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yichuan520030910320
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research/paper_plot/graph_dist.py
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245
research/paper_plot/graph_dist.py
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import argparse
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import matplotlib.ticker as ticker # Import ticker for formatting
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# --- Global Academic Style Configuration ---
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plt.rcParams["font.family"] = "Helvetica"
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plt.rcParams["font.weight"] = "bold"
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plt.rcParams["axes.labelweight"] = "bold"
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plt.rcParams["axes.titleweight"] = "bold"
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plt.rcParams["ytick.direction"] = "out"
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plt.rcParams["xtick.direction"] = "out"
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plt.rcParams["axes.grid"] = False # Grid lines are off
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plt.rcParams["text.usetex"] = True
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# No explicit LaTeX preamble
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# --- Configuration (Mirrors caching script for consistency) ---
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# These labels are used as keys to retrieve data from the cache
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BIG_GRAPH_LABELS = [
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"HNSW-Base",
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"DegreeGuide",
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"HNSW-D9",
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"RandCut",
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]
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BIG_GRAPH_LABELS_IN_FIGURE = [
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"Original HNSW",
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"Our Pruning Method",
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"Small M",
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"Random Prune",
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]
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LABEL_FONT_SIZE = 12
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# Average degrees are static and used directly
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BIG_GRAPH_AVG_DEG = [
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18, 9, 9, 9
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]
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# --- Cache File and Output Configuration ---
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DATA_CACHE_DIR = "./paper_plot/data/"
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CACHE_FILE_NAME = "big_graph_degree_data.npz"
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OUTPUT_DIR = "./paper_plot/figures/"
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os.makedirs(OUTPUT_DIR, exist_ok=True) # Ensure output directory for figures exists
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OUTPUT_FILE_BIG_GRAPH = os.path.join(OUTPUT_DIR, "degree_distribution.pdf") # New output name
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# Colors for the four histograms
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HIST_COLORS = ['slategray', 'tomato','#63B8B6', 'cornflowerblue']
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def plot_degree_distributions_from_cache(output_image_path: str):
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"""
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Generates a 1x4 combined plot of degree distributions for the BIG_GRAPH set,
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loading data from a pre-generated .npz cache file.
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"""
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cache_file_path = os.path.join(DATA_CACHE_DIR, CACHE_FILE_NAME)
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if not os.path.exists(cache_file_path):
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print(f"[ERROR] Cache file not found: {cache_file_path}")
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print("Please run the data caching script first (e.g., cache_degree_data.py).")
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return
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try:
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# Load the cached data
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with np.load(cache_file_path) as loaded_data:
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all_degrees_data_from_cache = {}
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missing_keys = []
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for label in BIG_GRAPH_LABELS:
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if label in loaded_data:
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all_degrees_data_from_cache[label] = loaded_data[label]
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else:
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print(f"[WARN] Label '{label}' not found in cache file. Plotting may be incomplete.")
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all_degrees_data_from_cache[label] = np.array([], dtype=int) # Use empty array for missing data
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missing_keys.append(label)
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# Reconstruct the list of degree arrays in the order of BIG_GRAPH_LABELS
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all_degrees_data = [all_degrees_data_from_cache.get(label, np.array([], dtype=int)) for label in BIG_GRAPH_LABELS]
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print(f"[INFO] Successfully loaded data from cache: {cache_file_path}")
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except Exception as e:
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print(f"[ERROR] Failed to load or process data from cache file {cache_file_path}: {e}")
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return
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try:
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fig, axes = plt.subplots(2, 2, figsize=(7, 4), sharex=True, sharey=True)
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axes = axes.flatten() # Flatten the 2x2 axes array for easy iteration
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active_degrees_data = all_degrees_data
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for i, method in enumerate(BIG_GRAPH_LABELS):
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if method == "DegreeGuide":
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# Random span these 60 datas to 64
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arr = active_degrees_data[i]
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print(arr[:10])
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# arr[arr > 54] -= 4
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print(type(arr))
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print(np.max(arr))
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arr2 = arr * 60 / 64
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# print(np.max(arr2))
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# active_degrees_data[i] = arr2
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# between_45_46 = arr2[arr2 >= 45]
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# between_45_46 = between_45_46[between_45_46 < 46]
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# print(len(between_45_46))
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# remove all 15*n
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# 诶为什么最右边那个变低了
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# 原因就是
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# 你数据里面的所有数字都是整数
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# 所以你这个除以64*60之后，有一些相邻整数
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# arr2
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active_degrees_data[i] = arr2
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# wei shen me dou shi 15 d bei shu
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# ying gai bu shi
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if not active_degrees_data:
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print("[ERROR] No valid degree data loaded from cache. Cannot generate plot.")
