code: #*#*#*./examples/evaluate.py
  """Official evaluation script for SQuAD version 2.0.
  
  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 argparse
  import collections
  import json
  import numpy as np
  import os
  import re
  import string
  import sys
  
  OPTS = None
  def parse_args():
    parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
    parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
    parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
    parser.add_argument('--out-file', '-o', metavar='eval.json',
                        help='Write accuracy metrics to file (default is stdout).')
    parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
                        help='Model estimates of probability of no answer.')
    parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
                        help='Predict "" if no-answer probability exceeds this (default = 1.0).')
    parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
                        help='Save precision-recall curves to directory.')
    parser.add_argument('--verbose', '-v', action='store_true')
    if len(sys.argv) == 1:
      parser.print_help()
      sys.exit(1)
    return parser.parse_args()
  
  
  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):
    gold_toks = get_tokens(a_gold)  #答案list
    pred_toks = get_tokens(a_pred)
    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)###c.Counter([1,2,33])& c.Counter([2,3,4])  res: Counter({2: 1})
    num_same = sum(common.values())  ###所有出现的相同词的总个数
    if len(gold_toks) == 0 or len(pred_toks) == 0:  ##无答案问题直接对比
      # 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)  #准确率
    recall = 1.0 * num_same / len(gold_toks)   #召回率
    f1 = (2 * precision * recall) / (precision + recall)  #f1
    return f1
  
  def get_raw_scores(dataset, preds):
    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 preds:
            print('Missing prediction for %s' % qid)
            continue
          a_pred = preds[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):
    new_scores = {}
    for qid, s in scores.items():
      pred_na = na_probs[qid] > na_prob_thresh  ##有答案变成了无答案的情况
      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):
    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 plot_pr_curve(precisions, recalls, out_image, title):
    plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
    plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.xlim([0.0, 1.05])
    plt.ylim([0.0, 1.05])
    plt.title(title)
    plt.savefig(out_image)
    plt.clf()
  
  def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
                                 out_image=None, title=None):
    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)
    if out_image:
      plot_pr_curve(precisions, recalls, out_image, title)
    return {'ap': 100.0 * avg_prec}
  
  def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
                                    qid_to_has_ans, out_image_dir):
    if out_image_dir and not os.path.exists(out_image_dir):
      os.makedirs(out_image_dir)
    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,
        out_image=os.path.join(out_image_dir, 'pr_exact.png'),
        title='Precision-Recall curve for Exact Match score')
    pr_f1 = make_precision_recall_eval(
        f1_raw, na_probs, num_true_pos, qid_to_has_ans,
        out_image=os.path.join(out_image_dir, 'pr_f1.png'),
        title='Precision-Recall curve for F1 score')
    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,
        out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
        title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
    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 histogram_na_prob(na_probs, qid_list, image_dir, name):
    if not qid_list:
      return
    x = [na_probs[k] for k in qid_list]
    weights = np.ones_like(x) / float(len(x))
    plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
    plt.xlabel('Model probability of no-answer')
    plt.ylabel('Proportion of dataset')
    plt.title('Histogram of no-answer probability: %s' % name)
    plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
    plt.clf()
  
  def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
    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])  #从小到大按照diff排序
    for i, qid in enumerate(qid_list):
      if qid not in scores: continue
      if qid_to_has_ans[qid]:
        diff = scores[qid]
      else:
        if preds[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, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
    best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
    best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
    main_eval['best_exact'] = best_exact
    main_eval['best_exact_thresh'] = exact_thresh
    main_eval['best_f1'] = best_f1
    main_eval['best_f1_thresh'] = f1_thresh
  
  def main():
    with open(OPTS.data_file) as f:
      dataset_json = json.load(f)
      dataset = dataset_json['data']
    with open(OPTS.pred_file) as f:
      preds = json.load(f)
    if OPTS.na_prob_file:
      with open(OPTS.na_prob_file) as f:
        na_probs = json.load(f)
    else:
      na_probs = {k: 0.0 for k in preds}
    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, preds)  #得到每个答案的extract和f1   list
    exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
                                          OPTS.na_prob_thresh)
    f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
                                       OPTS.na_prob_thresh)
  
  
    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')
    if OPTS.na_prob_file:
      find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
    if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
                                    qid_to_has_ans, OPTS.out_image_dir)
      histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
      histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
    if OPTS.out_file:
      with open(OPTS.out_file, 'w') as f:
        json.dump(out_eval, f)
    else:
      print(json.dumps(out_eval, indent=2))
  
  
  def judgeOnline(data_file,pred_file,na_prob_file,output_dir,epoch,train_steps):
  
      if not os.path.exists(os.path.join(output_dir,"eval_res")):
          os.makedirs(os.path.join(output_dir,"eval_res"))
      output = os.path.join(output_dir,"eval_res")
  
      out_file = os.path.join(output,"eval.json")
      out_image_dir = None
      na_prob_thresh = 1.0
  
      with open(data_file) as f:
  
          dataset_json = json.load(f)
          dataset = dataset_json['data']
      with open(pred_file) as f:
          preds = json.load(f)
      with open(na_prob_file) as f:
          na_probs = json.load(f)
  
  
      exact_raw, f1_raw = get_raw_scores(dataset, preds)
  
      qid_to_has_ans = make_qid_to_has_ans(dataset)  # maps qid to True/False  区分dev中的有无答案
      has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]  #有答案的问题
      no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]  #无答案的问题
      exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
                                            na_prob_thresh)###这里没用因为默认的是1.0 详情可参考该函数
      f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
                                         na_prob_thresh)###这里没用因为默认的是1.0  详情可参考该函数
      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')
      if na_prob_file: ##如果给出null_odds.json文件
          find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)  ##获取最好的thresh
      if na_prob_file and out_image_dir:
          run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
                                        qid_to_has_ans, out_image_dir)
          histogram_na_prob(na_probs, has_ans_qids, out_image_dir, 'hasAns')
          histogram_na_prob(na_probs, no_ans_qids, out_image_dir, 'noAns')
      if out_file:
          with open(out_file, 'a') as fout:
              fout.write("epoch:{} steps:{} evaluation res:{}\n".format(epoch,train_steps,json.dumps(out_eval, sort_keys=True, indent=2)))
              import logging
              logger = logging.getLogger(__name__)
              logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                                  datefmt='%m/%d/%Y %H:%M:%S',
                                  level=logging.INFO )
              logger.info("write evaluation result to " + out_file + "OK!")
      else:
          print(json.dumps(out_eval, indent=2))
      return out_eval
  
  if __name__ == '__main__':
    OPTS = parse_args()
    if OPTS.out_image_dir:
      import matplotlib
      matplotlib.use('Agg')
      import matplotlib.pyplot as plt
    print(vars(OPTS))
    main()
apis: matplotlib.pyplot.savefig numpy.ones_like matplotlib.pyplot.clf matplotlib.pyplot.xlim matplotlib.pyplot.title matplotlib.pyplot.step matplotlib.pyplot.ylabel matplotlib.pyplot.ylim matplotlib.pyplot.hist matplotlib.use matplotlib.pyplot.fill_between matplotlib.pyplot.xlabel