
        gold_path: path to mkqa annotation file, such as mkqa.jsonl.gz

    Returns:
        A mapping from example id to MKQAAnnotation for all languages
    """
    assert os.path.exists(gold_path)

    all_gold_annotations = collections.defaultdict(dict)
    gzipped_input_file = open(gold_path, "rb")
    with GzipFile(fileobj=gzipped_input_file) as input_file:
        for line in input_file:
            example = json.loads(line)

            for language in MKQA_LANGUAGES:
                valid_answers, answer_types = [], []
                for answer in example["answers"][language]:
                    # Binary (Yes/No) answer text is always "yes" / "no"
                    # If answer['text'] is None then it `""` represents No Answer
                    valid_answers.append(answer["text"] or "")
                    valid_answers.extend(answer.get("aliases", []))
                    answer_types.append(answer["type"])

                annotation = MKQAAnnotation(
                    example_id=str(example["example_id"]),
                    types=list(set(answer_types)),
                    answers=list(set(valid_answers)),
                )

                all_gold_annotations[language][annotation.example_id] = annotation

    for lang, annotations in all_gold_annotations.items():
        if len(annotations) != 10000:
            logging.warning(
                f"The annotations file you've provided contains {len(all_gold_annotations)} for language {lang} examples, where 10000 are expected."
            )
    return all_gold_annotations


def read_predictions(predictions_path: str) -> Dict[str, MKQAPrediction]:
    """Read model prediction json file

    Args:
        predictions_path: path to prediction json line file
        {
            "example_id": <example id>,
            "prediction": <prediction text>,
            "binary_answer": <"yes", "no", "">,
            "no_answer_prob": <prob>
        }

    Returns:
        A mapping from example id to MKQAPrediction
    """
    assert os.path.exists(predictions_path)
    logging.info(f"Reading predictions from file: {predictions_path}")
    with open(predictions_path, "r") as f:
        predictions = [json.loads(l) for l in f.readlines()]

    predict_labels = {}
    for prediction in predictions:
        binary_answer = prediction["binary_answer"].lower() if prediction["binary_answer"] else None
        assert binary_answer in {
            "yes",
            "no",
            None,
        }, f"Binary prediction can only be yes, no, or None. Provided answer was {binary_answer}"

        predict_label = MKQAPrediction(
            example_id=str(prediction["example_id"]),
            prediction=prediction["prediction"] or "",
            binary_answer=binary_answer,
            no_answer_prob=prediction.get("no_answer_prob", 0),
        )

        predict_labels[predict_label.example_id] = predict_label

    return predict_labels


def compute_mkqa_scores_for_language(
    predictions: Dict[str, MKQAPrediction],
    gold_annotations: Dict[str, MKQAAnnotation],
    language: str,
) -> (Dict[str, float], Dict[str, float]):
    """
    Compute Exact Match and token overlap F1 scores per answer, similar to SQuAD

    Args:
        predictions: mapping from example id to MKQAPrediction
        gold_annotations: mapping from example id to MKQAAnnotation
        language: evaluation language in MKQA_LANGUAGES

    Returns:
        Squad like em and f1
    """
    predict_texts, answer_texts = [], []
    for example_id, gold_annotation in gold_annotations.items():
        assert example_id in predictions, f"{example_id} is missing from prediction"
        predict_texts.append(
            predictions[example_id].binary_answer or predictions[example_id].prediction
        )
        answer_texts.append(gold_annotation.answers)

    text_metrics = eval_util.get_text_metrics(predict_texts, answer_texts, language)
    f1_scores = {example_id: text_metrics["f1"][i] for i, example_id in enumerate(gold_annotations)}
    em_scores = {
        example_id: text_metrics["exact_match"][i] for i, example_id in enumerate(gold_annotations)
    }
    return em_scores, f1_scores


def compute_best_threshold(
    predictions: Dict[str, MKQAPrediction],
    raw_em: Dict[str, float],
    raw_f1: Dict[str, float],
    no_answer_probs: Dict[str, float],
    qid_is_answerable: Dict[str, bool],
) -> Dict[str, float]:
    """Compute the averaged text metrics at the best threshold chosen to maximize F1.
    The threshold is varied over No Answer probabilities as provided in the predictions file.

