| from ...smp import * |
| from .multiple_choice import extract_answer_from_item |
| import numpy as np |
| import re |
|
|
| FAIL_MSG = 'Failed to obtain answer via API.' |
|
|
| DURATIONS = [ |
| 'short', |
| 'medium', |
| 'long', |
| ] |
|
|
| DOMAINS = [ |
| 'Knowledge', |
| 'Film & Television', |
| 'Sports Competition', |
| 'Artistic Performance', |
| 'Life Record', |
| 'Multilingual' |
| ] |
|
|
| SUB_CATEGORIES = [ |
| 'Humanity & History', |
| 'Literature & Art', |
| 'Biology & Medicine', |
| 'Finance & Commerce', |
| 'Astronomy', |
| 'Geography', |
| 'Law', |
| 'Life Tip', |
| 'Technology', |
| 'Animation', |
| 'Movie & TV Show', |
| 'Documentary', |
| 'News Report', |
| 'Esports', |
| 'Basketball', |
| 'Football', |
| 'Athletics', |
| 'Other Sports', |
| 'Stage Play', |
| 'Magic Show', |
| 'Variety Show', |
| 'Acrobatics', |
| 'Handicraft', |
| 'Food', |
| 'Fashion', |
| 'Daily Life', |
| 'Travel', |
| 'Pet & Animal', |
| 'Exercise', |
| 'Multilingual' |
| ] |
|
|
| TASK_CATEGORIES = [ |
| 'Temporal Perception', |
| 'Spatial Perception', |
| 'Attribute Perception', |
| 'Action Recognition', |
| 'Object Recognition', |
| 'OCR Problems', |
| 'Counting Problem', |
| 'Temporal Reasoning', |
| 'Spatial Reasoning', |
| 'Action Reasoning', |
| 'Object Reasoning', |
| 'Information Synopsis', |
| ] |
|
|
|
|
| def get_dimension_rating(data_path): |
| data = load(data_path) |
|
|
| duration_rating = {k: {} for k in DURATIONS} |
| for duration in DURATIONS + ['overall']: |
| duration_rating[duration] = { |
| 'overall': '', |
| 'domain': {k: [] for k in DOMAINS}, |
| 'sub_category': {k: [] for k in SUB_CATEGORIES}, |
| 'task_type': {k: [] for k in TASK_CATEGORIES} |
| } |
|
|
| for i in range(len(data)): |
|
|
| domain = data.iloc[i]['domain'] |
| sub_ctg = data.iloc[i]['sub_category'] |
| task_ctg = data.iloc[i]['task_type'] |
|
|
| duration = data.iloc[i]['duration'] |
| duration_rating[duration]['domain'][domain].append(data.iloc[i]['score']) |
| duration_rating[duration]['sub_category'][sub_ctg].append(data.iloc[i]['score']) |
| duration_rating[duration]['task_type'][task_ctg].append(data.iloc[i]['score']) |
|
|
| duration_rating['overall']['domain'][domain].append(data.iloc[i]['score']) |
| duration_rating['overall']['sub_category'][sub_ctg].append(data.iloc[i]['score']) |
| duration_rating['overall']['task_type'][task_ctg].append(data.iloc[i]['score']) |
|
|
| for duration in DURATIONS + ['overall']: |
|
|
| overall_res_dur = f'{np.mean([x for x in sum(duration_rating[duration]["domain"].values(), []) if x >= 0]):.3f}' |
| duration_rating[duration]['overall'] = overall_res_dur |
|
|
| for domain in DOMAINS: |
| domain_res_dur = f'{np.mean([x for x in duration_rating[duration]["domain"][domain] if x >= 0]):.3f}' |
| duration_rating[duration]['domain'][domain] = domain_res_dur |
|
|
| for sub_ctg in SUB_CATEGORIES: |
| sub_res_dur = f'{np.mean([x for x in duration_rating[duration]["sub_category"][sub_ctg] if x >= 0]):.3f}' |
| duration_rating[duration]['sub_category'][sub_ctg] = sub_res_dur |
|
|
| for task_ctg in TASK_CATEGORIES: |
| task_res_dur = f'{np.mean([x for x in duration_rating[duration]["task_type"][task_ctg] if x >= 0]):.3f}' |
| duration_rating[duration]['task_type'][task_ctg] = task_res_dur |
|
|
| return duration_rating |
|
|
|
|
| def extract_option(model, input_item, dataset_name): |
| options = input_item['question'].split('\n')[1:] |
| for id, option in enumerate(options): |
| option_id = chr(ord('A') + id) + '.' |
| if option.find(option_id) >= 0: |
| input_item[chr(ord('A') + id)] = option[option.find(option_id) + len(option_id):].strip('. \n') |
| return extract_answer_from_item(model, input_item, dataset_name)['opt'] |
|
|
|
|
| def extract_characters_regex(s): |
| s = s.strip() |
| answer_prefixes = [ |
| 'The best answer is', |
| 'The correct answer is', |
| 'The answer is', |
| 'The answer', |
| 'The best option is' |
| 'The correct option is', |
| 'Best answer:' |
| 'Best option:', |
| 'Answer:', |
| 'Option:', |
| ] |
| for answer_prefix in answer_prefixes: |
| s = s.replace(answer_prefix, '') |
|
|
| if len(s.split()) > 10 and not re.search('[ABCD]', s): |
| return '' |
| matches = re.search(r'[ABCD]', s) |
| if matches is None: |
| return '' |
| return matches[0] |
|
|
