Update drop.py

examples/ags/benchmark/drop.py

@@ -1,65 +1,166 @@
 import json
 import asyncio
 import pandas as pd
-from typing import List, Tuple, Callable, Dict, Any, Set
+import string
+import re
+from typing import List, Tuple, Callable, Dict, Any, Set, Union
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 from tqdm.asyncio import tqdm_asyncio
 
 from examples.ags.benchmark.utils import generate_random_indices
 
-def is_number(text: str) -> bool:
+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 _is_number(text: str) -> bool:
     try:
         float(text)
         return True
     except ValueError:
         return False
 
-def normalize_answer(text):
-    import re
-    import string
+def _normalize_number(text: str) -> str:
+    if _is_number(text):
+        return str(float(text))
+    else:
+        return text
 
-    def remove_articles(text):
-        return re.sub(r"\b(a|an|the)\b", " ", text)
+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 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: str) -> str:
+    return text.lower()
 
-    def lower(text):
-        return text.lower()
 
-    def tokenize(text):
-        return re.split(" |-", text)
+def _tokenize(text: str) -> List[str]:
+    return re.split(" |-", text)
 
-    def normalize_number(text: str) -> str:
-        if is_number(text):
-            return str(float(text))
-        else:
-            return 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)
+        _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 answer_to_bags(answer: str) -> Set[str]:
-    raw_spans = [answer]
-
-    normalized_spans = []
+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_span = _normalize_answer(raw_span)
         normalized_spans.append(normalized_span)
         token_bags.append(set(normalized_span.split()))
     return normalized_spans, token_bags
 
+#!/usr/bin/python
+
+from collections import defaultdict
+from typing import Any, Dict, List, Set, Tuple, Union, Optional
+import json
+import argparse
+import string
+import re
+
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+
+
+# 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.
+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
@@ -68,8 +169,8 @@ def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> List[float]:
     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] = f1_score(pred_item, gold_item)
+            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))])
@@ -77,20 +178,8 @@ def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> List[float]:
         max_scores[row] = max(max_scores[row], scores[row, column])
     return max_scores
 
-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 f1_score(predicted_bag: Set[str], gold_bag: Set[str]) -> float:
+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
@@ -100,9 +189,108 @@ def f1_score(predicted_bag: Set[str], gold_bag: Set[str]) -> float:
         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
+    f1 = (
+        (2 * precision * recall) / (precision + recall)
+        if not (precision == 0.0 and recall == 0.0)
+        else 0.0
+    )
     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 _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
+    )
+    return f1
+
+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 get_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"]
+                    )
+                ]
+            ),
+            "date",
+        )
+    else:
+        raise ValueError(
+            f"Answer type not found, should be one of number, spans or date at: {json.dumps(answer)}"
+        )
+
 def load_data(file_path: str, samples: int) -> List[Tuple[str, Dict[str, Any]]]:
     with open(file_path, mode="r") as file:
         data = json.load(file)
@@ -113,44 +301,39 @@ def load_data(file_path: str, samples: int) -> List[Tuple[str, Dict[str, Any]]]:
     return data
 
 async def evaluate_problem(question: str, passage: str, answers: List[Dict[str, Any]], graph: Callable) -> Tuple[str, str, float]:
-    def answer_json_to_strings(answer: Dict[str, Any]) -> Tuple[Tuple[str, ...], str]:
-        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"]
-                        )
-                    ]
-                ),
-                "date",
-            )
-        else:
-            raise ValueError(f"Answer type not found, should be one of number, spans or date at: {json.dumps(answer)}")
 
-    prediction = await graph(question, passage)
+    max_retries = 5
+    retries = 0
 
-    def get_f1_score(prediction: str, golden_answer: str) -> float:
-        predicted_bags = answer_to_bags(prediction)
-        gold_bags = answer_to_bags(golden_answer)
+    while retries < max_retries:
+        try:
+            prediction = await graph(question, passage)
 
-        f1_per_bag = _align_bags(predicted_bags[1], gold_bags[1])
-        score = np.mean(f1_per_bag)
-        return score
 
-    max_score = 0.0
-    best_answer = None
-    for answer in answers:
-        golden_answer, _ = answer_json_to_strings(answer)
-        golden_answer = golden_answer[0]
-        score = get_f1_score(prediction, golden_answer)
-        if score > max_score:
-            max_score = score
-            best_answer = golden_answer
+            max_score = 0.0
+            max_type = None
+            best_answer = None
+
+            for answer in answers:
+                golden_answer, golden_type = answer_json_to_strings(answer)
+                _, f1_score = get_metrics(prediction, golden_answer)
+                if golden_answer[0].strip() != "":
+                    max_score = max(max_score, f1_score)
+                    if max_score == f1_score:
+                        max_type = golden_type
+                        best_answer = golden_answer
+            break
+
+        except Exception as e:
+            retries += 1
+            print(f"Error generating prediction: {e}. Retrying... ({retries}/{max_retries})")
+
+            if retries == max_retries:
+                print("Maximum retries reached. Skipping this sample.")
+                best_answer = None
+                prediction = None
+                max_score = 0.0
+                break
 
     return best_answer, prediction, max_score
 
@@ -165,7 +348,7 @@ async def evaluate_all_passages(annotations: List[Tuple[str, Dict[str, Any]]], g
                 question = qa_pair["question"]
                 answers = [qa_pair["answer"]]
                 if "validated_answers" in qa_pair and qa_pair["validated_answers"]:
-                    answers.extend(qa_pair["validated_answers"])
+                    answers += qa_pair["validated_answers"]
                 best_answer, prediction, score = await evaluate_problem(question, passage, answers, graph)
                 results.append([id, question, prediction, best_answer, score])