109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
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#!/usr/bin/env python3
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import matplotlib.pyplot as plt
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from datetime import datetime
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import glob
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import json
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import reduce_edges
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def load_json(path):
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with open(path, 'r') as f:
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return json.load(f)
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def plot_in_out(n_crawlers, prc, data):
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times = []
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avg_in = []
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known_in = []
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known_out = []
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# same value, independent of algo
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algo = 'sr'
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for when, d in sorted(data.items(), key=lambda kv: kv[0]):
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times.append(when)
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avg_in.append(d[algo]['avg_in'])
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known_in.append(d[algo]['known_in'])
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known_out.append(d[algo]['known_out'])
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fig, ax = plt.subplots()
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a = 'SensorRank' if algo == 'sr' else 'RageRank'
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ax.set_ylabel(f'{a}')
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ax.plot(times, avg_in, label='Avg. In')
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# ax.plot(times, known_in, label='Known In') # TODO
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ax.plot(times, known_in, label='Known In')
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ax.plot(times, known_out, label='Known out')
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title = f'In And Out after adding {n_crawlers} crawlers with {prc * 100}% edges'
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ax.set_title(title)
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fig.autofmt_xdate()
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fig.legend()
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plt.savefig(f'./plot_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/in_out.png')
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def plot(n_crawlers, prc, algo, data):
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times = []
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avg_rank = []
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a_avg_in = []
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known_rank = []
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known_in = []
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known_out = []
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for when, d in sorted(data.items(), key=lambda kv: kv[0]):
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times.append(when)
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avg_rank.append(d[algo]['avg_rank'])
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a_avg_in.append(d[algo]['avg_in'])
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known_rank.append(d[algo]['known_rank'])
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known_in.append(d[algo]['known_in'])
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known_out.append(d[algo]['known_out'])
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# avg_out = sum(known_out) / len(known_out)
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# avg_in = sum(known_in) / len(known_in)
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fig, ax = plt.subplots()
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a = 'SensorRank' if algo == 'sr' else 'RageRank'
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ax.set_ylabel(f'{a}')
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ax.plot(times, avg_rank, label='Avg. Rank')
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# ax.plot(times, known_in, label='Known In') # TODO
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ax.plot(times, known_rank, label='Known Rank')
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# title = f'{a} after removing {percentage * 100}% edges and adding {added_percentage * 100}%\nin = {avg_in:.02f} out = {avg_out:.02f}'
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title = f'{a} with {n_crawlers} crawlers with {prc * 100}% edges each'
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ax.set_title(title)
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# ax2 = ax.twinx()
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# ax2.set_ylabel('Edges')
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# ax2.plot(times, known_in, label='Known In', color='red')
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# ax2.plot(times, known_out, label='Known Out', color='green')
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# # print(f'{len(times)=}, {len(a_avg_in)=}')
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# # assert len(times) == len(a_avg_in)
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# ax2.plot(times, a_avg_in, label='Avg. In', color='violet')
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fig.autofmt_xdate()
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fig.legend()
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plt.savefig(f'./plot_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/{algo}.png')
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# print('created sr plot')
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# plt.show()
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def main():
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for n_crawlers in [5,10,20,50]: #in reduce_edges.percentages:
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for prc in [0.1, 0.5, 0.9]:
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# for file in glob.glob(f'./edges_reduced/{reduced_percentage:.02f}/*.txt'):
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# for file in glob.glob(f'./data_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/*.json'):
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# for reduced_percentage in reduce_edges.percentages:
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# perc = reduce_edges.percentages.copy()
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# perc.append(1.0)
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# perc.append(1.2)
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# for added_percentage in perc:
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data = {}
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for file in glob.glob(f'./data_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/*.json'):
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# for file in glob.glob(f'./data_with_crawler/{reduced_percentage:.02f}/{added_percentage:.02f}/*.json'):
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when = datetime.fromtimestamp(float(file.split('/')[-1][:-5]))
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print(f'{n_crawlers=:.02f}, {prc=:.02f}, {when=}')
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data[when] = load_json(file)
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plot(n_crawlers, prc, 'sr', data)
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plot(n_crawlers, prc, 'pr', data)
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plot_in_out(n_crawlers, prc, data)
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if __name__ == '__main__':
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main()
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