    Args:
        predictions: mapping from example id to MKQAPrediction
        raw_em: mapping from example id to em
        raw_f1: mapping from example id to f1
        no_answer_probs: mapping from example id to no_answer_probs
        qid_is_answerable: mapping from example id to boolean indicator for whether the example is answerable

    Returns:
        text metrics at the best threshold of f1
    """
    id_to_predtext = {exid: p.binary_answer or p.prediction for exid, p in predictions.items()}
    ans_em_scores = {qid: raw_em[qid] for qid in raw_em if qid_is_answerable[qid]}
    ans_f1_scores = {qid: raw_f1[qid] for qid in raw_f1 if qid_is_answerable[qid]}
    unans_em_scores = {qid: raw_em[qid] for qid in raw_em if not qid_is_answerable[qid]}

    best_scores = eval_util.compute_best_score_and_threshold(
        id_to_predtext, raw_f1, no_answer_probs, qid_is_answerable
    )
    best_f1, f1_threshold = best_scores["best_score"], best_scores["best_threshold"]

    best_em_by_id = eval_util.apply_no_answer_threshold(
        raw_em, no_answer_probs, qid_is_answerable, f1_threshold
    )

    best_answerable_em_by_id = eval_util.apply_no_answer_threshold(
        ans_em_scores, no_answer_probs, qid_is_answerable, f1_threshold
    )

    best_answerable_f1_by_id = eval_util.apply_no_answer_threshold(
        ans_f1_scores, no_answer_probs, qid_is_answerable, f1_threshold
    )

    best_unanswerable_em_by_id = eval_util.apply_no_answer_threshold(
        unans_em_scores, no_answer_probs, qid_is_answerable, f1_threshold
    )

    return {
        "best_em": round(100.0 * np.mean(list(best_em_by_id.values())), 2),
        "best_f1": round(best_f1, 2),
        "best_answerable_em": round(100.0 * np.mean(list(best_answerable_em_by_id.values())), 2),
        "best_answerable_f1": round(100.0 * np.mean(list(best_answerable_f1_by_id.values())), 2),
        "best_unanswerable_em": round(
            100.0 * np.mean(list(best_unanswerable_em_by_id.values())), 2
        ),
        "best_f1_threshold": round(f1_threshold, 2),
    }


def evaluate(
    annotations: Dict[str, MKQAAnnotation],
    predictions: Dict[str, MKQAPrediction],
    language: str,
    out_dir: Optional[str] = None,
    verbose: bool = False,
    print_metrics: bool = True,
) -> Dict[str, Any]:
    """Evaluates predictions on the gold answers for the specified `language`.

    Args:
        annotations: a mapping from example id to corresponding MKQAAnnotation
        predictions: a mapping from example id to corresponding MKQAPrediction
        language: language code in MKQA_LANGUAGES
        out_dir: (Optional) Saves evaluation results into this directory.
            f1_plot.png: comparing answerable, unanswerable, and overall f1 across all thresholds
            metrics.json: reports best_em, best_f1, best_answerable_em, best_answerable_f1, best_unanswerable_em, and best_f1_threshold
            na_prob_hist_hasAns.png: histgram of no answer probability for answerable questions
            na_prob_hist_noAns.png: histgram of no answer probability for unanswerable questions
        print_metrics: (Optional) Print metrics to console

    Returns:
        A dictionary of metrics and individual f1 and em scores
    """

    # Argument validation
    assert language in MKQA_LANGUAGES

    raw_em_scores, raw_f1_scores = compute_mkqa_scores_for_language(
        predictions, annotations, language=language
    )

    qid_is_answerable = {
        ex_id: bool(annotation.answers != [""]) for ex_id, annotation in annotations.items()
    }

    # Find best thresholds
    no_answer_probs = {ex_id: p.no_answer_prob for ex_id, p in predictions.items()}

    metrics = compute_best_threshold(
        predictions, raw_em_scores, raw_f1_scores, no_answer_probs, qid_is_answerable
    )

    if out_dir:
        if not os.path.exists(out_dir):
            os.makedirs(out_dir, exist_ok=True)
        has_ans_qids = [ex_id for ex_id in qid_is_answerable if qid_is_answerable[ex_id]]
        no_ans_qids = [ex_id for ex_id in qid_is_answerable if not qid_is_answerable[ex_id]]
        if has_ans_qids:
            eval_util.plot_na_prob_histogram(no_answer_probs, has_ans_qids, out_dir, "hasAns")
        if no_ans_qids:
            eval_util.plot_na_prob_histogram(no_answer_probs, no_ans_qids, out_dir, "noAns")

