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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']
# revise there to set default dataset
check_box['required'] = ['Avg Score'] + [item for f in fields for item in (f'{f}-Score', f'{f}-Cost($)')]
check_box['avg'] = ['Avg Score']
check_box['all'] = check_box['avg'] + [item for f in fields for item in (f'{f}-Score', f'{f}-Cost($)')]
type_map = defaultdict(lambda: 'number')
type_map['Algorithm'] = 'html'
type_map['LLM'] = type_map['Vision Model'] = 'html'
type_map['Eval Date'] = 'str'
check_box['type_map'] = type_map
# df = generate_table(results, fields)
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', 'Parameters']
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', 'Parameters', 'Samples', 'All tokens', 'Cost($)']
check_box['all'] = ['Score', 'Pass rate', 'X-shot', 'Parameters', 'Samples', 'Total input tokens', 'Average input tokens', 'Total output tokens', 'Average output tokens', 'All tokens', 'Cost($)']
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['Parameters'] = 'str'
type_map['All tokens'] = 'number'
type_map['Cost($)'] = 'number'
check_box['type_map'] = type_map
return df, check_box
def generate_table(results, fields):
res = defaultdict(list)
for i, m in enumerate(results):
item = results[m]
meta = item['META']
for k in META_FIELDS:
res[k].append(meta[k])
scores, costs = [], []
for d in fields:
if d in item.keys():
res[d+"-Score"].append(item[d]["Score"])
res[d+"-Cost($)"].append(item[d]["Cost($)"])
scores.append(item[d]["Score"])
costs.append(item[d]["Cost($)"])
else:
res[d+"-Score"].append(None)
res[d+"-Cost($)"].append(None)
scores.append(None)
costs.append(None)
res['Avg Score'].append(round(np.mean(scores), 2) if None not in scores else None)
df = pd.DataFrame(res)
# Sort by Avg Score and assign rank
valid = df[~pd.isna(df['Avg Score'])].copy()
missing = df[pd.isna(df['Avg Score'])].copy()
# Assign rank to valid rows (using integer type)
valid = valid.sort_values('Avg Score', ascending=False)
valid['Rank'] = pd.Series(range(1, len(valid) + 1)[::-1], dtype=int)
# Assign last rank to missing rows (using integer type)
if not missing.empty:
missing['Rank'] = pd.Series([len(valid) + 1] * len(missing), dtype=int)
# Merge and sort by Rank
df = pd.concat([valid, missing])
df = df.sort_values('Rank')
# Rearrange column order to ensure Rank is the first column
columns = ['Rank', 'Algorithm', 'LLM', 'Eval Date', 'Avg Score'] # Fixed column order
for d in fields:
columns.extend([f"{d}-Score", f"{d}-Cost($)"]) # Add dataset-related columns
# Ensure all columns exist and reorder
existing_columns = [col for col in columns if col in df.columns]
remaining_columns = [col for col in df.columns if col not in columns]
df = df[existing_columns + remaining_columns] # Reorder columns
# Sort by Score in descending order
df = df.sort_values(['Avg Score'], ascending=[False])
# Add rank for each dataset separately
df['Rank'] = range(1, len(df) + 1)
# Rearrange column order
columns = ['Rank', 'Algorithm', 'LLM', 'Eval Date', 'Avg Score']
remaining_columns = [col for col in df.columns if col not in columns]
df = df[columns + remaining_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', 'Parameters']
remaining_columns = [col for col in df.columns if col not in columns]
df = df[columns + remaining_columns]
return df |