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import itertools
import os
import gradio as gr
import numpy as np
import pandas as pd
import plotly.express as px
from datasets import load_dataset
import style
TAB_STATE = 0 # FIXME
GSM8K_TASK_GROUP_NAME = "GSM8K" # FIXME
BELEBELE_TASK_GROUP_NAME = "BELEBELE" # FIXME
def init():
global repo_id, config_name, split_name, hidden_df, task_group_names_list, task_group_type_dict, task_groups_shots_dict, languages_list, model_type_dict
repo_id = os.getenv("OGX_LEADERBOARD_DATASET_NAME")
config_name = os.getenv("OGX_LEADERBOARD_DATASET_CONFIG")
split_name = os.getenv("OGX_LEADERBOARD_DATASET_SPLIT")
dataset = load_dataset(repo_id, config_name, split=split_name)
hidden_df = dataset.to_pandas()
task_group_names_list = hidden_df["Task_Group"].unique().tolist()
task_group_type_df = hidden_df[["Task_Group", "Task_Type"]].drop_duplicates()
task_group_type_dict = task_group_type_df.set_index("Task_Group")["Task_Type"].to_dict()
task_groups_shots_df = hidden_df[hidden_df["Few_Shot"] == True][["Task_Group", "Number_Shots"]].drop_duplicates()
task_groups_shots_dict = task_groups_shots_df.set_index("Task_Group")["Number_Shots"].to_dict()
languages_list = hidden_df["Language"].drop_duplicates().str.upper().tolist()
model_type_df = hidden_df[["Model_Name", "Model_Type"]].drop_duplicates()
model_type_dict = model_type_df.set_index("Model_Name")["Model_Type"].to_dict()
hidden_df = hidden_df.pivot_table(
columns=["Task_Group", "Few_Shot", "Language"],
index=["Model_Name"],
values="Value",
dropna=False,
).reset_index(inplace=False)
hidden_df["Type"] = hidden_df["Model_Name"].apply(lambda x: style.T_SYMBOLS[model_type_dict[x]])
def sort_cols(df: pd.DataFrame, fewshot: bool = False) -> pd.DataFrame:
task_cols = get_task_columns(df)
if fewshot:
renamer = {col: f"{col} ({task_groups_shots_dict[col]}-shot)" for col in task_cols if col in task_groups_shots_dict}
df.rename(columns=renamer, inplace=True)
task_cols = renamer.values()
return df.reindex(["Type", "Model_Name", "Average"] + sorted(task_cols), axis=1)
def get_task_columns(df: pd.DataFrame) -> pd.DataFrame:
l = list(df.columns)
l.remove("Model_Name")
l.remove("Average")
l.remove("Type")
return l
def get_models(df: pd.DataFrame) -> pd.DataFrame:
return df["Model_Name"].unique()
def filter_type(df: pd.DataFrame, model_types: list[str]) -> pd.DataFrame:
"""Keep only rows for which model type is in list of types"""
return df[df["Type"].isin(model_types)]
def search_model(df: pd.DataFrame, query: str) -> pd.DataFrame:
"""Keep only rows for which model name matches search query"""
query = query.replace(";", "|")
return df[df["Model_Name"].str.contains(query, case=False)]
def aggregate_langs(df: pd.DataFrame, tasks: list, langs: list):
"""Aggregates results over langs for each task in tasks.
If a language does not exist for a task, the aggregate for
that task will be shown as NaN.
"""
langs_lower = [item.lower() for item in langs]
df.columns = ["_".join(filter(None, col)) for col in df.columns]
colset = set(df.columns)
for t in tasks:
cols = [(f"{a}_{b}") for a, b in itertools.product([t], langs_lower)]
if set(cols).issubset(colset):
df.loc[:, t] = df[cols].mean(axis=1, skipna=False)
else:
df.loc[:, t] = np.nan
df.loc[:, "Average"] = df[tasks].mean(axis=1)
return df[["Type", "Model_Name", "Average"] + tasks]
def select_shots(df: pd.DataFrame, fewshot: bool = False):
cols = [col for col in df.columns if col[1] == fewshot] + []
# Move model name and type icon to the end
cols.append(("Model_Name", "", ""))
cols.append(("Type", "", ""))
return df[cols].droplevel(level=1, axis="columns")
def update_df(
tasks: list[str],
model_query: str,
langs: list[str],
model_types: list[str],
fewshot: bool = False,
format: bool = True,
) -> pd.DataFrame:
"""Return a filtered dataframe according to selected models, tasks and
languages. The format flag controls whether the output dataframe should
be formatted to tw significant figures.
