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import os |
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import json |
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import re |
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import sklearn |
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import collections |
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from sklearn.metrics import f1_score,accuracy_score |
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def get_f1(samples): |
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gold,pred = [],[] |
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for sample in samples: |
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if sample['gold'] in sample['pred']: |
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gold.append(sample['gold']) |
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pred.append(sample['gold']) |
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else: |
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gold.append(sample['gold']) |
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pred.append(sample['pred']) |
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micro_f1 = f1_score(gold,pred,average='micro') |
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macro_f1 = f1_score(gold,pred,average='macro') |
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print(f"Micro-F1: {micro_f1:.4f}") |
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print(f"Macro-F1: {macro_f1:.4f}") |
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return micro_f1, macro_f1 |
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def get_cls_acc(samples): |
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gold,pred = [],[] |
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for sample in samples: |
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if sample['gold'] in sample['pred']: |
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gold.append(sample['gold']) |
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pred.append(sample['gold']) |
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else: |
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gold.append(sample['gold']) |
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pred.append(sample['pred']) |
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pool = {} |
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for g,p in zip(gold,pred): |
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if g not in pool.keys(): |
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pool[g] = {'g':[],'p':[]} |
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pool[g]['g'].append(g) |
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pool[g]['p'].append(p) |
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result = {} |
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counter = {} |
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for k,v in pool.items(): |
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g,p = v['g'],v['p'] |
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acc = accuracy_score(g,p) |
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result[k] = acc |
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counter[k] = len(g) |
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result = sorted([(k,v,counter[k]) for k,v in result.items()],key=lambda x:x[2],reverse=True) |
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_result = [r for r in result][:20] |
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import pandas as pd |
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prob = [r[1] for r in result] |
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number = [r[2] for r in result] |
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crime = [r[0] for r in result] |
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data = {'crime':crime,'prob':prob,'number':number} |
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df = pd.DataFrame(data) |
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df.to_excel('./output/eval/lawbench.xlsx',index=False) |
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for r in result: |
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k,v = r[0],r[1] |
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print('{x} : {y}'.format(x=k,y=v)) |
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print(accuracy_score(gold,pred)) |
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def get_bias_with_acc(samples): |
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def load_result(path): |
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samples = [] |
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samples_with_tag = [] |
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with open(path,'r') as f: |
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for l in f.readlines(): |
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line = json.loads(l) |
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y = line['y'] |
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y_ = line['pred'] if line['pred'] else '' |
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y = [s[3:-4] for s in y.split(';')] |
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for c in y: |
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samples.append({'gold':c,'pred':y_[3:-4]}) |
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if re.search('<e>.*</e>',y_): |
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samples_with_tag.append({'gold':c,'pred':y_}) |
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return samples |
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def get_acc_in_cls(samples): |
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gold,pred = [],[] |
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for sample in samples: |
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if sample['gold'] in sample['pred']: |
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gold.append(sample['gold']) |
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pred.append(sample['gold']) |
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else: |
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gold.append(sample['gold']) |
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pred.append(sample['pred']) |
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pool = {} |
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for g,p in zip(gold,pred): |
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if g not in pool.keys(): |
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pool[g] = {'g':[],'p':[]} |
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pool[g]['g'].append(g) |
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pool[g]['p'].append(p) |
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result = {} |
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counter = {} |
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for k,v in pool.items(): |
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g,p = v['g'],v['p'] |
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acc = accuracy_score(g,p) |
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result[k] = acc |
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counter[k] = len(g) |
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import numpy |
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result = sorted([(k,v,numpy.log(counter[k]/len(gold))) for k,v in result.items()],key=lambda x:x[2],reverse=True) |
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return result |
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zeroshot = load_result('') |
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oneshot = load_result('') |
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cot = load_result('') |
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our = load_result('') |
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zeroshot = get_acc_in_cls(zeroshot) |
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oneshot = get_acc_in_cls(oneshot) |
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cot = get_acc_in_cls(cot) |
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our = get_acc_in_cls(our) |
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crimes = {c[0]:c[-1] for c in zeroshot} |
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crimes_pool = [] |
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log = [] |
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acc_zero = [] |
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acc_one = [] |
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acc_cot = [] |
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acc_our = [] |
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for c,crime in crimes.items(): |
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crimes_pool.append(c) |
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log.append(crime) |
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for i,s in enumerate(zeroshot): |
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if c in s[0]: |
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acc_zero.append(s[1]) |
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break |
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if i == len(zeroshot) and c not in s[0]: |
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raise ValueError |
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for i,s in enumerate(oneshot): |
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if c in s[0]: |
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acc_one.append(s[1]) |
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break |
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if i == len(oneshot) and c not in s[0]: |
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raise ValueError |
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if len(acc_one) < len(acc_zero): |
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acc_one.append(0) |
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for i,s in enumerate(cot): |
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if c in s[0]: |
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acc_cot.append(s[1]) |
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break |
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if i == len(cot) and c not in s[0]: |
