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manager.py
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manager.py
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from __future__ import print_function
from properties import Properties
from kliep import Kliep
from ensemble import Ensemble
from stream import Stream
from sklearn import svm, grid_search
import time, sys, datetime
import numpy as np
import random, math
import gaussianModel as gm
#from py4j.java_gateway import JavaGateway, GatewayParameters, CallbackServerParameters
class Manager(object):
def __init__(self, sourceFile, targetFile):
self.SDataBufferArr = None #2D array representation of self.SDataBuffer
self.SDataLabels = None
self.TDataBufferArr = None #2D array representation of self.TDataBuffer
self.TDataLabels = None
self.useKliepCVSigma = Properties.useKliepCVSigma
self.kliep = None
self.useSvmCVParams = Properties.useSvmCVParams
self.ensemble = Ensemble(Properties.ENSEMBLE_SIZE)
self.initialWindowSize = int(Properties.INITIAL_DATA_SIZE)
self.maxWindowSize = int(Properties.MAX_WINDOW_SIZE)
self.enableForceUpdate = int(Properties.enableForceUpdate)
self.forceUpdatePeriod = int(Properties.forceUpdatePeriod)
"""
- simulate source and target streams from corresponding files.
"""
print("Reading the Source Dataset")
self.source = Stream(sourceFile, Properties.INITIAL_DATA_SIZE)
print("Reading the Target Dataset")
self.target = Stream(targetFile, Properties.INITIAL_DATA_SIZE)
print("Finished Reading the Target Dataset")
Properties.MAXVAR = self.source.initialData.shape[0]
"""
Detect drift on a given data stream.
Returns the change point index on the stream array.
"""
def __detectDrift(self, slidingWindow, flagStream):
changePoint = -1
if flagStream == 0:
changePoint = self.changeDetector.detectSourceChange(slidingWindow)
elif flagStream == 1:
changePoint = self.changeDetector.detectTargetChange(slidingWindow)
else:
raise Exception('flagStream var has value ' + str(flagStream) + ' that is not supported.')
return changePoint
"""
Write value (accuracy or confidence) to a file with DatasetName as an identifier.
"""
def __saveResult(self, acc, datasetName):
with open(datasetName + '_' + Properties.OUTFILENAME, 'a') as f:
f.write(str(acc) + "\n")
f.close()
def convListOfDictToNDArray(self, listOfDict):
arrayRep = []
if not listOfDict:
return arrayRep
arrayRep = np.array([[float(v)] for k,v in listOfDict[0].items() if k!=-1])
for i in range(1, len(listOfDict)):
arrayRep = np.append(arrayRep, np.array([[float(v)] for k,v in listOfDict[i].items() if k!=-1]), axis=1)
return arrayRep
def collectLabels(self, listOfDict):
labels = []
for d in listOfDict:
labels.append(str(d[-1]))
return labels
"""
The main method handling multistream classification using KLIEP.
"""
def startFusion(self, datasetName, probFromSource):
#save the timestamp
globalStartTime = time.time()
Properties.logger.info('Global Start Time: ' + datetime.datetime.fromtimestamp(globalStartTime).strftime('%Y-%m-%d %H:%M:%S'))
#open files for saving accuracy and confidence
fAcc = open(datasetName + '_' + Properties.OUTFILENAME, 'w')
fConf = open(datasetName + '_confidence' + '_' + Properties.OUTFILENAME, 'w')
#initialize gaussian models
gmOld = gm.GaussianModel()
gmUpdated = gm.GaussianModel()
#variable to track forceupdate period
idxLastUpdate = 0
#Get data buffer
self.SDataBufferArr = self.source.initialData
self.SDataLabels = self.source.initialDataLabels
self.TDataBufferArr = self.target.initialData
#first choose a suitable value for sigma
self.kliep = Kliep(Properties.kliepParEta, Properties.kliepParLambda, Properties.kliepParB, Properties.kliepParThreshold, Properties.kliepDefSigma)
