| 1 | #!/usr/bin/env python3 |
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| 2 | |
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| 3 | import sys |
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| 4 | import os |
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| 5 | import time |
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| 6 | import random |
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| 7 | import tempfile |
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| 8 | import argparse |
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| 9 | import socket |
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| 10 | import json |
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| 11 | |
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| 12 | import numpy |
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| 13 | import matplotlib.mlab as mlab |
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| 14 | import matplotlib.pyplot as plt |
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| 15 | |
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| 16 | |
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| 17 | VERSION = "{DEVELOPMENT}" |
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| 18 | if VERSION == "{DEVELOPMENT}": |
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| 19 | script_dir = '.' |
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| 20 | try: |
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| 21 | script_dir = os.path.dirname(os.path.realpath(__file__)) |
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| 22 | except: |
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| 23 | try: |
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| 24 | script_dir = os.path.dirname(os.path.abspath(sys.argv[0])) |
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| 25 | except: |
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| 26 | pass |
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| 27 | sys.path.append("%s/../lib" % script_dir) |
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| 28 | |
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| 29 | from nanownlib import * |
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| 30 | from nanownlib.stats import * |
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| 31 | import nanownlib.storage |
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| 32 | |
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| 33 | |
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| 34 | parser = argparse.ArgumentParser( |
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| 35 | description="") |
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| 36 | parser.add_argument('db_file', default=None, |
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| 37 | help='') |
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| 38 | parser.add_argument('unusual_case', nargs='?', type=str, default=None, |
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| 39 | help='The test case that is most unusual from the others. (default: auto detect)') |
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| 40 | options = parser.parse_args() |
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| 41 | db = nanownlib.storage.db(options.db_file) |
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| 42 | if options.unusual_case == None: |
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| 43 | unusual_case,delta = findUnusualTestCase(db) |
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| 44 | |
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| 45 | |
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| 46 | def differences(db, unusual_case, rtt_type='packet'): |
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| 47 | ret_val = [s['unusual_'+rtt_type]-s['other_'+rtt_type] for s in db.subseries('train', unusual_case)] |
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| 48 | ret_val += [s['unusual_'+rtt_type]-s['other_'+rtt_type] for s in db.subseries('test', unusual_case)] |
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| 49 | return ret_val |
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| 50 | |
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| 51 | def null_differences(db, unusual_case, rtt_type='packet'): |
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| 52 | ret_val = [s['unusual_'+rtt_type]-s['other_'+rtt_type] for s in db.subseries('train_null', unusual_case)] |
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| 53 | return ret_val |
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| 54 | |
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| 55 | |
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| 56 | def timeSeries(db, probe_type, unusual_case): |
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| 57 | cursor = db.conn.cursor() |
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| 58 | query=""" |
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| 59 | SELECT time_of_day,packet_rtt AS uc,(SELECT avg(packet_rtt) FROM probes,analysis |
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| 60 | WHERE analysis.probe_id=probes.id AND probes.test_case!=:unusual_case AND probes.type=:probe_type AND sample=u.sample) AS oc |
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| 61 | FROM (SELECT time_of_day,probes.sample,packet_rtt FROM probes,analysis |
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| 62 | WHERE analysis.probe_id=probes.id AND probes.test_case =:unusual_case AND probes.type=:probe_type) u |
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| 63 | """ |
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| 64 | |
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| 65 | params = {"probe_type":probe_type,"unusual_case":unusual_case} |
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| 66 | cursor.execute(query, params) |
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| 67 | for row in cursor: |
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| 68 | yield {'time_of_day':row['time_of_day'],unusual_case:row['uc'],'other_cases':row['oc']} |
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| 69 | #samples,derived,null_derived = parse_data(input1) |
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| 70 | |
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| 71 | #trust = trustValues(derived, sum) |
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| 72 | #weights = linearWeights(derived, trust, 0.25) |
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| 73 | #print('(test): %f' % weightedMean(derived,weights)) |
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| 74 | |
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| 75 | diffs = list(differences(db, unusual_case)) |
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| 76 | reported_diffs = list(differences(db, unusual_case, 'reported')) |
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| 77 | #shorts = [s['packet_rtt'] for s in samples.values() if s['test_case']=='short'] |
