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b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619528400.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.2544325487770778, "known_in": 149, "known_out": 50, "avg_rank": 0.16863883189093973, "avg_in": 116.14469453376206}, "pr": {"known_rank": 0.4707494149434448, "known_in": 149, "known_out": 50, "avg_rank": 0.33289148098088955, "avg_in": 116.14469453376206}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619532000.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619532000.0.json new file mode 100644 index 00000000..6552d76e --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619532000.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.22914630621739218, "known_in": 141, "known_out": 50, "avg_rank": 0.15604403672547915, "avg_in": 111.19417475728156}, "pr": {"known_rank": 0.45788449157612976, "known_in": 141, "known_out": 50, "avg_rank": 0.3237608916130226, "avg_in": 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b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619539200.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.1969352775205234, "known_in": 126, "known_out": 50, "avg_rank": 0.1323363480163801, "avg_in": 101.60260586319218}, "pr": {"known_rank": 0.43707563553855355, "known_in": 126, "known_out": 50, "avg_rank": 0.30639389043798637, "avg_in": 101.60260586319218}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619542800.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619542800.0.json new file mode 100644 index 00000000..d54ee569 --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619542800.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.22498793864790304, "known_in": 134, "known_out": 50, "avg_rank": 0.145252159973825, "avg_in": 105.1270358306189}, "pr": {"known_rank": 0.4660168431695868, "known_in": 134, "known_out": 50, "avg_rank": 0.3160716063740416, "avg_in": 105.1270358306189}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619546400.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619546400.0.json new file mode 100644 index 00000000..2388dd41 --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619546400.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.21110721116649636, "known_in": 132, "known_out": 50, "avg_rank": 0.14875402333601537, "avg_in": 105.4342105263158}, "pr": {"known_rank": 0.4379258080055944, "known_in": 132, "known_out": 50, "avg_rank": 0.315993665105245, "avg_in": 105.4342105263158}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619550000.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619550000.0.json new file mode 100644 index 00000000..fce894c1 --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619550000.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.2355091668855965, "known_in": 136, "known_out": 50, "avg_rank": 0.15004832399548448, "avg_in": 105.45}, "pr": {"known_rank": 0.47157336169529784, "known_in": 136, "known_out": 50, "avg_rank": 0.31842808879435025, "avg_in": 105.45}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619553600.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619553600.0.json new file mode 100644 index 00000000..f3d9d56a --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619553600.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.250857273219965, "known_in": 133, "known_out": 50, "avg_rank": 0.15958154491986357, "avg_in": 105.04040404040404}, "pr": {"known_rank": 0.49352618799570985, "known_in": 133, "known_out": 50, "avg_rank": 0.3249376222626419, "avg_in": 105.04040404040404}} \ No newline at end of file diff --git a/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619557200.0.json b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619557200.0.json new file mode 100644 index 00000000..65f3eb57 --- /dev/null +++ b/codes/node-ranking/data_with_crawler/050_crawlers/0.90_edges/1619557200.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.24103541088829442, "known_in": 130, "known_out": 50, "avg_rank": 0.15773975781597374, "avg_in": 102.66666666666667}, "pr": {"known_rank": 0.48558376373250123, "known_in": 130, "known_out": 50, "avg_rank": 0.32657964706213566, "avg_in": 102.66666666666667}} \ No newline at end of file diff --git a/codes/node-ranking/edges.tar.bz2 b/codes/node-ranking/edges.tar.bz2 new file mode 100644 index 00000000..9d5e98fa Binary files /dev/null and b/codes/node-ranking/edges.tar.bz2 differ diff --git a/codes/node-ranking/mean_and_deriv.py b/codes/node-ranking/mean_and_deriv.py new file mode 100644 index 00000000..6cf2b1bd --- /dev/null +++ b/codes/node-ranking/mean_and_deriv.