| """Official evaluation script for SQuAD version 2.0.
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|
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| In addition to basic functionality, we also compute additional statistics and
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| plot precision-recall curves if an additional na_prob.json file is provided.
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| This file is expected to map question ID's to the model's predicted probability
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| that a question is unanswerable.
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| """
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| import argparse
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| import collections
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| import json
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| import numpy as np
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| import os
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| import re
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| import string
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| import sys
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|
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| OPTS = None
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|
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| def parse_args():
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| parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
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| parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
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| parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
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| parser.add_argument('--out-file', '-o', metavar='eval.json',
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| help='Write accuracy metrics to file (default is stdout).')
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| parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
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| help='Model estimates of probability of no answer.')
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| parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
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| help='Predict "" if no-answer probability exceeds this (default = 1.0).')
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| parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
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| help='Save precision-recall curves to directory.')
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| parser.add_argument('--verbose', '-v', action='store_true')
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| if len(sys.argv) == 1:
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| parser.print_help()
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| sys.exit(1)
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| return parser.parse_args()
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|
|
| def make_qid_to_has_ans(dataset):
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| qid_to_has_ans = {}
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| for article in dataset:
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| for p in article['paragraphs']:
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| for qa in p['qas']:
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| qid_to_has_ans[qa['id']] = bool(qa['answers'])
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| return qid_to_has_ans
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|
|
| def normalize_answer(s):
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| """Lower text and remove punctuation, articles and extra whitespace."""
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| def remove_articles(text):
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| regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
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| return re.sub(regex, ' ', text)
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| def white_space_fix(text):
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| return ' '.join(text.split())
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| def remove_punc(text):
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| exclude = set(string.punctuation)
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| return ''.join(ch for ch in text if ch not in exclude)
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| def lower(text):
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| return text.lower()
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| return white_space_fix(remove_articles(remove_punc(lower(s))))
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|
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| def get_tokens(s):
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| if not s: return []
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| return normalize_answer(s).split()
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|
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| def compute_exact(a_gold, a_pred):
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| return int(normalize_answer(a_gold) == normalize_answer(a_pred))
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|
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| def compute_f1(a_gold, a_pred):
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| gold_toks = get_tokens(a_gold)
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| pred_toks = get_tokens(a_pred)
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| common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
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| num_same = sum(common.values())
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| if len(gold_toks) == 0 or len(pred_toks) == 0:
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|
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| return int(gold_toks == pred_toks)
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| if num_same == 0:
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| return 0
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| precision = 1.0 * num_same / len(pred_toks)
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| recall = 1.0 * num_same / len(gold_toks)
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| f1 = (2 * precision * recall) / (precision + recall)
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| return f1
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|
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| def get_raw_scores(dataset, preds):
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| exact_scores = {}
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| f1_scores = {}
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| for article in dataset:
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| for p in article['paragraphs']:
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| for qa in p['qas']:
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| qid = qa['id']
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| gold_answers = [a['text'] for a in qa['answers']
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| if normalize_answer(a['text'])]
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| if not gold_answers:
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|
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| gold_answers = ['']
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| if qid not in preds:
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| print('Missing prediction for %s' % qid)
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| continue
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| a_pred = preds[qid]
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|
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| exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
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| f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
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| return exact_scores, f1_scores
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|
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| def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
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| new_scores = {}
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| for qid, s in scores.items():
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| pred_na = na_probs[qid] > na_prob_thresh
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| if pred_na:
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| new_scores[qid] = float(not qid_to_has_ans[qid])
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| else:
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| new_scores[qid] = s
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| return new_scores
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|
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| def make_eval_dict(exact_scores, f1_scores, qid_list=None):
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| if not qid_list:
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| total = len(exact_scores)
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| return collections.OrderedDict([
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| ('exact', 100.0 * sum(exact_scores.values()) / total),
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| ('f1', 100.0 * sum(f1_scores.values()) / total),
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| ('total', total),
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| ])
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| else:
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| total = len(qid_list)
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| return collections.OrderedDict([
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| ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
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| ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
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| ('total', total),
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| ])
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|
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| def merge_eval(main_eval, new_eval, prefix):
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| for k in new_eval:
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| main_eval['%s_%s' % (prefix, k)] = new_eval[k]
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|
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| def plot_pr_curve(precisions, recalls, out_image, title):
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| plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
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| plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
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| plt.xlabel('Recall')
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| plt.ylabel('Precision')
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| plt.xlim([0.0, 1.05])
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| plt.ylim([0.0, 1.05])
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| plt.title(title)
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| plt.savefig(out_image)
