Delete squad_evaluate_v2_0.py

squad_evaluate_v2_0.py

@@ -1,249 +0,0 @@
-# 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
-# limitations under the License.
-
-"""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