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 + +import matplotlib.pyplot as plt +import rank_with_crawlers +import statistics +import multiprocessing +from random import sample +import glob +import reduce_edges +import rank_with_churn +from datetime import datetime +import json +from node_ranking import ( + # rank as rank_nr, + page_rank, + sensor_rank, + find_rank, + parse_csv, + csv_loader, + build_graph, + Node, + RankedNode, +) + + +def analyze(g): + known = rank_with_crawlers.find_known(g, rank_with_churn.KNOWN) + # avg_r = rank_with_churn.avg_without_known(g) + # avg_in = rank_with_churn.avg_in(g) + # kn_in = known_in(g, known) + # kn_out = known_out(g, known) + d = list(map(lambda node: node.rank, g.nodes())) + mean = statistics.mean(d) + stddev = statistics.stdev(d) + return { + 'known_rank': known.rank, + # 'known_in': kn_in, + # 'known_out': kn_out, + # 'avg_rank': avg_r, + # 'avg_in': avg_in, + 'mean': mean, + 'stdev': stddev, + } + + +def perform(path): + when = datetime.fromtimestamp(float(path.split('/')[-1][:-4])) + print(f'{when=}, {path=}') + edges = reduce_edges.load_data(path) + g = build_graph(edges) + g_sr = sensor_rank(sensor_rank(g)) + g_pr = page_rank(page_rank(g)) + res_sr = analyze(g_sr) + res_pr = analyze(g_pr) + path = f'./mean_and_deriv/{when.timestamp()}.json' + return { + path: { + 'sr': res_sr, + 'pr': res_pr, + } + } + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + +# def plot(path): +# data = load_json(path) +# when = datetime.fromtimestamp(float(path.split('/')[-1][:-5])) + + +def main_plot(): + data = {} + for file in glob.glob('./mean_and_deriv/*.json'): + when = datetime.fromtimestamp(float(file.split('/')[-1][:-5])) + data[when] = load_json(file) + times = [] + means_sr = [] + stder_sr = [] + for t, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(t) + means_sr.append(d['sr']['mean']) + stder_sr.append(d['sr']['stdev']) + + plt.errorbar(times, means_sr, stder_sr) + plt.show() + + + +def main(): + params = [] + for file in glob.glob('./edges/*.txt'): + params.append(file) + + with multiprocessing.Pool(processes=8) as pool: + l_path_data = pool.map(perform, params) + for path_data in l_path_data: + for path, data in path_data.items(): + with open(path, 'w') as f: + json.dump(data, f) + + +if __name__ == '__main__': + main_plot() diff --git a/codes/node-ranking/mean_and_deriv/1618956000.0.json b/codes/node-ranking/mean_and_deriv/1618956000.0.json new file mode 100644 index 00000000..0c3c0557 --- /dev/null +++ b/codes/node-ranking/mean_and_deriv/1618956000.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.15506456549169106, "mean": 0.12771974126787422, "stdev": 0.06434479487876067}, "pr": {"known_rank": 0.24827899603256456, "mean": 0.21373056994818634, "stdev": 0.09919317084263922}} \ No newline at end of file diff --git a/codes/node-ranking/mean_and_deriv/1618959600.0.json b/codes/node-ranking/mean_and_deriv/1618959600.0.json new file mode 100644 index 00000000..b44338a6 --- /dev/null +++ b/codes/node-ranking/mean_and_deriv/1618959600.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.14204186762960533, "mean": 0.10446055559989793, "stdev": 0.06102026722761613}, "pr": {"known_rank": 0.26487668736597775, "mean": 0.20226130653266347, "stdev": 0.10857389469218698}} \ No newline at end of file diff --git a/codes/node-ranking/mean_and_deriv/1618963200.0.json b/codes/node-ranking/mean_and_deriv/1618963200.0.json new file mode 100644 index 00000000..675db2b1 --- /dev/null +++ b/codes/node-ranking/mean_and_deriv/1618963200.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.13420252916380745, "mean": 0.09679064661686244, "stdev": 0.05992438190481883}, "pr": {"known_rank": 0.25612175438101253, "mean": 0.19678217821782187, "stdev": 0.11322643241252353}} \ No newline at end of file diff --git a/codes/node-ranking/mean_and_deriv/1618966800.0.json 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b/codes/node-ranking/mean_and_deriv/1619557200.0.json new file mode 100644 index 00000000..d692af7e --- /dev/null +++ b/codes/node-ranking/mean_and_deriv/1619557200.0.json @@ -0,0 +1 @@ +{"sr": {"known_rank": 0.07038733860891296, "mean": 0.04084597404142832, "stdev": 0.033358206137188734}, "pr": {"known_rank": 0.24279188186625061, "mean": 0.14489795918367354, "stdev": 0.11417097820637205}} \ No newline at end of file diff --git a/codes/node-ranking/place_crawlers.py b/codes/node-ranking/place_crawlers.py new file mode 100644 index 00000000..cc94e757 --- /dev/null +++ b/codes/node-ranking/place_crawlers.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 + +import json +import multiprocessing +from datetime import datetime +from random import sample, seed +import rank_with_churn +import glob +from node_ranking import ( + RankedNode, + Node, + # page_rank, + # sensor_rank, + # find_rank, + parse_csv, + # csv_loader, + build_graph, + # Node, + # RankedNode, +) + +def load_data(path): + data = [] + with open(path, 'r') as f: + for line in f.readlines(): + data.append(parse_csv(line)) + + return data + +def edges_from(g, node): + return list(filter(lambda e: e[1] == node, g.edges())) + +def create_crawlers(graph, n_crawlers, n_peers): + nodes = list(filter(lambda n: n.node != rank_with_churn.KNOWN.node, graph.nodes())) + crawlers = [] + for i in range(n_crawlers): + ip = f'0.0.0.