128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import scipy.stats as stats
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from scipy.stats import shapiro
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def read_dimacs(file):
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idx = 0
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with open(f"{folder}/{file}", "r") as f:
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for line in f:
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line = line.strip()
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if line.startswith("c"):
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continue
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elif line.startswith("p"):
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_, _, n, e = line.split()
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nodes = int(n)
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edges = int(e)
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data = [0] * edges
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elif line.startswith("a"):
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_, u, v, w = line.split()
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data[idx] = int(w)
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idx += 1
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return ((nodes, edges), data)
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def visualize_weights(file):
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(nodes, edges), data = read_dimacs(file)
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data = np.array(data)
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log_data = np.log(data)
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skew = stats.skew(data)
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skew_log = stats.skew(log_data)
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kurt = stats.kurtosis(data) # excess kurtosis (정규=0)
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kurt_log = stats.kurtosis(log_data)
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print(f'\n--- Skewness / Kurtosis ---')
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print(f'Skewness (original): {skew:.4f} (정규 기준: 0)')
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print(f'Skewness (log): {skew_log:.4f} (정규 기준: 0)')
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print(f'Kurtosis (original): {kurt:.4f} (정규 기준: 0, excess)')
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print(f'Kurtosis (log): {kurt_log:.4f} (정규 기준: 0, excess)')
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save_folder = f"{res_folder}/{file}"
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os.makedirs(save_folder, exist_ok=True)
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plt.figure(figsize=(8, 5))
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plt.hist(data, bins=100)
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plt.title("Edge Weight Distribution (Original Scale)")
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plt.xlabel("Weight")
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plt.ylabel("Frequency")
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plt.tight_layout()
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plt.savefig(f"{save_folder}/hist_original.png")
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plt.close()
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plt.figure(figsize=(8, 5))
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stats.probplot(data, dist="norm", plot=plt)
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plt.title("Q-Q Plot (Original Scale)")
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plt.xlabel("Theoretical Quantiles (Normal)")
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plt.ylabel("Sample Quantiles (Weight)")
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plt.savefig(f"{save_folder}/Q-Q_original.png")
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plt.close()
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plt.figure(figsize=(8, 5))
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plt.hist(log_data, bins=100)
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plt.title("Log(Weight) Distribution")
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plt.xlabel("log(Weight)")
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plt.ylabel("Frequency")
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plt.tight_layout()
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plt.savefig(f"{save_folder}/hist_log.png")
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plt.close()
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plt.figure(figsize=(8, 5))
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stats.probplot(log_data, dist="norm", plot=plt)
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plt.title("Q-Q Plot (Log Weight)")
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plt.xlabel("Theoretical Quantiles (Normal)")
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plt.ylabel("Sample Quantiles (Weight)")
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plt.savefig(f"{save_folder}/Q-Q_log.png")
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plt.close()
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# plt.figure(figsize=(8, 5))
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# plt.hist(log_data, bins=100, density=True)
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# plt.title("Log(Weight) Density")
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# plt.xlabel("log(Weight)")
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# plt.ylabel("Density")
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# plt.tight_layout()
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# plt.savefig(f"{save_folder}/hist_log_density.png")
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# plt.close()
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print(f"finish {file}")
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return {
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"file": file,
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"nodes": nodes,
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"edges": edges,
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"density": edges / (nodes * (nodes - 1)),
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"mean": np.mean(data),
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"std": np.std(data),
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"min": np.min(data),
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"max": np.max(data),
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"skew": stats.skew(data),
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"skew_log": stats.skew(log_data),
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}
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folder = "experiments/real_data/data/dimacs_data"
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files = [
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"USA-road-d.BAY.gr",
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"USA-road-d.CAL.gr",
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"USA-road-d.COL.gr",
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"USA-road-d.CTR.gr",
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"USA-road-d.E.gr",
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"USA-road-d.FLA.gr",
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"USA-road-d.LKS.gr",
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"USA-road-d.NE.gr",
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"USA-road-d.NW.gr",
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"USA-road-d.NY.gr",
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"USA-road-d.USA.gr",
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"USA-road-d.W.gr"
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]
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res_folder = "results/real_data/derived/dimacs_graph_distribution"
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os.makedirs(res_folder, exist_ok=True)
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rows = []
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for file in files:
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row = visualize_weights(file)
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rows.append(row)
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df = pd.DataFrame(rows)
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df.to_csv(f"{res_folder}/distribution.csv", index=False)
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