Update scorer.py (#1)

- Update scorer.py (d3b1c8ac63c144a436705cd295d905212c217e43)

scorer.py

@@ -1,81 +1,101 @@
 import json
 import re
 import string
+import warnings
 
 import numpy as np
 
-def normalize_text(text: str) -> str:
-    "From QuAC"
-    def remove_articles(text: str) -> str:
-        return re.sub(r"\b(a|an|the)\b", " ", text)
 
-    def white_space_fix(text: str) -> str:
-        return " ".join(text.split())
+def normalize_number_str(number_str: str) -> float:
+    # we replace these common units and commas to allow
+    # conversion to float
+    for char in ["$", "%", ","]:
+        number_str = number_str.replace(char, "")
+    try:
+        return float(number_str)
+    except ValueError:
+        print(f"String {number_str} cannot be normalized to number str.")
+        return float("inf")
 
-    def homogeneize_numbers(text: str) -> str:
-        try:
-            return str(float(text))
-        except ValueError:
-            return text
-
-    def remove_punc(text: str) -> str:
-        exclude = set(string.punctuation)
-        return "".join(ch for ch in text if ch not in exclude)
-    
-    def remove_punc2(text: str) -> str:
-        "From Grégoire's code, removes all punctuation, nicer than remove_punc"
-        translator = str.maketrans('', '', string.punctuation)
-        return text.translate(translator)
-
-    def lower(text: str) -> str:
-        return text.lower()
-
-    def _tokenize(text):
-        return re.split(" ", text)
-
-    tokens = [white_space_fix(remove_articles(homogeneize_numbers(remove_punc2(lower(t))))) for t in _tokenize(text)]
-    return " ".join([t for t in tokens if t != ""]).strip()
-
-def extract_answer(input_str: str, prompt_sep: str = 'FINAL ANSWER: ') -> str:
-    answer = input_str.split(prompt_sep)[-1].strip()
-    return answer
 
-def extract_bow(input_str: str) -> list[str]:
-    return input_str.split(" ")
+def split_string(
+    s: str,
+    char_list: list[str] = [",", ";"],
+) -> list[str]:
+    pattern = f"[{''.join(char_list)}]"
+    return re.split(pattern, s)
 
-def numbers_equals_in_bow(gold_list: list, pred_list: list) -> bool:
-    # Numbers in prediction bag of words
-    pred_numbers = []
-    for text in pred_list:
-        try:
-            pred_numbers.append(str(float(text)))
-        except ValueError:
-            continue
 
-    for text in gold_list:
+def question_scorer(
+    model_answer: str,
+    ground_truth: str,
+) -> bool:
+    def is_float(element: any) -> bool:
         try:
-            number = str(float(text))
-            if number not in pred_numbers:
-                return False
+            float(element)
+            return True
         except ValueError:
-            continue
-
-    return True
-
-def affix_quasi_exact_match(gold: str, pred: str) -> float:
-    if not pred:
-        return 0
-
-    normalized_pred = normalize_text(pred)
-    normalized_gold = normalize_text(gold)
-    bow_pred = extract_bow(pred)
-    bow_gold = extract_bow(gold)
-
-    if normalized_pred.startswith(normalized_gold) or normalized_pred.endswith(normalized_gold):
-        if numbers_equals_in_bow(bow_gold, bow_pred):
-            return 1
-    
-    return 0
-
-def question_scorer(gold: str, pred: str) -> float:
-    return affix_quasi_exact_match(gold, pred)
+            return False
+
+    # if gt is a number
+    if is_float(ground_truth):
+        print(f"Evaluating {model_answer} as a number.")
+        normalized_answer = normalize_number_str(model_answer)
+        return normalized_answer == float(ground_truth)
+
+    # if gt is a list
+    elif any(char in ground_truth for char in [",", ";"]):
+        print(f"Evaluating {model_answer} as a comma separated list.")
+        # question with the fish: normalization removes punct
+
+        gt_elems = split_string(ground_truth)
+        ma_elems = split_string(model_answer)
+
+        # check length is the same
+        if len(gt_elems) != len(ma_elems):
+            warnings.warn(
+                "Answer lists have different lengths, returning False.", UserWarning
+            )
+            return False
+
+        # compare each element as float or str
+        comparisons = []
+        for ma_elem, gt_elem in zip(ma_elems, gt_elems):
+            if is_float(gt_elem):
+                normalized_ma_elem = normalize_number_str(ma_elem)
+                comparisons.append(normalized_ma_elem == float(gt_elem))
+            else:
+                # we do not remove punct since comparisons can include punct
+                comparisons.append(
+                    normalize_str(ma_elem, remove_punct=False)
+                    == normalize_str(gt_elem, remove_punct=False)
+                )
+        return all(comparisons)
+
+    # if gt is a str
+    else:
+        print(f"Evaluating {model_answer} as a string.")
+        return normalize_str(model_answer) == normalize_str(ground_truth)
+
+
+def normalize_str(input_str, remove_punct=True) -> str:
+    """
+    Normalize a string by:
+    - Removing all white spaces
+    - Optionally removing punctuation (if remove_punct is True)
+    - Converting to lowercase
+    Parameters:
+    - input_str: str, the string to normalize
+    - remove_punct: bool, whether to remove punctuation (default: True)
+    Returns:
+    - str, the normalized string
+    """
+    # Remove all white spaces. Required e.g for seagull vs. sea gull
+    no_spaces = re.sub(r"\s", "", input_str)
+
+    # Remove punctuation, if specified.
+    if remove_punct:
+        translator = str.maketrans("", "", string.punctuation)
+        return no_spaces.lower().translate(translator)
+    else:
+        return no_spaces.lower()