From 3684fc85cf38ecba29c41d9ea696ebbb774ce788 Mon Sep 17 00:00:00 2001 From: "(Bill) Yuchen Lin" Date: Sat, 9 Nov 2024 02:07:09 -0800 Subject: [PATCH] z3 figures --- zebra_logic_analysis/_uni_figure.z3.py | 155 +++++++++++++----- ...n_4o_mini.accuracy_hists.z3_conflicts.png} | Bin ...4o_mini.accuracy_hists.z3_propagations.png | Bin 0 -> 119750 bytes .../llama.accuracy_hists.z3_conflicts.png | Bin 0 -> 65840 bytes .../llama.accuracy_hists.z3_propagations.png | Bin 0 -> 68264 bytes .../o1_mini.hidden_cot.z3.png | Bin 0 -> 563983 bytes .../o1_preview.hidden_cot.z3.png | Bin 0 -> 605981 bytes .../openai.accuracy_hists.z3_conflicts.png | Bin 0 -> 81045 bytes .../openai.accuracy_hists.z3_propagations.png | Bin 0 -> 86041 bytes 9 files changed, 110 insertions(+), 45 deletions(-) rename zebra_logic_analysis/{bon_4o_mini.accuracy_hists.z3.png => bon_4o_mini.accuracy_hists.z3_conflicts.png} (100%) create mode 100644 zebra_logic_analysis/bon_4o_mini.accuracy_hists.z3_propagations.png create mode 100644 zebra_logic_analysis/llama.accuracy_hists.z3_conflicts.png create mode 100644 zebra_logic_analysis/llama.accuracy_hists.z3_propagations.png create mode 100644 zebra_logic_analysis/o1_mini.hidden_cot.z3.png create mode 100644 zebra_logic_analysis/o1_preview.hidden_cot.z3.png create mode 100644 zebra_logic_analysis/openai.accuracy_hists.z3_conflicts.png create mode 100644 zebra_logic_analysis/openai.accuracy_hists.z3_propagations.png diff --git a/zebra_logic_analysis/_uni_figure.z3.py b/zebra_logic_analysis/_uni_figure.z3.py index 966a0a4..37b35aa 100644 --- a/zebra_logic_analysis/_uni_figure.z3.py +++ b/zebra_logic_analysis/_uni_figure.z3.py @@ -40,7 +40,7 @@ def load_data(model_list, base_path): data_by_model[model] = data return data_by_model -def plot_hidden_reasoning_vs_search_space(data, output_file_name): +def plot_hidden_reasoning_vs_z3_v1(data, output_file_name, z3_key="conflicts"): hidden_reasoning_token = [d["hidden_reasoning_token"] for d in data] size = [d["size"] for d in data] search_space_sizes = [search_space_size(s) for s in size] @@ -53,67 +53,131 @@ def plot_hidden_reasoning_vs_search_space(data, output_file_name): 'z3_conflicts': z3_conflicts, 'solved': solved_status }) + # Bin the z3_conflicts and calculate mean hidden reasoning tokens for each bin + df["z3_conflicts_bin"] = pd.qcut(df["z3_conflicts"], q=15, duplicates='drop') + tokens_by_conflicts = df.groupby("z3_conflicts_bin")["hidden_reasoning_token"].mean() + bin_centers = df.groupby("z3_conflicts_bin")["z3_conflicts"].mean() plt.figure(figsize=(10, 6)) - sns.scatterplot(data=df, x='search_space_size', y='hidden_reasoning_token', - hue='solved', style='solved', - palette={True: '#4a90e2', False: '#d9534f'}, - markers={True: 'o', False: 'X'}, hue_order=[True, False], legend='full', s=100) - plt.legend(title='Solution Status', labels=['Incorrect', 'Correct']) - plt.xscale('log') - plt.xlabel('Search Space Size (log scale)') - plt.ylabel('# Hidden CoT Tokens') - plt.ylim(0, 20000) - plt.grid(True) + # Plot average hidden reasoning tokens vs z3_conflicts + plt.plot(bin_centers, tokens_by_conflicts, marker='o', color='#4a90e2', linewidth=2) - log_x = np.log10(df['search_space_size']) - coeffs = np.polyfit(log_x, df['hidden_reasoning_token'], deg=2) - poly = np.poly1d(coeffs) - x_fit = np.linspace(log_x.min(), log_x.max(), 500) - y_fit = poly(x_fit) - plt.plot(10**x_fit, y_fit, color='green', label='Polynomial Fit (degree 2)', linewidth=2) + plt.xlabel("Z3 Conflicts") + plt.ylabel("Average Hidden CoT Tokens") + plt.title("Hidden CoT Tokens vs. Z3 Conflicts") + plt.grid(True) plt.savefig(output_file_name, dpi=300) print(f"Saved the plot to {output_file_name}") - -def plot_hidden_reasoning_vs_search_space(data, output_file_name): - # visible_reasoning_token = [d["visible_reasoning_token"] for d in data] - # define visible reasoning token as the sum of the number of tokens in the output - - + # plt.figure(figsize=(10, 