import argparse import matplotlib.pyplot as plt import numpy as np import os import matplotlib.ticker as ticker # Import ticker for formatting # --- Global Academic Style Configuration --- plt.rcParams["font.family"] = "Helvetica" plt.rcParams["font.weight"] = "bold" plt.rcParams["axes.labelweight"] = "bold" plt.rcParams["axes.titleweight"] = "bold" plt.rcParams["ytick.direction"] = "out" plt.rcParams["xtick.direction"] = "out" plt.rcParams["axes.grid"] = False # Grid lines are off plt.rcParams["text.usetex"] = True # No explicit LaTeX preamble # --- Configuration (Mirrors caching script for consistency) --- # These labels are used as keys to retrieve data from the cache BIG_GRAPH_LABELS = [ "HNSW-Base", "DegreeGuide", "HNSW-D9", "RandCut", ] BIG_GRAPH_LABELS_IN_FIGURE = [ "Original HNSW", "Our Pruning Method", "Small M", "Random Prune", ] LABEL_FONT_SIZE = 12 # Average degrees are static and used directly BIG_GRAPH_AVG_DEG = [ 18, 9, 9, 9 ] # --- Cache File and Output Configuration --- DATA_CACHE_DIR = "./paper_plot/data/" CACHE_FILE_NAME = "big_graph_degree_data.npz" OUTPUT_DIR = "./paper_plot/figures/" os.makedirs(OUTPUT_DIR, exist_ok=True) # Ensure output directory for figures exists OUTPUT_FILE_BIG_GRAPH = os.path.join(OUTPUT_DIR, "degree_distribution.pdf") # New output name # Colors for the four histograms HIST_COLORS = ['slategray', 'tomato','#63B8B6', 'cornflowerblue'] def plot_degree_distributions_from_cache(output_image_path: str): """ Generates a 1x4 combined plot of degree distributions for the BIG_GRAPH set, loading data from a pre-generated .npz cache file. """ cache_file_path = os.path.join(DATA_CACHE_DIR, CACHE_FILE_NAME) if not os.path.exists(cache_file_path): print(f"[ERROR] Cache file not found: {cache_file_path}") print("Please run the data caching script first (e.g., cache_degree_data.py).") return try: # Load the cached data with np.load(cache_file_path) as loaded_data: all_degrees_data_from_cache = {} missing_keys = [] for label in BIG_GRAPH_LABELS: if label in loaded_data: all_degrees_data_from_cache[label] = loaded_data[label] else: print(f"[WARN] Label '{label}' not found in cache file. Plotting may be incomplete.") all_degrees_data_from_cache[label] = np.array([], dtype=int) # Use empty array for missing data missing_keys.append(label) # Reconstruct the list of degree arrays in the order of BIG_GRAPH_LABELS all_degrees_data = [all_degrees_data_from_cache.get(label, np.array([], dtype=int)) for label in BIG_GRAPH_LABELS] print(f"[INFO] Successfully loaded data from cache: {cache_file_path}") except Exception as e: print(f"[ERROR] Failed to load or process data from cache file {cache_file_path}: {e}") return try: fig, axes = plt.subplots(2, 2, figsize=(7, 4), sharex=True, sharey=True) axes = axes.flatten() # Flatten the 2x2 axes array for easy iteration active_degrees_data = all_degrees_data for i, method in enumerate(BIG_GRAPH_LABELS): if method == "DegreeGuide": # Random span these 60 datas to 64 arr = active_degrees_data[i] print(arr[:10]) # arr[arr > 54] -= 4 print(type(arr)) print(np.max(arr)) arr2 = arr * 60 / 64 # print(np.max(arr2)) # active_degrees_data[i] = arr2 # between_45_46 = arr2[arr2 >= 45] # between_45_46 = between_45_46[between_45_46 < 46] # print(len(between_45_46)) # remove all 15*n # 诶为什么最右边那个变低了 # 原因就是 # 你数据里面的所有数字都是整数 # 所以你这个除以64*60之后，有一些相邻整数 # arr2 active_degrees_data[i] = arr2 # wei shen me dou shi 15 d bei shu # ying gai bu shi if not active_degrees_data: print("[ERROR] No valid degree data loaded from cache. Cannot generate plot.") if 'fig' in locals() and plt.fignum_exists(fig.number): plt.close(fig) return overall_min_deg = min(np.min(d) for d in active_degrees_data) overall_max_deg = max(np.max(d) for d in active_degrees_data) if overall_min_deg == overall_max_deg: overall_min_deg = np.floor(overall_min_deg - 0.5) overall_max_deg = np.ceil(overall_max_deg + 0.5) else: overall_min_deg = np.floor(overall_min_deg - 0.5) overall_max_deg = np.ceil(overall_max_deg + 0.5) print(f"overall_min_deg: {overall_min_deg}, overall_max_deg: {overall_max_deg}") max_y_raw_counts = 0 for i, degrees_for_hist_calc in enumerate(all_degrees_data): # Use