{i+1}' + crawler = RankedNode(Node(ip, 1337), rank_with_churn.INITIAL_RANK) + crawlers.append(crawler) + candidates = sample(nodes, n_peers) + for candidate in candidates: + graph.add_edge(crawler, candidate) + return crawlers + +def add_crawlers(when, edges, n_crawlers, prc_edges): + print(f'{n_crawlers=:03d} {prc_edges=:.02f} {when=}') + g = build_graph(edges, initial_rank=rank_with_churn.INITIAL_RANK) + n_nodes = len(list(g.nodes())) + create_crawlers(g, n_crawlers, int(n_nodes * prc_edges)) + path = f'./edges_with_crawler/{n_crawlers:03d}_crawlers/{prc_edges:.02f}_edges/{when.timestamp()}.txt' + data = [] + for [s, d] in g.edges(): + data.append(f'{s.node.ip},{s.node.port},{d.node.ip},{d.node.port}\n') + return {path: data} + +def wrapper_add_crawlers(params): + return add_crawlers(params[0], params[1], params[2], params[3]) + +percentages = [0.0, 0.3, 0.5, 0.75] +# percentages = [0.0] +def main(): + params = [] + for file in glob.glob('./edges/*.txt'): + edges = load_data(file) + print(f'loaded edges for {file}') + when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + for crawlers in [5, 10, 20, 50]: + for prc_edges in [0.1, 0.5, 0.9]: + params.append([when, edges, crawlers, prc_edges]) + print(f'created {len(params)} params') + # add_crawlers(when, edges.copy(), crawlers, prc_edges) + # for percentage in percentages: + # remove_edges(file, percentage) + with multiprocessing.Pool(processes=8) as pool: + l_path_data = pool.map(wrapper_add_crawlers, params) + for path_data in l_path_data: + for path, rows in path_data.items(): + with open(path, 'w') as f: + for row in rows: + f.write(row) + + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/plot_in_out_avg.py b/codes/node-ranking/plot_in_out_avg.py new file mode 100644 index 00000000..e8344eb9 --- /dev/null +++ b/codes/node-ranking/plot_in_out_avg.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python3 + +from datetime import datetime +import matplotlib.pyplot as plt +import numpy as np +import json + +def load_data(path): + with open(path, 'r') as f: + return json.load(f) + + +def main(): + data = load_data('./tmp_plot.json') + times = [] + avg_in = [] + avg_out = [] + known_in = [] + known_out = [] + number_of_nodes = [] + for k, v in sorted(data.items(), key = lambda kv: kv[0]): + times.append(datetime.fromtimestamp(float(k))) + avg_in.append(v['avg_in']) + avg_out.append(v['avg_out']) + known_in.append(v['known_in']) + known_out.append(v['known_out']) + number_of_nodes.append(v['number_of_nodes']) + + + fig, ax = plt.subplots() + ax.plot(times, avg_in, label='Avg. In') + # ax.plot(times, avg_out, label='Avg. Out') + ax.plot(times, known_in, label='Known In') + ax.plot(times, known_out, label='Known Out') + ax.plot(times, number_of_nodes, label='Number of nodes') + ax.set_title(f'Average edge count per hour') + fig.autofmt_xdate() + fig.legend() + plt.savefig(f'./tmp_plot.png') + # print('created sr plot') + plt.show() + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/plot_reduced.py b/codes/node-ranking/plot_reduced.py new file mode 100644 index 00000000..3a9f1079 --- /dev/null +++ b/codes/node-ranking/plot_reduced.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 + +import matplotlib.pyplot as plt +from datetime import datetime +import glob +import json +import reduce_edges + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + + +def plot_in_out(percentage, added_percentage, data): + times = [] + avg_in = [] + known_in = [] + known_out = [] + # same value, independent of algo + algo = 'sr' + for when, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(when) + avg_in.append(d[algo]['avg_in']) + known_in.append(d[algo]['known_in']) + known_out.append(d[algo]['known_out']) + + fig, ax = plt.subplots() + a = 'SensorRank' if algo == 'sr' else 'RageRank' + ax.set_ylabel(f'{a}') + ax.plot(times, avg_in, label='Avg. In') + # ax.plot(times, known_in, label='Known In') # TODO + ax.plot(times, known_in, label='Known In') + ax.plot(times, known_out, label='Known out') + title = f'In And Out after removing {percentage * 100}% edges and adding {added_percentage * 100}%' + ax.set_title(title) + + fig.autofmt_xdate() + fig.legend() + plt.savefig(f'./plot_reduced/{percentage:.02f}/{added_percentage:.02f}/in_out.png') + + +def plot(percentage, added_percentage, algo, data): + times = [] + avg_rank = [] + a_avg_in = [] + known_rank = [] + known_in = [] + known_out = [] + for when, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(when) + avg_rank.append(d[algo]['avg_rank']) + a_avg_in.append(d[algo]['avg_in']) + known_rank.append(d[algo]['known_rank']) + known_in.append(d[algo]['known_in']) + known_out.append(d[algo]['known_out']) + + # avg_out = sum(known_out) / len(known_out) + # avg_in = sum(known_in) / len(known_in) + + fig, ax = plt.subplots() + a = 'SensorRank' if algo == 'sr' else 'RageRank' + ax.set_ylabel(f'{a}') + ax.plot(times, avg_rank, label='Avg. Rank') + # ax.plot(times, known_in, label='Known In') # TODO + ax.plot(times, known_rank, label='Known Rank') + # title = f'{a} after removing {percentage * 100}% edges and adding {added_percentage * 100}%\nin = {avg_in:.02f} out = {avg_out:.02f}' + title = f'{a} after removing {percentage * 100}% edges and adding {added_percentage * 100}%' + ax.set_title(title) + + # ax2 = ax.twinx() + # ax2.set_ylabel('Edges') + # ax2.plot(times, known_in, label='Known In', color='red') + # ax2.plot(times, known_out, label='Known Out', color='green') + # # print(f'{len(times)=}, {len(a_avg_in)=}') + # # assert