Changeset 10 for trunk/lib/nanownlib


Ignore:
Timestamp:
07/13/15 19:16:30 (9 years ago)
Author:
tim
Message:

.

Location:
trunk/lib/nanownlib
Files:
3 edited

Legend:

Unmodified
Added
Removed
  • trunk/lib/nanownlib/__init__.py

    r6 r10  
    44import sys
    55import time
     6import traceback
    67import random
    78import argparse
     
    209210def removeDuplicatePackets(packets):
    210211    #return packets
    211     suspect = None
     212    suspect = ''
    212213    seen = {}
    213214    # XXX: Need to review this deduplication algorithm and make sure it is correct
    214215    for p in packets:
    215216        key = (p['sent'],p['tcpseq'],p['tcpack'],p['payload_len'])
    216         if (key not in seen)\
    217            or p['sent']==1 and (seen[key]['observed'] < p['observed'])\
    218            or p['sent']==0 and (seen[key]['observed'] > p['observed']):
    219             #if (key not in seen) or (seen[key]['observed'] > p['observed']):
     217        if (key not in seen):
    220218            seen[key] = p
    221    
    222     if len(seen) < len(packets):
    223         suspect = 'd'
    224         #sys.stderr.write("INFO: removed %d duplicate packets.\n" % (len(packets) - len(seen)))
     219            continue
     220        if p['sent']==1 and (seen[key]['observed'] > p['observed']): #earliest sent
     221            seen[key] = p
     222            suspect += 's'
     223            continue
     224        if p['sent']==0 and (seen[key]['observed'] > p['observed']): #earliest rcvd
     225            seen[key] = p
     226            suspect += 'r'
     227            continue
     228   
     229    #if len(seen) < len(packets):
     230    #   sys.stderr.write("INFO: removed %d duplicate packets.\n" % (len(packets) - len(seen)))
    225231
    226232    return suspect,seen.values()
     
    230236    suspect,packets = removeDuplicatePackets(packets)
    231237
    232     #sort_key = lambda d: (d['tcpseq'],d['tcpack'])
    233     sort_key = lambda d: (d['observed'],d['tcpseq'])
     238    sort_key = lambda d: (d['tcpseq'],d['observed'])
    234239    sent = sorted((p for p in packets if p['sent']==1 and p['payload_len']>0), key=sort_key)
    235240    rcvd = sorted((p for p in packets if p['sent']==0 and p['payload_len']>0), key=sort_key)
    236241
    237     if len(sent) <= trim_sent:
    238         last_sent = sent[-1]
    239     else:
    240         last_sent = sent[trim_sent]
    241 
    242     if len(rcvd) <= trim_rcvd:
    243         last_rcvd = rcvd[0]
    244     else:
    245         last_rcvd = rcvd[len(rcvd)-1-trim_rcvd]
     242    alt_key = lambda d: (d['observed'],d['tcpseq'])
     243    rcvd_alt = sorted((p for p in packets if p['sent']==0 and p['payload_len']>0), key=alt_key)
     244
     245    s_off = trim_sent
     246    if s_off >= len(sent):
     247        s_off = -1
     248    last_sent = sent[s_off]
     249
     250    r_off = len(rcvd) - trim_rcvd - 1
     251    if r_off <= 0:
     252        r_off = 0
     253    last_rcvd = rcvd[r_off]
     254    if last_rcvd != rcvd_alt[r_off]:
     255        suspect += 'R'
    246256   
    247257    packet_rtt = last_rcvd['observed'] - last_sent['observed']
     
    272282    query="""
    273283      SELECT packet_rtt-(SELECT avg(packet_rtt) FROM probes,trim_analysis
    274                          WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case!=:unusual_case AND sample=u.sample AND probes.type in ('train','test'))
    275       FROM (SELECT probes.sample,packet_rtt FROM probes,trim_analysis WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case=:unusual_case AND probes.type in ('train','test')) u
     284                         WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case!=:unusual_case AND sample=u.s AND probes.type in ('train','test'))
     285      FROM (SELECT probes.sample s,packet_rtt FROM probes,trim_analysis WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case=:unusual_case AND probes.type in ('train','test') AND 1 NOT IN (select 1 from probes p,trim_analysis t WHERE p.sample=s AND t.probe_id=p.id AND t.suspect LIKE '%R%')) u
     286    """
     287    query="""
     288      SELECT packet_rtt-(SELECT avg(packet_rtt) FROM probes,trim_analysis
     289                         WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case!=:unusual_case AND sample=u.s AND probes.type in ('train','test'))
     290      FROM (SELECT probes.sample s,packet_rtt FROM probes,trim_analysis WHERE sent_trimmed=:strim AND rcvd_trimmed=:rtrim AND trim_analysis.probe_id=probes.id AND probes.test_case=:unusual_case AND probes.type in ('train','test')) u
    276291    """
    277292
     