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| 78 | #longs = [s['packet_rtt'] for s in samples.values() if s['test_case']=='long'] |
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| 79 | |
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| 80 | |
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| 81 | def basicStatistics(): |
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| 82 | print('packet_rtt diff midhinge: %10.2f' % midsummary(diffs)) |
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| 83 | print('packet_rtt diff quadsummary: %10.2f' % quadsummary(diffs)) |
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| 84 | print('packet_rtt diff septasummary: %10.2f' % septasummary(diffs)) |
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| 85 | print('packet_rtt diff MAD: %10.2f' % mad(diffs)) |
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| 86 | try: |
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| 87 | print('reported diff midhinge: %10.2f' % midsummary(reported_diffs)) |
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| 88 | print('reported diff quadsummary: %10.2f' % quadsummary(reported_diffs)) |
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| 89 | print('reported diff septasummary: %10.2f' % septasummary(reported_diffs)) |
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| 90 | print('reported diff MAD: %10.2f' % mad(reported_diffs)) |
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| 91 | |
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| 92 | #import cProfile |
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| 93 | #start = time.time() |
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| 94 | #kresults = kfilter({},diffs) |
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| 95 | #print('packet_rtt diff kfilter: ', numpy.mean(kresults['est']), kresults['var']) |
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| 96 | #print('packet_rtt diff kfilter: ', kresults['est'][-1], kresults['var'][-1]) |
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| 97 | #kresults = kfilter({},reported_diffs) |
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| 98 | #print('reported diff kfilter: ', numpy.mean(kresults['est']), kresults['var'][-1]) |
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| 99 | #print('reported diff kfilter: ', kresults['est'][-1], kresults['var'][-1]) |
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| 100 | #print("kfilter time: %f" % (time.time()-start)) |
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| 101 | except: |
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| 102 | pass |
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| 103 | |
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| 104 | #print('tsval diff mean: %f' % numpy.mean(differences(db, 'long', 'tsval'))) |
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| 105 | #print('tsval null diff mean: %f' % numpy.mean(null_differences(db, 'long', 'tsval'))) |
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| 106 | #print('tsval diff weighted mean: %f' % tsvalwmean(db.subseries('train','long')+db.subseries('test','long'))) |
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| 107 | #print('tsval null diff weighted mean: %f' % tsvalwmean(db.subseries('train_null','long'))) |
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| 108 | |
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| 109 | basicStatistics() |
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| 110 | |
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| 111 | def exampleBoxTestHistogram(low,high): |
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| 112 | num_bins = 300 |
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| 113 | all = db.subseries('train',unusual_case)+db.subseries('test',unusual_case) |
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| 114 | s = [s['other_packet'] for s in all] |
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| 115 | l = [s['unusual_packet'] for s in all] |
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| 116 | |
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| 117 | s_low,s_high = numpy.percentile(s, (low,high)) |
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| 118 | l_low,l_high = numpy.percentile(l, (low,high)) |
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| 119 | |
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| 120 | s.sort() |
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| 121 | cut_off_low = s[int(len(diffs)*0.002)] |
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| 122 | cut_off_high = s[int(len(diffs)*0.998)] |
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| 123 | |
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| 124 | plt.clf() |
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| 125 | # the histogram of the data |
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| 126 | #n, bins, patches = plt.hist(s, num_bins, normed=1, color='blue', histtype='step', alpha=0.8, |
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| 127 | # label='Test Case 1') |
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| 128 | #n, bins, patches = plt.hist(l, num_bins, normed=1, color='red', histtype='step', alpha=0.8, |
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| 129 | # label='Test Case 2') |
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| 130 | # |
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| 131 | n, bins, patches = plt.hist((s,l), num_bins, normed=1, color=('blue','red'), histtype='step', alpha=0.8, |
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| 132 | label=('Test Case 1','Test Case 2'), range=(cut_off_low,cut_off_high)) |
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| 133 | |
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| 134 | from matplotlib.patches import FancyBboxPatch |
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| 135 | currentAxis = plt.gca() |
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| 136 | currentAxis.add_patch(FancyBboxPatch((s_low, 0), s_high-s_low, 0.0001, boxstyle='square', facecolor="blue", alpha=0.4)) |
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| 137 | currentAxis.add_patch(FancyBboxPatch((l_low, 0), l_high-l_low, 0.0001, boxstyle='square', facecolor="red", alpha=0.4)) |
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| 138 | |
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| 139 | |
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| 140 | plt.xlabel('RTT Difference') |
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| 141 | plt.ylabel('Probability') |
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| 142 | #plt.title(r'Box Test Example - Overlapping Boxes') |
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| 143 | |
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| 144 | # Tweak spacing to prevent clipping of ylabel |
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| 145 | plt.subplots_adjust(left=0.15) |