len(times) == len(a_avg_in) + # ax2.plot(times, a_avg_in, label='Avg. In', color='violet') + + fig.autofmt_xdate() + fig.legend() + plt.savefig(f'./plot_reduced/{percentage:.02f}/{added_percentage:.02f}/{algo}.png') + # print('created sr plot') + # plt.show() + + +def main(): + for reduced_percentage in reduce_edges.percentages: + perc = reduce_edges.percentages.copy() + perc.append(1.0) + perc.append(1.2) + for added_percentage in perc: + data = {} + for file in glob.glob(f'./data_reduced/{reduced_percentage:.02f}/{added_percentage:.02f}/*.json'): + when = datetime.fromtimestamp(float(file.split('/')[-1][:-5])) + print(f'{reduced_percentage=:.02f}, {added_percentage=:.02f}, {when=}') + data[when] = load_json(file) + + plot(reduced_percentage, added_percentage, 'sr', data) + plot(reduced_percentage, added_percentage, 'pr', data) + plot_in_out(reduced_percentage, added_percentage, data) + + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/plot_reduced/0.00/0.00/in_out.png b/codes/node-ranking/plot_reduced/0.00/0.00/in_out.png new file 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python3 + +import matplotlib.pyplot as plt +from datetime import datetime +import glob +import json +import reduce_edges + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + + +def plot_in_out(n_crawlers, prc, data): + times = [] + avg_in = [] + known_in = [] + known_out = [] + # same value, independent of algo + algo = 'sr' + for when, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(when) + avg_in.append(d[algo]['avg_in']) + known_in.append(d[algo]['known_in']) + known_out.append(d[algo]['known_out']) + + fig, ax = plt.subplots() + a = 'SensorRank' if algo == 'sr' else 'RageRank' + ax.set_ylabel(f'{a}') + ax.plot(times, avg_in, label='Avg. In') + # ax.plot(times, known_in, label='Known In') # TODO + ax.plot(times, known_in, label='Known In') + ax.plot(times, known_out, label='Known out') + title = f'In And Out after adding {n_crawlers} crawlers with {prc * 100}% edges' + ax.set_title(title) + + fig.autofmt_xdate() + fig.legend() + plt.savefig(f'./plot_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/in_out.png') + + +def plot(n_crawlers, prc, algo, data): + times = [] + avg_rank = [] + a_avg_in = [] + known_rank = [] + known_in = [] + known_out = [] + for when, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(when) + avg_rank.append(d[algo]['avg_rank']) + a_avg_in.append(d[algo]['avg_in']) + known_rank.append(d[algo]['known_rank']) + known_in.append(d[algo]['known_in']) + known_out.append(d[algo]['known_out']) + + # avg_out = sum(known_out) / len(known_out) + # avg_in = sum(known_in) / len(known_in) + + fig, ax = plt.subplots() + a = 'SensorRank' if algo == 'sr' else 'RageRank' + ax.set_ylabel(f'{a}') + ax.plot(times, avg_rank, label='Avg. Rank') + # ax.plot(times, known_in, label='Known In') # TODO + ax.plot(times, known_rank, label='Known Rank') + # title = f'{a} after removing {percentage * 100}% edges and adding {added_percentage * 100}%\nin = {avg_in:.02f} out = {avg_out:.02f}' + title = f'{a} with {n_crawlers} crawlers with {prc * 100}% edges each' + ax.set_title(title) + + # ax2 = ax.twinx() + # ax2.set_ylabel('Edges') + # ax2.plot(times, known_in, label='Known In', color='red') + # ax2.plot(times, known_out, label='Known Out', color='green') + # # print(f'{len(times)=}, {len(a_avg_in)=}') + # # assert len(times) == len(a_avg_in) + # ax2.plot(times, a_avg_in, label='Avg. In', color='violet') + + fig.autofmt_xdate() + fig.legend() + plt.savefig(f'./plot_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/{algo}.png') + # print('created sr plot') + # plt.show() + + +def main(): + for n_crawlers in [5,10,20,50]: #in reduce_edges.percentages: + for prc in [0.1, 0.5, 0.9]: + # for file in glob.glob(f'./edges_reduced/{reduced_percentage:.02f}/*.txt'): + # for file in glob.glob(f'./data_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/*.json'): + # for reduced_percentage in reduce_edges.percentages: + # perc = reduce_edges.percentages.copy() + # perc.append(1.0) + # perc.append(1.2) + # for added_percentage in perc: + data = {} + for file in glob.glob(f'./data_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/*.json'): + # for file in glob.glob(f'./data_with_crawler/{reduced_percentage:.02f}/{added_percentage:.02f}/*.json'): + when = datetime.fromtimestamp(float(file.split('/')[-1][:-5])) + print(f'{n_crawlers=:.02f}, {prc=:.02f}, {when=}') + data[when] = load_json(file) + + plot(n_crawlers, prc, 'sr', data) + plot(n_crawlers, prc, 'pr', data) + plot_in_out(n_crawlers, prc, data) + + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/in_out.png b/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/in_out.png new file mode 100644 index 00000000..d41685f3 Binary files /dev/null and b/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/in_out.png differ diff --git a/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/pr.png b/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/pr.png new file mode 100644 index 00000000..0dc8d256 Binary files /dev/null and b/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/pr.png differ diff --git a/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/sr.png b/codes/node-ranking/plot_with_crawler/005_crawlers/0.10_edges/sr.png new file mode 100644 index 00000000..9355728f Binary 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--git a/codes/node-ranking/plot_with_crawler/050_crawlers/0.90_edges/sr.png b/codes/node-ranking/plot_with_crawler/050_crawlers/0.90_edges/sr.png new file mode 100644 index 00000000..f6a4ce1e Binary files /dev/null and b/codes/node-ranking/plot_with_crawler/050_crawlers/0.90_edges/sr.png differ diff --git a/codes/node-ranking/rank_reduced.py b/codes/node-ranking/rank_reduced.py new file mode 100644 index 00000000..96adcbd0 --- /dev/null +++ b/codes/node-ranking/rank_reduced.