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raise ValueError |
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for i,s in enumerate(our): |
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if c in s[0]: |
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acc_our.append(s[1]) |
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break |
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if i == len(our) and c not in s[0]: |
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raise ValueError |
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import pandas as pd |
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idx = [i for i in range(len(crimes_pool))] |
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data = {'crime':crimes_pool,'prob':log,'zs':acc_zero,'os':acc_one,'ct':acc_cot,'our':acc_our,'id':idx} |
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df = pd.DataFrame(data) |
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df.to_excel('',index=False) |
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def get_effect_with_f1(): |
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def load_result(path): |
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samples = [] |
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samples_with_tag = [] |
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with open(path,'r') as f: |
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for l in f.readlines(): |
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line = json.loads(l) |
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y = line['y'] |
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y_ = line['pred'] if line['pred'] else '' |
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y = [s[3:-4] for s in y.split(';')] |
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for c in y: |
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samples.append({'gold':c,'pred':y_[3:-4]}) |
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if re.search('<e>.*</e>',y_): |
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samples_with_tag.append({'gold':c,'pred':y_}) |
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return samples, samples_with_tag |
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dzeroshot,dzeroshottag = load_result('') |
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dzeromi,dzeroma = get_f1(dzeroshot) |
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d_zeromi,d_zeroma = get_f1(dzeroshottag) |
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doneshot,doneshottag = load_result('') |
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donemi,donema = get_f1(doneshot) |
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d_onemi,d_onema = get_f1(doneshottag) |
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dcotshot,dcotshottag = load_result('') |
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dcotmi,dcotma = get_f1(dcotshot) |
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d_cotmi,d_cotma = get_f1(dcotshottag) |
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dourshot,dourshottag = load_result('') |
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dourmi,dourma = get_f1(dourshot) |
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d_ourmi,d_ourma = get_f1(dourshottag) |
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zeroshot,zeroshottag = load_result('') |
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zeromi,zeroma = get_f1(zeroshot) |
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_zeromi,_zeroma = get_f1(zeroshottag) |
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oneshot,oneshottag = load_result('') |
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onemi,onema = get_f1(oneshot) |
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_onemi,_onema = get_f1(oneshottag) |
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cotshot,cotshottag = load_result('') |
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cotmi,cotma = get_f1(cotshot) |
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_cotmi,_cotma = get_f1(cotshottag) |
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ourshot,ourshottag = load_result('') |
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ourmi,ourma = get_f1(ourshot) |
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_ourmi,_ourma = get_f1(ourshottag) |
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acc_zero_effect = (dzeromi - zeromi) / (dzeromi - zeromi) |
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acc_one_effect = (donemi - zeromi) / (dzeromi - zeromi) |
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acc_cot_effect = (dcotmi - zeromi) / (dzeromi - zeromi) |
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acc_our_effect = (dourmi - zeromi) / (dzeromi - zeromi) |
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acc_zero_effect = (dzeroma - zeroma) / (dzeroma - zeroma) |
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acc_one_effect = (donema - zeroma) / (dzeroma - zeroma) |
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acc_cot_effect = (dcotma - zeroma) / (dzeroma - zeroma) |
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acc_our_effect = (dourma - zeroma) / (dzeroma - zeroma) |
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print(acc_zero_effect) |
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print(acc_one_effect) |
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print(acc_cot_effect) |
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print(acc_our_effect) |
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def crime_topk_acc(samples,crimes=None,name='deepseek'): |
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gold,pred = [],[] |
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for sample in samples: |
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if sample['gold'] in sample['pred']: |
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gold.append(sample['gold']) |
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pred.append(sample['gold']) |
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else: |
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gold.append(sample['gold']) |
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pred.append(sample['pred']) |
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pool = {} |
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for g,p in zip(gold,pred): |
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if g not in pool.keys(): |
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pool[g] = {'g':[],'p':[]} |
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pool[g]['g'].append(g) |
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pool[g]['p'].append(p) |
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if crimes is None: |
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crimes = {k:i for i,k in enumerate(pool.keys())} |
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result = {} |
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for k,v in pool.items(): |
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counter = collections.Counter() |
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g,p = v['g'],v['p'] |
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for pi in p: |
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counter[pi] += 1 |
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for c in crimes: |
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if c not in counter.keys(): |
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counter[c] = 0 |
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result[k] = [counter[c] for c in crimes] |
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import pandas as pd |
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df = pd.DataFrame(result) |
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df.to_excel('./output/eval/warm_acc_{x}.xlsx'.format(x=name),index=False) |
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return result,crimes |
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def main(): |
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path = '' |
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def load_data(path): |
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crimes_counter = collections.Counter() |
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samples = [] |
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samples_with_tag = [] |
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with open(path,'r') as f: |
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for l in f.readlines(): |
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line = json.loads(l) |
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y = line['y'] |
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y_ = line['pred'] if line['pred'] else '' |
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y = [s[3:-4] for s in y.split(';')] |
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for c in y: |
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crimes_counter[c] += 1 |
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samples.append({'gold':c,'pred':y_[3:-4]}) |
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if re.search('<e>.*</e>',y_): |
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samples_with_tag.append({'gold':c,'pred':y_}) |
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return samples,samples_with_tag |
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samples,samples_with_tag = load_data(path) |
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print('/'.join(path.split('/')[-2:])) |
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get_f1(samples) |
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print('-'*30) |
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get_f1(samples_with_tag) |
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print('-'*30) |
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get_cls_acc(samples) |
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print('-'*30) |
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get_bias_with_acc(samples) |
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if __name__ =='__main__': |
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main() |
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