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| 146 | plt.legend() |
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| 147 | plt.show() |
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| 148 | #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') |
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| 149 | |
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| 150 | |
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| 151 | #exampleBoxTestHistogram(6,8) |
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| 152 | |
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| 153 | |
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| 154 | def testKalman4D(params=None): |
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| 155 | from pykalman import KalmanFilter |
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| 156 | train = db.subseries('train','long', offset=0) |
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| 157 | test = db.subseries('test','long', offset=0) |
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| 158 | null = db.subseries('train_null','long', offset=0) |
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| 159 | measurements = numpy.asarray([(s['unusual_packet'],s['other_packet'],s['unusual_tsval'],s['other_tsval']) for s in (train+test)]) |
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| 160 | null_measurements = numpy.asarray([(s['unusual_packet'],s['other_packet'],s['unusual_tsval'],s['other_tsval']) for s in null]) |
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| 161 | |
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| 162 | if params == None: |
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| 163 | kf = KalmanFilter(n_dim_obs=4, n_dim_state=4, |
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| 164 | initial_state_mean=[quadsummary([s['unusual_packet'] for s in train]), |
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| 165 | quadsummary([s['other_packet'] for s in train]), |
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| 166 | numpy.mean([s['unusual_tsval'] for s in train]), |
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| 167 | numpy.mean([s['other_tsval'] for s in train])]) |
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| 168 | kf = KalmanFilter(n_dim_obs=4, n_dim_state=4) |
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| 169 | |
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| 170 | start=time.time() |
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| 171 | kf = kf.em(measurements[0:len(train)]+null_measurements[0:50000], n_iter=10, |
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| 172 | em_vars=('transition_matrices', |
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| 173 | 'observation_matrices', |
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| 174 | 'transition_offsets', |
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| 175 | 'observation_offsets', |
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| 176 | 'transition_covariance', |
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| 177 | 'observation_covariance', |
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| 178 | 'initial_state_mean', |
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| 179 | 'initial_state_covariance')) |
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| 180 | params = {'transition_matrices': kf.transition_matrices.tolist(), |
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| 181 | 'observation_matrices': kf.observation_matrices.tolist(), |
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| 182 | 'transition_offsets': kf.transition_offsets.tolist(), |
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| 183 | 'observation_offsets': kf.observation_offsets.tolist(), |
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| 184 | 'transition_covariance': kf.transition_covariance.tolist(), |
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| 185 | 'observation_covariance': kf.observation_covariance.tolist(), |
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| 186 | 'initial_state_mean': kf.initial_state_mean.tolist(), |
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| 187 | 'initial_state_covariance': kf.initial_state_covariance.tolist()} |
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| 188 | print("Learned Params:\n") |
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| 189 | import pprint |
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| 190 | pprint.pprint(params) |
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| 191 | print("pykalman em time: %f" % (time.time()-start)) |
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| 192 | |
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| 193 | #kf = KalmanFilter(n_dim_obs=2, n_dim_state=2, **params) |
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| 194 | |
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| 195 | num_obs=5000 |
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| 196 | for offset in range(50000,100000+num_obs,num_obs): |
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| 197 | start=time.time() |
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| 198 | m = measurements[offset:offset+num_obs] |
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| 199 | #params['initial_state_mean']=[quadsummary([s[0] for s in m]), |
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| 200 | # quadsummary([s[1] for s in m]), |
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| 201 | # numpy.mean([s[2] for s in m]), |
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| 202 | # numpy.mean([s[3] for s in m])] |
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| 203 | kf = KalmanFilter(n_dim_obs=4, n_dim_state=4, **params) |
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| 204 | (smoothed_state_means, smoothed_state_covariances) = kf.smooth(m) |
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| 205 | #print("pykalman smooth time: %f" % (time.time()-start)) |
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| 206 | up = numpy.mean([m[0] for m in smoothed_state_means]) |
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| 207 | op = numpy.mean([m[1] for m in smoothed_state_means]) |
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| 208 | #print("packet_rtt pykalman final:", smoothed_state_means[-1][0]-smoothed_state_means[-1][1]) |
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| 209 | print("packet_rtt pykalman mean:", up-op) |
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| 210 | print("packet_rtt mean:", numpy.mean([s[0]-s[1] for s in m])) |
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| 211 | #up = numpy.mean([m[2] for m in smoothed_state_means]) |
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| 212 | #op = numpy.mean([m[3] for m in smoothed_state_means]) |
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| 213 | #print("tsval_rtt pykalman final:", smoothed_state_means[-1][2]-smoothed_state_means[-1][3]) |