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 + +import statistics +import multiprocessing +from random import sample +import glob +import reduce_edges +import rank_with_churn +from datetime import datetime +import json +from node_ranking import ( + rank as rank_nr, + page_rank, + # sensor_rank, + find_rank, + parse_csv, + csv_loader, + build_graph, + Node, + RankedNode, +) + +def sr(graph, node, number_of_nodes): + try: + return node.rank * (len(list(graph.predecessors(node))) / number_of_nodes) + except ZeroDivisionError as e: + print(f'{node=}, {number_of_nodes=}') + raise e + +def sensor_rank(graph): + number_of_nodes = graph.number_of_nodes() + return rank_nr( + page_rank(graph), + lambda g, node: sr(g, node, number_of_nodes) + ) + +def find_known(g, known): + nodes = list(filter(lambda n: n.node == known.node, g.nodes())) + n = len(nodes) + assert n == 1 + return nodes[0] + +def known_out(g, known): + return len(list(g.successors(known))) + +def known_in(g, known): + return len(list(g.predecessors(known))) + +def high_succ(g, n, known): + result = [] + counter = 0 + # choose nodes with many successors to reduce pr + for node in sorted(g.nodes(), key=lambda node: len(list(g.successors(node))), reverse=True): + if node.node == known.node: + print('skipping known') + continue + if counter >= n: + break + counter += 1 + result.append(node) + return result + +def analyze(g): + known = find_known(g, rank_with_churn.KNOWN) + # avg_r = rank_with_churn.avg_without_known(g) + avg_in = rank_with_churn.avg_in(g) + kn_in = known_in(g, known) + kn_out = known_out(g, known) + d = list(map(lambda node: node.rank, g.nodes())) + mean = statistics.mean(d) + stddev = statistics.stdev(d) + return { + 'known_rank': known.rank, + 'known_in': kn_in, + 'known_out': kn_out, + # 'avg_rank': avg_r, + 'avg_in': avg_in, + 'mean': mean, + 'stdev': stddev, + } + + +def create_crawlers(graph, n_crawlers, n_edges): + nodes = list(filter(lambda n: n.node != rank_with_churn.KNOWN.node, graph.nodes())) + crawlers = [] + for i in range(n_crawlers): + ip = f'0.0.0.{i+1}' + crawler = RankedNode(Node(ip, 1337), rank_with_churn.INITIAL_RANK) + crawlers.append(crawler) + candidates = sample(nodes, n_edges) + for candidate in candidates: + graph.add_edge(crawler, candidate) + return crawlers + + + +def rank(path, added_percentage): + edges = reduce_edges.load_data(path) + g = build_graph(edges, initial_rank=rank_with_churn.INITIAL_RANK) + + # edges = list(filter(lambda e: e[1] == rank_with_churn.KNOWN, g.edges())) + # for_removal = sample(edges, int(len(edges) * remove_edges_percentage)) + # print(f'removing {len(for_removal)} incoming edges') + # for edge in for_removal: + # g.remove_edge(edge[0], edge[1]) + n_known_in = len(list(filter(lambda e: e[1] == rank_with_churn.KNOWN, g.edges()))) + # avg_out = rank_with_churn.avg_out(g) + churned_peers = int(n_known_in * added_percentage) + known_pred = len(list(g.predecessors(rank_with_churn.KNOWN))) + c_out = int(known_pred * added_percentage) + crawlers = create_crawlers(g, churned_peers, c_out) + print(f'{added_percentage=}, {churned_peers=}') + # assert added_percentage == 0 or churned_peers != 0 + + # if churned_peers > 0: + # # nodes = list(g.nodes()) + # # destinations = sample(nodes, churned_peers) + # destinations = high_succ(g, churned_peers, rank_with_churn.KNOWN) + # print(f'!!!!!! adding destinations: {destinations}') + print(f'adding {len(crawlers)=} crawlers with {c_out=} successors') + for node in crawlers: + g.add_edge(rank_with_churn.KNOWN, node) + + print('pr start') + g_pr = page_rank(page_rank(g)) + print('sr start') + g_sr = sensor_rank(sensor_rank(g)) + print('analyze pr start') + res_pr = analyze(g_pr) + print('analyze sr start') + res_sr = analyze(g_sr) + print('done!') + res = {'sr': res_sr, 'pr': res_pr} + return res + +def main(): + # pool = multiprocessing.Pool(processes=4) + params = [] + for reduced_percentage in [0.0]: #in reduce_edges.percentages: + for file in glob.glob(f'./edges_reduced/{reduced_percentage:.02f}/*.txt'): + params.append([reduced_percentage, file]) + # p = Proc(reduced_percentage, file) + # p.start() + # for added_percentage in reduce_edges.percentages: + # when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + # print(f'{reduced_percentage=:.02f}, {added_percentage=:.02f}, {when=}') + # result = rank(file, added_percentage) + # with open(f'./data_reduced/{reduced_percentage:.02f}/{added_percentage:.02f}/{when.timestamp()}.json', 'w') as f: + # json.dump(result, f) + with multiprocessing.Pool(processes=8) as pool: + l_path_data = pool.map(wohoo, params) + for path_data in l_path_data: + for path, data in path_data.items(): + with open(path, 'w') as f: + json.dump(data, f) + + +def wohoo(p): + reduced_percentage = p[0] + file = p[1] + path_data = {} + # ps = reduce_edges.percentages.copy() + ps = [0.3, 0.5, 0.75] + ps.append(0.1) + ps.append(1.0) + ps.append(1.2) + # ps.append(2.0) + for added_percentage in ps: + when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + print(f'{reduced_percentage=:.02f}, {added_percentage=:.02f}, {when=}') + result = rank(file, added_percentage) + path = f'./data_reduced/{reduced_percentage:.02f}/{added_percentage:.02f}/{when.timestamp()}.json' + path_data[path] = result + # with open() as f: + # json.dump(result, f) + return path_data + + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/rank_with_churn.py b/codes/node-ranking/rank_with_churn.py index 75a4fb9a..00770ef2 100644 --- a/codes/node-ranking/rank_with_churn.py +++ b/codes/node-ranking/rank_with_churn.py @@ -66,6 +66,20 @@ def avg_out(g): avg = reduce(lambda acc, x: acc + len(list(g.successors(x))), nodes, 0.) / n return avg +def known_out(g): + nodes = list(filter(lambda n: n.node == KNOWN.node, g.nodes())) + n = len(nodes) + assert n == 1 + avg = reduce(lambda acc, x: acc + len(list(g.successors(x))), nodes, 0.) / n + return avg + +def known_in(g): + nodes = list(filter(lambda n: n.node == KNOWN.node, g.nodes())) + n = len(nodes) + assert n == 1 + avg = reduce(lambda acc, x: acc + len(list(g.predecessors(x))), nodes, 0.) / n + return avg + def known_rank(g): for n in g.nodes(): if n.node == KNOWN.node: @@ -140,11 +154,39 @@ def do_work(path, initial_rank, percent_edges, remove_edges, known): print(f'finished {path} {initial_rank} {percent_edges} {known}') -def main(): - for file in glob.glob('./edges/*.txt'): - for perc in [0.5, 0.75, 0.8, 1.0, 1.5, 2.0, 2.5]: - do_work(file, INITIAL_RANK, perc, REMOVE_EDGES, KNOWN) +def main(): + data = {} + # times = [] + # avg_in = [] + # avg_out = [] + # sensor_in = [] + # sensor_out = [] + + initial_rank = 0.5 + for file in glob.glob('./edges/*.txt'): + when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + print(when) + edges = load_data(file) + g = build_graph(edges, initial_rank=initial_rank) + navg_in = avg_in(g) + navg_out = avg_out(g) + nknown_in = known_in(g) + nknown_out = known_out(g) + x = { + 'avg_in': navg_in, + 'avg_out': navg_out, + 'known_in': nknown_in, + 'known_out': nknown_out, + 'number_of_nodes': len(g), + } + + data[when.timestamp()] = x + # for perc in [0.5, 0.75, 0.8, 1.0, 1.5, 2.0, 2.5]: + # do_work(file, INITIAL_RANK, perc, REMOVE_EDGES, KNOWN) + + with open('./tmp_plot.json', 'w') as f: + json.dump(data, f) if __name__ == '__main__': main() diff --git a/codes/node-ranking/rank_with_crawlers.py b/codes/node-ranking/rank_with_crawlers.py new file mode 100644 index 00000000..0fb19b82 --- /dev/null +++ b/codes/node-ranking/rank_with_crawlers.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 + +import statistics +import multiprocessing +from random import sample +import glob +import reduce_edges +import rank_with_churn +from datetime import datetime +import json +from node_ranking import ( + rank as rank_nr, + page_rank, + # sensor_rank, + find_rank, + parse_csv, + csv_loader, + build_graph, + Node, + RankedNode, +) + +def sr(graph, node, number_of_nodes): + try: + return node.rank * (len(list(graph.predecessors(node))) / number_of_nodes) + except ZeroDivisionError as e: + print(f'{node=}, {number_of_nodes=}') + raise e + +def sensor_rank(graph): + number_of_nodes = graph.number_of_nodes() + return rank_nr( + page_rank(graph), + lambda g, node: sr(g, node, number_of_nodes) + ) + +def find_known(g, known): + nodes = list(filter(lambda n: n.node == known.node, g.nodes())) + n = len(nodes) + assert n == 1 + return nodes[0] + +def known_out(g, known): + return len(list(g.successors(known))) + +def known_in(g, known): + return len(list(g.predecessors(known))) + +def high_succ(g, n, known): + result = [] + counter = 0 + # choose nodes with many successors to reduce pr + for node in sorted(g.nodes(), key=lambda node: len(list(g.successors(node))), reverse=True): + if node.node == known.node: + print('skipping known') + continue + if counter >= n: + break + counter += 1 + result.append(node) + return result + +def analyze(g): + known = find_known(g, rank_with_churn.KNOWN) + # avg_r = rank_with_churn.avg_without_known(g) + avg_in = rank_with_churn.avg_in(g) + kn_in = known_in(g, known) + kn_out = known_out(g, known) + d = list(map(lambda node: node.rank, g.nodes())) + mean = statistics.mean(d) + stddev = statistics.stdev(d) + return { + 'known_rank': known.rank, + 'known_in': kn_in, + 'known_out': kn_out, + # 'avg_rank': avg_r, + 'avg_in': avg_in, + 'mean': mean, + 'stdev': stddev, + } + + +def create_crawlers(graph, n_crawlers, n_edges): + nodes = list(filter(lambda n: n.node != rank_with_churn.KNOWN.node, graph.nodes())) + crawlers = [] + for i in range(n_crawlers): + ip = f'0.0.0.