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| from ...smp import * | |
| 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]):.2f}' | |
| 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]):.2f}' | |
| 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]):.2f}' | |
| 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]):.2f}' | |
| duration_rating[duration]['task_type'][task_ctg] = task_res_dur | |
| return duration_rating | |
| 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] | |