MMLU-by-task-Leaderboard / plotting_utils.py
Corey Morris
Extracted plotting functions from moral_app to plotting_utils to improve organization and testability
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import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
import plotly.graph_objects as go
def plot_top_n(df, target_column, n=10):
top_n = df.nlargest(n, target_column)
# Initialize the bar plot
fig, ax1 = plt.subplots(figsize=(10, 5))
# Set width for each bar and their positions
width = 0.28
ind = np.arange(len(top_n))
# Plot target_column and MMLU_average on the primary y-axis with adjusted positions
ax1.bar(ind - width, top_n[target_column], width=width, color='blue', label=target_column)
ax1.bar(ind, top_n['MMLU_average'], width=width, color='orange', label='MMLU_average')
# Set the primary y-axis labels and title
ax1.set_title(f'Top {n} performing models on {target_column}')
ax1.set_xlabel('Model')
ax1.set_ylabel('Score')
# Create a secondary y-axis for Parameters
ax2 = ax1.twinx()
# Plot Parameters as bars on the secondary y-axis with adjusted position
ax2.bar(ind + width, top_n['Parameters'], width=width, color='red', label='Parameters')
# Set the secondary y-axis labels
ax2.set_ylabel('Parameters', color='red')
ax2.tick_params(axis='y', labelcolor='red')
# Set the x-ticks and their labels
ax1.set_xticks(ind)
ax1.set_xticklabels(top_n.index, rotation=45, ha="right")
# Adjust the legend
fig.tight_layout()
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# Show the plot
st.pyplot(fig)
# Function to create an unfilled radar chart
def create_radar_chart_unfilled(df, model_names, metrics):
fig = go.Figure()
min_value = df.loc[model_names, metrics].min().min()
max_value = df.loc[model_names, metrics].max().max()
for model_name in model_names:
values_model = df.loc[model_name, metrics]
fig.add_trace(go.Scatterpolar(
r=values_model,
theta=metrics,
name=model_name
))
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True,
range=[min_value, max_value]
)),
showlegend=True,
width=800, # Change the width as needed
height=600 # Change the height as needed
)
return fig
# Function to create a line chart
def create_line_chart(df, model_names, metrics):
line_data = []
for model_name in model_names:
values_model = df.loc[model_name, metrics]
for metric, value in zip(metrics, values_model):
line_data.append({'Model': model_name, 'Metric': metric, 'Value': value})
line_df = pd.DataFrame(line_data)
fig = px.line(line_df, x='Metric', y='Value', color='Model', title='Comparison of Models', line_dash_sequence=['solid'])
fig.update_layout(showlegend=True)
return fig
def create_plot(df, x_values, y_values, models=None, title=None):
if models is not None:
df = df[df.index.isin(models)]
# remove rows with NaN values
df = df.dropna(subset=[x_values, y_values])
plot_data = pd.DataFrame({
'Model': df.index,
x_values: df[x_values],
y_values: df[y_values],
})
plot_data['color'] = 'purple'
fig = px.scatter(plot_data, x=x_values, y=y_values, color='color', hover_data=['Model'], trendline="ols")
# If title is not provided, use x_values vs. y_values as the default title
if title is None:
title = x_values + " vs. " + y_values
layout_args = dict(
showlegend=False,
xaxis_title=x_values,
yaxis_title=y_values,
xaxis=dict(),
yaxis=dict(),
title=title,
height=500,
width=1000,
)
fig.update_layout(**layout_args)
# Add a dashed line at 0.25 for the y_values
x_min = df[x_values].min()
x_max = df[x_values].max()
y_min = df[y_values].min()
y_max = df[y_values].max()
if x_values.startswith('MMLU'):
fig.add_shape(
type='line',
x0=0.25, x1=0.25,
y0=y_min, y1=y_max,
line=dict(
color='red',
width=2,
dash='dash'
)
)
if y_values.startswith('MMLU'):
fig.add_shape(
type='line',
x0=x_min, x1=x_max,
y0=0.25, y1=0.25,
line=dict(
color='red',
width=2,
dash='dash'
)
)
return fig