        # plot the answerable, unanswerable and overall f1 curve
        ans_f1_scores = {qid: raw_f1_scores[qid] for qid in raw_f1_scores if qid_is_answerable[qid]}
        unans_em_scores = {
            qid: raw_em_scores[qid] for qid in raw_em_scores if not qid_is_answerable[qid]
        }

        eval_util.plot_f1(
            ans_f1_scores, unans_em_scores, no_answer_probs, qid_is_answerable, out_dir
        )

    if verbose:
        default_metrics = eval_util.summarize_default_metrics(
            raw_em_scores, raw_f1_scores, qid_is_answerable, metrics,
        )

        metrics.update(default_metrics)

    if print_metrics:
        print(json.dumps(metrics, indent=4))

    if out_dir:
        with open(os.path.join(out_dir, "metrics.json"), "w") as f:
            f.write(json.dumps(metrics, indent=4))

    return metrics


if __name__ == "__main__":
    args = parse_args()
    annotations = read_annotations(args.annotation_file)[args.language]
    predictions = read_predictions(args.predictions_file)
    evaluate(
        annotations, predictions, args.language, args.out_dir, args.verbose,
    )


================================================
FILE: mkqa_eval_all_languages.py
================================================
import argparse
import logging
import os
import sys
from collections import defaultdict
from multiprocessing.pool import Pool
from typing import Optional, Dict

import pandas as pd
from tabulate import tabulate

import mkqa_eval
import mkqa_eval_util as eval_util


def parse_args():
    parser = argparse.ArgumentParser("Official evaluation script for all MKQA languages.")
    parser.add_argument(
        "-a",
        "--annotation_file",
        metavar="mkqa.jsonl.gz",
        required=True,
        help="Input annotations MKQA JSON Lines gzip file.",
    )
    parser.add_argument(
        "-p",
        "--predictions_dir",
        metavar="preds/",
        required=True,
        help="Model predictions for each language",
    )
    parser.add_argument(
        "-o",
        "--out-dir",
        metavar="results/",
        help="Write evaluation metrics to files (default is stdout).",
    )
    parser.add_argument("--verbose", "-v", action="store_true")

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)
    return parser.parse_args()


def read_prediction_dir(predictions_dir: str) -> Dict[str, Dict[str, mkqa_eval.MKQAAnnotation]]:
    """Read a directory that contains predictions for all languages

    Args:
        predictions_dir:  a directory that contains predictions for all languages

    Returns:
        A mapping from example id to MKQAPrediction for all languages
    """

    assert os.path.exists(predictions_dir)
    all_language_predictions = {}
    for language in mkqa_eval.MKQA_LANGUAGES:
        prediction_file = os.path.join(predictions_dir, f"{language}.jsonl")
        if not os.path.exists(prediction_file):
            logging.info(
                f"WARNING: Missing predictions file for language `{language}`. Expecting file `{prediction_file}``"
            )
            continue

        all_language_predictions[language] = mkqa_eval.read_predictions(prediction_file)

    return all_language_predictions


def evaluate_predictions(
    annotations: Dict[str, mkqa_eval.MKQAAnnotation],
    predictions: Dict[str, mkqa_eval.MKQAPrediction],
    language: str,
    out_dir: str = None,
    verbose: bool = False,
) -> Dict[str, Dict[str, float]]:
    """Evaluate predictions for one language from its prediction file and MKQAAnnotations

    Args:
        annotations: a mapping from example id to corresponding MKQAAnnotation
        predictions: a mapping from example id to corresponding MKQAPrediction
        language: language code in MKQA_LANGUAGES
        out_dir: (Optional) Saves evaluation results into this directory.
        verbose: (Optional) Collection additional metrics

    Returns:
        A dictionary of metrics for the specified language
    """
    metrics_by_language = {}

    metrics = mkqa_eval.evaluate(
        annotations=annotations,
        predictions=predictions,
        language=language,
        out_dir=os.path.join(out_dir, f"{language}_results/") if out_dir else None,
        verbose=verbose,
        print_metrics=False,
    )
    metrics_by_language[language] = metrics
    return metrics_by_language


def evaluate_all_languages(
    annotations_path: str,
    all_language_predictions: Dict[str, Dict[str, mkqa_eval.MKQAAnnotation]],
    out_dir: Optional[str] = None,
    verbose: bool = False,
    serialize: bool = False,
):
    """Evaluate all predictions for all supported languages

