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import copy as cp
import json, sys
from collections import defaultdict
from urllib.request import urlopen
import gradio as gr
import numpy as np
import pandas as pd
from meta_data import DEFAULT_TASK
def listinstr(lst, s):
assert isinstance(lst, list)
for item in lst:
if item in s:
return True
return False
def load_results():
#data = json.loads(urlopen(URL).read())
with open('results.json', 'r') as file:
data = json.load(file)
return data
def nth_large(val, vals):
return sum([1 for v in vals if v > val]) + 1
def format_timestamp(timestamp):
date = timestamp[:-4] + '.' + timestamp[-4:-2] + '.' + timestamp[-2:]
return date
# def BUILD_L1_DF(results, fields):
# check_box = {}
# check_box['essential'] = ['Model']
# # revise there to set default dataset
# check_box['required'] = DEFAULT_TASK
# check_box['all'] = DEFAULT_TASK
# type_map = defaultdict(lambda: 'number')
# check_box['type_map'] = type_map
# df = generate_table(results, fields)
# return df, check_box
def BUILD_L2_DF(results, benchmark):
results=results[benchmark]
model_list=[]
all_fields=list(results.keys())
for task in results:
model_list+=list(results[task].keys())
model_list=list(set(model_list))
res = defaultdict(list)
if benchmark not in ["RedCode","NYU CTF Bench","PrimeVul","SWE-bench-verified"]:
res['Model']=model_list
elif benchmark=="SWE-bench-verified":
res['Agent']=model_list
elif benchmark == "PrimeVul":
used=[]
for task in all_fields:
for model in results[task]:
for extra in results[task][model]:
if [model,extra] not in used:
res['Model'].append(model)
res['Method'].append(extra)
used.append([model,extra])
else:
used=[]
for task in all_fields:
for model in results[task]:
for extra in results[task][model]:
if [model,extra] not in used:
res['Model'].append(model)
res['Agent'].append(extra)
used.append([model,extra])
if benchmark not in ["RedCode","NYU CTF Bench",'PrimeVul']:
for task in all_fields:
for model in model_list:
if model in results[task]:
res[task].append(results[task][model])
else:
res[task].append(None)
else:
for task in all_fields:
for model, extra in used:
if model in results[task] and extra in results[task][model]:
res[task].append(results[task][model][extra])
else:
res[task].append(None)
df = pd.DataFrame(res)
rank_criteria=all_fields[0]
valid, missing = df[~pd.isna(df[rank_criteria])], df[pd.isna(df[rank_criteria])]
valid = valid.sort_values(rank_criteria)
valid = valid.iloc[::-1]
if len(all_fields):
missing = missing.iloc[::-1]
df = pd.concat([valid, missing])
required_fields = all_fields
check_box = {}
if benchmark=="SWE-bench-verified":
check_box['essential'] = ['Agent']
elif benchmark=='PrimeVul':
check_box['essential'] = ['Model','Method']
elif benchmark in ["RedCode","NYU CTF Bench"]:
check_box['essential'] = ['Model','Agent']
else:
check_box['essential'] = ['Model']
check_box['required'] = required_fields
check_box['all'] = all_fields
type_map = defaultdict(lambda: 'number')
check_box['type_map'] = type_map
return df, check_box
def generate_table(results, fields):
model_list=[]
task_list=fields
benchmark_list=[]
for task in results:
for benchmark in results[task]:
if benchmark!='category':
benchmark_list+=[benchmark]
model_list+=list(results[task][benchmark].keys())
model_list=list(set(model_list))
res = defaultdict(list)
res['Model']=model_list
average_score={}
cnt={}
for task in task_list:
task_score=[]
for model in model_list:
score=[]
for benchmark in results[task]:
if benchmark != 'category':
if model not in results[task][benchmark]:
score.append(None)
elif not isinstance(results[task][benchmark][model], (int, float)):
score.append((results[task][benchmark][model]["autonomous"]+results[task][benchmark][model]["assisted"])/2)
else:
score.append(results[task][benchmark][model])
if not any (item is not None for item in score):
score=None
else:
score=np.mean([s for s in score if s is not None])
if model not in average_score:
average_score[model]=score
cnt[model]=1
else:
average_score[model]=((average_score[model]*cnt[model])+score)/(cnt[model]+1)
cnt[model]+=1
task_score.append(score)
res[task]=task_score
#res['Avg Score']=[average_score[model] for model in model_list]
#res['Avg Rank'] = [sorted(res['Avg Score'], reverse=True).index(score) + 1 for score in res['Avg Score']]
df = pd.DataFrame(res)
# valid, missing = df[~pd.isna(df['Avg Score'])], df[pd.isna(df['Avg Score'])]
# valid = valid.sort_values('Avg Score')
# valid = valid.iloc[::-1]
# if len(fields):
# missing = missing.sort_values('MMBench_V11' if 'MMBench_V11' in fields else fields[0])
# missing = missing.iloc[::-1]
# df = pd.concat([valid, missing])
return df
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