6)) + # sns.scatterplot(data=df, x='search_space_size', y='hidden_reasoning_token', + # hue='solved', style='solved', + # palette={True: '#4a90e2', False: '#d9534f'}, + # markers={True: 'o', False: 'X'}, hue_order=[True, False], legend='full', s=100) + # plt.legend(title='Solution Status', labels=['Incorrect', 'Correct']) + # plt.xscale('log') + # plt.xlabel('Search Space Size (log scale)') + # plt.ylabel('# Hidden CoT Tokens') + # plt.ylim(0, 20000) + # plt.grid(True) + + # log_x = np.log10(df['search_space_size']) + # coeffs = np.polyfit(log_x, df['hidden_reasoning_token'], deg=2) + # poly = np.poly1d(coeffs) + # x_fit = np.linspace(log_x.min(), log_x.max(), 500) + # y_fit = poly(x_fit) + # plt.plot(10**x_fit, y_fit, color='green', label='Polynomial Fit (degree 2)', linewidth=2) + + # plt.savefig(output_file_name, dpi=300) + # print(f"Saved the plot to {output_file_name}") + + +def plot_hidden_reasoning_vs_z3(data, output_file_name, z3_key="conflicts"): + hidden_reasoning_token = [d["hidden_reasoning_token"] for d in data] size = [d["size"] for d in data] search_space_sizes = [search_space_size(s) for s in size] solved_status = [d["solved"] for d in data] + z3_conflicts = [z3_stat[d["id"]]["conflicts"] for d in data] df = pd.DataFrame({ - 'visible_reasoning_token': visible_reasoning_token, + 'hidden_reasoning_token': hidden_reasoning_token, 'search_space_size': search_space_sizes, + 'z3_conflicts': z3_conflicts, 'solved': solved_status }) plt.figure(figsize=(10, 6)) - sns.scatterplot(data=df, x='search_space_size', y='visible_reasoning_token', + + # First plot the individual points + sns.scatterplot(data=df, x='z3_conflicts', y='hidden_reasoning_token', hue='solved', style='solved', palette={True: '#4a90e2', False: '#d9534f'}, - markers={True: 'o', False: 'X'}, hue_order=[True, False], legend='full', s=100) - plt.legend(title='Solution Status', labels=['Incorrect', 'Correct']) - plt.xscale('log') - plt.xlabel('Search Space Size (log scale)') - plt.ylabel('# Visible Reasoning Tokens') - plt.ylim(0, 20000) + markers={True: 'o', False: 'X'}, + hue_order=[True, False], + legend='full', + alpha=0.5, # Make points semi-transparent + s=100) # Point size + + # Then plot the average line + df["z3_conflicts_bin"] = pd.qcut(df["z3_conflicts"], q=100, duplicates='drop') + tokens_by_conflicts = df.groupby("z3_conflicts_bin")["hidden_reasoning_token"].mean() + bin_centers = df.groupby("z3_conflicts_bin")["z3_conflicts"].mean() + plt.plot(bin_centers, tokens_by_conflicts, + color='green', linewidth=2, label='Average', + zorder=5) # Put line on top of points + + plt.xlabel("Z3 Conflicts") + plt.ylabel("Hidden CoT Tokens") + plt.title(f"Hidden CoT Tokens vs. Z3 Conflicts") plt.grid(True) + + # Update legend + handles, labels = plt.gca().get_legend_handles_labels() + plt.legend(handles=handles, labels=['Correct', 'Incorrect', 'Average'], + title='Solution Status') - log_x = np.log10(df['search_space_size']) - coeffs = np.polyfit(log_x, df['visible_reasoning_token'], deg=2) - poly = np.poly1d(coeffs) - x_fit = np.linspace(log_x.min(), log_x.max(), 500) - y_fit = poly(x_fit) - plt.plot(10**x_fit, y_fit, color='green', label='Polynomial Fit (degree 2)', linewidth=2) - - plt.savefig(output_file_name, dpi=300) + plt.savefig(output_file_name, dpi=300, bbox_inches='tight') print(f"Saved the plot to {output_file_name}") + +# def plot_hidden_reasoning_vs_search_space(data, output_file_name): +# # visible_reasoning_token = [d["visible_reasoning_token"] for d in data] +# # define visible reasoning token as the sum of the number of tokens in the output + + +# size = [d["size"] for d in data] +# search_space_sizes = [search_space_size(s) for s in size] +# solved_status = [d["solved"] for d in data] + +# df = pd.DataFrame({ +# 'visible_reasoning_token': visible_reasoning_token, +# 'search_space_size': search_space_sizes, +# 'solved': solved_status +# }) + +# plt.figure(figsize=(10, 6)) +# sns.scatterplot(data=df, x='search_space_size', y='visible_reasoning_token', +# hue='solved', style='solved', +# palette={True: '#4a90e2', False: '#d9534f'}, +# markers={True: 'o', False: 'X'}, hue_order=[True, False], legend='full', s=100) +# plt.legend(title='Solution Status', labels=['Incorrect', 'Correct']) +# plt.xscale('log') +# plt.xlabel('Search Space Size (log scale)') +# plt.ylabel('# Visible Reasoning Tokens') +# plt.ylim(0, 20000) +# plt.grid(True) + +# log_x = np.log10(df['search_space_size']) +# coeffs = np.polyfit(log_x, df['visible_reasoning_token'], deg=2) +# poly = np.poly1d(coeffs) +# x_fit = np.linspace(log_x.min(), log_x.max(), 500) +# y_fit = poly(x_fit) +# plt.plot(10**x_fit, y_fit, color='green', label='Polynomial Fit (degree 2)', linewidth=2) + +# plt.savefig(output_file_name, dpi=300) +# print(f"Saved the plot to {output_file_name}") + def plot_accuracy_vs_z3(data_by_model, model_list, output_file_name, max_space_size, z3_key="conflicts"): plt.figure(figsize=(10, 6)) for model in model_list: @@ -146,6 +210,7 @@ def plot_accuracy_vs_z3(data_by_model, model_list, output_file_name, max_space_s plt.ylabel("Accuracy (%)") plt.grid(True) plt.legend(title="Model") + output_file_name = output_file_name.replace(".z3.", f".z3_{z3_key}.") plt.savefig(output_file_name) print(f"Saved the plot to {output_file_name}") @@ -193,7 +258,7 @@ def main(): }) if args.analysis == 'hidden_reasoning': - plot_hidden_reasoning_vs_search_space(data_by_model[args.model_list[0]], args.output_file) + plot_hidden_reasoning_vs_z3(data_by_model[args.model_list[0]], args.output_file) elif args.analysis == 'accuracy': plot_accuracy_vs_z3(data_by_model, args.model_list, args.output_file, args.max_space_size) elif args.analysis == 'reasoning_length': @@ -206,8 +271,8 @@ def main(): """ # For showing hidden reasoning token vs search space size -python zebra_logic_analysis/_uni_figure.py --analysis hidden_reasoning --model_list o1-preview-2024-09-12-v2 --output_file zebra_logic_analysis/o1_preview.hidden_cot.png -python zebra_logic_analysis/_uni_figure.py --analysis hidden_reasoning --model_list o1-mini-2024-09-12-v3 --output_file zebra_logic_analysis/o1_mini.hidden_cot.png +python zebra_logic_analysis/_uni_figure.z3.py --analysis hidden_reasoning --model_list o1-preview-2024-09-12-v2 --output_file zebra_logic_analysis/o1_preview.hidden_cot.z3.png +python zebra_logic_analysis/_uni_figure.z3.py --analysis hidden_reasoning --model_list o1-mini-2024-09-12-v3 --output_file zebra_logic_analysis/o1_mini.hidden_cot.z3.png @@ -226,13 +291,13 @@ def main(): python zebra_logic_analysis/_uni_figure.z3.py --analysis accuracy \ --model_list "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=1" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=4" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=8" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=16" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=32" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=64" "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=128" "o1-mini-2024-09-12-v3" \ --output_file zebra_logic_analysis/bon_4o_mini.accuracy_hists.z3.png --max_space_size 18 -# "bon_all/gpt-4o-mini-2024-07-18.best_of_n.K=256" \ + # For showing accuracy histograms -python zebra_logic_analysis/_uni_figure.py --analysis accuracy \ +python zebra_logic_analysis/_uni_figure.z3.py --analysis accuracy \ --model_list "bon_all/gpt-4o-2024-08-06.best_of_n.K=1" "bon_all/gpt-4o-2024-08-06.best_of_n.K=4" "bon_all/gpt-4o-2024-08-06.best_of_n.K=8" "bon_all/gpt-4o-2024-08-06.best_of_n.K=16" "bon_all/gpt-4o-2024-08-06.best_of_n.K=32" "bon_all/gpt-4o-2024-08-06.best_of_n.K=64" "bon_all/gpt-4o-2024-08-06.best_of_n.K=128" "o1-preview-2024-09-12-v2" \ - --output_file zebra_logic_analysis/bon_4o.accuracy_hists.png --max_space_size 18 + --output_file zebra_logic_analysis/bon_4o.accuracy_hists.z3.png --max_space_size 18 python zebra_logic_analysis/_uni_figure.py --analysis accuracy \ diff --git a/zebra_logic_analysis/bon_4o_mini.accuracy_hists.z3.png 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