#self.kliep = Kliep(Properties.kliepParEta, Properties.kliepParLambda, Properties.kliepParB, Properties.MAXVAR*Properties.kliepParThreshold, Properties.kliepDefSigma)
if self.useKliepCVSigma==1:
self.kliep.kliepDefSigma = self.kliep.chooseSigma(self.SDataBufferArr, self.TDataBufferArr)
#calculate alpha values
#self.kliep.kliepDefSigma = 0.1
Properties.logger.info('Estimating initial DRM')
gmOld.alphah, kernelMatSrcData, kernelMatTrgData, gmOld.refPoints = self.kliep.KLIEP(self.SDataBufferArr, self.TDataBufferArr)
#initialize the updated gaussian model
gmUpdated.setAlpha(gmOld.alphah)
gmUpdated.setRefPoints(gmOld.refPoints)
#now resize the windows appropriately
self.SDataBufferArr = self.SDataBufferArr[:, -Properties.MAX_WINDOW_SIZE:]
self.SDataLabels = self.SDataLabels[-Properties.MAX_WINDOW_SIZE:]
self.TDataBufferArr = self.TDataBufferArr[:, -Properties.MAX_WINDOW_SIZE:]
kernelMatSrcData = kernelMatSrcData[-Properties.MAX_WINDOW_SIZE:,:]
kernelMatTrgData = kernelMatTrgData[-Properties.MAX_WINDOW_SIZE:,:]
#meanDistSrcData = self.kliep.colWiseMeanTransposed(kernelMatSrcData)
Properties.logger.info('Initializing Ensemble with the first model')
#target model
#first calculate weight for source instances
weightSrcData = self.kliep.calcInstanceWeights(kernelMatSrcData, gmUpdated.alphah)
#since weightSrcData is a column matrix, convert it to a list before sending to generating new model
SDataBufferArrTransposed = self.SDataBufferArr.T
TDataBufferArrTransposed = self.TDataBufferArr.T
if self.useSvmCVParams == 1:
params = {'gamma': [2 ** 2, 2 ** -16], 'C': [2 ** -6, 2 ** 15]}
svr = svm.SVC()
opt = grid_search.GridSearchCV(svr, params)
opt.fit(SDataBufferArrTransposed.tolist(), self.SDataLabels)
optParams = opt.best_params_
self.ensemble.generateNewModelKLIEP(SDataBufferArrTransposed, self.SDataLabels,
TDataBufferArrTransposed, weightSrcData[0].tolist(),
optParams['C'], optParams['gamma'])
else:
self.ensemble.generateNewModelKLIEP(SDataBufferArrTransposed.tolist(), self.SDataLabels,
TDataBufferArrTransposed.tolist(), weightSrcData[0].tolist(),
Properties.svmDefC, Properties.svmDefGamma, Properties.svmKernel)
Properties.logger.info(self.ensemble.getEnsembleSummary())
sDataIndex = 0
tDataIndex = 0
trueTargetNum = 0
targetConfSum = 0
#enoughInstToUpdate is used to see if there are enough instances in the windows to
#estimate the weights
Properties.logger.info('Starting MultiStream Classification with FUSION')
while self.target.data.shape[1] > tDataIndex:
"""
if source stream is not empty, do proper sampling. Otherwise, just take
the new instance from the target isntance.
"""
if self.source.data.shape[1] > sDataIndex:
fromSource = random.uniform(0,1)<probFromSource
else:
print("\nsource stream sampling not possible")
fromSource = False
if fromSource:
# Source Stream: '.' means sampling from source
print('.', end="")
#print("Source data index: ", sDataIndex)
#print("\nlen(self.SDataBufferList) = ", len(self.SDataBufferList), ": source window slides")
#remove the first instance, and add the new instance in the buffers
newSrcDataArr = self.source.data[:, sDataIndex][np.newaxis].T
self.SDataBufferArr = self.SDataBufferArr[:, 1:]
self.SDataLabels = self.SDataLabels[1:]
kernelMatSrcData = kernelMatSrcData[1:, :]
#add new instance to the buffers
self.SDataBufferArr = np.append(self.SDataBufferArr, newSrcDataArr, axis=1)
self.SDataLabels.append(self.source.dataLabels[sDataIndex])
#update kernelMatSrcData
dist_tmp = np.power(np.tile(newSrcDataArr, (1, gmUpdated.refPoints.shape[1])) - gmUpdated.refPoints, 2)
dist_2 = np.sum(dist_tmp, axis=0, dtype='float64')
kernelSDataNewFromRefs = np.exp(-dist_2 / (2 * math.pow(self.kliep.kliepDefSigma, 2)), dtype='float64')
kernelMatSrcData = np.append(kernelMatSrcData, kernelSDataNewFromRefs[np.newaxis], axis=0)
#print("Satisfying the constrains.")
