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import re
import string
import numpy as np
from scipy.optimize import linear_sum_assignment
_ARTICLES = re.compile(r"\b(a|an|the)\b", re.UNICODE)
def process_docs(dataset):
def _process(doc):
return {
"id": doc["query_id"],
"passage": doc["passage"],
"question": doc["question"],
"answers": get_answers(doc),
}
return dataset.map(_process)
def get_answers(doc):
def _flatten_validated_answers(validated_answers):
"""Flattens a dict of lists of validated answers.
{"number": ['1', '8'], ...}
-> [{"number": ['1'], ...}, {"number": ['8'], ...}]
"""
valid_answers = []
for i in range(len(validated_answers["number"])):
valid_answers.append(
{
"number": validated_answers["number"][i],
"date": validated_answers["date"][i],
"spans": validated_answers["spans"][i],
}
)
return valid_answers
answers = []
answers_set = set()
candidates = [doc["answer"]] + _flatten_validated_answers(doc["validated_answers"])
for candidate in candidates:
answer = parse_answer(candidate)
if answer in answers_set:
continue
answers_set.add(answer)
answers.append(answer)
return answers
def parse_answer(answer):
# NOTE: Everything is returned as a tuple for uniformity and hashability.
if answer["number"] != "":
return (str(answer["number"]),)
if answer["spans"] != []:
return tuple(answer["spans"])
return (
" ".join(
[answer["date"]["day"], answer["date"]["month"], answer["date"]["year"]]
).strip(),
)
def process_results(doc, results):
preds, golds = results, doc["answers"]
max_em = 0
max_f1 = 0
for gold_answer in golds:
exact_match, f1_score = get_metrics(preds, gold_answer)
if gold_answer[0].strip():
max_em = max(max_em, exact_match)
max_f1 = max(max_f1, f1_score)
return {"em": max_em, "f1": max_f1}
def get_metrics(predicted, gold):
"""
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_to_bags(answer):
if isinstance(answer, (list, tuple)):
raw_spans = answer
else:
raw_spans = [answer]
normalized_spans = []
token_bags = []
for raw_span in raw_spans:
normalized_span = _normalize(raw_span)
normalized_spans.append(normalized_span)
token_bags.append(set(normalized_span.split()))
return normalized_spans, token_bags
def _align_bags(predicted, gold):
"""
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, gold_bag):
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, predicted_bag):
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 _is_number(text):
try:
float(text)
return True
except ValueError:
return False
def _remove_articles(text):
return _ARTICLES.sub(" ", text)
def _white_space_fix(text):
return " ".join(text.split())
def _remove_punc(text):
exclude = set(string.punctuation)
if not _is_number(text):
return "".join(ch for ch in text if ch not in exclude)
else:
return text
def _fix_number(text):
return str(float(text)) if _is_number(text) else text
def _tokenize(text):
return re.split(" |-", text)
def _normalize(answer):
tokens = [
_white_space_fix(_remove_articles(_fix_number(_remove_punc(token.lower()))))
for token in _tokenize(answer)
]
tokens = [token for token in tokens if token.strip()]
normalized = " ".join(tokens).strip()
return normalized
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