Spaces:
Running
Running
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| from .utils import undo_hyperlink | |
| def plot_avg_correlation(df1, df2): | |
| """ | |
| Plots the "average" column for each unique model that appears in both dataframes. | |
| Parameters: | |
| - df1: pandas DataFrame containing columns "model" and "average". | |
| - df2: pandas DataFrame containing columns "model" and "average". | |
| """ | |
| # Identify the unique models that appear in both DataFrames | |
| common_models = pd.Series(list(set(df1['model']) & set(df2['model']))) | |
| # Set up the plot | |
| plt.figure(figsize=(13, 6), constrained_layout=True) | |
| # axes from 0 to 1 for x and y | |
| plt.xlim(0.475, 0.8) | |
| plt.ylim(0.475, 0.8) | |
| # larger font (16) | |
| plt.rcParams.update({'font.size': 12, 'axes.labelsize': 14,'axes.titlesize': 14}) | |
| # plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1) | |
| # plt.tight_layout() | |
| # plt.margins(0,0) | |
| for model in common_models: | |
| # Filter data for the current model | |
| df1_model_data = df1[df1['model'] == model]['average'].values | |
| df2_model_data = df2[df2['model'] == model]['average'].values | |
| # Plotting | |
| plt.scatter(df1_model_data, df2_model_data, label=model) | |
| m_name = undo_hyperlink(model) | |
| if m_name == "No text found": | |
| m_name = "Random" | |
| # Add text above each point like | |
| # plt.text(x[i] + 0.1, y[i] + 0.1, label, ha='left', va='bottom') | |
| plt.text(df1_model_data - .005, df2_model_data, m_name, horizontalalignment='right', verticalalignment='center') | |
| # add correlation line to scatter plot | |
| # first, compute correlation | |
| corr = df1['average'].corr(df2['average']) | |
| # add correlation line based on corr | |
| plt.xlabel('HERM Eval. Set Avg.', fontsize=16) | |
| plt.ylabel('Pref. Test Sets Avg.', fontsize=16) | |
| # plt.legend(title='Model', bbox_to_anchor=(1.05, 1), loc='upper left') | |
| return plt |