[4] | 1 | #!/usr/bin/env python3 |
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| 2 | #-*- mode: Python;-*- |
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| 3 | |
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| 4 | import sys |
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| 5 | import os |
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| 6 | import time |
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| 7 | import random |
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| 8 | import statistics |
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| 9 | import functools |
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| 10 | import argparse |
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| 11 | import threading |
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| 12 | import queue |
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| 13 | import pprint |
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| 14 | import json |
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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 | import nanownlib.storage |
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| 31 | from nanownlib.stats import boxTest,multiBoxTest,subsample,bootstrap,bootstrap2,trimean,midhinge,midhingeTest,samples2Distributions,samples2MeanDiffs |
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| 32 | |
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| 33 | parser = argparse.ArgumentParser( |
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| 34 | description="") |
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| 35 | #parser.add_argument('-c', dest='cases', type=str, default='{"short":10000,"long":1010000}', |
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| 36 | # help='JSON representation of echo timing cases. Default: {"short":10000,"long":1010000}') |
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| 37 | parser.add_argument('session_data', default=None, |
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| 38 | help='Database file storing session information') |
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| 39 | options = parser.parse_args() |
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| 40 | |
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| 41 | |
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| 42 | |
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| 43 | class WorkerThreads(object): |
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| 44 | workq = None |
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| 45 | resultq = None |
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| 46 | target = None |
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| 47 | |
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| 48 | def __init__(self, num_workers, target): |
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| 49 | self.workq = queue.Queue() |
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| 50 | self.resultq = queue.Queue() |
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| 51 | self.target = target |
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| 52 | |
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| 53 | self.workers = [] |
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| 54 | for i in range(num_workers): |
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| 55 | t = threading.Thread(target=self._worker) |
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| 56 | t.daemon = True |
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| 57 | t.start() |
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| 58 | self.workers.append(t) |
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| 59 | |
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| 60 | def _worker(self): |
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| 61 | while True: |
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| 62 | item = self.workq.get() |
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| 63 | if item == None: |
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| 64 | self.workq.task_done() |
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| 65 | break |
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| 66 | |
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| 67 | job_id,args = item |
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| 68 | self.resultq.put((job_id, self.target(*args))) |
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| 69 | self.workq.task_done() |
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| 70 | |
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| 71 | def addJob(self, job_id, args): |
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| 72 | self.workq.put((job_id, args)) |
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| 73 | |
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| 74 | def wait(self): |
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| 75 | self.workq.join() |
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| 76 | |
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| 77 | def stop(self): |
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| 78 | for i in range(0,len(workers)): |
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| 79 | self.workq.put(None) |
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| 80 | for w in self.workers: |
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| 81 | w.join() |
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| 82 | |
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| 83 | |
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| 84 | def trainBoxTest(db, test_cases, longest, subsample_size): |
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| 85 | |
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| 86 | def trainAux(low,high,num_trials): |
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| 87 | estimator = functools.partial(boxTest, {'low':low, 'high':high}) |
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| 88 | estimates = bootstrap(estimator, db, 'train', test_cases, subsample_size, num_trials) |
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| 89 | null_estimates = bootstrap(estimator, db, 'train_null', test_cases, subsample_size, num_trials) |
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| 90 | |
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| 91 | #XXX: need to have a configurable policy on what we're looking for. |
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| 92 | # which is longest or which is shortest? |
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| 93 | bad_estimates = len([e for e in estimates if e != longest]) |
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| 94 | bad_null_estimates = len([e for e in null_estimates if e != None]) |
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| 95 | |
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| 96 | false_negatives = 100.0*bad_estimates/num_trials |
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| 97 | false_positives = 100.0*bad_null_estimates/num_trials |
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| 98 | return false_positives,false_negatives |
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| 99 | |
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| 100 | start = time.time() |
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| 101 | wt = WorkerThreads(2, trainAux) |
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| 102 | |
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| 103 | width = 2.0 |