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| 214 | #print("tsval_rtt pykalman mean:", up-op) |
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| 215 | #print("tsval_rtt mean:", numpy.mean([s[2]-s[3] for s in m])) |
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| 216 | |
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| 217 | for offset in range(0,len(null_measurements)+num_obs,num_obs): |
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| 218 | start=time.time() |
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| 219 | m = null_measurements[offset:offset+num_obs] |
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| 220 | #params['initial_state_mean']=[quadsummary([s[0] for s in m]), |
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| 221 | # quadsummary([s[1] for s in m]), |
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| 222 | # numpy.mean([s[2] for s in m]), |
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| 223 | # numpy.mean([s[3] for s in m])] |
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| 224 | kf = KalmanFilter(n_dim_obs=4, n_dim_state=4, **params) |
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| 225 | (smoothed_state_means, smoothed_state_covariances) = kf.smooth(m) |
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| 226 | up = numpy.mean([m[0] for m in smoothed_state_means]) |
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| 227 | op = numpy.mean([m[1] for m in smoothed_state_means]) |
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| 228 | #print("null packet_rtt pykalman final:", smoothed_state_means[-1][0]-smoothed_state_means[-1][1]) |
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| 229 | print("null packet_rtt pykalman mean:", up-op) |
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| 230 | print("null packet_rtt mean:", numpy.mean([s[0]-s[1] for s in m])) |
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| 231 | #up = numpy.mean([m[2] for m in smoothed_state_means]) |
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| 232 | #op = numpy.mean([m[3] for m in smoothed_state_means]) |
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| 233 | #print("null tsval_rtt pykalman final:", smoothed_state_means[-1][2]-smoothed_state_means[-1][3]) |
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| 234 | #print("null tsval_rtt pykalman mean:", up-op) |
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| 235 | #print("null tsval_rtt mean:", numpy.mean([s[2]-s[3] for s in m])) |
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| 236 | |
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| 237 | |
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| 238 | #testKalman4D(echo_vm_5k) |
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| 239 | |
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| 240 | |
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| 241 | |
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| 242 | def testKalman(params=None): |
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| 243 | from pykalman import AdditiveUnscentedKalmanFilter,KalmanFilter |
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| 244 | train = db.subseries('train','long', offset=0) |
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| 245 | test = db.subseries('test','long', offset=0) |
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| 246 | measurements = numpy.asarray([(s['unusual_packet'],s['other_packet']) for s in (train+test)]) |
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| 247 | |
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| 248 | #kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) |
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| 249 | kf = KalmanFilter(n_dim_obs=2, n_dim_state=2, |
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| 250 | initial_state_mean=[quadsummary([s['unusual_packet'] for s in train]), |
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| 251 | quadsummary([s['other_packet'] for s in train])]) |
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| 252 | #kf = AdditiveUnscentedKalmanFilter(n_dim_obs=2, n_dim_state=2) |
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| 253 | |
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| 254 | if params == None: |
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| 255 | start=time.time() |
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| 256 | kf = kf.em(measurements[0:len(train)], n_iter=10, |
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| 257 | em_vars=('transition_matrices', |
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| 258 | 'observation_matrices', |
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| 259 | 'transition_offsets', |
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| 260 | 'observation_offsets', |
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| 261 | 'transition_covariance', |
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| 262 | 'observation_covariance', |
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| 263 | 'initial_state_covariance')) |
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| 264 | params = {'transition_matrices': kf.transition_matrices.tolist(), |
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| 265 | 'observation_matrices': kf.observation_matrices.tolist(), |
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| 266 | 'transition_offsets': kf.transition_offsets.tolist(), |
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| 267 | 'observation_offsets': kf.observation_offsets.tolist(), |
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| 268 | 'transition_covariance': kf.transition_covariance.tolist(), |
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| 269 | 'observation_covariance': kf.observation_covariance.tolist(), |
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| 270 | 'initial_state_mean': kf.initial_state_mean.tolist(), |
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| 271 | 'initial_state_covariance': kf.initial_state_covariance.tolist()} |
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| 272 | print("Learned Params:\n") |
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| 273 | import pprint |
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| 274 | pprint.pprint(params) |
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| 275 | print("pykalman em time: %f" % (time.time()-start)) |
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| 276 | |
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| 277 | #kf = KalmanFilter(n_dim_obs=2, n_dim_state=2, **params) |
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| 278 | |
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| 279 | num_obs=10000 |
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| 280 | for offset in range(50000,100000+num_obs,num_obs): |
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| 281 | start=time.time() |
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| 282 | kf = KalmanFilter(n_dim_obs=2, n_dim_state=2, **params) |
