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83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
#! /usr/bin/python
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from algo import *
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import warnings
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import argparse
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warnings.filterwarnings("ignore")
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# df = pd.DataFrame(columns=["method" "alpha", "cpu_time", "mv", "tol"])
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def run_standard_pagerank(G, alphas):
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print("\nStarting the standard pagerank algorithm...")
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iter_dict = dict.fromkeys(alphas, 0)
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list_of_pageranks = []
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start1 = time.time()
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for alpha in alphas:
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x, iter, tol = pagerank(G, alpha, tol=1e-6)
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iter_dict[alpha] = iter
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list_of_pageranks.append(x)
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end1 = time.time()
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total_iter = sum(iter_dict.values())
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cpu_time = round(end1 - start1,1)
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mv = total_iter
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print("\nSTANDARD PAGERANK ALGORITHM RESULTS:\n")
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print("\tCPU time (s):", cpu_time)
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print("\tMatrix-vector multiplications:", mv)
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print("\tAlpha(s):", alphas)
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print("\tTolerance:", tol)
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for i in range(len(list_of_pageranks)):
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if not list_of_pageranks[i]:
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print("The algorithm did not converge for alpha =", alphas[i])
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# df.loc[len(df)] = ["Power Method", alphas, cpu_time, mv, tol]
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# df.to_csv(args.dataset + "_results.tsv", sep="\t", index=False)
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# print("\nThe results are saved in the file:", args.dataset + "_results.tsv")
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def run_shifted_powe(G, alphas):
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print("\nStarting the shifted power method... (this may take a while)")
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start2 = time.time()
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x, mv, alphas, tol = shifted_pow_pagerank(G, alphas, tol=1e-6)
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end2 = time.time()
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cpu_time = round(end2 - start2,1)
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print("\nSHIFTED PAGERANK ALGORITHM RESULTS:\n")
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print("\tCPU time (s):", cpu_time)
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print("\tMatrix-vector multiplications:", mv)
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print("\tAlphas(s):", alphas)
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print("\tTolerance:", tol)
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# df.loc[len(df)] = ["Shifted Power Method", alphas, cpu_time, mv, tol]
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# df.to_csv(args.dataset + "_results.tsv", sep="\t", index=False)
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# print("\nThe results are saved in the file:", args.dataset + "_results.tsv")
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# main function
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset", type=str, default="Stanford", help="Choose the dataset to work with. The options are: Stanford, NotreDame, BerkStan")
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parser.add_argument("--algo", type=str, default="both", help="Choose the algorithm to use. The options are: pagerank, shifted_pagerank, both")
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args = parser.parse_args()
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G = load_data(args.dataset)
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alphas = [0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]
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if args.algo == "pagerank":
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run_standard_pagerank(G, alphas)
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elif args.algo == "shifted_pagerank":
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run_shifted_powe(G, alphas)
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elif args.algo == "both":
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run_standard_pagerank(G, alphas)
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run_shifted_powe(G, alphas)
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