    Args:
        annotations_path: annotations MKQA JSON Lines gzip file.
        all_language_predictions: a mapping from example id to MKQAPrediction for all languages
        out_dir: (Optional) saves evaluation results into this directory.
        verbose: (Optional) print out additional metrics
        serialize: (Optional) disable parallel processing

    """
    assert os.path.exists(annotations_path), "Missing MKQA annotation file"

    if out_dir and not os.path.exists(out_dir):
        os.makedirs(out_dir, exist_ok=True)
    all_annotations = mkqa_eval.read_annotations(annotations_path)

    metrics_by_language = {}
    if not serialize:
        with Pool() as pool:
            values = pool.starmap(
                evaluate_predictions,
                [
                    (
                        all_annotations[language],
                        all_language_predictions[language],
                        language,
                        out_dir,
                        verbose,
                    )
                    for language in all_language_predictions.keys()
                ],
            )
    else:
        values = []
        for language in all_language_predictions.keys():
            values.append(
                evaluate_predictions(
                    all_annotations[language],
                    all_language_predictions[language],
                    language,
                    out_dir,
                    verbose,
                )
            )

    for value in values:
        metrics_by_language.update(value)

    # Here we compute the macro average over the languages for which prediction files were supplied for.
    # The official macro-average requires all 26 prediction files to be included.
    metrics_by_language["Macro Average"] = eval_util.aggregate_summaries(
        list(metrics_by_language.values())
    )

    logging.info("Metrics by languages:")
    metrics_summary = defaultdict(list)
    for language, metrics in metrics_by_language.items():
        metrics_summary["language"].append(language)
        for key, value in metrics.items():
            metrics_summary[key].append(value)

    metrics_df = pd.DataFrame(metrics_summary)
    print(tabulate(metrics_df, headers="keys", tablefmt="psql", showindex=False))

    if out_dir:
        metrics_df.to_json(os.path.join(out_dir, "metrics.json"))


if __name__ == "__main__":
    args = parse_args()
    all_language_predictions = read_prediction_dir(args.predictions_dir)
    evaluate_all_languages(
        args.annotation_file, all_language_predictions, args.out_dir, args.verbose,
    )


================================================
FILE: mkqa_eval_util.py
================================================
import collections
import os
import re
import string
from collections import Counter, OrderedDict
from multiprocessing import Pool
from typing import Dict, List, Optional

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

matplotlib.use("Agg")

MIXED_SEGMENTATION_LANGS = ["zh_cn", "zh_hk", "zh_tw", "ja", "th", "km"]

ARTICLE_REGEX_BY_LANG = {
    "en": r"\b(a|an|the)\b",
    "es": r"\b(un|una|unos|unas|el|la|los|las)\b",
    "vi": r"\b(của|là|cái|chiếc|những)\b",
    "de": r"\b(ein|eine|einen|einem|eines|einer|der|die|das|den|dem|des)\b",
    "ar": "\sال^|ال",
    "nl": r"\b(de|het|een|des|der|den)\b",
    "sv": r"\b(en|ett)\b",
    "da": r"\b(en|et)\b",
    "no": r"\b(en|et|ei)\b",
    "fr": r"\b(le|la|l'|les|du|de|d'|des|un|une|des)",
    "pt": r"\b(o|a|os|as|um|uma|uns|umas)\b",
    "it": r"\b(il|lo|la|l'|i|gli|le|del|dello|della|dell'|dei|degli|degl'|delle|un'|uno|una|un)",
    "fi": r"\b(se|yks|yksi)\b",
    "hu": r"\b(a|az|egy)\b",
}


def map_em_value(prediction, gold_answers, lang):
    em_value = compute_max_score_over_answers(calculate_em, prediction, gold_answers, lang)
    return float(em_value)


def map_f1_value(prediction, gold_answers, lang):
    f1_value = compute_max_score_over_answers(calculate_f1, prediction, gold_answers, lang)
    return float(f1_value)


def get_text_metrics(
    predictions: List[str], gold_answers: List[List[str]], lang: str, serial=True, workers=None
) -> Dict[str, List[float]]:
    """Compute metrics from the predicted and answer texts."""
    if serial:
        f1_scores = [
            map_f1_value(predictions[i], gold_answers[i], lang) for i in range(len(predictions))
        ]
        em_scores = [
            map_em_value(predictions[i], gold_answers[i], lang) for i in range(len(predictions))
        ]
    else:
        with Pool(workers) as p:
            f1_scores = p.starmap(
                map_f1_value,
                [(predictions[i], gold_answers[i], lang) for i in range(len(predictions))],
                chunksize=64,
            )
            em_scores = p.starmap(
                map_em_value,
                [(predictions[i], gold_answers[i], lang) for i in range(len(predictions))],
                chunksize=64,
            )

