Corey Morris
Extracted plotting functions from moral_app to plotting_utils to improve organization and testability
2b55a03
| 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 |