    280295    differences = [row[0] for row in cursor]
    281296   
    282     return trimean(differences),mad(differences)
     297    return ubersummary(differences),mad(differences)
    283298
    284299
     
    287302    db.conn.execute("CREATE INDEX IF NOT EXISTS packets_probe ON packets (probe_id)")
    288303    pcursor = db.conn.cursor()
    289     kcursor = db.conn.cursor()
     304    db.conn.commit()
    290305
    291306    pcursor.execute("SELECT tcpts_mean FROM meta")
     
    297312    pcursor.execute("DELETE FROM trim_analysis")
    298313    db.conn.commit()
     314
     315    def loadPackets(db):
     316        cursor = db.conn.cursor()
     317        cursor.execute("SELECT * FROM packets ORDER BY probe_id")
     318
     319        probe_id = None
     320        entry = []
     321        ret_val = []
     322        for p in cursor:
     323            if probe_id == None:
     324                probe_id = p['probe_id']
     325            if p['probe_id'] != probe_id:
     326                ret_val.append((probe_id,entry))
     327                probe_id = p['probe_id']
     328                entry = []
     329            entry.append(dict(p))
     330        ret_val.append((probe_id,entry))
     331        return ret_val
     332   
     333    start = time.time()
     334    packet_cache = loadPackets(db)
     335    print("packets loaded in: %f" % (time.time()-start))
    299336   
    300337    count = 0
    301338    sent_tally = []
    302339    rcvd_tally = []
    303     for pid, in pcursor.execute("SELECT id FROM probes"):
    304         kcursor.execute("SELECT * FROM packets WHERE probe_id=?", (pid,))
     340    for probe_id,packets in packet_cache:
    305341        try:
    306             analysis,s,r = analyzePackets(kcursor.fetchall(), timestamp_precision)
    307             analysis['probe_id'] = pid
     342            analysis,s,r = analyzePackets(packets, timestamp_precision)
     343            analysis['probe_id'] = probe_id
    308344            sent_tally.append(s)
    309345            rcvd_tally.append(r)
     346            db.addTrimAnalyses([analysis])
    310347        except Exception as e:
    311             print(e)
    312             sys.stderr.write("WARN: couldn't find enough packets for probe_id=%s\n" % pid)
     348            traceback.print_exc()
     349            sys.stderr.write("WARN: couldn't find enough packets for probe_id=%s\n" % probe_id)
    313350       
    314351        #print(pid,analysis)
    315         db.addTrimAnalyses([analysis])
    316352        count += 1
    317353    db.conn.commit()
     
    326362            if strim == 0 and rtrim == 0:
    327363                continue # no point in doing 0,0 again
    328             for pid, in pcursor.execute("SELECT id FROM probes"):
    329                 kcursor.execute("SELECT * FROM packets WHERE probe_id=?", (pid,))
     364            for probe_id,packets in packet_cache:
    330365                try:
    331                     analysis,s,r = analyzePackets(kcursor.fetchall(), timestamp_precision, strim, rtrim)
    332                     analysis['probe_id'] = pid
     366                    analysis,s,r = analyzePackets(packets, timestamp_precision, strim, rtrim)
     367                    analysis['probe_id'] = probe_id
    333368                except Exception as e:
    334369                    print(e)
     
    336371                   
    337372                db.addTrimAnalyses([analysis])
    338             db.conn.commit()
     373    db.conn.commit()
    339374
    340375    # Populate analysis table so findUnusualTestCase can give us a starting point
     
    359394    for strim in range(1,num_sent):
    360395        delta,mad = evaluations[(strim,0)]
    361         if abs(good_delta - delta) < abs(delta_margin*good_delta) and mad < good_mad:
     396        if delta*good_delta > 0.0 and (abs(good_delta) - abs(delta)) < abs(delta_margin*good_delta) and mad < good_mad:
    362397            best_strim = strim
    363398        else:
     