"""
# keep only selected shots
df = select_shots(hidden_df, fewshot)
# aggregate results over languages per task
df = aggregate_langs(df, tasks, langs)
# filter models by search bar and model type
df = search_model(df, model_query)
df = filter_type(df, model_types)
if format:
return sort_cols(df, fewshot).style.format(precision=2, decimal=".")
else:
return sort_cols(df, fewshot)
def make_plot(df: pd.DataFrame):
df.columns = df.loc["Model_Name"]
df = df.drop("Model_Name")
df = df.reset_index(names="task")
if len(df.columns) > 2:
fig = px.line(data_frame=df, x="task", y=df.columns, markers=True, width=1200)
else:
fig = px.bar(data_frame=df, x="task", y=df.columns[-1], width=1200)
fig.update_xaxes(type="category")
return fig
def update_plot(
tasks: list[str],
model_query: str,
langs: list[str],
model_types: list[str],
fewshot: bool = False,
):
df = update_df(tasks, model_query, langs, model_types, fewshot, False).transpose()
plot = make_plot(df)
return plot
def fix_zeroshot(tasks: list[str | int | float], fewshot: bool = False):
global TAB_STATE
selected_task_type = get_selected_task_type(TAB_STATE)
choices = task_groups_with_task_type(selected_task_type)
if not fewshot:
try:
choices.remove(GSM8K_TASK_GROUP_NAME)
except ValueError:
pass
if TAB_STATE == 0:
value = [v for v in tasks if v in choices]
if BELEBELE_TASK_GROUP_NAME not in value:
value += [BELEBELE_TASK_GROUP_NAME]
elif TAB_STATE == 1:
value = [v for v in tasks if v in choices]
else:
try:
choices.remove(BELEBELE_TASK_GROUP_NAME)
except ValueError:
pass
if TAB_STATE == 0:
value = [v for v in tasks if v in choices]
if GSM8K_TASK_GROUP_NAME not in value:
value += [GSM8K_TASK_GROUP_NAME]
elif TAB_STATE == 1:
value = [v for v in tasks if v in choices]
shown_tasks = gr.CheckboxGroup(
choices=choices,
value=value,
label="Select tasks to show",
elem_id="column-select",
interactive=True,
scale=50,
)
return shown_tasks
def update_tab_tasks(id: int, fewshot: bool = False):
# when the tab is changed, update the TAB_STATE accordingly
global TAB_STATE
TAB_STATE = id
selected_task_type = get_selected_task_type(TAB_STATE)
choices = task_groups_with_task_type(selected_task_type)
if not fewshot:
try:
choices.remove(GSM8K_TASK_GROUP_NAME)
except ValueError:
pass
else:
try:
choices.remove(BELEBELE_TASK_GROUP_NAME)
except ValueError:
pass
values = choices.copy()
shown_tasks = gr.CheckboxGroup(
choices=choices,
value=values,
label="Select tasks to show",
elem_id="column-select",
interactive=True,
scale=50,
)
if id == 0:
# switching to accuracy tab, default to fewshot
fewshot = gr.Radio(
choices=[("0-Shot", False), ("Few-shot", True)],
value=True,
label="Select evaluation type",
interactive=True,
scale=29,
)
elif id == 1:
# switching to translation tab, default to 0-shot and disable selection
fewshot = gr.Radio(
choices=[("0-Shot", False), ("Few-shot", True)],
value=False,
label="Select evaluation type",
interactive=False,
scale=29,
)
return [shown_tasks, fewshot]
def get_selected_task_type(task_type_id):
task_types = {0: "accuracy", 1: "misc"}
selected_task_type = task_types[task_type_id]
return selected_task_type
def task_groups_with_task_type(selected_task_type):
choices = [task_group_name for task_group_name, task_type in task_group_type_dict.items() if task_type == selected_task_type]
return choices
init()
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