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| 104 | performance = [] |
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| 105 | percentiles = list(range(0,50,2)) |
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| 106 | for low in percentiles: |
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| 107 | wt.addJob(low, (low,low+width,200)) |
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| 108 | wt.wait() |
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| 109 | while not wt.resultq.empty(): |
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| 110 | job_id,errors = wt.resultq.get() |
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| 111 | fp,fn = errors |
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| 112 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 113 | performance.sort() |
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| 114 | pprint.pprint(performance) |
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| 115 | print(time.time()-start) |
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| 116 | |
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| 117 | lows = [p[1] for p in performance[0:5]] |
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| 118 | widths = [w/10.0 for w in range(0,105,5)] |
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| 119 | performance = [] |
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| 120 | for width in widths: |
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| 121 | false_positives = [] |
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| 122 | false_negatives = [] |
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| 123 | for low in lows: |
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| 124 | wt.addJob(low,(low,low+width,150)) |
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| 125 | wt.wait() |
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| 126 | while not wt.resultq.empty(): |
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| 127 | job_id,errors = wt.resultq.get() |
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| 128 | fp,fn = errors |
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| 129 | false_negatives.append(fn) |
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| 130 | false_positives.append(fp) |
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| 131 | |
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| 132 | #print(width, false_negatives) |
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| 133 | #print(width, false_positives) |
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| 134 | performance.append(((statistics.mean(false_positives)+statistics.mean(false_negatives))/2.0, |
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| 135 | width, statistics.mean(false_negatives), statistics.mean(false_positives))) |
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| 136 | performance.sort() |
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| 137 | pprint.pprint(performance) |
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| 138 | good_width = performance[0][1] |
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| 139 | print("good_width:",good_width) |
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| 140 | |
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| 141 | |
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| 142 | lc = {} |
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| 143 | for low in lows: |
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| 144 | if low-1 > 0: |
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| 145 | lc[low-1] = None |
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| 146 | lc[low] = None |
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| 147 | lc[low+1] = None |
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| 148 | lows = lc.keys() |
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| 149 | |
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| 150 | performance = [] |
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| 151 | for low in lows: |
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| 152 | wt.addJob(low, (low,low+good_width,300)) |
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| 153 | wt.wait() |
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| 154 | while not wt.resultq.empty(): |
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| 155 | job_id,errors = wt.resultq.get() |
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| 156 | fp,fn = errors |
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| 157 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 158 | performance.sort() |
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| 159 | pprint.pprint(performance) |
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| 160 | best_low = performance[0][1] |
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| 161 | print("best_low:", best_low) |
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| 162 | |
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| 163 | |
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| 164 | widths = [good_width-0.4,good_width-0.3,good_width-0.2,good_width-0.1, |
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| 165 | good_width,good_width+0.1,good_width+0.2,good_width+0.3,good_width+0.4] |
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| 166 | performance = [] |
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| 167 | for width in widths: |
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| 168 | wt.addJob(width, (best_low,best_low+width,200)) |
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| 169 | wt.wait() |
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| 170 | while not wt.resultq.empty(): |
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| 171 | job_id,errors = wt.resultq.get() |
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| 172 | fp,fn = errors |
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| 173 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 174 | performance.sort() |
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| 175 | pprint.pprint(performance) |
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| 176 | best_width=performance[0][1] |
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| 177 | print("best_width:",best_width) |
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| 178 | print("final_performance:", performance[0][0]) |
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| 179 | |
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| 180 | return {"low":best_low,"high":best_low+good_width} |
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| 181 | |
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| 182 | |
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| 183 | def trainBoxTest2(db, unusual_case, greater, subsample_size): |
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| 184 | |
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| 185 | def trainAux(low,high,num_trials): |
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| 186 | estimator = functools.partial(multiBoxTest, {'low':low, 'high':high}, unusual_case, greater) |
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| 187 | estimates = bootstrap2(estimator, db, 'train', subsample_size, num_trials) |
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| 188 | null_estimates = bootstrap2(estimator, db, 'train_null', subsample_size, num_trials) |
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| 189 | |