    return {"f1": f1_scores, "exact_match": em_scores}


def summarize_default_metrics(
    em_scores, f1_scores, qid_is_answerable, metrics: Optional[Dict[str, float]] = None,
):
    """Summarize EM and F1 based on default threshold"""
    assert set(em_scores.keys()) == set(f1_scores.keys()) == set(qid_is_answerable.keys())
    ans_em_scores = {qid: em_scores[qid] for qid in em_scores if qid_is_answerable[qid]}
    ans_f1_scores = {qid: f1_scores[qid] for qid in f1_scores if qid_is_answerable[qid]}
    unans_em_scores = {qid: em_scores[qid] for qid in em_scores if not qid_is_answerable[qid]}

    summary = OrderedDict(
        [
            ("exact_match", round(100.0 * np.mean(list(em_scores.values())), 2)),
            ("f1", round(100.0 * np.mean(list(f1_scores.values())), 2)),
            ("answerable_exact_match", round(100.0 * np.mean(list(ans_em_scores.values())), 2)),
            ("answerable_f1", round(100.0 * np.mean(list(ans_f1_scores.values())), 2)),
            ("unanswerable_exact_match", round(100.0 * np.mean(list(unans_em_scores.values())), 2)),
        ]
    )

    if metrics:
        metrics.update(summary)
    return summary


def aggregate_summaries(dicts):
    summaries = collections.defaultdict(list)
    for d in dicts:
        for k, v in d.items():
            assert isinstance(v, float) or isinstance(v, int)
            summaries[k].append(v)
    results = {}
    for k, v in summaries.items():
        results[k] = round(float(np.mean(v)), 2)

    return results


def whitespace_tokenize(text):
    return text.split()


def mixed_segmentation(text):
    segs_out = []
    temp_str = ""
    for char in text:
        if temp_str != "":
            ss = whitespace_tokenize(temp_str)
            segs_out.extend(ss)
            temp_str = ""
        segs_out.append(char)

    if temp_str != "":
        ss = whitespace_tokenize(temp_str)
        segs_out.extend(ss)

    return segs_out


def normalize_answer_by_language(s, lang):
    """Lower text, remove punctuation, articles and extra whitespace.
    This function is customized by language.
    """

    def remove_articles(text, lang):
        article_regex = ARTICLE_REGEX_BY_LANG.get(lang)
        if article_regex:
            return re.sub(article_regex, " ", text)
        else:
            return text

    def white_space_fix(text, lang):

        if lang in MIXED_SEGMENTATION_LANGS:
            tokens = mixed_segmentation(text)
        else:
            tokens = whitespace_tokenize(text)
        return " ".join([t for t in tokens if t.strip() != ""])

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s)), lang), lang)


def plot_f1(answerable_f1_by_id, unanswerable_em_by_id, na_probs_by_id, qid_to_has_ans, image_dir):
    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
    qid_list = sorted(na_probs_by_id, key=lambda k: na_probs_by_id[k])
    question_counts = len(qid_list)
    answerable_f1 = []
    overall_f1 = []
    unanswerable_em = []
    thresholds = []

    sum_answerable_f1 = 0
    sum_unanswerable_em = num_no_ans
    for i, qid in enumerate(qid_list):
        thresholds.append(na_probs_by_id[qid])
        if qid in answerable_f1_by_id:
            sum_answerable_f1 += answerable_f1_by_id[qid]
        elif qid in unanswerable_em_by_id:
            sum_unanswerable_em += unanswerable_em_by_id[qid] - 1
        else:
            raise ValueError(f"{qid} is not in either answerable or unanswerable predictions")

        answerable_f1.append(sum_answerable_f1 / question_counts)
        unanswerable_em.append(sum_unanswerable_em / question_counts)
        overall_f1.append((sum_answerable_f1 + sum_unanswerable_em) / question_counts)

    plt.plot(thresholds, answerable_f1, color="green", label="Answerable F1")
    plt.plot(thresholds, unanswerable_em, color="red", label="Unanswerable F1")
    plt.plot(thresholds, overall_f1, color="blue", label="Overall F1")
    plt.legend()

