File size: 5,987 Bytes
78c1391 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
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
|