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| 190 | bad_estimates = len([e for e in estimates if e != 1]) |
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| 191 | bad_null_estimates = len([e for e in null_estimates if e != 0]) |
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| 192 | |
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| 193 | false_negatives = 100.0*bad_estimates/num_trials |
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| 194 | false_positives = 100.0*bad_null_estimates/num_trials |
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| 195 | return false_positives,false_negatives |
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| 196 | |
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| 197 | start = time.time() |
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| 198 | wt = WorkerThreads(2, trainAux) |
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| 199 | |
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| 200 | num_trials = 200 |
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| 201 | width = 2.0 |
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| 202 | performance = [] |
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| 203 | percentiles = list(range(0,50,2)) |
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| 204 | for low in percentiles: |
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| 205 | wt.addJob(low, (low,low+width,num_trials)) |
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| 206 | wt.wait() |
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| 207 | while not wt.resultq.empty(): |
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| 208 | job_id,errors = wt.resultq.get() |
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| 209 | fp,fn = errors |
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| 210 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 211 | performance.sort() |
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| 212 | pprint.pprint(performance) |
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| 213 | print(time.time()-start) |
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| 214 | |
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| 215 | num_trials = 150 |
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| 216 | lows = [p[1] for p in performance[0:5]] |
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| 217 | widths = [w/10.0 for w in range(0,105,5)] |
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| 218 | performance = [] |
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| 219 | for width in widths: |
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| 220 | false_positives = [] |
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| 221 | false_negatives = [] |
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| 222 | for low in lows: |
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| 223 | wt.addJob(low,(low,low+width,num_trials)) |
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| 224 | wt.wait() |
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| 225 | while not wt.resultq.empty(): |
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| 226 | job_id,errors = wt.resultq.get() |
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| 227 | fp,fn = errors |
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| 228 | false_negatives.append(fn) |
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| 229 | false_positives.append(fp) |
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| 230 | |
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| 231 | #print(width, false_negatives) |
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| 232 | #print(width, false_positives) |
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| 233 | performance.append(((statistics.mean(false_positives)+statistics.mean(false_negatives))/2.0, |
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| 234 | width, statistics.mean(false_negatives), statistics.mean(false_positives))) |
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| 235 | performance.sort() |
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| 236 | pprint.pprint(performance) |
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| 237 | good_width = performance[0][1] |
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| 238 | print("good_width:",good_width) |
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| 239 | |
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| 240 | |
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| 241 | lc = {} |
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| 242 | for low in lows: |
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| 243 | if low-1 >= 0: |
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| 244 | lc[low-1] = None |
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| 245 | lc[low] = None |
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| 246 | lc[low+1] = None |
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| 247 | lows = lc.keys() |
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| 248 | print("candidate lows:") |
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| 249 | pprint.pprint(lows) |
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| 250 | |
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| 251 | num_trials = 300 |
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| 252 | performance = [] |
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| 253 | for low in lows: |
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| 254 | wt.addJob(low, (low,low+good_width,num_trials)) |
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| 255 | wt.wait() |
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| 256 | while not wt.resultq.empty(): |
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| 257 | job_id,errors = wt.resultq.get() |
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| 258 | fp,fn = errors |
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| 259 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 260 | performance.sort() |
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| 261 | pprint.pprint(performance) |
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| 262 | best_low = performance[0][1] |
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| 263 | print("best_low:", best_low) |
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| 264 | |
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| 265 | num_trials = 200 |
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| 266 | widths = [good_width-0.4,good_width-0.3,good_width-0.2,good_width-0.1, |
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| 267 | good_width,good_width+0.1,good_width+0.2,good_width+0.3,good_width+0.4] |
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| 268 | performance = [] |
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| 269 | for width in widths: |
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| 270 | wt.addJob(width, (best_low,best_low+width,num_trials)) |
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| 271 | wt.wait() |
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| 272 | while not wt.resultq.empty(): |
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| 273 | job_id,errors = wt.resultq.get() |
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| 274 | fp,fn = errors |
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| 275 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 276 | performance.sort() |
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| 277 | pprint.pprint(performance) |
