import numpy as np import json import pandas as pd QUES_TYPES = ['MCQ','MCQ(multiple)','Integer','Numeric'] models = [ "Random", "GPT3_normal", "GPT3.5_normal", "GPT4_normal", "GPT4_CoT", 'GPT4_CoT_self_refine', "GPT4_CoT+OneShot", "GPT4_CoT+SC@8" ] def get_aggregate(answers, question_type, single_threshold=None, multiple_threshold=None): # Pass optional \tau_{single} and \tau_{multiple} parameters if needed for evaluation under risk. if question_type == 'MCQ(multiple)' or question_type == 'MCQ': letter_to_idx = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'None': 4} idx_to_letter = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'None'} abcd = [0,0,0,0,0] for ans in answers: if ans == 'None': abcd[letter_to_idx[ans]] += 1 else: for c in ans: abcd[letter_to_idx[c]] += 1 if question_type == 'MCQ': abcd = abcd[:-1] answer = idx_to_letter[np.argmax(abcd)] if single_threshold is not None: answer = answer if abcd[np.argmax(abcd)]/len(answers) >= single_threshold else "None" else: if multiple_threshold is not None: options_selected = [idx_to_letter[x] for x in range(len(abcd)) if abcd[x] >= len(answers)*multiple_threshold and idx_to_letter[x] != 'None'] else: options_selected = [idx_to_letter[x] for x in range(len(abcd)) if abcd[x] >= len(answers)/2 and idx_to_letter[x] != 'None'] if len(options_selected) == 0: answer = "None" else: answer = ''.join(sorted(options_selected)) else: # For integer and numeric answers, choose the most common response(other than None) while "None" in answers: answers.remove("None") if len(answers) == 0: answers = ["None"] unique, counts = np.unique(answers, return_counts=True) answer = unique[np.argmax(counts)] return answer def compute_score(gold, resp, question_type, year): assert question_type in QUES_TYPES if question_type == 'MCQ(multiple)': gold = set([c for c in ['A', 'B', 'C', 'D'] if c in gold]) resp = set([c for c in ['A', 'B', 'C', 'D'] if c in resp]) if resp == gold : return 1.0 else: if len(resp-gold) == 0: return 0.25*len(resp) return 0.0 # If response contains something not in the gold set, give 0 elif question_type == 'MCQ': gold = set([c for c in ['A', 'B', 'C', 'D'] if c in gold]) resp = set([c for c in ['A', 'B', 'C', 'D'] if c in resp]) return int(gold == resp) else: if resp == "None": return 0.0 g, r = float(gold), float(resp) return int(abs(g-r) <= 0.01) def construct_responses_table(): responses = {} for model in models: if "SC@" in model: pass elif "Random" == model: pass else: responses[model] = json.load(open(f"data/responses/{model}_responses/responses.json")) dataset = json.load(open('data/dataset.json')) extracts = { "Type": [], "Index": [], "Description": [], "Subject": [], "Gold": [], } for model in models: if "Random" == model: continue else: extracts[f'{model}'] = [] for i, q in enumerate(dataset): extracts['Type'].append(q['type']) extracts['Index'].append(q['index']) extracts['Description'].append(q['description']) extracts['Subject'].append(q['subject']) extracts['Gold'].append(q['gold']) for model in models: if "SC@" in model: continue elif "Random" == model: continue else: try: assert q['question'] == responses[model][i]['question'] except: print(q['question']) breakpoint() print(responses[model][i]['question']) breakpoint() try: extracts[f'{model}'].append(responses[model][i]['extract']) except: print(extracts) if "GPT4_CoT+SC" in model: num_responses = int(model.split("@")[1]) for i, q in enumerate(dataset): sc_responses = json.load(open('data/responses/GPT4_CoT+SC_responses/responses.json')) resp = sc_responses[i] answers = [resp['GPT4_CoT+SC_response']['choices'][k]['extract'] for k in range(num_responses)] answer = get_aggregate(answers, resp['type']) extracts[f'{model}'].append(answer) pd.DataFrame(extracts).to_csv('results/extracts.csv', index=False) return pd.read_csv('results/extracts.csv',dtype=str) responses = construct_responses_table() output = [] for i, response in responses.iterrows(): out = {} out["Type"] = response["Type"] out["Index"] = response["Index"] out["Description"] = response["Description"] out["Subject"] = response["Subject"] gold = response["Gold"] out["Gold"] = gold if response["Type"] == "MCQ": out["Random"] = 0.25 elif response["Type"] == "MCQ(multiple)": num_ans = len(gold) if num_ans == 1: out["Random"] = 0.0625 elif num_ans == 2: out["Random"] = 0.09375 elif num_ans == 3: out["Random"] = 0.203125 elif num_ans == 4: out["Random"] = 0.5 else: out["Random"] = 0 for model in models: if model == "Random": continue resp = response[f"{model}"] if not isinstance(resp, str): resp = "None" out[f"{model}"] = resp out[f'{model}'] = compute_score(gold,resp,out["Type"],out["Description"]) out[f'Max'] = 1 output.append(out) df = pd.DataFrame() df['Type'] = [x['Type'] for x in output] df['Index'] = [x['Index'] for x in output] df['Description'] = [x['Description'] for x in output] df['Subject'] = [x['Subject'] for x in output] df['Gold'] = [x['Gold'] for x in output] df['Random'] = [x['Random'] for x in output] for model in models: df[f"{model}"] = [ x.get(f"{model}", "None") for x in output] df[f"{model}"] = [x.get(f"{model}", 0) for x in output] df.to_csv(f"results/scores.csv", index=False) modes = ['overall', 'type_wise', 'subject_wise'] for mode in modes: col_dict = {} for model in models: col_dict[f'{model}'] = ['mean'] if mode != 'overall': col_dict[f'{models[0]}'].insert(0,'count') if mode == 'overall': grouped_multiple = df.agg(col_dict) elif mode == 'type_wise': grouped_multiple = df.groupby(['Type']).agg(col_dict) elif mode == 'subject_wise': grouped_multiple = df.groupby(['Subject']).agg(col_dict) if mode != 'overall': grouped_multiple.columns = ['count'] + models grouped_multiple = grouped_multiple.reset_index() grouped_multiple = grouped_multiple.round(3) grouped_multiple.to_csv(f"results/aggregated_scores_{mode}.csv", index=False) print("Done!")