    367402    for rtrim in range(1,num_rcvd):
    368403        delta,mad = evaluations[(best_strim,rtrim)]
    369         if (abs(delta) > abs(good_delta) or abs(good_delta - delta) < abs(delta_margin*good_delta)) and mad < good_mad:
     404        if delta*good_delta > 0.0 and (abs(good_delta) - abs(delta)) < abs(delta_margin*good_delta) and mad < good_mad:
    370405            best_rtrim = rtrim
    371406        else:
     
    425460    cursor = db.conn.cursor()
    426461    cursor.execute("SELECT packet_rtt FROM probes,analysis WHERE probes.id=analysis.probe_id AND probes.type in ('train','test')")
    427     global_tm = trimean([row['packet_rtt'] for row in cursor])
     462    global_tm = quadsummary([row['packet_rtt'] for row in cursor])
    428463
    429464    tm_abs = []
     
    432467    for tc in test_cases:
    433468        cursor.execute("SELECT packet_rtt FROM probes,analysis WHERE probes.id=analysis.probe_id AND probes.type in ('train','test') AND probes.test_case=?", (tc,))
    434         tm_map[tc] = trimean([row['packet_rtt'] for row in cursor])
     469        tm_map[tc] = quadsummary([row['packet_rtt'] for row in cursor])
    435470        tm_abs.append((abs(tm_map[tc]-global_tm), tc))
    436471
    437472    magnitude,tc = max(tm_abs)
    438473    cursor.execute("SELECT packet_rtt FROM probes,analysis WHERE probes.id=analysis.probe_id AND probes.type in ('train','test') AND probes.test_case<>?", (tc,))
    439     remaining_tm = trimean([row['packet_rtt'] for row in cursor])
     474    remaining_tm = quadsummary([row['packet_rtt'] for row in cursor])
    440475
    441476    ret_val = (tc, tm_map[tc]-remaining_tm)
  • trunk/lib/nanownlib/stats.py

    r8 r10  
    22import sys
    33import os
     4import functools
    45import math
    56import statistics
     
    133134
    134135
    135 def midhinge(values, distance=25):
    136     return (numpy.percentile(values, 50-distance) + numpy.percentile(values, 50+distance))/2.0
     136def midsummary(values, distance=25):
     137    #return (numpy.percentile(values, 50-distance) + numpy.percentile(values, 50+distance))/2.0
     138    l,h = numpy.percentile(values, (50-distance,50+distance))
     139    return (l+h)/2.0
    137140
    138141def trimean(values, distance=25):
    139     return (midhinge(values, distance) + statistics.median(values))/2
    140 
     142    return (midsummary(values, distance) + statistics.median(values))/2
     143
     144def ubersummary(values, distance=25):
     145    left2 = 50-distance
     146    left1 = left2/2.0
     147    left3 = (left2+50)/2.0
     148    right2 = 50+distance
     149    right3 = (right2+50)/2.0
     150    right1 = (right2+100)/2.0
     151    l1,l2,l3,r3,r2,r1 = numpy.percentile(values, (left1,left2,left3,right3,right2,right1))
     152    #print(left1,left2,left3,50,right3,right2,right1)
     153    #print(l1,l2,l3,m,r3,r2,r1)
     154    return (l1+l2*4+l3+r3+r2*4+r1)/12.0
     155    #return statistics.mean((l1,l2,l3,m,r3,r2,r1))
     156
     157def quadsummary(values, distance=25):
     158    left2 = 50-distance
     159    left1 = left2/2.0
     160    right2 = 50+distance
     161    right1 = (right2+100)/2.0
     162    l1,l2,r2,r1 = numpy.percentile(values, (left1,left2,right2,right1))
     163    #print(left1,left2,left3,50,right3,right2,right1)
     164    #print(l1,l2,l3,m,r3,r2,r1)
     165    return (l1+l2+r2+r1)/4.0
     166    #return statistics.mean((l1,l2,l3,m,r3,r2,r1))
     167
     168def quadsummary(values, distance=25):
     169    left1 = 50-distance
     170    left2 = (left1+50)/2.0
     171    right1 = 50+distance
     172    right2 = (right1+50)/2.0
     173    l1,l2,r2,r1 = numpy.percentile(values, (left1,left2,right2,right1))
     174    #print(left1,left2,left3,50,right3,right2,right1)
     175    #print(l1,l2,l3,m,r3,r2,r1)
     176    return (l1+l2+r2+r1)/4.0
     177    #return statistics.mean((l1,l2,l3,m,r3,r2,r1))
     178
     179   
    141180def weightedMean(derived, weights):
    142181    normalizer = sum(weights.values())/len(weights)
     
