multimodal-clem-leaderboard / src /leaderboard_utils.py
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import os
import pandas as pd
import requests, json
from io import StringIO
from datetime import datetime
def get_github_data():
"""
Get data from csv files on Github
Args:
None
Returns:
latest_df: singular list containing dataframe of the latest version of the leaderboard with only 4 columns
all_dfs: list of dataframes for previous versions + latest version including columns for all games
all_vnames: list of the names for the previous versions + latest version (For Details and Versions Tab Dropdown)
"""
uname = "clembench"
repo = "clembench-runs"
json_url = f"https://raw.githubusercontent.com/{uname}/{repo}/main/benchmark_runs.json"
resp = requests.get(json_url)
if resp.status_code == 200:
json_data = json.loads(resp.text)
versions = json_data['versions']
version_names = []
csv_url = f"https://raw.githubusercontent.com/{uname}/{repo}/main/"
for ver in versions:
version_names.append(ver['version'])
csv_path = ver['result_file'].split('/')[1:]
csv_path = '/'.join(csv_path)
# Sort by latest version
float_content = [float(s[1:]) for s in version_names]
float_content.sort(reverse=True)
version_names = ['v'+str(s) for s in float_content]
# Get date of latest version
for data in versions:
if data['version'] == version_names[0]:
date = data['date'] # Should be in YYYY/MM/DD format
date_obj = datetime.strptime(date, "%Y/%m/%d")
date = date_obj.strftime("%d %b %Y")
DFS = []
for version in version_names:
result_url = csv_url+ version + '/' + csv_path
csv_response = requests.get(result_url)
if csv_response.status_code == 200:
df = pd.read_csv(StringIO(csv_response.text))
df = process_df(df)
df = df.sort_values(by=list(df.columns)[1], ascending=False) # Sort by clemscore
DFS.append(df)
else:
print(f"Failed to read CSV file for version : {version}. Status Code : {resp.status_code}")
# Only keep relevant columns for the main leaderboard
latest_df_dummy = DFS[0]
all_columns = list(latest_df_dummy.columns)
keep_columns = all_columns[0:4]
latest_df_dummy = latest_df_dummy.drop(columns=[c for c in all_columns if c not in keep_columns])
latest_df = [latest_df_dummy]
all_dfs = []
all_vnames = []
for df, name in zip(DFS, version_names):
all_dfs.append(df)
all_vnames.append(name)
return latest_df, all_dfs, all_vnames, date
else:
print(f"Failed to read JSON file: Status Code : {resp.status_code}")
def process_df(df: pd.DataFrame) -> pd.DataFrame:
"""
Process dataframe
- Remove repition in model names
- Convert datatypes to sort by "float" instead of "str" for sorting
- Update column names
Args:
df: Unprocessed Dataframe (after using update_cols)
Returns:
df: Processed Dataframe
"""
# Change column type to float from str
list_column_names = list(df.columns)
model_col_name = list_column_names[0]
for col in list_column_names:
if col != model_col_name:
df[col] = df[col].astype(float)
# Remove repetition in model names, if any
models_list = []
for i in range(len(df)):
model_name = df.iloc[i][model_col_name]
splits = model_name.split('--')
splits = [split.replace('-t0.0', '') for split in splits] # Comment to not remove -t0.0
if splits[0] == splits[1]:
models_list.append(splits[0])
else:
models_list.append(splits[0] + "--" + splits[1])
df[model_col_name] = models_list
# Update column names
update = ['Model', 'Clemscore', '% Played', 'Quality Score']
game_metrics = list_column_names[4:]
for col in game_metrics:
splits = col.split(',')
update.append(splits[0].capitalize() + "" + splits[1])
map_cols = {}
for i in range(len(update)):
map_cols[list_column_names[i]] = str(update[i])
df = df.rename(columns=map_cols)
return df
def filter_search(df: pd.DataFrame, query: str) -> pd.DataFrame:
"""
Filter the dataframe based on the search query
Args:
df: Unfiltered dataframe
query: a string of queries separated by ";"
Return:
filtered_df: Dataframe containing searched queries in the 'Model' column
"""
queries = query.split(';')
list_cols = list(df.columns)
df_len = len(df)
filtered_models = []
models_list = list(df[list_cols[0]])
for q in queries:
q = q.lower()
q = q.strip()
for i in range(df_len):
model_name = models_list[i]
if q in model_name.lower():
filtered_models.append(model_name) # Append model names containing query q
filtered_df = df[df[list_cols[0]].isin(filtered_models)]
if query == "":
return df
return filtered_df