gmUpdated.alphah, kernelMatSrcData = self.kliep.satConstraints(self.SDataBufferArr, self.TDataBufferArr, gmUpdated.refPoints, gmUpdated.alphah, kernelMatSrcData)
sDataIndex += 1
else:
# Target Stream
print('#', end="") # '#' indicates new point from target
newTargetDataArr = self.target.data[:, tDataIndex][np.newaxis].T
# get Target Accuracy on the new instance
resTarget = self.ensemble.evaluateEnsembleKLIEP(np.reshape(newTargetDataArr, (1,-1)))
if isinstance(resTarget[0], float) and abs(resTarget[0]-self.target.dataLabels[tDataIndex])<0.0001:
trueTargetNum += 1
elif resTarget[0] == self.target.dataLabels[tDataIndex]:
trueTargetNum += 1
acc = float(trueTargetNum)/(tDataIndex+1)
if (tDataIndex%100)==0:
Properties.logger.info('\nTotal test instance: '+ str(tDataIndex+1) + ', correct: ' + str(trueTargetNum) + ', accuracy: ' + str(acc))
fAcc.write(str(acc)+ "\n")
conf = resTarget[1] # confidence
# save confidence
targetConfSum += conf
fConf.write(str(float(targetConfSum)/(tDataIndex+1))+ "\n")
#update alpha, and satisfy constraints
#print("Update alpha and satisfy constrains")
gmUpdated.alphah, kernelMatSrcData = self.kliep.updateAlpha(self.SDataBufferArr, self.TDataBufferArr, newTargetDataArr, gmUpdated.refPoints, gmUpdated.alphah, kernelMatSrcData)
#print("\nlen(self.TDataBufferList) = ", len(self.TDataBufferList), ": target window slides")
#remove the first instance from buffers
self.TDataBufferArr = self.TDataBufferArr[:, 1:]
#update ref points
gmUpdated.refPoints = gmUpdated.refPoints[:, 1:]
# update kernelMatSrcData, as ref points has been updated
kernelMatSrcData = kernelMatSrcData[:, 1:]
# update kernelMatTrgData, as ref points has been updated
kernelMatTrgData = kernelMatTrgData[1:, 1:]
#update ref points
gmUpdated.refPoints = np.append(gmUpdated.refPoints, newTargetDataArr, axis=1)
#add to kernelMatSrcData for the last ref point
dist_tmp = np.power(
np.tile(newTargetDataArr,
(1, self.SDataBufferArr.shape[1])) - self.SDataBufferArr, 2)
dist_2 = np.sum(dist_tmp, axis=0, dtype='float64')
kernel_dist_2 = np.exp(-dist_2 / (2 * math.pow(self.kliep.kliepDefSigma, 2)), dtype='float64')
kernelMatSrcData = np.append(kernelMatSrcData, kernel_dist_2[np.newaxis].T, axis=1)
#now update kernelMatTrgData, as ref points has been updated
#first add distance from the new ref points to all the target points
dist_tmp = np.power(
np.tile(newTargetDataArr,
(1, self.TDataBufferArr.shape[1])) - self.TDataBufferArr, 2)
dist_2 = np.sum(dist_tmp, axis=0, dtype='float64')
kernel_dist_2 = np.exp(-dist_2 / (2 * math.pow(self.kliep.kliepDefSigma, 2)), dtype='float64')
kernelMatTrgData = np.append(kernelMatTrgData, kernel_dist_2[np.newaxis].T, axis=1)
#now add distances for the newly added instance to all the ref points
#add the new instance to the buffers
self.TDataBufferArr = np.append(self.TDataBufferArr, newTargetDataArr, axis=1)
dist_tmp = np.power(np.tile(newTargetDataArr, (1, gmUpdated.refPoints.shape[1])) - gmUpdated.refPoints, 2)
dist_2 = np.sum(dist_tmp, axis=0, dtype='float64')
kernelTDataNewFromRefs = np.exp(-dist_2 / (2 * math.pow(self.kliep.kliepDefSigma, 2)), dtype='float64')
kernelMatTrgData = np.append(kernelMatTrgData, kernelTDataNewFromRefs[np.newaxis], axis=0)
tDataIndex += 1
#print "sDataIndex: ", str(sDataIndex), ", tDataIndex: ", str(tDataIndex)
enoughInstToUpdate = self.SDataBufferArr.shape[1]>=Properties.kliepParB and self.TDataBufferArr.shape[1]>=Properties.kliepParB
if enoughInstToUpdate:
#print("Enough points in source and target sliding windows. Attempting to detect any change of distribution.")
