Update QA

examples/ags/benchmark/drop.py

@@ -0,0 +1,297 @@
+import json
+import asyncio
+import pandas as pd
+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
+
+global cost
+cost = 0
+
+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_number(text: str) -> str:
+    if _is_number(text):
+        return str(float(text))
+    else:
+        return 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 _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 _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
+    )
+    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)
+        data = list(data.items())
+
+    random_indices = generate_random_indices(len(data), samples)
+    data = [data[i] for i in random_indices]
+    return data
+
+async def evaluate_problem(question: str, passage: str, answers: List[Dict[str, Any]], graph: Callable) -> Tuple[str, str, float]:
+
+    max_retries = 5
+    retries = 0
+
+    while retries < max_retries:
+        try:
+            global cost
+            prediction, cost = await graph(question, passage)
+
+
+            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
+
+async def evaluate_all_passages(annotations: List[Tuple[str, Dict[str, Any]]], graph: Callable, max_concurrent_tasks: int = 50) -> List[List[Any]]:
+    semaphore = asyncio.Semaphore(max_concurrent_tasks)
+    results = []
+
+    async def sem_evaluate(id: str, annotation: Dict[str, Any]):
+        async with semaphore:
+            passage = annotation["passage"]
+            for qa_pair in annotation["qa_pairs"]:
+                question = qa_pair["question"]
+                answers = [qa_pair["answer"]]
+                if "validated_answers" in qa_pair and 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])
+
+    tasks = [sem_evaluate(id, annotation) for id, annotation in annotations]
+    await tqdm_asyncio.gather(*tasks, desc="Evaluating DROP passages", total=len(annotations))
+
+    return results
+
+def save_results_to_csv(results: List[List[Any]], path: str) -> float:
+    df = pd.DataFrame(results, columns=["id", "question", "prediction", "best_answer", "score"])
+    average_score = df["score"].mean()
+
+    output_file = f"{path}/{average_score:.5f}.csv"
+    df.to_csv(output_file, index=False)
+    print(f"Results saved to {output_file}")
+
+    return average_score
+
+async def drop_evaluation(graph: Callable, file_path: str, samples: int, path: str) -> float:
+    data = load_data(file_path, samples)
+    results = await evaluate_all_passages(data, graph, max_concurrent_tasks=20)
+    average_score = save_results_to_csv(results, path=path)
+    print(f"Average score on DROP dataset: {average_score:.5f}")
+    global cost
+    print(f"Total cost: {cost}")
+    return average_score

examples/ags/benchmark/hotpotqa.py

@@ -0,0 +1,129 @@
+import json
+import asyncio
+import aiofiles
+import pandas as pd
+import numpy as np
+from typing import List, Tuple, Callable, Set
+from collections import Counter
+from tqdm.asyncio import tqdm_asyncio
+from scipy.optimize import linear_sum_assignment
+import string
+import re
+
+
+from examples.ags.benchmark.utils import generate_random_indices
+
+global cost
+cost = 0
+
+def is_number(text: str) -> bool:
+    try:
+        float(text)
+        return True
+    except ValueError:
+        return False
+
+def normalize_answer(s):
+    """
+    Normalize answers for evaluation.
+    """
+
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", 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 f1_score(prediction, ground_truth):
+    """
+    Compute the F1 score between prediction and ground truth answers.
+    """
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+async def load_data(file_path: str, samples=20, total_length=1000) -> List[dict]:
+    data = []
+    async with aiofiles.open(file_path, mode="r") as file:
+        async for line in file:
+            data.append(json.loads(line))
+    data = data[:total_length] 
+    random_indices = generate_random_indices(len(data), samples)
+    data = [data[i] for i in random_indices]
+    return data
+
+async def evaluate_problem(input: str, context_str: str, graph: Callable, expected_output: str):
+    max_retries = 5
+    retries = 0
+
+    while retries < max_retries:
+        try:
+            global cost
+            prediction, cost = await graph(input, context_str) if graph else "None"
+            score = f1_score(prediction, expected_output)
+
+            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.")
+                prediction = None
+                score = 0
+                break
+
+    return input, prediction, expected_output, score
+
+async def evaluate_all_problems(data: List[dict], graph: Callable, max_concurrent_tasks: int = 50):
+    semaphore = asyncio.Semaphore(max_concurrent_tasks)
+
+    async def sem_evaluate(problem):
+        async with semaphore:
+            input_text = problem["question"]
+            expected_output = problem["answer"]
+            paragraphs = [item[1] for item in problem["context"] if isinstance(item[1], list)]
+            context_str = "\n".join(" ".join(paragraph) for paragraph in paragraphs)
+            return await evaluate_problem(input_text, context_str, graph, expected_output)
+
+    tasks = [sem_evaluate(problem) for problem in data]
+
+    return await tqdm_asyncio.gather(*tasks, desc="Evaluating HotpotQA problems", total=len(data))
+
+def save_results_to_csv(results: List[Tuple[str, str, str, float]], path: str) -> float:
+    df = pd.DataFrame(
+        results, columns=["question", "prediction", "expected_output", "score"]
+    )
+    average_score = df["score"].mean()
+
+    output_file = f"{path}/{average_score:.5f}.csv"
+    df.to_csv(output_file, index=False)
+    print(f"Results saved to {output_file}")
+
+    return average_score
+
+async def hotpotqa_evaluation(graph: Callable, file_path: str, samples: int, path: str) -> float:
+    data = await load_data(file_path, samples)
+    results = await evaluate_all_problems(data, graph, max_concurrent_tasks=20)
+    average_score = save_results_to_csv(results, path=path)
+    print(f"Average score on HotpotQA dataset: {average_score:.5f}")
+    global cost
+    print(f"Total cost: {cost}")
+    return average_score