143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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from collections import defaultdict
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from datetime import datetime
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from functools import reduce
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from node_ranking import (
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page_rank,
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sensor_rank,
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find_rank,
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parse_csv,
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csv_loader,
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build_graph,
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Node,
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RankedNode,
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)
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def load_data(path):
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data = defaultdict(list)
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with open(path, 'r') as f:
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for line in f.readlines():
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when = datetime.strptime(line.split(',')[0]+'00', '%Y-%m-%d %H:%M:%S%z')
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data[when].append(
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parse_csv(line, source_ip_index=1, source_port_index=2, dest_ip_index=3, dest_port_index=4)
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)
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return data
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churned_peers = 50
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iterations = 5
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def main():
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initial_rank = 0.5
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data = load_data('./part-dist-edges.csv')
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print(f'loaded data. {len(data.keys())} buckets')
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for bucket, edges in data.items():
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edges = list(edges)
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print(f'bucket: {bucket}')
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print(f'edges: {len(edges)}')
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known = Node('34.204.196.211', 9796)
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known_r = RankedNode(known, initial_rank)
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g = build_graph(edges, initial_rank=initial_rank)
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cnt = 0
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destinations = []
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for node in g:
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destinations.append(node)
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cnt += 1
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if cnt >= churned_peers:
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break
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for node in destinations:
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g.add_edge(known_r, node)
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count_map = {}
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for node in g:
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count_map[node] = len(list(g.successors(node)))
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sum_out = 0
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for v in count_map.values():
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sum_out += v
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min_out = min(count_map.items(), key=lambda kv: kv[1])
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max_out = max(count_map.items(), key=lambda kv: kv[1])
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nodes = len(g)
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initial_rank = 0.5
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with open(f'./churn_rank/{churned_peers}_edges_{bucket.timestamp()}.txt', 'w') as out:
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table = []
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pr_data = []
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sr_data = []
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for iteration in range(iterations):
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g = page_rank(g)
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avg_pr = reduce(lambda acc, n: acc + n.rank, g, 0.) / nodes
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# max_pr = max(g, key = lambda n : n.rank).rank
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max_pr = find_rank(g)
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percentiles = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]
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perc = np.percentile(np.array(sorted(map(lambda n: n.rank, g))), percentiles)
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pr_data.append(f'PR: InitialRank: {initial_rank}, Iteration: {iteration}, Percentiles {percentiles}: {perc}')
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# print(f'InitialRank: {initial_rank}, Iteration: {iteration}, Avg. PR: {avg_pr}')
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# print(f'InitialRank: {initial_rank}, Iteration: {iteration}, max PR: {max_pr}')
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sr = sensor_rank(g)
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avg_sr = reduce(lambda acc, n: acc + n.rank, sr, 0.) / nodes
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# max_sr = max(sr, key = lambda n : n.rank).rank
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max_sr = find_rank(sr)
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perc = np.percentile(np.array(sorted(map(lambda n: n.rank, sr))), percentiles)
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sr_data.append(f'SR: InitialRank: {initial_rank}, Iteration: {iteration}, Percentiles {percentiles}: {perc}')
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# print(f'InitialRank: {initial_rank}, Iteration: {iteration}, Avg. SR: {avg_sr}')
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# print(f'InitialRank: {initial_rank}, Iteration: {iteration}, max SR: {max_sr}')
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table.append(f'{iteration+1} & {avg_pr:.8f} & {max_pr:.8f} & {avg_sr:.8f} & {max_sr:.8f} \\\\')
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# out.write(f'{iteration+1} & {avg_pr:.8f} & {max_pr:.8f} & {avg_sr:.8f} & {max_sr:.8f} \\\\\n')
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# with open(f'./{initial_rank}_{iteration+1}_pr.txt', 'w') as f:
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# for n in g:
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# f.write(f'{n.rank}' + '\n')
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# with open(f'./{initial_rank}_{iteration+1}_sr.txt', 'w') as f:
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# for n in sr:
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# f.write(f'{n.rank}' + '\n')
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avg_out = float(sum_out) / len(count_map.keys())
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known_out = count_map[known_r]
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print(f'\tavg_out: {avg_out}')
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print(f'\tmin_out: {min_out}')
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print(f'\tmax_out: {max_out}')
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print(f'\tknown_out: {known} {known_out}')
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out.write(f'bucket: {bucket}\n')
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out.write(f'\tavg_out: {avg_out}\n')
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out.write(f'\tmin_out: {min_out}\n')
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out.write(f'\tmax_out: {max_out}\n')
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out.write(f'\tknown_out: {known} {known_out}\n')
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print()
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print()
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out.write('\n')
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out.write('\n')
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for row in table:
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print(row)
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out.write(f'{row}\n')
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print()
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print()
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out.write('\n')
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out.write('\n')
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for row in sr_data:
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print(row)
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out.write(f'{row}\n')
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print()
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print()
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out.write('\n')
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out.write('\n')
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for row in pr_data:
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print(row)
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out.write(f'{row}\n')
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print()
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print()
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print()
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print()
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if __name__ == "__main__":
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main()
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