    plt.xlabel("No Answer Threshold")
    plt.ylabel("F1")
    plt.title("F1 plot for different answer types")
    plt.savefig(os.path.join(image_dir, "f1_plot.png"))
    plt.clf()


def calculate_em(prediction, gold_answer, language):
    norm_pred = normalize_answer_by_language(prediction, language)
    norm_answer = normalize_answer_by_language(gold_answer, language)
    return int(norm_pred == norm_answer)


def calculate_f1(prediction, gold_answer, language):
    gold_toks = normalize_answer_by_language(gold_answer, language).split() if gold_answer else []
    pred_toks = normalize_answer_by_language(prediction, language).split() if prediction else []
    common = Counter(gold_toks) & Counter(pred_toks)
    num_common = sum(common.values())

    if len(gold_toks) == 0 or len(pred_toks) == 0:
        # If the prediction or gold_answer is No Answer, then F1 is 1 if they agree, 0 otherwise
        return int(gold_toks == pred_toks)
    if num_common == 0:
        return 0.0

    recall = 1.0 * num_common / len(gold_toks)
    precision = 1.0 * num_common / len(pred_toks)
    return (2.0 * precision * recall) / (precision + recall)


def compute_max_score_over_answers(metric_fn, prediction, ground_truths, language):
    assert len(ground_truths) > 0, "Gold truth answers list should never be empty."
    scores_by_answer = [
        metric_fn(prediction, ground_truth, language) for ground_truth in ground_truths
    ]
    return max(scores_by_answer)


def compute_best_score_and_threshold(
    predictions, scores, no_answer_probs, qid_has_answer
) -> Dict[str, float]:
    # Begin at threshold of 0, where all predictions are No Answer.
    best_threshold = 0.0
    current_score = best_score = sum(1 for k in qid_has_answer if not qid_has_answer[k])

    exs_sorted_by_na_prob = sorted(no_answer_probs, key=lambda k: no_answer_probs[k])
    for qid in exs_sorted_by_na_prob:

        if qid_has_answer[qid]:  # Gold truth is answer, and we predict an answer
            score_diff = scores[qid]
        elif predictions[qid]:  # If gold truth is No Answer, but we predict an answer
            score_diff = -1
        else:  # If gold truth and prediction are both No Answer
            score_diff = 0
        current_score += score_diff

        # Update best score and threshold if new max value
        if current_score > best_score:
            best_threshold = no_answer_probs[qid]
            best_score = current_score

    return {
        "best_score": 100.0 * best_score / len(scores),
        "best_threshold": best_threshold,
    }


def apply_no_answer_threshold(scores, no_answer_probs, qid_has_answer, no_answer_thresh):
    new_scores = {}
    for qid, s in scores.items():
        pred_no_answer = no_answer_probs[qid] > no_answer_thresh
        new_scores[qid] = float(not qid_has_answer[qid]) if pred_no_answer else s
    return new_scores


def plot_na_prob_histogram(no_answer_probs, qid_list, outdir, name):
    x = [no_answer_probs[k] for k in qid_list]
    weights = np.ones_like(x) / float(len(x))
    plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
    plt.xlabel("No Answer Probability")
    plt.ylabel("Proportion of Dataset")
    plt.title(f"No Answer Probability Histogram: {name}")
    plt.savefig(os.path.join(outdir, f"na_prob_histogram_{name}.png"))
    plt.clf()


================================================
FILE: requirements.txt
================================================
matplotlib
numpy
pandas
pytest
tabulate

================================================
FILE: sample_predictions/en.jsonl
================================================
{"example_id": -8817357831042426028, "prediction": "365.256 days", "binary_answer": null, "no_answer_prob": -10.777592301368713}
{"example_id": 1326178481868679533, "prediction": "50 metres ( 164.0 ft ) in length", "binary_answer": null, "no_answer_prob": -4.017424821853638}
{"example_id": 5156881367671150240, "prediction": "April 4 , 1973", "binary_answer": null, "no_answer_prob": -9.927809596061707}
{"example_id": 7896399267156135965, "prediction": "Konrad Zuse", "binary_answer": null, "no_answer_prob": -10.966487884521484}
{"example_id": -1865341688150735283, "prediction": "The Matrix Reloaded and The Matrix Revolutions", "binary_answer": null, "no_answer_prob": -5.570307493209839}
{"example_id": 2544891513471080945, "prediction": "Storm botnet or Storm worm botnet", "binary_answer": null, "no_answer_prob": -8.836552143096924}
{"example_id": 5977990164505192934, "prediction": "Channel 4 in the UK", "binary_answer": null, "no_answer_prob": 1.809189796447754}
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FILE: sample_predictions/es.jsonl
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================================================
FILE: tests/test_mkqa_eval.py
================================================
import os

