From 49e45e147f835699347a2131ad1e735bf39bf3a7 Mon Sep 17 00:00:00 2001 From: Valentin Brandl Date: Thu, 21 Apr 2022 12:15:45 +0200 Subject: [PATCH] Updates --- codes/node-ranking/plot_reduced.py | 89 ++++++++++++++++++++++++++++- codes/node-ranking/rank_reduced.py | 54 ++++++++--------- codes/node-ranking/reduce_edges.py | 49 ++++++++++++---- content.tex | 2 +- report.pdf | Bin 1252299 -> 1230841 bytes 5 files changed, 154 insertions(+), 40 deletions(-) diff --git a/codes/node-ranking/plot_reduced.py b/codes/node-ranking/plot_reduced.py index 3a9f1079..407ed1d5 100644 --- a/codes/node-ranking/plot_reduced.py +++ b/codes/node-ranking/plot_reduced.py @@ -82,7 +82,7 @@ def plot(percentage, added_percentage, algo, data): # plt.show() -def main(): +def main2(): for reduced_percentage in reduce_edges.percentages: perc = reduce_edges.percentages.copy() perc.append(1.0) @@ -99,5 +99,92 @@ def main(): plot_in_out(reduced_percentage, added_percentage, data) + + +def plot2(percentage, algo, data): + times = [] + mean = [] + stdev = [] + a_avg_in = [] + known_rank = [] + known_in = [] + known_out = [] + for when, d in sorted(data.items(), key=lambda kv: kv[0]): + times.append(when) + mean.append(d[algo]['mean']) + stdev.append(d[algo]['stdev']) + 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, mean, label='Avg. Rank') + # ax.errorbar(times, mean, stdev, label='Avg. Rank') + ax.plot(times, mean, 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' + ax.set_title(title) + + fig.autofmt_xdate() + fig.legend() + path = f'./plot_reduced/{percentage:.02f}/{algo}.png' + with reduce_edges.open_mkdir(path, 'w'): + print('created') + plt.savefig(path) + plt.close(fig) + + +def plot_in_out2(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' + ax.set_title(title) + + fig.autofmt_xdate() + fig.legend() + path = f'./plot_reduced/{percentage:.02f}/in_out.png' + with reduce_edges.open_mkdir(path, 'w'): + print('created') + plt.savefig(path) + plt.close(fig) + + +def main(): + for perc in reduce_edges.percentages: + data = {} + for file in glob.glob(f'./data_reduced/{perc:.02f}/*.json'): + when = datetime.fromtimestamp(float(file.split('/')[-1][:-5])) + print(f'{perc=:.02f}, {when=}') + data[when] = load_json(file) + + plot2(perc, 'sr', data) + plot2(perc, 'pr', data) + plot_in_out2(perc, data) + if __name__ == '__main__': main() diff --git a/codes/node-ranking/rank_reduced.py b/codes/node-ranking/rank_reduced.py index 96adcbd0..cbe262f7 100644 --- a/codes/node-ranking/rank_reduced.py +++ b/codes/node-ranking/rank_reduced.py @@ -94,7 +94,7 @@ def create_crawlers(graph, n_crawlers, n_edges): -def rank(path, added_percentage): +def rank(path): edges = reduce_edges.load_data(path) g = build_graph(edges, initial_rank=rank_with_churn.INITIAL_RANK) @@ -103,13 +103,13 @@ def rank(path, added_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=}') + # 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: @@ -117,9 +117,9 @@ def rank(path, added_percentage): # # 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(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)) @@ -136,7 +136,7 @@ def rank(path, added_percentage): def main(): # pool = multiprocessing.Pool(processes=4) params = [] - for reduced_percentage in [0.0]: #in reduce_edges.percentages: + for reduced_percentage 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) @@ -151,29 +151,29 @@ def main(): 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: + with reduce_edges.open_mkdir(path, 'w') as f: json.dump(data, f) def wohoo(p): - reduced_percentage = p[0] - file = p[1] - path_data = {} + reduced_percentage, file = p + # 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 = [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 + # for added_percentage in ps: + # print(f'{reduced_percentage=:.02f}, {added_percentage=:.02f}, {when=}') + # result = rank(file, added_percentage) + # path_data[path] = result # with open() as f: # json.dump(result, f) - return path_data + when = datetime.fromtimestamp(float(file.split('/')[-1][:-4])) + path = f'./data_reduced/{reduced_percentage:.02f}/{when.timestamp()}.json' + result = rank(file) + return {path: result} if __name__ == '__main__': diff --git a/codes/node-ranking/reduce_edges.py b/codes/node-ranking/reduce_edges.py index 187e20ad..8f6a5ce0 100644 --- a/codes/node-ranking/reduce_edges.py +++ b/codes/node-ranking/reduce_edges.py @@ -1,5 +1,7 @@ #!/usr/bin/env python3 +import os +import multiprocessing from datetime import datetime from random import sample, seed import rank_with_churn @@ -15,10 +17,15 @@ from node_ranking import ( # RankedNode, ) + +def open_mkdir(path, mode): + os.makedirs(os.path.dirname(path), exist_ok=True) + return open(path, mode) + def load_data(path): data = [] with open(path, 'r') as f: - for line in f.readlines(): + for line in f: data.append(parse_csv(line)) return data @@ -26,26 +33,46 @@ def load_data(path): def edges_from(g, node): return list(filter(lambda e: e[1] == node, g.edges())) -def remove_edges(path, percentage): +def remove_edges(path, edges, percentage): when = datetime.fromtimestamp(float(path.split('/')[-1][:-4])) - print(f'{when=}, {percentage=}') - edges = load_data(path) + log = f'{when=}, {percentage=}' + print(log) + # 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: + path = f'./edges_reduced/{percentage:.02f}/{when.timestamp()}.txt' + rows = [] + with open_mkdir(path, '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) + f.write(f'{s.node.ip},{s.node.port},{d.node.ip},{d.node.port}\n') + # f.write(row) + return f'done: {log}' -percentages = [0.0, 0.3, 0.5, 0.75] + +def work(params): + # print(f'starting work {params=}') + path, edges, percentage = params + remove_edges(path, edges, percentage) + + +percentages = [0.0, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9] # percentages = [0.0] def main(): - for percentage in percentages: - for file in glob.glob('./edges/*.txt'): - remove_edges(file, percentage) + params = [] + for file in glob.glob('./edges/*.txt'): + edges = load_data(file) + for percentage in percentages: + params.append([file, edges, percentage]) + # remove_edges(file, percentage) + + print('created params') + with multiprocessing.Pool(processes=8) as pool: + res = pool.map(work, params) + for r in res: + print(r) if __name__ == '__main__': diff --git a/content.tex b/content.tex index c2e5c8c4..01148f18 100644 --- a/content.tex +++ b/content.tex @@ -603,7 +603,7 @@ The following candidates to place on the neighbor list will be investigated: \subsubsection{Other Sensors or Crawlers} -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. +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. 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