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| 283 | m = measurements[offset:offset+num_obs] |
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| 284 | (smoothed_state_means, smoothed_state_covariances) = kf.smooth(m) |
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| 285 | print("pykalman smooth time: %f" % (time.time()-start)) |
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| 286 | up = numpy.mean([m[0] for m in smoothed_state_means]) |
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| 287 | op = numpy.mean([m[1] for m in smoothed_state_means]) |
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| 288 | print("packet_rtt pykalman final:", smoothed_state_means[-1][0]-smoothed_state_means[-1][1]) |
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| 289 | print("packet_rtt pykalman mean:", up-op) |
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| 290 | print("packet_rtt mean:", numpy.mean([s[0]-s[1] for s in m])) |
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| 291 | |
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| 292 | |
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| 293 | #testKalman(ten_iter) |
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| 294 | |
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| 295 | |
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| 296 | def getTCPTSPrecision(): |
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| 297 | cursor = db.conn.cursor() |
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| 298 | query="""SELECT tcpts_mean FROM meta""" |
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| 299 | cursor.execute(query) |
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| 300 | row = cursor.fetchone() |
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| 301 | if row: |
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| 302 | return row[0] |
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| 303 | return None |
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| 304 | |
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| 305 | |
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| 306 | def tsFilteredHistogram(): |
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| 307 | tcpts_precision = getTCPTSPrecision() |
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| 308 | |
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| 309 | num_bins = 500 |
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| 310 | all = db.subseries('train','long')+db.subseries('test','long') |
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| 311 | diffs = [s['unusual_packet']-s['other_packet'] for s in all] |
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| 312 | ts0_diffs = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']-s['other_tsval'] == 0] |
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| 313 | #ts1_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(s['unusual_tsval']-s['other_tsval']) > 0] |
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| 314 | #ts2_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision)) <= 1.0] |
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| 315 | ts1_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(int(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision))) == 1] |
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| 316 | ts2_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(int(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision))) >= 2] |
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| 317 | #ts3_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(int(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision))) == 3] |
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| 318 | #ts4_diffs = [s['unusual_packet']-s['other_packet'] for s in all if abs(int(round((s['unusual_tsval']-s['other_tsval'])/tcpts_precision))) == 4] |
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| 319 | |
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| 320 | #ts_mode = statistics.mode([s['unusual_tsval'] for s in all]+[s['other_tsval'] for s in all]) |
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| 321 | #ts_diff_mode = statistics.mode([s['unusual_tsval']-s['other_tsval'] for s in all]) |
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| 322 | #ts_common_mode = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']<=ts_mode and s['other_tsval']<=ts_mode] |
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| 323 | #ts_common_diff_mode = [s['unusual_packet']-s['other_packet'] for s in all if s['unusual_tsval']-s['other_tsval']==ts_diff_mode] |
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| 324 | |
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| 325 | #print('packet_rtt diff quadsummary: %f' % quadsummary(diffs)) |
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| 326 | #print('packet_rtt tsval diff=0 quadsummary: %f' % quadsummary(ts0_diffs)) |
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| 327 | #print('packet_rtt tsval diff>0 quadsummary: %f' % quadsummary(ts1_diffs)) |
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| 328 | #print('packet_rtt tsval diff<=1 quadsummary: %f' % quadsummary(ts2_diffs)) |
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| 329 | #print('packet_rtt tsval mode quadsummary: %f' % quadsummary(ts_common_mode)) |
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| 330 | #print(len(diffs), len(ts0_diffs)+len(ts1_diffs)) |
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| 331 | diffs.sort() |
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| 332 | cut_off_low = diffs[int(len(diffs)*0.008)] |
|---|
| 333 | cut_off_high = diffs[int(len(diffs)*0.992)] |
|---|
| 334 | |
|---|
| 335 | plt.clf() |
|---|
| 336 | # the histogram of the data |
|---|
| 337 | n, bins, patches = plt.hist(diffs, num_bins, normed=0, color='black', histtype='step', alpha=0.8, |
|---|
| 338 | range=(cut_off_low,cut_off_high), label='All Packets') |
|---|
| 339 | n, bins, patches = plt.hist(ts0_diffs, num_bins, normed=0, color='blue', histtype='step', alpha=0.8, |
|---|
| 340 | range=(cut_off_low,cut_off_high), label='TSval Difference == 0') |
|---|
| 341 | n, bins, patches = plt.hist(ts1_diffs, num_bins, normed=0, color='orange', histtype='step', alpha=0.8, |
|---|
| 342 | range=(cut_off_low,cut_off_high), label='TSval Difference == 1') |
|---|
| 343 | n, bins, patches = plt.hist(ts2_diffs, num_bins, normed=0, color='red', histtype='step', alpha=0.8, |
|---|
| 344 | range=(cut_off_low,cut_off_high), label='TSval Difference >= 2') |
|---|
| 345 | #n, bins, patches = plt.hist(ts3_diffs, num_bins, normed=0, color='red', histtype='step', alpha=0.8, |
|---|
| 346 | # range=(cut_off_low,cut_off_high), label='tsval diff == 3') |
|---|
| 347 | #n, bins, patches = plt.hist(ts4_diffs, num_bins, normed=0, color='brown', histtype='step', alpha=0.8, |
|---|
| 348 | # range=(cut_off_low,cut_off_high), label='tsval diff == 4') |
|---|
| 349 | #n, bins, patches = plt.hist(ts_common_mode, num_bins, normed=0, color='green', histtype='step', alpha=0.8, |