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| 278 | best_width=performance[0][1] |
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| 279 | print("best_width:",best_width) |
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| 280 | print("final_performance:", performance[0][0]) |
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| 281 | |
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| 282 | params = json.dumps({"low":best_low,"high":best_low+good_width}) |
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| 283 | return {'algorithm':"boxtest", |
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| 284 | 'params':params, |
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| 285 | 'sample_size':subsample_size, |
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| 286 | 'num_trials':num_trials, |
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| 287 | 'trial_type':"train", |
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| 288 | 'false_positives':performance[0][3], |
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| 289 | 'false_negatives':performance[0][2]} |
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| 290 | |
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| 291 | |
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| 292 | def trainMidhinge(db, unusual_case, greater, subsample_size): |
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| 293 | |
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| 294 | def trainAux(distance, threshold, num_trials): |
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| 295 | estimator = functools.partial(midhingeTest, {'distance':distance,'threshold':threshold}, unusual_case, greater) |
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| 296 | estimates = bootstrap2(estimator, db, 'train', subsample_size, num_trials) |
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| 297 | null_estimates = bootstrap2(estimator, db, 'train_null', subsample_size, num_trials) |
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| 298 | |
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| 299 | bad_estimates = len([e for e in estimates if e != 1]) |
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| 300 | bad_null_estimates = len([e for e in null_estimates if e != 0]) |
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| 301 | |
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| 302 | false_negatives = 100.0*bad_estimates/num_trials |
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| 303 | false_positives = 100.0*bad_null_estimates/num_trials |
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| 304 | return false_positives,false_negatives |
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| 305 | |
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| 306 | #determine expected delta based on differences |
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| 307 | start = time.time() |
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| 308 | mean_diffs = list(samples2MeanDiffs(subsample(db, 'train'), 'packet_rtt', unusual_case)) |
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| 309 | threshold = trimean(mean_diffs)/2.0 |
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| 310 | print("initial threshold:", threshold) |
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| 311 | print("median threshold:", statistics.median(mean_diffs)/2.0) |
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| 312 | print("midhinge threshold:", midhinge(mean_diffs)/2.0) |
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| 313 | print("trimean threshold:", trimean(mean_diffs)/2.0) |
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| 314 | |
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| 315 | mean_diffs = list(samples2MeanDiffs(subsample(db, 'train_null'), 'packet_rtt', unusual_case)) |
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| 316 | print(len(mean_diffs)) |
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| 317 | print("null mean:", statistics.mean(mean_diffs)) |
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| 318 | print("null median:", statistics.median(mean_diffs)) |
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| 319 | print("null midhinge:", midhinge(mean_diffs)) |
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| 320 | print("null trimean:", trimean(mean_diffs)) |
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| 321 | print(time.time()-start) |
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| 322 | |
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| 323 | |
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| 324 | start = time.time() |
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| 325 | wt = WorkerThreads(1, trainAux) |
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| 326 | |
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| 327 | num_trials = 200 |
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| 328 | performance = [] |
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| 329 | #for distance in range(1,46,4): |
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| 330 | for distance in range(25,46,4): |
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| 331 | wt.addJob(distance, (distance,threshold,num_trials)) |
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| 332 | wt.wait() |
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| 333 | while not wt.resultq.empty(): |
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| 334 | job_id,errors = wt.resultq.get() |
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| 335 | fp,fn = errors |
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| 336 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 337 | performance.sort() |
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| 338 | pprint.pprint(performance) |
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| 339 | print(time.time()-start) |
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| 340 | good_distance = performance[0][1] |
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| 341 | print("good_distance:",good_distance) |
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| 342 | |
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| 343 | |
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| 344 | num_trials = 200 |
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| 345 | start = time.time() |
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| 346 | performance = [] |
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| 347 | for t in range(80,125,5): |
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| 348 | wt.addJob(threshold*(t/100.0), (good_distance,threshold*(t/100.0),num_trials)) |
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| 349 | wt.wait() |
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| 350 | while not wt.resultq.empty(): |
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| 351 | job_id,errors = wt.resultq.get() |
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| 352 | fp,fn = errors |
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| 353 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 354 | performance.sort() |
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| 355 | pprint.pprint(performance) |
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| 356 | print(time.time()-start) |
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| 357 | good_threshold = performance[0][1] |
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| 358 | print("good_threshold:", good_threshold) |