{i+1}' + crawler = RankedNode(Node(ip, 1337), rank_with_churn.INITIAL_RANK) + crawlers.append(crawler) + candidates = sample(nodes, n_edges) + for candidate in candidates: + graph.add_edge(crawler, candidate) + return crawlers + + +def crawlers(n_crawlers): + c = [] + for i in range(n_crawlers): + c.append(RankedNode(Node(f'0.0.0.{i+1}', 1337), rank_with_churn.INITIAL_RANK)) + return c + + +def rank(path, n_crawlers): + edges = reduce_edges.load_data(path) + g = build_graph(edges, initial_rank=rank_with_churn.INITIAL_RANK) + + cs = crawlers(n_crawlers) + print(f'adding {len(cs)} crawlers as edges') + for node in cs: + g.add_edge(rank_with_churn.KNOWN, node) + + print('pr start') + g_pr = page_rank(page_rank(g)) + print('sr start') + g_sr = sensor_rank(sensor_rank(g)) + print('analyze pr start') + res_pr = analyze(g_pr) + print('analyze sr start') + res_sr = analyze(g_sr) + print('done!') + res = {'sr': res_sr, 'pr': res_pr} + return res + +def main(): + # pool = multiprocessing.Pool(processes=4) + params = [] + for n_crawlers in [5,10,20,50]: #in reduce_edges.percentages: + for prc in [0.1, 0.5, 0.9]: + # for file in glob.glob(f'./edges_reduced/{reduced_percentage:.02f}/*.txt'): + for file in glob.glob(f'./edges_with_crawler/{n_crawlers:03d}_crawlers/{prc:.02f}_edges/*.txt'): + params.append([n_crawlers, prc, file]) + # p = Proc(reduced_percentage, file) + # p.start() + # for added_percentage in reduce_edges.percentages: + # when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + # print(f'{reduced_percentage=:.02f}, {added_percentage=:.02f}, {when=}') + # result = rank(file, added_percentage) + # with open(f'./data_reduced/{reduced_percentage:.02f}/{added_percentage:.02f}/{when.timestamp()}.json', 'w') as f: + # json.dump(result, f) + with multiprocessing.Pool(processes=8) as pool: + l_path_data = pool.map(wohoo, params) + for path_data in l_path_data: + for path, data in path_data.items(): + with open(path, 'w') as f: + json.dump(data, f) + + +def wohoo(p): + n_crawlers = p[0] + prc_edges = p[1] + file = p[2] + path_data = {} + # ps = reduce_edges.percentages.copy() + # ps = [0.3, 0.5, 0.75] + # ps.append(0.1) + # ps.append(1.0) + # ps.append(1.2) + # # ps.append(2.0) + # for added_percentage in ps: + when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + print(f'{n_crawlers=:03d}, {prc_edges=:.02f}, {when=}') + result = rank(file, n_crawlers) + path = f'./data_with_crawler/{n_crawlers:03d}_crawlers/{prc_edges:.02f}_edges/{when.timestamp()}.json' + path_data[path] = result + # with open() as f: + # json.dump(result, f) + return path_data + + +if __name__ == '__main__': + main() diff --git a/codes/node-ranking/reduce_edges.py b/codes/node-ranking/reduce_edges.py new file mode 100644 index 00000000..187e20ad --- /dev/null +++ b/codes/node-ranking/reduce_edges.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 + +from datetime import datetime +from random import sample, seed +import rank_with_churn +import glob +from node_ranking import ( + # page_rank, + # sensor_rank, + # find_rank, + parse_csv, + # csv_loader, + build_graph, + # Node, + # RankedNode, +) + +def load_data(path): + data = [] + with open(path, 'r') as f: + for line in f.readlines(): + data.append(parse_csv(line)) + + return data + +def edges_from(g, node): + return list(filter(lambda e: e[1] == node, g.edges())) + +def remove_edges(path, percentage): + when = datetime.fromtimestamp(float(path.split('/')[-1][:-4])) + print(f'{when=}, {percentage=}') + edges = load_data(path) + g = build_graph(edges) + edges = edges_from(g, rank_with_churn.KNOWN) + for_removal = sample(edges, int(len(edges) * percentage)) + for edge in for_removal: + g.remove_edge(edge[0], edge[1]) + with open(f'./edges_reduced/{percentage:.02f}/{when.timestamp()}.txt', 'w') as f: + for [s, d] in g.edges(): + row = f'{s.node.ip},{s.node.port},{d.node.ip},{d.node.port}\n' + f.write(row) + +percentages = [0.0, 0.3, 0.5, 0.75] +# percentages = [0.0] +def main(): + for percentage in percentages: + for file in glob.glob('./edges/*.txt'): + remove_edges(file, percentage) + + +if __name__ == '__main__': + main() diff --git a/codes/partition/__pycache__/variance.cpython-39.pyc b/codes/partition/__pycache__/variance.cpython-39.pyc new file mode 100644 index 00000000..7aa6f4fe Binary files /dev/null and b/codes/partition/__pycache__/variance.cpython-39.pyc differ diff --git a/codes/partition/partition.py b/codes/partition/partition.py index 246d7593..9138a21b 100644 --- a/codes/partition/partition.py +++ b/codes/partition/partition.py @@ -4,6 +4,7 @@ from collections import defaultdict import hashlib import matplotlib.pyplot as plt import numpy as np +import variance # int(ipaddress.IPv4Address("192.168.0.1")) @@ -26,65 +27,74 @@ def partition(crawlers, ips): def main(): - ips = load_ips('./ips.csv') + # ips = load_ips('./ips.csv') + ips = load_ips('./random.csv') c2 = ['c0', 'c1'] c4 = ['c0', 'c1', 'c2', 'c3'] c6 = ['c0', 'c1', 'c2', 'c3', 'c4', 'c5'] c10 = ['c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9'] + c100 = [] + for i in range(100): + c100.append(f'c{i:02d}') - for cs in [c2, c4, c6, c10]: + # for cs in [c2, c4, c6, c10]: + for cs in [c100]: print(f'{len(cs)} crawlers') part = partition(cs, ips) - - ind = np.arange(len(cs)) - fig, ax = plt.subplots() - crawlers = sorted(cs) - - ax.invert_yaxis() - ax.set_ylabel('Crawlers') - ax.set_xlabel('Peers') - ax.set_title(f'IP Partitioning for {len(cs)} crawlers') - ax.set_yticks(ind) - - # ax.set_xticks(ind) #, labels=cs) - - ns = [] - - for c, i in sorted(part.items(), key=lambda kv: kv[0]): - print(f'\t{c}: {len(i)}') - ns.append(len(i)) - - # p1 = ax.bar(ind, ns, 0.35) - hbars = ax.barh(ind, ns) - ax.bar_label(hbars, label_type='center') - # plt.show() - plt.savefig(f'./ip_part_c{len(cs):02}.png') + counts = [len(v) for k, v in part.items()] + variance.variance(counts) + # for c, i in sorted(part.items(), key=lambda kv: kv[0]): - # p1 = ax.bar(ind, i, 0.35, label=c) +# ind = np.arange(len(cs)) +# fig, ax = plt.subplots() +# crawlers = sorted(cs) + +# ax.invert_yaxis() +# ax.set_ylabel('Crawlers') +# ax.set_xlabel('Peers') +# ax.set_title(f'IP Partitioning for {len(cs)} crawlers') +# ax.set_yticks(ind) + +# # ax.set_xticks(ind) #, labels=cs) + +# ns = [] + +# for c, i in sorted(part.items(), key=lambda kv: kv[0]): +# print(f'\t{c}: {len(i)}') +# ns.append(len(i)) + +# # p1 = ax.bar(ind, ns, 0.35) +# hbars = ax.barh(ind, ns) +# ax.bar_label(hbars, label_type='center') +# # plt.show() +# plt.savefig(f'./ip_part_c{len(cs):02}.png') - # people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim') +# # p1 = ax.bar(ind, i, 0.35, label=c) + + +# # people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim') -# hbars = ax.barh(y_pos, performance, xerr=error, align='center') -# ax.set_yticks(y_pos, labels=people) -# ax.invert_yaxis() # labels read top-to-bottom -# ax.set_xlabel('Performance') -# ax.set_title('How fast do you want to go today?') +# # hbars = ax.barh(y_pos, performance, xerr=error, align='center') +# # ax.set_yticks(y_pos, labels=people) +# # ax.invert_yaxis() # labels read top-to-bottom +# # ax.set_xlabel('Performance') +# # ax.set_title('How fast do you want to go today?') -# # Label with specially formatted floats -# ax.bar_label(hbars, fmt='%.2f') -# ax.set_xlim(right=15) # adjust xlim to fit labels +# # # Label with specially formatted floats +# # ax.bar_label(hbars, fmt='%.2f') +# # ax.set_xlim(right=15) # adjust xlim to fit labels - # plt.show() +# # plt.show() - print() - print() - print() - print() +# print() +# print() +# print() +# print() if __name__ == '__main__': diff --git a/codes/partition/variance.py b/codes/partition/variance.py new file mode 100644 index 00000000..3b375007 --- /dev/null +++ b/codes/partition/variance.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 + +from math import sqrt +# from itertools import islice + +def variance(xs): + n = len(xs) + mu = sum(xs) / n + # mu = 265.8 + # print(sum(map(lambda x: (x - mu)**2, xs))) + s = sum(map(lambda x: (x - mu)**2, xs)) + var = s / n + deriv = sqrt(var) + # var = sqrt( / len(xs)) + + print('\\begin{align*}') + print(f' n &= {n} \\\\') + print(f' \\mu &= \\frac{{{sum(xs)}}}{{n}} = {mu:.1f} \\\\') + print(f' s &= \\sum\\limits_{{i=1}}^{{n}} {{(x_i - \\mu)}}^2 \\\\') + # print [mylist[i:i+4] ] + first = True + for i in range(0, n, 3): + symbol = '=' if first else '+' + l = xs[i:i+3] + a = ' + '.join(map(lambda x: f'{{({x} - {mu:.1f})}}^2', l)) + first = False + print(f' &{symbol} {a} \\\\') + + print(f' &= {s:.1f} \\\\') + + # a = ' + '.join(map(lambda x: f'({x} - {mu})^2', xs)) + print(f' \\sigma^2 &= \\frac{{s}}{{n}} = {var:.1f} \\\\') + print(f' \\sigma &= \\sqrt{{\\sigma^2}} = {deriv:.1f}') + print('\\end{align*}') + + print() + print() + print() + print() + + # \mu &= 1595 / 4 = 398.75 \\ + # \sigma &= \sqrt{\frac{(403 - 398.75)^2 + (369 - 398.75)^2 + (405 - 398.75)^2 + (418 - 398.75)^2}{4}} = 10.5 + +def main(): + # for xs in [[808, 787], [403, 369, 405, 418], [258, 273, 257, 264, 293, 250], [140, 175, 186, 166, 159, 152, 172, 148, 151, 146]]: + # for xs in [[258, 273, 257, 264, 293, 250]]: + for xs in [[499322, 500678], [249504, 250451, 249818, 250227], [166430, 166861, 166269, 166937, 166623, 166880], [100424, 99650, 99307, 100305, 99403, 100562, 100277, 99875, 99911, 100286]]: + variance(xs) + +if __name__ == '__main__': + main() diff --git a/content.tex b/content.tex index c5c80782..c2e5c8c4 100644 --- a/content.tex +++ b/content.tex @@ -606,6 +606,11 @@ The following candidates to place on the neighbor list will be investigated: Returning all the other sensors when responding to peer list requests, thereby effectively creating a complete graph \(K_\abs{C}\) among the workers, creates valid outgoing edges. The resulting graph will still form a \ac{wcc} with now edges back into the main network. +PageRank is the sum of a node's predecessors ranks divided by the amount of successors each predecessor's successors. +Predecessors with many successors should therefore reduce the rank. +By their nature, crawlers have many successors, so they are good candidates to reduce the PageRank of a sensor. +\todo{crawlers as predecessors} + %{{{ churned peers \subsubsection{Churned Peers After IP Rotation} diff --git a/report.pdf b/report.pdf index de2a19e3..e31ae109 100644 Binary files a/report.pdf and b/report.pdf differ