add musique metric

musique.py

@@ -13,6 +13,10 @@
 # limitations under the License.
 """TODO: Add a description here."""
 
+import re
+import string
+import collections
+from typing import Callable
 import evaluate
 import datasets
 
@@ -26,40 +30,37 @@ year={2020}
 }
 """
 
-# TODO: Add description of the module here
 _DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+Question-answering metrics (`Exact Match` and `F1`) for Musique-Answerable dataset. 
+
+The implementation is taken from Musique repository.
+https://github.com/StonyBrookNLP/musique
 """
 
 
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
 Calculates how good are predictions given some references, using certain scores
 Args:
-    predictions: list of predictions to score. Each predictions
-        should be a string with tokens separated by spaces.
-    references: list of reference for each prediction. Each
-        reference should be a string with tokens separated by spaces.
+    predictions: list of predicted answers.
+    references: list of ground truth answers. Each reference should be a list of 
+        ground truth answers for the corresponding prediction.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    exact_match: Exact match score,
+    f1: F1 score over tokens
 Examples:
-    Examples should be written in doctest format, and should illustrate how
-    to use the function.
-
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
+    >>> my_new_module = evaluate.load("musique")
+    >>> results = my_new_module.compute(
+        references=[["New York City", "NYC"], ["Einstein", "Albert Einstein"]], 
+        predictions=["New York City", "Albert Einstein"],
+    )
     >>> print(results)
-    {'accuracy': 1.0}
+    {'exact_match': 1.0, 'f1': 1.0}
 """
 
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class musique(evaluate.Metric):
-    """TODO: Short description of my evaluation module."""
+    """TODO: Question answering metrics (EM and F1) for Musique-Answerable dataset."""
 
     def _info(self):
         # TODO: Specifies the evaluate.EvaluationModuleInfo object
@@ -70,26 +71,105 @@ class musique(evaluate.Metric):
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value('int64'),
-                'references': datasets.Value('int64'),
-            }),
+            features=datasets.Features(
+                {
+                    "predictions": datasets.features.Sequence(datasets.Value("string")),
+                    "references": datasets.features.Sequence(
+                        datasets.features.Sequence(datasets.Value("string"))
+                    ),
+                }
+            ),
             # Homepage of the module for documentation
             homepage="http://module.homepage",
             # Additional links to the codebase or references
             codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            reference_urls=["http://path.to.reference.url/new_module"],
         )
 
     def _download_and_prepare(self, dl_manager):
         """Optional: download external resources useful to compute the scores"""
-        # TODO: Download external resources if needed
         pass
 
     def _compute(self, predictions, references):
         """Returns the scores"""
-        # TODO: Compute the different scores of the module
-        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
+
+        if len(predictions) != len(references):
+            raise ValueError(
+                "The number of predictions and references should be the same."
+            )
+
+        if len(predictions) == 0:
+            return {"exact_match": 0.0, "f1": 0.0}
+
+        exact_scores = [
+            metric_max_over_ground_truths(compute_exact, prediction, reference)
+            for prediction, reference in zip(predictions, references)
+        ]
+        f1_scores = [
+            metric_max_over_ground_truths(compute_f1, prediction, reference)
+            for prediction, reference in zip(predictions, references)
+        ]
         return {
-            "accuracy": accuracy,
-        }
+            "exact_match": sum(exact_scores) / len(exact_scores),
+            "f1": sum(f1_scores) / len(f1_scores),
+        }
+
+
+# Source: https://github.com/StonyBrookNLP/musique/blob/main/metrics/answer.py
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
+        return re.sub(regex, " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    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))))
+
+
+def get_tokens(s):
+    if not s:
+        return []
+    return normalize_answer(s).split()
+
+
+def compute_exact(a_gold, a_pred):
+    return int(normalize_answer(a_gold) == normalize_answer(a_pred))
+
+
+def compute_f1(a_gold, a_pred):
+    gold_toks = get_tokens(a_gold)
+    pred_toks = get_tokens(a_pred)
+    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+    num_same = sum(common.values())
+    if len(gold_toks) == 0 or len(pred_toks) == 0:
+        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
+        return int(gold_toks == pred_toks)
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def metric_max_over_ground_truths(
+    metric_fn: Callable[[str, str], float],
+    prediction: str,
+    ground_truths: list[str],
+) -> float:
+    scores_for_ground_truths = [
+        metric_fn(prediction, ground_truth) for ground_truth in ground_truths
+    ]
+    return max(scores_for_ground_truths)

tests.py

@@ -1,17 +1,7 @@
 test_cases = [
     {
-        "predictions": [0, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0}
+        "predictions": ["New York City", "Albert Einstein"],
+        "references": [["New York City", "NYC"], ["Einstein", "Albert Einstein"]],
+        "result": {"exact_match": 1.0, "f1": 1.0},
     },
-    {
-        "predictions": [1, 1],
-        "references": [1, 1],
-        "result": {"metric_score": 1}
-    },
-    {
-        "predictions": [1, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0.5}
-    }
-]
+]