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| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import numpy as np | |
| import scipy.stats as stats | |
| import warnings | |
| warnings.simplefilter("ignore", category=Warning) | |
| def custom_agg(x): | |
| result = stats.hmean(x) | |
| return result | |
| def create_plots(big_df, selected_methods): | |
| big_df = big_df[big_df['Method'].isin(selected_methods)] | |
| # we want 1-Rouge-P | |
| big_df["ROUGE-P Forget"] = 1 - big_df["ROUGE-P Forget"] | |
| metrics = list(big_df.columns) | |
| metrics.remove("Method") | |
| metrics.remove("Model") | |
| metrics.remove("Forget Rate") | |
| metrics.remove("LR") | |
| metrics.remove("Epoch") | |
| metrics.remove("Compute") | |
| print(metrics) | |
| # Apply the custom aggregation function across each row, excluding the first column | |
| row_custom_agg = big_df.iloc[:, -len(metrics):].apply(custom_agg, axis=1) | |
| # If you want to add these results back to your original DataFrame | |
| big_df['MAPO'] = row_custom_agg | |
| big_df["LR"] = big_df["LR"].astype(float) | |
| # big_df = big_df[big_df["LR"] >= 1e-5] | |
| big_df["ROUGE-P Forget"] = 1 - big_df["ROUGE-P Forget"] | |
| big_df.reset_index(inplace=True) | |
| print(big_df[["Method", "Model", "Forget Rate", "LR", "Epoch", "ROUGE-P Forget", "MAPO"]].round(2).to_markdown()) | |
| # print(big_df.groupby(['Method', 'Model', 'Forget Rate']).head()) | |
| result = big_df.loc[big_df.groupby(['Method', 'Model', 'Forget Rate'])['MAPO'].idxmax()] | |
| print(result[["Method", "Model", "Forget Rate", "LR", "Epoch", "MAPO"]].round(6).to_markdown()) | |
| # exit() | |
| plot_legend = False | |
| fs = 18 if plot_legend else 22 | |
| metrics.append("MAPO") | |
| # Set the style of the visualization | |
| sns.set_theme(style="whitegrid") | |
| plt.rcParams['font.family'] = 'Times New Roman' | |
| for metric_to_plot in metrics: | |
| sub_df = result[big_df["Model"] == "Llama-2-7B"] | |
| fig, ax = plt.subplots(figsize=(15, 5)) | |
| sns.barplot(x="Method", y=metric_to_plot, hue="Forget Rate", data=sub_df, ax=ax, legend=plot_legend) | |
| ax.set_ylabel(metric_to_plot, fontsize=fs) | |
| ax.set_ylim(0.0, 1.0) | |
| ax.set_xlabel("", fontsize=fs) | |
| ax.set_xticklabels(ax.get_xticklabels(), fontsize=fs) | |
| ax.set_yticklabels(ax.get_yticklabels(), fontsize=fs-4) | |
| ax.spines[['right', 'top']].set_visible(False) | |
| if plot_legend: | |
| plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1), title="Forget Rate (%)") | |
| plt.title(metric_to_plot + " on Llama-2-7B", fontsize=fs) | |
| plt.tight_layout() | |
| plt.savefig(f"barplots/{metric_to_plot}-Llama-2-7B{'legend' if plot_legend else ''}.pdf") | |
| print(f"\includegraphics[width=\\textwidth]{{figures/barplots/{metric_to_plot}-Llama-2-7B{'legend' if plot_legend else ''}.pdf}}") | |
| plt.close(fig) | |
| for model in ["Llama-2-7B", "Phi"]: | |
| sub_df = result[result["Model"] == model][["Method", "Forget Rate", "MAPO"]] | |
| # print(sub_df.round(6).to_latex(index=False)) | |
| sub_df.reset_index(inplace=True) | |
| # Reorienting the dataframe | |
| sub_df_reoriented = sub_df.pivot(index="Method", columns='Forget Rate', values='MAPO') | |
| # Output a latex table of the MAPO values by Method and Forget Rate | |
| print(sub_df_reoriented.round(4).to_latex(index=True)) |