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| 359 | |
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| 360 | |
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| 361 | num_trials = 200 |
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| 362 | start = time.time() |
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| 363 | performance = [] |
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| 364 | for d in range(-4,5): |
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| 365 | wt.addJob(good_distance+d, (good_distance+d,good_threshold,num_trials)) |
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| 366 | wt.wait() |
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| 367 | while not wt.resultq.empty(): |
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| 368 | job_id,errors = wt.resultq.get() |
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| 369 | fp,fn = errors |
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| 370 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 371 | performance.sort() |
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| 372 | pprint.pprint(performance) |
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| 373 | print(time.time()-start) |
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| 374 | best_distance = performance[0][1] |
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| 375 | print("best_distance:",best_distance) |
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| 376 | |
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| 377 | num_trials = 200 |
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| 378 | start = time.time() |
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| 379 | performance = [] |
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| 380 | for t in range(95,106): |
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| 381 | wt.addJob(good_threshold*(t/100.0), (best_distance,good_threshold*(t/100.0),num_trials)) |
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| 382 | wt.wait() |
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| 383 | while not wt.resultq.empty(): |
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| 384 | job_id,errors = wt.resultq.get() |
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| 385 | fp,fn = errors |
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| 386 | performance.append(((fp+fn)/2.0, job_id, fn, fp)) |
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| 387 | performance.sort() |
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| 388 | pprint.pprint(performance) |
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| 389 | print(time.time()-start) |
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| 390 | best_threshold = performance[0][1] |
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| 391 | print("best_threshold:", best_threshold) |
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| 392 | |
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| 393 | params = json.dumps({'distance':best_distance,'threshold':best_threshold}) |
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| 394 | return {'algorithm':"midhinge", |
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| 395 | 'params':params, |
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| 396 | 'sample_size':subsample_size, |
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| 397 | 'num_trials':num_trials, |
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| 398 | 'trial_type':"train", |
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| 399 | 'false_positives':performance[0][3], |
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| 400 | 'false_negatives':performance[0][2]} |
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| 401 | |
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| 402 | |
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| 403 | #classifiers = {'boxtest':{'train':trainBoxTest2, 'test':multiBoxTest}, |
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| 404 | # 'midhinge':{'train':trainMidhinge, 'test':midhinge}} |
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| 405 | |
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| 406 | |
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| 407 | db = nanownlib.storage.db(options.session_data) |
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| 408 | #cursor = db.cursor() |
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| 409 | #cursor.execute("SELECT min(sample) min, max(sample) max FROM probes") |
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| 410 | #train_start,test_end = cursor.fetchone() |
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| 411 | #train_end = int(test_end-train_start) |
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| 412 | #test_start = train_end+1 |
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| 413 | #subsample_size = min(10000,(train_end-train_start+1)/4) |
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| 414 | |
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| 415 | start = time.time() |
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| 416 | unusual_case,unusual_diff = findUnusualTestCase(db) |
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| 417 | greater = (unusual_diff > 0) |
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| 418 | print("unusual_case:", unusual_case) |
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| 419 | print("unusual_diff:", unusual_diff) |
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| 420 | end = time.time() |
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| 421 | print(":", end-start) |
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| 422 | |
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| 423 | start = time.time() |
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| 424 | results = trainMidhinge(db, unusual_case, greater, 6000) |
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| 425 | db.addClassifierResults(results) |
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| 426 | print("midhinge result:", results) |
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| 427 | end = time.time() |
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| 428 | print(":", end-start) |
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| 429 | |
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| 430 | start = time.time() |
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| 431 | results = trainBoxTest2(db, unusual_case, greater, 6000) |
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| 432 | db.addClassifierResults(results) |
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| 433 | print("multi box test result:", results) |
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| 434 | end = time.time() |
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| 435 | print(":", end-start) |
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| 436 | |
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| 437 | #start = time.time() |
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| 438 | #print("box test params:", trainBoxTest(db, test_cases, 'long', 100)) |
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| 439 | #end = time.time() |
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| 440 | #print(":", end-start) |
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| 441 | |
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| 442 | |
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