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| plt.clf()
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|
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| def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
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| out_image=None, title=None):
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| qid_list = sorted(na_probs, key=lambda k: na_probs[k])
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| true_pos = 0.0
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| cur_p = 1.0
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| cur_r = 0.0
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| precisions = [1.0]
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| recalls = [0.0]
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| avg_prec = 0.0
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| for i, qid in enumerate(qid_list):
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| if qid_to_has_ans[qid]:
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| true_pos += scores[qid]
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| cur_p = true_pos / float(i+1)
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| cur_r = true_pos / float(num_true_pos)
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| if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
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|
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| avg_prec += cur_p * (cur_r - recalls[-1])
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| precisions.append(cur_p)
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| recalls.append(cur_r)
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| if out_image:
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| plot_pr_curve(precisions, recalls, out_image, title)
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| return {'ap': 100.0 * avg_prec}
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|
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| def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
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| qid_to_has_ans, out_image_dir):
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| if out_image_dir and not os.path.exists(out_image_dir):
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| os.makedirs(out_image_dir)
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| num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
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| if num_true_pos == 0:
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| return
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| pr_exact = make_precision_recall_eval(
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| exact_raw, na_probs, num_true_pos, qid_to_has_ans,
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| out_image=os.path.join(out_image_dir, 'pr_exact.png'),
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| title='Precision-Recall curve for Exact Match score')
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| pr_f1 = make_precision_recall_eval(
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| f1_raw, na_probs, num_true_pos, qid_to_has_ans,
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| out_image=os.path.join(out_image_dir, 'pr_f1.png'),
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| title='Precision-Recall curve for F1 score')
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| oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
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| pr_oracle = make_precision_recall_eval(
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| oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
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| out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
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| title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
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| merge_eval(main_eval, pr_exact, 'pr_exact')
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| merge_eval(main_eval, pr_f1, 'pr_f1')
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| merge_eval(main_eval, pr_oracle, 'pr_oracle')
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|
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| def histogram_na_prob(na_probs, qid_list, image_dir, name):
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| if not qid_list:
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| return
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| x = [na_probs[k] for k in qid_list]
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| weights = np.ones_like(x) / float(len(x))
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| plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
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| plt.xlabel('Model probability of no-answer')
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| plt.ylabel('Proportion of dataset')
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| plt.title('Histogram of no-answer probability: %s' % name)
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| plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
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| plt.clf()
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|
|
| def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
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| num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
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| cur_score = num_no_ans
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| best_score = cur_score
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| best_thresh = 0.0
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| qid_list = sorted(na_probs, key=lambda k: na_probs[k])
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| for i, qid in enumerate(qid_list):
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| if qid not in scores: continue
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| if qid_to_has_ans[qid]:
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| diff = scores[qid]
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| else:
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| if preds[qid]:
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| diff = -1
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| else:
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| diff = 0
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| cur_score += diff
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| if cur_score > best_score:
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| best_score = cur_score
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| best_thresh = na_probs[qid]
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| return 100.0 * best_score / len(scores), best_thresh
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|
|
| def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
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| best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
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| best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
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| main_eval['best_exact'] = best_exact
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| main_eval['best_exact_thresh'] = exact_thresh
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| main_eval['best_f1'] = best_f1
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| main_eval['best_f1_thresh'] = f1_thresh
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|
|
| def main():
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| with open(OPTS.data_file) as f:
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| dataset_json = json.load(f)
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| dataset = dataset_json['data']
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| with open(OPTS.pred_file) as f:
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| preds = json.load(f)
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| if OPTS.na_prob_file:
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| with open(OPTS.na_prob_file) as f:
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| na_probs = json.load(f)
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| else:
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| na_probs = {k: 0.0 for k in preds}
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| qid_to_has_ans = make_qid_to_has_ans(dataset)
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| has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
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| no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
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| exact_raw, f1_raw = get_raw_scores(dataset, preds)
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| exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
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| OPTS.na_prob_thresh)
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| f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
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| OPTS.na_prob_thresh)
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| out_eval = make_eval_dict(exact_thresh, f1_thresh)
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| if has_ans_qids:
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| has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
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| merge_eval(out_eval, has_ans_eval, 'HasAns')
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| if no_ans_qids:
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| no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
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| merge_eval(out_eval, no_ans_eval, 'NoAns')
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| if OPTS.na_prob_file:
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| find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
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| if OPTS.na_prob_file and OPTS.out_image_dir:
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| run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
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| qid_to_has_ans, OPTS.out_image_dir)
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| histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
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| histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
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| if OPTS.out_file:
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| with open(OPTS.out_file, 'w') as f:
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| json.dump(out_eval, f)
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| else:
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| print(json.dumps(out_eval, indent=2))
|
|
|
| if __name__ == '__main__':
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| OPTS = parse_args()
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| if OPTS.out_image_dir:
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| import matplotlib
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| matplotlib.use('Agg')
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| import matplotlib.pyplot as plt
|
| main() |