|---|
| 350 | # range=(cut_off_low,cut_off_high), label='tsval common mode') |
|---|
| 351 | #n, bins, patches = plt.hist(ts_common_diff_mode, num_bins, normed=0, color='green', histtype='step', alpha=0.8, |
|---|
| 352 | # range=(cut_off_low,cut_off_high), label='tsval common diff mode') |
|---|
| 353 | plt.xlabel('RTT Difference') |
|---|
| 354 | #plt.ylabel('Probability') |
|---|
| 355 | #plt.title(r'Histogram - distribution of differences by tsval') |
|---|
| 356 | |
|---|
| 357 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 358 | plt.subplots_adjust(left=0.15) |
|---|
| 359 | plt.legend() |
|---|
| 360 | plt.show() |
|---|
| 361 | #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') |
|---|
| 362 | |
|---|
| 363 | tsFilteredHistogram() |
|---|
| 364 | |
|---|
| 365 | |
|---|
| 366 | def exampleSummaryHistogram(): |
|---|
| 367 | num_bins = 300 |
|---|
| 368 | all = db.subseries('train','long')+db.subseries('test','long') |
|---|
| 369 | diffs = [s['unusual_packet']-s['other_packet'] for s in all] |
|---|
| 370 | |
|---|
| 371 | diffs.sort() |
|---|
| 372 | cut_off_low = diffs[int(len(diffs)*0.003)] |
|---|
| 373 | cut_off_high = diffs[int(len(diffs)*0.997)] |
|---|
| 374 | |
|---|
| 375 | plt.clf() |
|---|
| 376 | # the histogram of the data |
|---|
| 377 | n, bins, patches = plt.hist(diffs, num_bins, normed=0, color='black', histtype='step', alpha=0.8, |
|---|
| 378 | range=(cut_off_low,cut_off_high), label='all') |
|---|
| 379 | |
|---|
| 380 | plt.xlabel('RTT Difference') |
|---|
| 381 | plt.ylabel('Probability') |
|---|
| 382 | #plt.title(r'Histogram - distribution of differences by tsval') |
|---|
| 383 | |
|---|
| 384 | w = 25 |
|---|
| 385 | l1,r1,l2,r2,l3,r3 = numpy.percentile(diffs, (50-w,50+w,50-w/2,50+w/2,(50-w)/2,(50+w)/2+50)) |
|---|
| 386 | #plt.plot([l1, 0], [l1, 0.0001], "k--") |
|---|
| 387 | #plt.plot([r1, 0], [r1, 0.0001], "k--") |
|---|
| 388 | from matplotlib.patches import FancyBboxPatch |
|---|
| 389 | currentAxis = plt.gca() |
|---|
| 390 | currentAxis.add_patch(FancyBboxPatch((l1, 0), 2500, 5000, boxstyle='square', facecolor="blue", alpha=0.4, edgecolor='none')) |
|---|
| 391 | currentAxis.add_patch(FancyBboxPatch((r1, 0), 2500, 5000, boxstyle='square', facecolor="blue", alpha=0.4, edgecolor='none')) |
|---|
| 392 | currentAxis.add_patch(FancyBboxPatch((l2, 0), 2500, 5000, boxstyle='square', facecolor="green", alpha=0.4, edgecolor='none')) |
|---|
| 393 | currentAxis.add_patch(FancyBboxPatch((r2, 0), 2500, 5000, boxstyle='square', facecolor="green", alpha=0.4, edgecolor='none')) |
|---|
| 394 | currentAxis.add_patch(FancyBboxPatch((l3, 0), 2500, 5000, boxstyle='square', facecolor="green", alpha=0.4, edgecolor='none')) |
|---|
| 395 | currentAxis.add_patch(FancyBboxPatch((r3, 0), 2500, 5000, boxstyle='square', facecolor="green", alpha=0.4, edgecolor='none')) |
|---|
| 396 | currentAxis.add_patch(FancyBboxPatch((50, 0), 2500, 5000, boxstyle='square', facecolor="black", alpha=0.4, edgecolor='none')) |
|---|
| 397 | currentAxis.add_patch(FancyBboxPatch((numpy.mean((l1,r1,l2,r2)), 0), 2500, 5000, boxstyle='square', facecolor="red", alpha=0.4, edgecolor='none')) |
|---|
| 398 | #currentAxis.add_patch(FancyBboxPatch((numpy.mean((1000)), 0), 1500, 5000, boxstyle='square', facecolor="black", alpha=0.4, edgecolor='none')) |
|---|
| 399 | |
|---|
| 400 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 401 | plt.subplots_adjust(left=0.15) |
|---|
| 402 | #plt.legend() |
|---|
| 403 | plt.show() |
|---|
| 404 | #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') |
|---|
| 405 | |
|---|
| 406 | #exampleSummaryHistogram() |
|---|
| 407 | |
|---|
| 408 | |
|---|
| 409 | |
|---|
| 410 | #all_data = longs+shorts |
|---|
| 411 | #all_data.sort() |
|---|
| 412 | #cut_off_low = all_data[0] |
|---|
| 413 | #cut_off_high = all_data[int(len(all_data)*0.997)] |
|---|
| 414 | |
|---|
| 415 | |
|---|
| 416 | def plotSingleProbe(probe_id=None): |
|---|
| 417 | if probe_id == None: |
|---|
| 418 | cursor = db.conn.cursor() |
|---|
| 419 | query="""SELECT probe_id FROM analysis WHERE suspect='' ORDER BY probe_id DESC limit 1 OFFSET 10""" |
|---|
| 420 | cursor.execute(query) |
|---|
| 421 | probe_id = cursor.fetchone()[0] |
|---|
| 422 | |
|---|
| 423 | cursor = db.conn.cursor() |
|---|
| 424 | query="""SELECT observed,payload_len FROM packets WHERE probe_id=? AND sent=1""" |
|---|
| 425 | cursor.execute(query, (probe_id,)) |
|---|
| 426 | pkts = cursor.fetchall() |
|---|
| 427 | sent_payload = [row[0] for row in pkts if row[1] != 0] |
|---|
| 428 | sent_other = [row[0] for row in pkts if row[1] == 0] |
|---|
| 429 | |
|---|
| 430 | query="""SELECT observed,payload_len FROM packets WHERE probe_id=? AND sent=0""" |
|---|
| 431 | cursor.execute(query, (probe_id,)) |
|---|
| 432 | pkts = cursor.fetchall() |
|---|
| 433 | rcvd_payload = [row[0] for row in pkts if row[1] != 0] |
|---|
| 434 | rcvd_other = [row[0] for row in pkts if row[1] == 0] |
|---|
| 435 | |
|---|
| 436 | #query="""SELECT reported,time_of_day FROM probes WHERE id=?""" |
|---|
| 437 | #cursor.execute(query, (probe_id,)) |
|---|
| 438 | #reported,tod = cursor.fetchone() |
|---|
| 439 | #userspace_times = [sent_times[0]-reported/3.0, sent_times[0]+reported] |
|---|
| 440 | |
|---|
| 441 | print("single probe counts:",len(sent_payload),len(sent_other),len(rcvd_payload),len(rcvd_other)) |
|---|
| 442 | plt.clf() |
|---|
| 443 | plt.title("Single HTTP Request - Packet Times") |
|---|
| 444 | sp = plt.eventplot(sent_payload, colors=('red',), lineoffsets=8, linewidths=2, alpha=0.6,label='sent') |
|---|
| 445 | so = plt.eventplot(sent_other, colors=('red',), lineoffsets=6, linewidths=2, alpha=0.6,label='sent') |
|---|
| 446 | rp = plt.eventplot(rcvd_payload, colors=('blue',), lineoffsets=4, linewidths=2, alpha=0.6,label='received') |
|---|
| 447 | ro = plt.eventplot(rcvd_other, colors=('blue',), lineoffsets=2, linewidths=2, alpha=0.6,label='received') |
|---|
| 448 | #plt.legend((s,r), ('sent','received')) |
|---|
| 449 | #plt.savefig('../img/http-packet-times.svg') |
|---|
| 450 | plt.show() |
|---|
| 451 | |
|---|
| 452 | #plotSingleProbe() |
|---|
| 453 | |
|---|
| 454 | |
|---|
| 455 | def graphTestResults(): |
|---|
| 456 | basename = os.path.basename(options.db_file) |
|---|
| 457 | basename,ext = os.path.splitext(basename) |
|---|
| 458 | |
|---|
| 459 | chartname = "/home/tim/blindspot/research/timing-analysis/paper/figures/results/%s.svg" % (basename) |
|---|
| 460 | #print(chartname) |
|---|
| 461 | |
|---|
| 462 | plt.clf() |
|---|
| 463 | plt.title("Test Results") |
|---|
| 464 | plt.xlabel('sample size') |
|---|
| 465 | plt.ylabel('error rate') |
|---|
| 466 | legend = [] |
|---|
| 467 | colors = ['red','blue','green','purple','orange','black','brown'] |
|---|
| 468 | color_id = 0 |
|---|
| 469 | |
|---|
| 470 | best_obs,best_error = evaluateTestResults(db) |
|---|
| 471 | best_obs = sorted(best_obs, key=lambda x: x['num_observations']) |
|---|
| 472 | best_error = sorted(best_error, key=lambda x: x['error']) |
|---|
| 473 | winner = None |
|---|
