Open-Arabic-LLM-Leaderboard / visualizations.py
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Add GPT-4 & human eval tab
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import math
import numpy as np
import pandas as pd
import plotly.express as px
# 1
def compute_pairwise_win_fraction(battles):
# Times each model wins as Model A
a_win_ptbl = pd.pivot_table(
battles[battles["win"] == "model_a"],
index="model_a",
columns="model_b",
aggfunc="size",
fill_value=0,
)
# Table counting times each model wins as Model B
b_win_ptbl = pd.pivot_table(
battles[battles["win"] == "model_b"],
index="model_a",
columns="model_b",
aggfunc="size",
fill_value=0,
)
# Table counting number of A-B pairs
num_battles_ptbl = pd.pivot_table(battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0)
# Computing the proportion of wins for each model as A and as B
# against all other models
row_beats_col_freq = (a_win_ptbl + b_win_ptbl.T) / (num_battles_ptbl + num_battles_ptbl.T)
# Arrange ordering according to proprition of wins
prop_wins = row_beats_col_freq.mean(axis=1).sort_values(ascending=False)
model_names = list(prop_wins.keys())
row_beats_col = row_beats_col_freq.loc[model_names, model_names]
return row_beats_col
def visualize_pairwise_win_fraction(battles, title):
row_beats_col = compute_pairwise_win_fraction(battles)
fig = px.imshow(row_beats_col, color_continuous_scale="RdBu", text_auto=".2f", title=title)
fig.update_layout(
xaxis_title="Model B",
yaxis_title="Model A",
xaxis_side="top",
title_y=0.07,
title_x=0.5,
)
fig.update_traces(hovertemplate="Model A: %{y}<br>Model B: %{x}<br>Fraction of A Wins: %{z}<extra></extra>")
return fig
# 2
def switch_model_a_b(df):
df_switch = df.copy()
# switch with probability 0.5
for i, row in df.iterrows():
if np.random.rand() < 0.5:
df_switch.at[i, "model_a"] = row["model_b"]
df_switch.at[i, "model_b"] = row["model_a"]
if row["win"] == "model_a":
df_switch.at[i, "win"] = "model_b"
elif row["win"] == "model_b":
df_switch.at[i, "win"] = "model_a"
return df_switch
def visualize_battle_count(battles, title):
ptbl = pd.pivot_table(battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0)
battle_counts = ptbl + ptbl.T
ordering = battle_counts.sum().sort_values(ascending=False).index
fig = px.imshow(battle_counts.loc[ordering, ordering], title=title, text_auto=True, width=600)
fig.update_layout(
xaxis_title="Model B",
yaxis_title="Model A",
xaxis_side="top",
title_y=0.07,
title_x=0.5,
)
fig.update_traces(hovertemplate="Model A: %{y}<br>Model B: %{x}<br>Count: %{z}<extra></extra>")
return fig
# 3
def get_bootstrap_result(battles, func_compute_elo, num_round):
rows = [func_compute_elo(battles.sample(frac=1.0, replace=True)) for _ in range(num_round)]
df = pd.DataFrame(rows)
return df[df.median().sort_values(ascending=False).index]
def visualize_bootstrap_scores(df, title):
bars = (
pd.DataFrame(
dict(
lower=df.quantile(0.025),
rating=df.quantile(0.5),
upper=df.quantile(0.975),
)
)
.reset_index(names="model")
.sort_values("rating", ascending=False)
)
bars["error_y"] = bars["upper"] - bars["rating"]
bars["error_y_minus"] = bars["rating"] - bars["lower"]
bars["rating_rounded"] = np.round(bars["rating"], 2)
fig = px.scatter(
bars,
x="model",
y="rating",
error_y="error_y",
error_y_minus="error_y_minus",
text="rating_rounded",
title=title,
)
fig.update_layout(xaxis_title="Model", yaxis_title="Rating")
return fig
# 4
def visualize_rating_count(df, title):
df_all_value_counts = pd.concat([df["model_a"], df["model_b"]]).value_counts()
fig = px.bar(df_all_value_counts, title=title, text_auto=True)
min_y = df_all_value_counts.min()
max_y = df_all_value_counts.max()
y_end = math.ceil(min_y / 100) * 100
y_begin = math.floor(max_y / 100) * 100
fig.update_layout(xaxis_title="model", yaxis_title="Rating Count", showlegend=False)
fig.update_yaxes(range=[y_begin, y_end])
# save the plot for the blog:
fig.write_html("model_counts.html", full_html=False, include_plotlyjs="cdn")
return fig