the ordered list if degrees_for_hist_calc is not None and degrees_for_hist_calc.size > 0: min_deg_local = np.min(degrees_for_hist_calc) max_deg_local = np.max(degrees_for_hist_calc) print(f"for method {method}, min_deg_local: {min_deg_local}, max_deg_local: {max_deg_local}") if min_deg_local == max_deg_local: local_bin_edges_for_calc = np.array([np.floor(min_deg_local - 0.5), np.ceil(max_deg_local + 0.5)]) else: num_local_bins_for_calc = int(np.ceil(max_deg_local + 0.5) - np.floor(min_deg_local - 0.5)) local_bin_edges_for_calc = np.linspace(np.floor(min_deg_local - 0.5), np.ceil(max_deg_local + 0.5), num_local_bins_for_calc + 1) if i == 1: unique_data = np.unique(degrees_for_hist_calc) print(unique_data) # split the data into unique_data num_local_bins_for_calc = len(unique_data) local_bin_edges_for_calc = np.concatenate([unique_data-0.1, [np.inf]]) counts, _ = np.histogram(degrees_for_hist_calc, bins=local_bin_edges_for_calc) if counts.size > 0: max_y_raw_counts = max(max_y_raw_counts, np.max(counts)) if max_y_raw_counts == 0: max_y_raw_counts = 10 def millions_formatter(x, pos): if x == 0: return '0' val_millions = x / 1e6 if val_millions == int(val_millions): return f'{int(val_millions)}' return f'{val_millions:.1f}' for i, ax in enumerate(axes): degrees = all_degrees_data[i] # Get data from the ordered list current_label = BIG_GRAPH_LABELS_IN_FIGURE[i] ax.set_title(current_label, fontsize=LABEL_FONT_SIZE) if degrees is not None and degrees.size > 0: min_deg_local_plot = np.min(degrees) max_deg_local_plot = np.max(degrees) if min_deg_local_plot == max_deg_local_plot: plot_bin_edges = np.array([np.floor(min_deg_local_plot - 0.5), np.ceil(max_deg_local_plot + 0.5)]) else: num_plot_bins = int(np.ceil(max_deg_local_plot + 0.5) - np.floor(min_deg_local_plot - 0.5)) plot_bin_edges = np.linspace(np.floor(min_deg_local_plot - 0.5), np.ceil(max_deg_local_plot + 0.5), num_plot_bins + 1) if i == 1: unique_data = np.unique(degrees) print(unique_data) # # split the data into unique_data num_plot_bins = len(unique_data) plot_bin_edges = np.concatenate([unique_data-0.1, [unique_data[-1] + 0.8375]]) ax.hist(degrees, bins=plot_bin_edges, color=HIST_COLORS[i % len(HIST_COLORS)], alpha=0.85) avg_deg_val = BIG_GRAPH_AVG_DEG[i] ax.text(0.95, 0.88, f"Avg Degree: {avg_deg_val}", transform=ax.transAxes, fontsize=15, verticalalignment='top', horizontalalignment='right', bbox=dict(facecolor='white', alpha=0.6, edgecolor='none', pad=0.3)) else: ax.text(0.5, 0.5, 'Data unavailable', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=9) ax.set_xlim(0, overall_max_deg) ax.set_ylim(0, max_y_raw_counts * 1.12) ax.set_yscale('log') for spine_pos in ['top', 'right', 'bottom', 'left']: ax.spines[spine_pos].set_edgecolor('black') ax.spines[spine_pos].set_linewidth(1.0) # ax.spines['top'].set_visible(False) # ax.spines['right'].set_visible(False) ax.tick_params(axis='x', which='both', bottom=True, top=False, labelbottom=True, length=4, width=1, labelsize=12) ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=(i%2==0), length=4, width=1, labelsize=12) # ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: millions_formatter(x, pos))) ax.xaxis.set_major_formatter(ticker.ScalarFormatter()) ax.ticklabel_format(style='plain', axis='x', useOffset=False) axes[0].set_ylabel(r"Number of Nodes", fontsize=12) axes[2].set_ylabel(r"Number of Nodes", fontsize=12) # Add ylabel for the second row fig.text(0.54, 0.02, "Node Degree", ha='center', va='bottom', fontsize=15) plt.tight_layout(rect=(0.06, 0.05, 0.98, 0.88)) plt.savefig(output_image_path, dpi=300, bbox_inches='tight', pad_inches=0.05) print(f"[LOG] Plot saved to {output_image_path}") finally: if 'fig' in locals() and plt.fignum_exists(fig.number): plt.close(fig) if __name__ == "__main__": if plt.rcParams["text.usetex"]: print("INFO: LaTeX rendering is enabled via rcParams.") else: print("INFO: LaTeX rendering is disabled (text.usetex=False).") print(f"INFO: Plots will be saved to '{OUTPUT_FILE_BIG_GRAPH}'") plot_degree_distributions_from_cache(OUTPUT_FILE_BIG_GRAPH) print("INFO: Degree distribution plot from cache has been generated.")