    170209
    171210
    172 def estimateMidhinge(derived):
    173     return midhinge([(d['long']-d['short']) for d in derived.values()])
     211def estimateMidsummary(derived):
     212    return midsummary([(d['long']-d['short']) for d in derived.values()])
    174213
    175214
     
    348387    rest = [s['other_cases'] for s in samples]
    349388   
    350     uc_high = numpy.percentile(uc, params['high'])
    351     rest_low = numpy.percentile(rest, params['low'])
     389    uc_high,uc_low = numpy.percentile(uc, (params['high'],params['low']))
     390    rest_high,rest_low = numpy.percentile(rest, (params['high'],params['low']))
    352391    if uc_high < rest_low:
    353392        if greater:
     
    356395            return 1
    357396
    358     uc_low = numpy.percentile(uc, params['low'])
    359     rest_high = numpy.percentile(rest, params['high'])
    360397    if rest_high < uc_low:
    361398        if greater:
     
    369406# Returns 1 if unusual_case is unusual in the expected direction
    370407#         0 otherwise
    371 def midhingeTest(params, greater, samples):
     408def summaryTest(f, params, greater, samples):
    372409    diffs = [s['unusual_case']-s['other_cases'] for s in samples]
    373410
    374     mh = midhinge(diffs, params['distance'])
    375     #mh = trimean(diffs, params['distance'])
     411    mh = f(diffs, params['distance'])
    376412    if greater:
    377413        if mh > params['threshold']:
     
    385421            return 0
    386422
     423midsummaryTest = functools.partial(summaryTest, midsummary)
     424trimeanTest = functools.partial(summaryTest, trimean)
     425ubersummaryTest = functools.partial(summaryTest, ubersummary)
     426quadsummaryTest = functools.partial(summaryTest, quadsummary)
    387427
    388428def rmse(expected, measurements):
     
    392432def nrmse(expected, measurements):
    393433    return rmse(expected, measurements)/(max(measurements)-min(measurements))
     434
     435
     436class KalmanFilter1D:
     437    def __init__(self, x0, P, R, Q):
     438        self.x = x0
     439        self.P = P
     440        self.R = R
     441        self.Q = Q
     442
     443    def update(self, z):
     444        self.x = (self.P * z + self.x * self.R) / (self.P + self.R)
     445        self.P = 1. / (1./self.P + 1./self.R)
     446
     447    def predict(self, u=0.0):
     448        self.x += u
     449        self.P += self.Q
     450
     451
     452def kfilter(params, observations):
     453    x = numpy.array(observations)
     454    movement = 0
     455    est = []   
     456    var = []
     457    kf = KalmanFilter1D(x0 = quadsummary(x), # initial state
     458                        #P  = 10000,          # initial variance
     459                        P  = 10,          # initial variance
     460                        R  = numpy.std(x),   # msensor noise
     461                        Q  = 0)              # movement noise
     462    for round in range(1):
     463        for d in x:
     464            kf.predict(movement)
     465            kf.update(d)
     466            est.append(kf.x)
     467            var.append(kf.P)
     468
     469    return({'est':est, 'var':var})
     470
     471
     472def kalmanTest(params, greater, samples):
     473    diffs = [s['unusual_case']-s['other_cases'] for s in samples]
     474
     475    m = kfilter(params, diffs)['est'][-1]
     476    if greater:
     477        if m > params['threshold']:
     478            return 1
     479        else:
     480            return 0
     481    else:
     482        if m < params['threshold']:
     483            return 1
     484        else:
     485            return 0
     486
     487
     488def kalmanTest2(params, greater, samples):
     489    diffs = [s['unusual_case']-s['other_cases'] for s in samples]
     490
     491    estimates = []
     492    size = 500
     493    for i in range(100):
     494        off = random.randrange(0,len(diffs))
     495        sub = diffs[off:size]
     496        if len(sub) < size:
     497            sub += diffs[0:size-len(sub)]
     498        estimates.append(kfilter(params, sub)['est'][-1])
     499           
     500    m = quadsummary(estimates)
     501    if greater:
     502        if m > params['threshold']:
     503            return 1
     504        else:
     505            return 0
     506    else:
     507        if m < params['threshold']:
     508            return 1
     509        else:
     510            return 0
     511
  • trunk/lib/nanownlib/storage.py

    r9 r10  
    44import os
    55import uuid
     6import random
    67import threading
    78import sqlite3
    89
    910import numpy
     11# Don't trust numpy's seeding
     12numpy.random.seed(random.SystemRandom().randint(0,2**32-1))
    1013
    1114def _newid():
     