import tests.testdata as test_data
from mkqa_eval import (
    compute_mkqa_scores_for_language,
    MKQAAnnotation,
    MKQAPrediction,
    read_predictions,
    read_annotations,
    evaluate,
)

package_path = list(test_data.__path__)[0]


def test_compute_mkqa_scores():
    test_cases = [
        # Test 1: Perfect textual prediction
        {
            "prediction": MKQAPrediction(
                example_id="1",
                prediction="first dummy answer",
                binary_answer=None,
                no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="1", types=[], answers=["first dummy answer", "second dummy answer"],
            ),
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 2: Partially correct prediction
        {
            "prediction": MKQAPrediction(
                example_id="2",
                prediction="alternative answer",
                binary_answer=None,
                no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="2", types=[], answers=["dummy answer one", "alternative answer two"],
            ),
            "expected_f1": 0.8,
            "expected_em": 0.0,
        },
        # Test 3: Partially correct with stopword and punctuation removal
        {
            "prediction": MKQAPrediction(
                example_id="3",
                prediction="an answer ?? without stopwords",
                binary_answer=None,
                no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="3", types=[], answers=["answer -- without, stopwords!!"],
            ),
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 4: Correct No Answer prediction
        {
            "prediction": MKQAPrediction(
                example_id="4", prediction="", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(example_id="4", types=[], answers=[""],),
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 5: Incorrect No Answer prediction
        {
            "prediction": MKQAPrediction(
                example_id="5", prediction="", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="5", types=[], answers=["first dummy answer", "second dummy answer"],
            ),
            "expected_f1": 0.0,
            "expected_em": 0.0,
        },
        # Test 6: Incorrect No Answer prediction, when answer is binary
        {
            "prediction": MKQAPrediction(
                example_id="6", prediction="", binary_answer=None, no_answer_prob=1,
            ),
            "gold_truth": MKQAAnnotation(example_id="6", types=[], answers=["yes"],),
            "expected_f1": 0.0,
            "expected_em": 0.0,
        },
        # Test 7: Correct binary answer prediction
        {
            "prediction": MKQAPrediction(
                example_id="7", prediction="wrong answer", binary_answer="yes", no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(example_id="7", types=[], answers=["yes"],),
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 8: Incorrect binary answer prediction
        {
            "prediction": MKQAPrediction(
                example_id="8",
                prediction="distractor answer",
                binary_answer="no",
                no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(example_id="8", types=[], answers=["yes"],),
            "expected_f1": 0.0,
            "expected_em": 0.0,
        },
        # Test 9: Binary answer prediction takes precedence, but is incorrect
        {
            "prediction": MKQAPrediction(
                example_id="9", prediction="", binary_answer="no", no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(example_id="1", types=[], answers=[""],),
            "expected_f1": 0.0,
            "expected_em": 0.0,
        },
        # Test 10: No Answer probability is 1, but it is not relevant to computing initial scores in `compute_mkqa_scores_for_language`
        {
            "prediction": MKQAPrediction(
                example_id="10",
                prediction="final prediction",
                binary_answer=None,
                no_answer_prob=1.0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="10", types=[], answers=["penultimate", "final prediction"],
            ),
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
    ]

    test_preds = {ex["prediction"].example_id: ex["prediction"] for ex in test_cases}
    test_golds = {ex["gold_truth"].example_id: ex["gold_truth"] for ex in test_cases}
    expected_f1s = {ex["gold_truth"].example_id: ex["expected_f1"] for ex in test_cases}
    expected_ems = {ex["gold_truth"].example_id: ex["expected_em"] for ex in test_cases}
    test_em_scores, test_f1_scores = compute_mkqa_scores_for_language(test_preds, test_golds, "en")

    for ex_id in test_golds:
        assert (
            test_em_scores[ex_id] == expected_ems[ex_id]
        ), f"Example ID = {ex_id} | EM | Result = {test_em_scores[ex_id]} | Expected = {expected_ems[ex_id]}"
        assert (
            test_f1_scores[ex_id] == expected_f1s[ex_id]
        ), f"Example ID = {ex_id} | F1 | Result = {test_f1_scores[ex_id]} | Expected = {expected_f1s[ex_id]}"