| 474 | for bo in best_obs: |
|---|
| 475 | sys.stdout.write("%(num_observations)d obs / %(classifier)s / %(params)s" % bo) |
|---|
| 476 | if winner == None: |
|---|
| 477 | sys.stdout.write(" (winner)") |
|---|
| 478 | winner = bo |
|---|
| 479 | print() |
|---|
| 480 | |
|---|
| 481 | for be in best_error: |
|---|
| 482 | sys.stdout.write("%(error)f%% error / %(classifier)s / %(params)s" % be) |
|---|
| 483 | if winner == None: |
|---|
| 484 | sys.stdout.write(" (winner)") |
|---|
| 485 | winner = be |
|---|
| 486 | print() |
|---|
| 487 | |
|---|
| 488 | all = sorted(best_obs+best_error, key=lambda x: x['classifier']) |
|---|
| 489 | max_obs = 0 |
|---|
| 490 | for result in all: |
|---|
| 491 | query=""" |
|---|
| 492 | SELECT num_observations,(false_positives+false_negatives)/2.0 FROM classifier_results |
|---|
| 493 | WHERE trial_type='test' |
|---|
| 494 | AND classifier=:classifier |
|---|
| 495 | AND params=:params |
|---|
| 496 | ORDER BY num_observations |
|---|
| 497 | """ |
|---|
| 498 | cursor = db.conn.cursor() |
|---|
| 499 | cursor.execute(query, {'classifier':result['classifier'],'params':result['params']}) |
|---|
| 500 | |
|---|
| 501 | num_obs = [] |
|---|
| 502 | performance = [] |
|---|
| 503 | for row in cursor: |
|---|
| 504 | max_obs = max(max_obs, row[0]) |
|---|
| 505 | num_obs.append(row[0]) |
|---|
| 506 | performance.append(row[1]) |
|---|
| 507 | #print(num_obs,performance) |
|---|
| 508 | path = plt.scatter(num_obs, performance, color=colors[color_id], s=4, alpha=0.8, linewidths=3.0) |
|---|
| 509 | plt.plot(num_obs, performance, color=colors[color_id], alpha=0.8) |
|---|
| 510 | legend.append((result['classifier'],path)) |
|---|
| 511 | color_id = (color_id+1) % len(colors) |
|---|
| 512 | |
|---|
| 513 | plt.legend([l[1] for l in legend], [l[0] for l in legend], scatterpoints=1, fontsize='x-small') |
|---|
| 514 | plt.plot([0, max_obs], [5.0, 5.0], "k--") |
|---|
| 515 | plt.xlabel('Number of Observations') |
|---|
| 516 | plt.ylabel('Error Rate') |
|---|
| 517 | #plt.savefig(chartname) |
|---|
| 518 | plt.show() |
|---|
| 519 | |
|---|
| 520 | graphTestResults() |
|---|
| 521 | |
|---|
| 522 | sys.exit(0) |
|---|
| 523 | |
|---|
| 524 | short_overtime = [(sample['time_of_day'],sample['short']) for sample in timeSeries(db,'train','short')] |
|---|
| 525 | long_overtime = [(sample['time_of_day'],sample['long']) for sample in timeSeries(db,'train','long')] |
|---|
| 526 | diff_overtime = [(sample['time_of_day'],sample['long']-sample['other_cases']) for sample in timeSeries(db,'train','long')] |
|---|
| 527 | short_overtime.sort() |
|---|
| 528 | long_overtime.sort() |
|---|
| 529 | diff_overtime.sort() |
|---|
| 530 | |
|---|
| 531 | plt.clf() |
|---|
| 532 | plt.title("Packet RTT over time") |
|---|
| 533 | plt.xlabel('Time of Day') |
|---|
| 534 | plt.ylabel('RTT') |
|---|
| 535 | s = plt.scatter([t for t,rtt in short_overtime], [rtt for t,rtt in short_overtime], s=1, color='red', alpha=0.6) |
|---|
| 536 | l = plt.scatter([t for t,rtt in long_overtime], [rtt for t,rtt in long_overtime], s=1, color='blue', alpha=0.6) |
|---|
| 537 | d = plt.scatter([t for t,rtt in diff_overtime], [rtt for t,rtt in diff_overtime], s=1, color='purple', alpha=0.6) |
|---|
| 538 | plt.legend((s,l,d), ('short','long','difference'), scatterpoints=1) |
|---|
| 539 | #plt.savefig('paper/figures/comcast-powerboost1.png') |
|---|
| 540 | plt.show() |
|---|
| 541 | |
|---|
| 542 | |
|---|
| 543 | |
|---|
| 544 | plt.clf() |
|---|
| 545 | plt.title("Simple HTTP Request") |
|---|
| 546 | plt.xlabel('Time of Day') |
|---|
| 547 | plt.ylabel('') |
|---|
| 548 | s = plt.scatter(sent_times, [2]*len(sent_times), s=3, color='red', alpha=0.9) |
|---|
| 549 | r = plt.scatter(rcvd_times, [1]*len(rcvd_times), s=3, color='blue', alpha=0.9) |
|---|
| 550 | plt.legend((s,r), ('sent','received'), scatterpoints=1) |
|---|
| 551 | plt.show() |
|---|
| 552 | |
|---|
| 553 | sys.exit(0) |
|---|
| 554 | short_overtime,long_overtime,diff_overtime = None,None,None |
|---|
| 555 | |
|---|
| 556 | |
|---|
| 557 | num_bins = 300 |
|---|
| 558 | reported_diffs.sort() |
|---|
| 559 | cut_off_low = reported_diffs[int(len(diffs)*0.003)] |
|---|
| 560 | cut_off_high = reported_diffs[int(len(diffs)*0.997)] |
|---|
| 561 | |
|---|
| 562 | plt.clf() |
|---|
| 563 | # the histogram of the data |
|---|
| 564 | n, bins, patches = plt.hist(reported_diffs, num_bins, normed=1, color='black', histtype='step', alpha=0.8, |
|---|
| 565 | range=(cut_off_low,cut_off_high)) |
|---|
| 566 | plt.xlabel('RTT Difference') |
|---|
| 567 | plt.ylabel('Probability') |
|---|
| 568 | plt.title(r'Histogram - distribution of differences') |
|---|
| 569 | |
|---|
| 570 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 571 | plt.subplots_adjust(left=0.15) |
|---|
| 572 | #plt.legend() |
|---|
| 573 | plt.show() |
|---|
| 574 | #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') |
|---|
| 575 | |
|---|
| 576 | |
|---|
| 577 | |
|---|
| 578 | |
|---|
| 579 | num_bins = 300 |
|---|
| 580 | diffs.sort() |
|---|
| 581 | cut_off_low = diffs[int(len(diffs)*0.003)] |
|---|
| 582 | cut_off_high = diffs[int(len(diffs)*0.997)] |
|---|
| 583 | |
|---|
| 584 | plt.clf() |
|---|
| 585 | # the histogram of the data |
|---|
| 586 | n, bins, patches = plt.hist(diffs, num_bins, normed=1, color='purple', histtype='step', alpha=0.8, |
|---|
| 587 | range=(cut_off_low,cut_off_high)) |
|---|
| 588 | plt.xlabel('RTT Difference') |
|---|
| 589 | plt.ylabel('Probability') |
|---|
| 590 | plt.title(r'Histogram - distribution of differences') |
|---|
| 591 | |
|---|
| 592 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 593 | plt.subplots_adjust(left=0.15) |
|---|
| 594 | #plt.legend() |
|---|
| 595 | plt.show() |
|---|
| 596 | #plt.savefig('paper/graphs/dists-vs-dist-of-diffs2.svg') |
|---|
| 597 | |
|---|
| 598 | sys.exit(0) |
|---|
| 599 | |
|---|
| 600 | |
|---|
| 601 | |
|---|
| 602 | num_bins = 150 |
|---|
| 603 | # the histogram of the data |
|---|
| 604 | n, bins, patches = plt.hist((shorts,longs), num_bins, normed=1, label=['short', 'long'], color=['red','blue'], histtype='step', alpha=0.8, |
|---|
| 605 | range=(cut_off_low,cut_off_high)) |
|---|
| 606 | #n, bins, patches = plt.hist(shorts2+longs2, num_bins, normed=1, facecolor='blue', histtype='step', alpha=0.3) |
|---|
| 607 | # add a 'best fit' line |
|---|
| 608 | #y = mlab.normpdf(bins, mu, sigma) |
|---|
| 609 | #plt.plot(bins, y, 'r--') |
|---|
| 610 | plt.xlabel('packet_rtt') |
|---|
| 611 | plt.ylabel('Probability') |
|---|
| 612 | plt.title(r'Histogram - RTT short and long') |
|---|
| 613 | |
|---|
| 614 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 615 | plt.subplots_adjust(left=0.15) |
|---|
| 616 | plt.legend() |
|---|
| 617 | #plt.show() |
|---|
| 618 | plt.savefig('paper/figures/comcast-powerboost2.svg') |
|---|
| 619 | |
|---|
| 620 | |
|---|
| 621 | |
|---|
| 622 | |
|---|
| 623 | num_trials = 200 |
|---|
| 624 | |
|---|
| 625 | |
|---|
| 626 | subsample_sizes = (50,150,300,500,700,1000,2000,3000,5000,7000,10000,15000,20000) |
|---|
| 627 | estimator = functools.partial(boxTest, 0.07, 0.08) |