    1821    _population_sizes = None
    1922    _population_cache = None
     23    _offset_cache = None
     24    _cur_offsets = None
    2025   
    2126    def __init__(self, path):
     
    2631        self._population_sizes = {}
    2732        self._population_cache = {}
     33        self._offset_cache = {}
     34        self._cur_offsets = {}
    2835       
    2936        if not exists:
     
    7986            self.conn.execute(
    8087                """CREATE TABLE classifier_results (id BLOB PRIMARY KEY,
    81                                                     algorithm TEXT,
     88                                                    classifier TEXT,
     89                                                    trial_type TEXT,
     90                                                    num_observations INTEGER,
     91                                                    num_trials INTEGER,
    8292                                                    params TEXT,
    83                                                     sample_size INTEGER,
    84                                                     num_trials INTEGER,
    85                                                     trial_type TEXT,
    8693                                                    false_positives REAL,
    8794                                                    false_negatives REAL)
     
    109116
    110117    def subseries(self, probe_type, unusual_case, size=None, offset=None, field='packet_rtt'):
    111         if (probe_type,unusual_case,field) not in self._population_cache:
     118        cache_key = (probe_type,unusual_case,field)
     119       
     120        if cache_key not in self._population_cache:
    112121            query="""
    113122            SELECT %(field)s AS unusual_case,
     
    121130            cursor = self.conn.cursor()
    122131            cursor.execute(query, params)
    123             self._population_cache[(probe_type,unusual_case,field)] = [dict(row) for row in cursor.fetchall()]
    124 
    125         population = self._population_cache[(probe_type,unusual_case,field)]
     132            p = [dict(row) for row in cursor.fetchall()]
     133            self._population_cache[cache_key] = p
     134            self._offset_cache[cache_key] = tuple(numpy.random.random_integers(0,len(p)-1, len(p)/5))
     135            self._cur_offsets[cache_key] = 0
     136
     137        population = self._population_cache[cache_key]
    126138
    127139        if size == None or size > len(population):
    128140            size = len(population)
    129141        if offset == None or offset >= len(population) or offset < 0:
    130             offset = numpy.random.random_integers(0,len(population)-1)
    131 
     142            offset = self._offset_cache[cache_key][self._cur_offsets[cache_key]]
     143            self._cur_offsets[cache_key] = (offset + 1) % len(self._offset_cache[cache_key])
     144       
    132145        try:
    133             ret_val = population[offset:offset+size]
     146            offset = int(offset)
     147            size = int(size)
    134148        except Exception as e:
    135149            print(e, offset, size)
    136            
     150            return None
     151       
     152        ret_val = population[offset:offset+size]
    137153        if len(ret_val) < size:
    138154            ret_val += population[0:size-len(ret_val)]
    139155       
    140156        return ret_val
    141 
    142    
     157   
     158   
     159    def resetOffsets(self):
     160        for k in self._cur_offsets.keys():
     161            self._cur_offsets[k] = 0
     162
     163           
    143164    def clearCache(self):
    144165        self._population_cache = {}
     166        self._offset_cache = {}
     167        self._cur_offsets = {}
    145168
    146169       
     
    188211        self.conn.commit()
    189212        return ret_val
     213
     214    def fetchClassifierResult(self, classifier, trial_type, num_observations):
     215        query = """
     216          SELECT * FROM classifier_results
     217          WHERE classifier=? AND trial_type=? AND num_observations=?
     218          ORDER BY false_positives+false_negatives
     219          LIMIT 1;
     220        """
     221        cursor = self.conn.cursor()
     222        cursor.execute(query, (classifier, trial_type, num_observations))
     223        ret_val = cursor.fetchone()
     224
     225        if ret_val != None:
     226            ret_val = dict(ret_val)
     227        return ret_val
     228
     229    def deleteClassifierResults(self, classifier, trial_type, num_observations=None):
     230        params = {"classifier":classifier,"trial_type":trial_type,"num_observations":num_observations}
     231        query = """
     232          DELETE FROM classifier_results
     233          WHERE classifier=:classifier AND trial_type=:trial_type
     234        """
     235        if num_observations != None:
     236            query += " AND num_observations=:num_observations"
     237       
     238        self.conn.execute(query, params)
     239        self.conn.commit()
     240       
Note: See TracChangeset for help on using the changeset viewer.