official/nlp/tools/squad_evaluate_v2_0.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""Evaluation script for SQuAD version 2.0.
The functions are copied and modified from
https://raw.githubusercontent.com/white127/SQUAD-2.0-bidaf/master/evaluate-v2.0.py
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import collections
import re
import string
from absl import logging
def _make_qid_to_has_ans(dataset):
qid_to_has_ans = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid_to_has_ans[qa['id']] = bool(qa['answers'])
return qid_to_has_ans
def _normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', 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 _get_tokens(s):
if not s: return []
return _normalize_answer(s).split()
def _compute_exact(a_gold, a_pred):
return int(_normalize_answer(a_gold) == _normalize_answer(a_pred))
def _compute_f1(a_gold, a_pred):
"""Compute F1-score."""
gold_toks = _get_tokens(a_gold)
pred_toks = _get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if not gold_toks or not pred_toks:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def _get_raw_scores(dataset, predictions):
"""Compute raw scores."""
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if _normalize_answer(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
if qid not in predictions:
logging.error('Missing prediction for %s', qid)
continue
a_pred = predictions[qid]
# Take max over all gold answers
exact_scores[qid] = max(_compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(_compute_f1(a, a_pred) for a in gold_answers)
return exact_scores, f1_scores
def _apply_no_ans_threshold(
scores, na_probs, qid_to_has_ans, na_prob_thresh=1.0):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def _make_eval_dict(exact_scores, f1_scores, qid_list=None):
"""Make evaluation result dictionary."""
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores.values()) / total),
('f1', 100.0 * sum(f1_scores.values()) / total),
('total', total),
])
else:
total = len(qid_list)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
('total', total),
])
def _merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval['%s_%s' % (prefix, k)] = new_eval[k]
def _make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans):
"""Make evaluation dictionary containing average recision recall."""
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
true_pos = 0.0
cur_p = 1.0
cur_r = 0.0
precisions = [1.0]
recalls = [0.0]
avg_prec = 0.0
for i, qid in enumerate(qid_list):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
cur_p = true_pos / float(i+1)
cur_r = true_pos / float(num_true_pos)
if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(cur_p)
recalls.append(cur_r)
return {'ap': 100.0 * avg_prec}
def _run_precision_recall_analysis(
main_eval, exact_raw, f1_raw, na_probs, qid_to_has_ans):
"""Run precision recall analysis and return result dictionary."""
num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
pr_exact = _make_precision_recall_eval(
exact_raw, na_probs, num_true_pos, qid_to_has_ans)
pr_f1 = _make_precision_recall_eval(
f1_raw, na_probs, num_true_pos, qid_to_has_ans)
oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
pr_oracle = _make_precision_recall_eval(
oracle_scores, na_probs, num_true_pos, qid_to_has_ans)
_merge_eval(main_eval, pr_exact, 'pr_exact')
_merge_eval(main_eval, pr_f1, 'pr_f1')
_merge_eval(main_eval, pr_oracle, 'pr_oracle')
def _find_best_thresh(predictions, scores, na_probs, qid_to_has_ans):
"""Find the best threshold for no answer probability."""
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for qid in qid_list:
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if predictions[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def _find_all_best_thresh(
main_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = _find_best_thresh(
predictions, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = _find_best_thresh(
predictions, f1_raw, na_probs, qid_to_has_ans)
main_eval['final_exact'] = best_exact
main_eval['final_exact_thresh'] = exact_thresh
main_eval['final_f1'] = best_f1
main_eval['final_f1_thresh'] = f1_thresh
def evaluate(dataset, predictions, na_probs=None):
"""Evaluate prediction results."""
new_orig_data = []
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
if qa['id'] in predictions:
new_para = {'qas': [qa]}
new_article = {'paragraphs': [new_para]}
new_orig_data.append(new_article)
dataset = new_orig_data
if na_probs is None:
na_probs = {k: 0.0 for k in predictions}
qid_to_has_ans = _make_qid_to_has_ans(dataset) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = _get_raw_scores(dataset, predictions)
exact_thresh = _apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans)
f1_thresh = _apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans)
out_eval = _make_eval_dict(exact_thresh, f1_thresh)
if has_ans_qids:
has_ans_eval = _make_eval_dict(
exact_thresh, f1_thresh, qid_list=has_ans_qids)
_merge_eval(out_eval, has_ans_eval, 'HasAns')
if no_ans_qids:
no_ans_eval = _make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
_merge_eval(out_eval, no_ans_eval, 'NoAns')
_find_all_best_thresh(
out_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans)
_run_precision_recall_analysis(
out_eval, exact_raw, f1_raw, na_probs, qid_to_has_ans)
return out_eval