From e3dd922cba9eeb8b76ed8212a81ee4cf6f30de2f Mon Sep 17 00:00:00 2001 From: "(Bill) Yuchen Lin" Date: Sat, 9 Nov 2024 02:16:35 -0800 Subject: [PATCH] make the figures more smooth by add bins --- zebra_logic_analysis/_uni_figure.py | 46 ++++++++++++++++-- .../bon_4o_mini.accuracy_hists.png | Bin 159311 -> 149215 bytes zebra_logic_analysis/llama.accuracy_hists.png | Bin 88138 -> 80445 bytes .../o1_preview.hidden_cot.png | Bin 462876 -> 464005 bytes 4 files changed, 42 insertions(+), 4 deletions(-) diff --git a/zebra_logic_analysis/_uni_figure.py b/zebra_logic_analysis/_uni_figure.py index 23f0d12..c4e7529 100644 --- a/zebra_logic_analysis/_uni_figure.py +++ b/zebra_logic_analysis/_uni_figure.py @@ -70,11 +70,9 @@ def plot_hidden_reasoning_vs_search_space(data, output_file_name): print(f"Saved the plot to {output_file_name}") -def plot_hidden_reasoning_vs_search_space(data, output_file_name): +def plot_hidden_reasoning_vs_search_space_v2(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] @@ -108,7 +106,7 @@ def plot_hidden_reasoning_vs_search_space(data, output_file_name): plt.savefig(output_file_name, dpi=300) print(f"Saved the plot to {output_file_name}") -def plot_accuracy_vs_search_space(data_by_model, model_list, output_file_name, max_space_size): +def plot_accuracy_vs_search_space_v1(data_by_model, model_list, output_file_name, max_space_size): plt.figure(figsize=(10, 6)) for model in model_list: model_data = data_by_model[model] @@ -132,6 +130,46 @@ def plot_accuracy_vs_search_space(data_by_model, model_list, output_file_name, m plt.savefig(output_file_name) print(f"Saved the plot to {output_file_name}") +def plot_accuracy_vs_search_space(data_by_model, model_list, output_file_name, max_space_size): + plt.figure(figsize=(10, 6)) + + # Define bins in log space + bin_edges = np.logspace(0, max_space_size, num=15) # 20 bins from 10^0 to 10^max_space_size + + for model in model_list: + model_data = data_by_model[model] + df = pd.DataFrame(model_data) + df["search_space_size"] = df["size"].apply(search_space_size) + + # Bin the data + df['space_size_bin'] = pd.cut(df['search_space_size'], bins=bin_edges, labels=bin_edges[:-1]) + + # Calculate accuracy for each bin - modified to handle empty groups + accuracy_data = [] + for name, group in df.groupby("space_size_bin"): + if len(group) > 0: # Only process non-empty groups + accuracy_data.append({ + "search_space_size": name, + "accuracy": group["solved"].sum() / len(group) * 100 + }) + + accuracy_data = pd.DataFrame(accuracy_data) + if not accuracy_data.empty: # Only plot if we have data + clean_name = clean_model_name(model) + sns.lineplot(data=accuracy_data, x="search_space_size", y="accuracy", + marker="o", label=clean_name) + + plt.xscale("log") + plt.xlim(1, 10**max_space_size) + plt.xlabel("Search Space Size (log scale)") + plt.ylabel("Accuracy (%)") + plt.title("Accuracy vs. Search Space Size") + plt.grid(True) + plt.legend(title="Model") + plt.tight_layout() + plt.savefig(output_file_name) + print(f"Saved the plot to {output_file_name}") + def plot_reasoning_length_vs_search_space(data_by_model, model_list, output_file_name, max_space_size): plt.figure(figsize=(20, 5)) for model in model_list: diff --git a/zebra_logic_analysis/bon_4o_mini.accuracy_hists.png b/zebra_logic_analysis/bon_4o_mini.accuracy_hists.png index cae417ce84fe3555d44ebd52b47b42e07cf074d5..7a9a778582b424f965927a195407d5c000de48f1 100644 GIT binary patch literal 149215 zcmdq}Wmr~g_XUoEfrN;FfPjK@N=uh0UD6>TUDDkuB_$=LgrtOYNh8wTUD8N*!iCH+)V6@PfQ&g zZ0vcNnJxeK15DO-Cd{qN>Pm1CG#d#Gdn6<*J;cArSwi28uK zI{f#~C?#|58@^-uzmItD?)Fjr?_>6jaOA50U5HWziA^~6e;?D)g;7@j@2bkkZAjYx z_mLin@SF7iJ}TaR^8fRtNdGVRQZ$$2X=er62ec2+HOt>>%nV1Q2Xu6I8*NUM9c)b# 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