changeDetected, changeScore, kernelMatTrgData = self.kliep.changeDetection(self.TDataBufferArr, gmOld.refPoints, gmOld.alphah, gmUpdated.refPoints, gmUpdated.alphah, kernelMatTrgData)
#print("Change Score: ", changeScore)
#instances from more than one class are needed for svm training
if len(set(self.SDataLabels))>1 and (changeDetected or (self.enableForceUpdate and (tDataIndex + sDataIndex - idxLastUpdate)>self.forceUpdatePeriod)): #or (tDataIndex>0 and (targetConfSum/tDataIndex)<0.1):
fConf.write(str(7777777.0) + "\n")
Properties.logger.info(
'\n-------------------------- Change of Distribution ------------------------------------')
Properties.logger.info('Change of distribution found')
Properties.logger.info(
'sDataIndex=' + str(sDataIndex) + '\ttDataIndex=' + str(tDataIndex))
Properties.logger.info('Change Detection Score: ' + str(changeScore) + ', Threshold: ' + str(self.kliep.kliepParThreshold))
#Build a new model
#First calculate the weights for each source instances
gmOld.alphah, kernelMatSrcData, kernelMatTrgData, gmOld.refPoints = self.kliep.KLIEP(self.SDataBufferArr,
self.TDataBufferArr)
#update the updated gaussian model as well
gmUpdated.setAlpha(gmOld.alphah)
gmUpdated.setRefPoints(gmOld.refPoints)
weightSrcData = self.kliep.calcInstanceWeights(kernelMatSrcData, gmUpdated.alphah)
#Build a new model
Properties.logger.info('Training a model due to change detection')
SDataBufferArrTransposed = self.SDataBufferArr.T
TDataBufferArrTransposed = self.TDataBufferArr.T
if self.useSvmCVParams==1:
params = {'gamma': [2 ** 2, 2 ** -16], 'C': [2 ** -6, 2 ** 15]}
svr = svm.SVC()
opt = grid_search.GridSearchCV(svr, params)
opt.fit(SDataBufferArrTransposed.tolist(), self.SDataLabels)
optParams = opt.best_params_
self.ensemble.generateNewModelKLIEP(SDataBufferArrTransposed.tolist(), self.SDataLabels,
TDataBufferArrTransposed.tolist(), weightSrcData[0].tolist(),
optParams['C'], optParams['gamma'])
else:
self.ensemble.generateNewModelKLIEP(SDataBufferArrTransposed.tolist(), self.SDataLabels,
TDataBufferArrTransposed.tolist(), weightSrcData[0].tolist(),
Properties.svmDefC, Properties.svmDefGamma,
Properties.svmKernel)
Properties.logger.info(self.ensemble.getEnsembleSummary())
#update the idx
idxLastUpdate = tDataIndex + sDataIndex
changeDetected = False
#keep the latest 1/4th of data and update the arrays and lists
#Properties.logger.info('Updating source and target sliding windows')
"""
In the target window, we want to keep (3x/4) instances, where x is the number of gaussian kernel centers,
So that we will try for detecting change point again after (x/4) instances. Since there might be a diff
between arrival rate in the source and target, we calculate number of points to retain in the source
keeping that in mind.
"""
#numberOfPointsInTargetToRetain = Properties.kliepParB - int(((1-probFromSource)*3*Properties.kliepParB)/4)
#numberOfPointsInSourceToRetain = Properties.kliepParB - int((probFromSource*3*Properties.kliepParB)/4)
#save the timestamp
fConf.close()
fAcc.close()
globalEndTime = time.time()
Properties.logger.info(
'\nGlobal Start Time: ' + datetime.datetime.fromtimestamp(globalEndTime).strftime('%Y-%m-%d %H:%M:%S'))
Properties.logger.info('Total Time Spent: ' + str(globalEndTime-globalStartTime) + ' seconds')
Properties.logger.info('Done !!')