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import copy as cp
import json
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 OVERALL_MATH_SCORE_FILE, DEFAULT_MATH_BENCH, META_FIELDS
def listinstr(lst, s):
assert isinstance(lst, list)
for item in lst:
if item in s:
return True
return False
def load_results(file_name=OVERALL_MATH_SCORE_FILE):
data = json.loads(open(file_name, "r").read())
return data
def format_timestamp(timestamp):
date = timestamp[:10]
time = timestamp[11:13] + ':' + timestamp[14:16] + ':' + timestamp[17:19]
return date + ' ' + time
def nth_large(val, vals):
return sum([1 for v in vals if v > val]) + 1
def BUILD_L1_DF(results, fields):
check_box = {}
check_box['essential'] = ['Algorithm', 'LLM', 'Eval Date']
# First check which columns exist in the actual data structure
sample_data = next(iter(results.values()))
available_fields = []
for field in fields:
if field in sample_data:
available_fields.append(field)
# Build column names, ensure they match exactly with those in generate_table function
score_columns = [f"{field}-Score" for field in available_fields]
cost_columns = [f"{field}-Cost($)" for field in available_fields]
combined_columns = score_columns + cost_columns
combined_columns_sorted = sorted(combined_columns, key=lambda x: x.split('-')[0])
check_box['required'] = ['Avg Score'] + combined_columns_sorted
check_box['all'] = ['Avg Score'] + combined_columns_sorted
type_map = defaultdict(lambda: 'number')
type_map['Algorithm'] = 'html'
type_map['LLM'] = type_map['Vision Model'] = 'html'
type_map['Eval Date'] = 'str'
type_map['Avg Score'] = 'number'
type_map['gsm8k-Score'] = 'number'
type_map['AQuA-Score'] = 'number'
type_map['gsm8k-Cost($)'] = 'number'
type_map['AQuA-Cost($)'] = 'number'
check_box['type_map'] = type_map
return check_box
def BUILD_L2_DF(results, fields):
res = defaultdict(list)
# Iterate over each algorithm and its corresponding models
for algo_name, algo_data in results.items():
for model_name, model_data in algo_data.items():
# Get META information
meta = model_data['META']
# Create a record for each dataset
for dataset in fields:
if dataset not in model_data:
continue
# Add metadata
for k, v in meta.items():
res[k].append(v)
# Add dataset name
res['Dataset'].append(dataset)
# Get dataset data
dataset_data = model_data[dataset]
# Add all fields
for field, value in dataset_data.items():
res[field].append(value)
# Create DataFrame
df = pd.DataFrame(res)
# Sort by Dataset and Score in descending order
df = df.sort_values(['Dataset', 'Score'], ascending=[True, False])
# Add rank for each dataset separately
df['Rank'] = df.groupby('Dataset').cumcount() + 1
# Rearrange column order
columns = ['Rank', 'Algorithm', 'Dataset', 'LLM', 'Eval Date', 'Score', 'Pass rate', 'X-shot']
remaining_columns = [col for col in df.columns if col not in columns]
df = df[columns + remaining_columns]
# Set checkbox configuration
check_box = {}
check_box['essential'] = ['Algorithm', 'Dataset', 'LLM', 'Eval Date']
check_box['required'] = check_box['essential'] + ['Score', 'Pass rate', 'X-shot', 'Samples', 'All tokens', 'Cost($)']
check_box['all'] = ['Score', 'Pass rate', 'X-shot', 'Samples', 'Total input tokens', 'Average input tokens', 'Total output tokens', 'Average output tokens', 'All tokens', 'Cost($)']
type_map = defaultdict(lambda: 'number')
type_map['Algorithm'] = 'html'
type_map['LLM'] = type_map['Vision Model'] = 'html'
type_map['Eval Date'] = 'str'
type_map['Dataset'] = 'str'
type_map['All tokens'] = 'number'
type_map['Cost($)'] = 'number'
check_box['type_map'] = type_map
return df, check_box
def generate_table(results, fields):
res = defaultdict(list)
for i, m in enumerate(results):
item = results[m]
meta = item['META']
for k in META_FIELDS:
res[k].append(meta[k])
scores, costs = [], []
# Ensure column names format matches with BUILD_L1_DF
for d in fields:
if d in item:
score = item[d].get("Score")
cost = item[d].get("Cost($)")
res[f"{d}-Score"].append(score)
res[f"{d}-Cost($)"].append(cost)
if score is not None:
scores.append(score)
if cost is not None:
costs.append(cost)
else:
res[f"{d}-Score"].append(None)
res[f"{d}-Cost($)"].append(None)
# Calculate average score
res['Avg Score'].append(round(np.mean(scores), 2) if scores else None)
df = pd.DataFrame(res)
# Sorting and ranking logic remains unchanged
valid = df[~pd.isna(df['Avg Score'])].copy()
missing = df[pd.isna(df['Avg Score'])].copy()
valid = valid.sort_values('Avg Score', ascending=False)
valid['Rank'] = range(1, len(valid) + 1)
if not missing.empty:
missing['Rank'] = len(valid) + 1
df = pd.concat([valid, missing])
df = df.sort_values('Rank')
# 重新排列列顺序
columns = ['Rank', 'Algorithm', 'LLM', 'Eval Date', 'Avg Score']
for d in fields:
columns.extend([f"{d}-Score", f"{d}-Cost($)"])
existing_columns = [col for col in columns if col in df.columns]
df = df[existing_columns]
return df
def generate_table_detail(results, fields):
res = defaultdict(list)
# Iterate over each algorithm and its corresponding models
for algo_name, algo_data in results.items():
for model_name, model_data in algo_data.items():
# Get META information
meta = model_data['META']
# Create a record for each dataset
for dataset in fields:
if dataset not in model_data:
continue
# Add metadata
for k, v in meta.items():
res[k].append(v)
# Add dataset name
res['Dataset'].append(dataset)
# Get dataset data
dataset_data = model_data[dataset]
# Add all fields
for field, value in dataset_data.items():
res[field].append(value)
# Create DataFrame
df = pd.DataFrame(res)
# Sort by Dataset and Score in descending order
df = df.sort_values(['Dataset', 'Score'], ascending=[True, False])
# Add rank for each dataset separately
df['Rank'] = df.groupby('Dataset').cumcount() + 1
# Rearrange column order
columns = ['Rank', 'Dataset', 'Algorithm', 'LLM', 'Eval Date', 'Score', 'Pass rate', 'X-shot']
remaining_columns = [col for col in df.columns if col not in columns]
df = df[columns + remaining_columns]
return df |