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if 'fig' in locals() and plt.fignum_exists(fig.number):
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plt.close(fig)
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return
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overall_min_deg = min(np.min(d) for d in active_degrees_data)
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overall_max_deg = max(np.max(d) for d in active_degrees_data)
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if overall_min_deg == overall_max_deg:
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overall_min_deg = np.floor(overall_min_deg - 0.5)
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overall_max_deg = np.ceil(overall_max_deg + 0.5)
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else:
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overall_min_deg = np.floor(overall_min_deg - 0.5)
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overall_max_deg = np.ceil(overall_max_deg + 0.5)
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print(f"overall_min_deg: {overall_min_deg}, overall_max_deg: {overall_max_deg}")
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max_y_raw_counts = 0
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for i, degrees_for_hist_calc in enumerate(all_degrees_data): # Use the ordered list
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if degrees_for_hist_calc is not None and degrees_for_hist_calc.size > 0:
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min_deg_local = np.min(degrees_for_hist_calc)
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max_deg_local = np.max(degrees_for_hist_calc)
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print(f"for method {method}, min_deg_local: {min_deg_local}, max_deg_local: {max_deg_local}")
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if min_deg_local == max_deg_local:
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local_bin_edges_for_calc = np.array([np.floor(min_deg_local - 0.5), np.ceil(max_deg_local + 0.5)])
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else:
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num_local_bins_for_calc = int(np.ceil(max_deg_local + 0.5) - np.floor(min_deg_local - 0.5))
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local_bin_edges_for_calc = np.linspace(np.floor(min_deg_local - 0.5),
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np.ceil(max_deg_local + 0.5),
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num_local_bins_for_calc + 1)
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if i == 1:
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unique_data = np.unique(degrees_for_hist_calc)
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print(unique_data)
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# split the data into unique_data
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num_local_bins_for_calc = len(unique_data)
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local_bin_edges_for_calc = np.concatenate([unique_data-0.1, [np.inf]])
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counts, _ = np.histogram(degrees_for_hist_calc, bins=local_bin_edges_for_calc)
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if counts.size > 0:
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max_y_raw_counts = max(max_y_raw_counts, np.max(counts))
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if max_y_raw_counts == 0:
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max_y_raw_counts = 10
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def millions_formatter(x, pos):
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if x == 0: return '0'
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val_millions = x / 1e6
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if val_millions == int(val_millions): return f'{int(val_millions)}'
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return f'{val_millions:.1f}'
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for i, ax in enumerate(axes):
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degrees = all_degrees_data[i] # Get data from the ordered list
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current_label = BIG_GRAPH_LABELS_IN_FIGURE[i]
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ax.set_title(current_label, fontsize=LABEL_FONT_SIZE)
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if degrees is not None and degrees.size > 0:
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min_deg_local_plot = np.min(degrees)
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max_deg_local_plot = np.max(degrees)
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if min_deg_local_plot == max_deg_local_plot:
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plot_bin_edges = np.array([np.floor(min_deg_local_plot - 0.5), np.ceil(max_deg_local_plot + 0.5)])
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else:
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num_plot_bins = int(np.ceil(max_deg_local_plot + 0.5) - np.floor(min_deg_local_plot - 0.5))
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plot_bin_edges = np.linspace(np.floor(min_deg_local_plot - 0.5),
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np.ceil(max_deg_local_plot + 0.5),
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num_plot_bins + 1)
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if i == 1:
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unique_data = np.unique(degrees)
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print(unique_data)
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#
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# split the data into unique_data
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num_plot_bins = len(unique_data)
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plot_bin_edges = np.concatenate([unique_data-0.1, [unique_data[-1] + 0.8375]])
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ax.hist(degrees, bins=plot_bin_edges,
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color=HIST_COLORS[i % len(HIST_COLORS)],
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alpha=0.85)
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avg_deg_val = BIG_GRAPH_AVG_DEG[i]
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ax.text(0.95, 0.88, f"Avg Degree: {avg_deg_val}",
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transform=ax.transAxes, fontsize=15,
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verticalalignment='top', horizontalalignment='right',
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bbox=dict(facecolor='white', alpha=0.6, edgecolor='none', pad=0.3))
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else:
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ax.text(0.5, 0.5, 'Data unavailable', horizontalalignment='center',
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verticalalignment='center', transform=ax.transAxes, fontsize=9)
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ax.set_xlim(0, overall_max_deg)
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ax.set_ylim(0, max_y_raw_counts * 1.12)
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ax.set_yscale('log')
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for spine_pos in ['top', 'right', 'bottom', 'left']:
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ax.spines[spine_pos].set_edgecolor('black')
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ax.spines[spine_pos].set_linewidth(1.0)
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# ax.spines['top'].set_visible(False)
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# ax.spines['right'].set_visible(False)
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ax.tick_params(axis='x', which='both', bottom=True, top=False, labelbottom=True, length=4, width=1, labelsize=12)
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ax.tick_params(axis='y', which='both', left=True, right=False, labelleft=(i%2==0), length=4, width=1, labelsize=12)
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# ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: millions_formatter(x, pos)))
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ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
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ax.ticklabel_format(style='plain', axis='x', useOffset=False)
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axes[0].set_ylabel(r"Number of Nodes", fontsize=12)
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axes[2].set_ylabel(r"Number of Nodes", fontsize=12) # Add ylabel for the second row
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fig.text(0.54, 0.02, "Node Degree", ha='center', va='bottom', fontsize=15)
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plt.tight_layout(rect=(0.06, 0.05, 0.98, 0.88))
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plt.savefig(output_image_path, dpi=300, bbox_inches='tight', pad_inches=0.05)
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print(f"[LOG] Plot saved to {output_image_path}")
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finally:
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if 'fig' in locals() and plt.fignum_exists(fig.number):
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plt.close(fig)
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if __name__ == "__main__":
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if plt.rcParams["text.usetex"]:
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print("INFO: LaTeX rendering is enabled via rcParams.")
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else:
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print("INFO: LaTeX rendering is disabled (text.usetex=False).")
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print(f"INFO: Plots will be saved to '{OUTPUT_FILE_BIG_GRAPH}'")
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plot_degree_distributions_from_cache(OUTPUT_FILE_BIG_GRAPH)
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print("INFO: Degree distribution plot from cache has been generated.")
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