|---|
| 628 | performance = [] |
|---|
| 629 | for subsample_size in subsample_sizes: |
|---|
| 630 | estimates = bootstrap(derived, subsample_size, num_trials, estimator) |
|---|
| 631 | performance.append(100.0*len([e for e in estimates if e == 1])/num_trials) |
|---|
| 632 | |
|---|
| 633 | null_performance = [] |
|---|
| 634 | for subsample_size in subsample_sizes: |
|---|
| 635 | null_estimates = bootstrap(null_derived, subsample_size, num_trials, estimator) |
|---|
| 636 | null_performance.append(100.0*len([e for e in null_estimates if e == 0])/num_trials) |
|---|
| 637 | |
|---|
| 638 | plt.clf() |
|---|
| 639 | plt.title("boxTest bootstrap") |
|---|
| 640 | plt.xlabel('sample size') |
|---|
| 641 | plt.ylabel('performance') |
|---|
| 642 | plt.scatter(subsample_sizes, performance, s=2, color='red', alpha=0.6) |
|---|
| 643 | plt.scatter(subsample_sizes, null_performance, s=2, color='blue', alpha=0.6) |
|---|
| 644 | plt.show() |
|---|
| 645 | |
|---|
| 646 | |
|---|
| 647 | |
|---|
| 648 | subsample_sizes = (50,150,300,400,500,700,1000,2000,3000,4000,5000,7000,10000) |
|---|
| 649 | estimator = diffMedian |
|---|
| 650 | performance = [] |
|---|
| 651 | for subsample_size in subsample_sizes: |
|---|
| 652 | estimates = bootstrap(derived, subsample_size, num_trials, estimator) |
|---|
| 653 | performance.append(100.0*len([e for e in estimates if e > expected_mean*0.9 and e < expected_mean*1.1])/num_trials) |
|---|
| 654 | |
|---|
| 655 | plt.clf() |
|---|
| 656 | plt.title("diff median bootstrap") |
|---|
| 657 | plt.xlabel('sample size') |
|---|
| 658 | plt.ylabel('performance') |
|---|
| 659 | plt.scatter(subsample_sizes, performance, s=1, color='red', alpha=0.6) |
|---|
| 660 | plt.show() |
|---|
| 661 | |
|---|
| 662 | |
|---|
| 663 | |
|---|
| 664 | |
|---|
| 665 | subsample_sizes = (50,150,300,400,500,700,1000,2000,3000,4000,5000,7000,10000) |
|---|
| 666 | weight_funcs = (linearWeights, prunedWeights) |
|---|
| 667 | for wf in weight_funcs: |
|---|
| 668 | estimator = functools.partial(estimateMean, hypotenuse, wf, 0.40) |
|---|
| 669 | performance = [] |
|---|
| 670 | for subsample_size in subsample_sizes: |
|---|
| 671 | estimates = bootstrap(derived, subsample_size, num_trials, estimator) |
|---|
| 672 | performance.append(100.0*len([e for e in estimates if e > expected_mean*0.9 and e < expected_mean*1.1])/num_trials) |
|---|
| 673 | |
|---|
| 674 | plt.clf() |
|---|
| 675 | plt.title(repr(wf)) |
|---|
| 676 | plt.xlabel('sample size') |
|---|
| 677 | plt.ylabel('performance') |
|---|
| 678 | plt.scatter(subsample_sizes, performance, s=1, color='red', alpha=0.6) |
|---|
| 679 | plt.show() |
|---|
| 680 | |
|---|
| 681 | |
|---|
| 682 | |
|---|
| 683 | num_bins = 300 |
|---|
| 684 | # the histogram of the data |
|---|
| 685 | n, bins, patches = plt.hist((tsshorts,tslongs), num_bins, normed=1, label=['short', 'long'], color=['red','blue'], histtype='step', alpha=0.8) |
|---|
| 686 | #n, bins, patches = plt.hist(shorts2+longs2, num_bins, normed=1, facecolor='blue', histtype='step', alpha=0.3) |
|---|
| 687 | # add a 'best fit' line |
|---|
| 688 | #y = mlab.normpdf(bins, mu, sigma) |
|---|
| 689 | #plt.plot(bins, y, 'r--') |
|---|
| 690 | plt.xlabel('packet_rtt') |
|---|
| 691 | plt.ylabel('Probability') |
|---|
| 692 | plt.title(r'Histogram - tsval_rtt short vs long') |
|---|
| 693 | |
|---|
| 694 | # Tweak spacing to prevent clipping of ylabel |
|---|
| 695 | plt.subplots_adjust(left=0.15) |
|---|
| 696 | plt.legend() |
|---|
| 697 | plt.show() |
|---|
| 698 | |
|---|
| 699 | |
|---|
| 700 | |
|---|
| 701 | |
|---|
| 702 | #### |
|---|
| 703 | #trust_methods = [min,max,sum,difference,product] |
|---|
| 704 | trust_methods = [sum,product,hypotenuse] |
|---|
| 705 | colors = ['red','blue','green','purple','orange','black'] |
|---|
| 706 | weight_methods = [prunedWeights, linearWeights] |
|---|
| 707 | alphas = [i/100.0 for i in range(0,100,2)] |
|---|
| 708 | |
|---|
| 709 | |
|---|
| 710 | |
|---|
| 711 | |
|---|
| 712 | plt.clf() |
|---|
| 713 | plt.title(r'Trust Method Comparison - Linear') |
|---|
| 714 | plt.xlabel('Alpha') |
|---|
| 715 | plt.ylabel('Mean error') |
|---|
| 716 | paths = [] |
|---|
| 717 | for tm in trust_methods: |
|---|
| 718 | trust = trustValues(derived, tm) |
|---|
| 719 | series = [] |
|---|
| 720 | for alpha in alphas: |
|---|
| 721 | weights = linearWeights(derived, trust, alpha) |
|---|
| 722 | series.append(weightedMean(derived, weights) - expected_mean) |
|---|
| 723 | |
|---|
| 724 | paths.append(plt.scatter(alphas, series, s=1, color=colors[len(paths)],alpha=0.6)) |
|---|
| 725 | |
|---|
| 726 | plt.legend(paths, [repr(tm) for tm in trust_methods], scatterpoints=1) |
|---|
| 727 | plt.show() |
|---|
| 728 | |
|---|
| 729 | |
|---|
| 730 | |
|---|
| 731 | plt.clf() |
|---|
| 732 | plt.title(r'Trust Method Comparison - Pruned') |
|---|
| 733 | plt.xlabel('Alpha') |
|---|
| 734 | plt.ylabel('Mean error') |
|---|
| 735 | paths = [] |
|---|
| 736 | for tm in trust_methods: |
|---|
| 737 | trust = trustValues(derived, tm) |
|---|
| 738 | series = [] |
|---|
| 739 | for alpha in alphas: |
|---|
| 740 | weights = prunedWeights(derived, trust, alpha) |
|---|
| 741 | series.append(weightedMean(derived, weights) - expected_mean) |
|---|
| 742 | |
|---|
| 743 | paths.append(plt.scatter(alphas, series, s=1, color=colors[len(paths)],alpha=0.6)) |
|---|
| 744 | |
|---|
| 745 | plt.legend(paths, [repr(tm) for tm in trust_methods], scatterpoints=1) |
|---|
| 746 | plt.show() |
|---|
| 747 | |
|---|
| 748 | |
|---|
| 749 | sys.exit(0) |
|---|
| 750 | |
|---|
| 751 | plt.clf() |
|---|
| 752 | plt.title(r'Trust Method Comparison - Inverted') |
|---|
| 753 | plt.xlabel('Alpha') |
|---|
| 754 | plt.ylabel('Mean error') |
|---|
| 755 | paths = [] |
|---|
| 756 | for tm in trust_methods: |
|---|
| 757 | trust = trustValues(derived, tm) |
|---|
| 758 | series = [] |
|---|
| 759 | for alpha in alphas: |
|---|
| 760 | weights = invertedWeights(derived, trust, alpha) |
|---|
| 761 | series.append(weightedMean(derived, weights) - expected_mean) |
|---|
| 762 | |
|---|
| 763 | paths.append(plt.scatter(alphas, series, s=1, color=colors[len(paths)],alpha=0.6)) |
|---|
| 764 | |
|---|
| 765 | plt.legend(paths, [repr(tm) for tm in trust_methods], scatterpoints=1) |
|---|
| 766 | plt.show() |
|---|
| 767 | |
|---|
| 768 | |
|---|
| 769 | plt.clf() |
|---|
| 770 | plt.title(r'Trust Method Comparison - Arctangent') |
|---|
| 771 | plt.xlabel('Alpha') |
|---|
| 772 | plt.ylabel('Mean error') |
|---|
| 773 | paths = [] |
|---|
| 774 | for tm in trust_methods: |
|---|
| 775 | trust = trustValues(derived, tm) |
|---|
| 776 | series = [] |
|---|
| 777 | for alpha in alphas: |
|---|
| 778 | weights = arctanWeights(derived, trust, alpha) |
|---|
| 779 | series.append(weightedMean(derived, weights) - expected_mean) |
|---|
| 780 | |
|---|
| 781 | paths.append(plt.scatter(alphas, series, s=1, color=colors[len(paths)],alpha=0.6)) |
|---|
| 782 | |
|---|
| 783 | plt.legend(paths, [repr(tm) for tm in trust_methods], scatterpoints=1) |
|---|
| 784 | plt.show() |
|---|