def test_compute_mkqa_scores_in_different_languages():
    test_cases = [
        # Test 1: Test Spanish text normalization
        {
            "prediction": MKQAPrediction(
                example_id="1", prediction="esto es manzana", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="1", types=[], answers=["esto es una manzana", "esta es otra manzana"],
            ),
            "lang": "es",
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 2: Test Arabic normalization
        {
            "prediction": MKQAPrediction(
                example_id="2", prediction="تفاحة", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="2", types=[], answers=["التفاحة", "هذه تفاحة"],
            ),
            "lang": "ar",
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 3: Test French normalization
        {
            "prediction": MKQAPrediction(
                example_id="3",
                prediction="c'est de la pomme",
                binary_answer=None,
                no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="3", types=[], answers=["c'est la pomme", "c'est une autre pomme"],
            ),
            "lang": "fr",
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 4: Test Hungarian normalization
        {
            "prediction": MKQAPrediction(
                example_id="4", prediction="ez egy alma", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="4", types=[], answers=["ez alma", "ez egy újabb alma"],
            ),
            "lang": "hu",
            "expected_f1": 1.0,
            "expected_em": 1.0,
        },
        # Test 5: Test Chinese Mandarin mixed segmentation f1
        {
            "prediction": MKQAPrediction(
                example_id="5", prediction="这个一个苹果", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(example_id="5", types=[], answers=["这是还是苹果", "这是另一个苹果"],),
            "lang": "zh_cn",
            "expected_f1": 0.7692307692307692,
            "expected_em": 0,
        },
        # Test 6: Test Khmer mixed segmentation f1
        {
            "prediction": MKQAPrediction(
                example_id="6", prediction="នេះគឺជាផ្លែប៉ោម", binary_answer=None, no_answer_prob=0,
            ),
            "gold_truth": MKQAAnnotation(
                example_id="7", types=[], answers=["នេះគឺជាផ្លែប៉ោមមួយទៀត"],
            ),
            "lang": "km",
            "expected_f1": 0.8333333333333333,
            "expected_em": 0,
        },
    ]

    for case in test_cases:
        example_id = case["prediction"].example_id
        predictions = {example_id: case["prediction"]}
        gold_annotations = {example_id: case["gold_truth"]}
        lang = case["lang"]

        em_scores, f1_scores = compute_mkqa_scores_for_language(predictions, gold_annotations, lang)
        assert (
            em_scores[example_id] == case["expected_em"]
        ), f"Example ID = {example_id} | EM | Result = {em_scores[example_id]} | Expected = {case['expected_em']}"
        assert (
            f1_scores[example_id] == case["expected_f1"]
        ), f"Example ID = {example_id} | F1 | Result = {f1_scores[example_id]} | Expected = {case['expected_f1']}"


def test_compute_metrics_end_2_end():
    predictions_path = os.path.join(package_path, "en_prediction.jsonl")
    annotations_path = os.path.join(package_path, "test_mkqa.jsonl.gz")
    language = "en"
    predictions = read_predictions(predictions_path)
    annotations = read_annotations(annotations_path)

    metrics = evaluate(annotations[language], predictions, language)
    expected_metrics = {
        "best_em": 66.67,
        "best_f1": 80.95,
        "best_answerable_em": 33.33,
        "best_answerable_f1": 61.9,
        "best_unanswerable_em": 100.0,
        "best_f1_threshold": -6.91,
    }
    assert expected_metrics == dict(metrics)


================================================
FILE: tests/testdata/en_prediction.jsonl
================================================
{"example_id": 7631360939705650347, "prediction": "Ridley Scott", "binary_answer": null, "no_answer_prob": -11.557607769966125}
{"example_id": -7449157003522518870, "prediction": "Hafþór Júlíus `` Thor '' Björnsson", "binary_answer": null, "no_answer_prob": -6.912837386131287}
{"example_id": 7721692200040168186, "prediction": "The visa policy of the United Kingdom", "binary_answer": null, "no_answer_prob": 1.1354331970214844}
{"example_id": 2961317153244610375, "prediction": "August 11 , 2018 ( 2018 - 08 - 11 ) October 4 , 2018", "binary_answer": null, "no_answer_prob": -5.99773907661438}
{"example_id": 2904274678882358283, "prediction": "6", "binary_answer": null, "no_answer_prob": 2.5260353088378906}
{"example_id": 36228854473990432, "prediction": "October 1 , 1961", "binary_answer": null, "no_answer_prob": -6.819772958755493}