151 lines
5.2 KiB
Python
151 lines
5.2 KiB
Python
from properties import Properties
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import math, numpy as np
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from scipy.stats import beta, binom
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from decimal import Decimal
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import sys, random, time
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class ChangeDetection(object):
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def __init__(self, gamma, sensitivity, maxWindowSize):
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self.gamma = gamma
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self.sensitivity = sensitivity
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self.maxWindowSize = maxWindowSize
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"""
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Functions to estimate beta distribution parameters
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"""
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def __calcBetaDistAlpha(self, list, sampleMean, sampleVar):
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if sampleMean == -1:
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sampleMean = np.mean(list)
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if sampleVar == -1:
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sampleVar = np.var(list)
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c = (sampleMean * (1-sampleMean)/sampleVar) - 1
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return sampleMean * c
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def __calcBetaDistBeta(self, list, alphaChange, sampleMean):
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if sampleMean == -1:
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sampleMean = np.mean(list)
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return alphaChange * ((1.0/sampleMean) - 1)
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"""
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input: The dynamic sliding window containing confidence of target classifier
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output: -1 if no change found, otherwise the change point
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"""
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def detectTargetChange(self, slidingWindow):
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estimatedChangePoint = -1
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N = len(slidingWindow)
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cushion = max(Properties.CUSHION, int(math.floor(N ** self.gamma)))
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#If mean confidence fall below 0.3, must retrain the classifier, so return a changepoint
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if N > self.maxWindowSize:
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Properties.logger.info('Current target Window Size is: ' + str(N) + ', which exceeds max limit, so update classifier')
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return 0
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if N > 2*cushion and np.mean(slidingWindow[0:N]) <= Properties.CONFCUTOFF:
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Properties.logger.info('Current target Window Size is: ' + str(N))
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Properties.logger.info('But overall confidence fell below ' + str(Properties.CONFCUTOFF) + ', so update classifier')
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return 0
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threshold = -math.log(self.sensitivity)
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w = 0.0
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kAtMaxW = -1
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kindex = np.arange(cushion, N - cushion + 1)
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for k in kindex:
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xbar0 = np.mean(slidingWindow[:k])
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var0 = np.var(slidingWindow[:k])
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xbar1 = np.mean(slidingWindow[k:])
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var1 = np.var(slidingWindow[k:])
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if xbar1 <= 0.9*xbar0:
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skn = 0.0
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alphaPreChange = self.__calcBetaDistAlpha(slidingWindow[:k], xbar0, var0)
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betaPreChange = self.__calcBetaDistBeta(slidingWindow[:k], alphaPreChange, xbar0)
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alphaPostChange = self.__calcBetaDistAlpha(slidingWindow[k:], xbar1, var1)
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betaPostChange = self.__calcBetaDistBeta(slidingWindow[k:], alphaPostChange, xbar1)
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try:
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swin = map(float, slidingWindow[k:])
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denom = [beta.pdf(s, alphaPreChange, betaPreChange) for s in swin]
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numer = [beta.pdf(s, alphaPostChange, betaPostChange) for s in swin]
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nor_denom = np.array([1e-50 if (h-0)<1e-50 else h for h in denom])
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l_ratios = numer/nor_denom
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l_ratios_no_zeros = np.array([1e-50 if (h-0)<1e-50 else h for h in l_ratios])
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ll_ratios = np.log(l_ratios_no_zeros)
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skn = sum(ll_ratios)
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except:
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e = sys.exc_info()
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print str(e[1])
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raise Exception('Error in calculating skn')
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if skn > w:
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w = skn
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kAtMaxW = k
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if w >= threshold and kAtMaxW != -1:
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estimatedChangePoint = kAtMaxW
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Properties.logger.info('Estimated change point is ' + str(estimatedChangePoint) + ', detected at ' + str(N))
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return estimatedChangePoint
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"""
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input: The dynamic sliding window containing accuracy of source classifier
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output: -1 if no change found, otherwise the change point
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"""
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def detectSourceChange(self, slidingWindow):
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estimatedChangePoint = -1
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N = len(slidingWindow)
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cushion = max(Properties.CUSHION, int(math.floor(N ** self.gamma)))
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#If mean confidence fall below 0.3, must retrain the classifier, so return a changepoint
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if N > self.maxWindowSize:
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Properties.logger.info('Current target Window Size is: ' + str(N) + ', which exceeds max limit, so update classifier')
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return 0
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if N > 2*cushion and np.mean(slidingWindow) <= Properties.CONFCUTOFF:
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Properties.logger.info('Current target Window Size is: ' + str(N))
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Properties.logger.info('But overall confidence fell below ' + str(Properties.CONFCUTOFF) + ', so update classifier')
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return 0
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threshold = -math.log(self.sensitivity)
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w = 0.0
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kAtMaxW = -1
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kindex = np.arange(cushion, N - cushion + 1)
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for k in kindex:
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xbar0 = np.mean(slidingWindow[:k])
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xbar1 = np.mean(slidingWindow[k:])
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# means should set 1=accurate, 0=erroneous
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if xbar1 <= 0.9*xbar0:
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skn = 0.0
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try:
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swin = map(float, slidingWindow[k:])
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denom = [binom.pmf(s, k, xbar0) for s in swin]
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numer = [binom.pmf(s, N-k, xbar1) for s in swin]
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nor_denom = np.array([1e-50 if (h - 0) < 1e-50 else h for h in denom])
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l_ratios = numer/nor_denom
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l_ratios_no_zeros = np.array([1e-50 if (h-0)<1e-50 else h for h in l_ratios])
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ll_ratios = np.log(l_ratios_no_zeros)
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skn = sum(ll_ratios)
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except:
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e = sys.exc_info()
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print str(e[1])
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raise Exception('Error in calculating skn')
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if skn > w:
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w = skn
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kAtMaxW = k
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if w >= threshold and kAtMaxW != -1:
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estimatedChangePoint = kAtMaxW
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Properties.logger.info('Estimated change point is ' + str(estimatedChangePoint) + ', detected at: ' + str(N))
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Properties.logger.info('Value of w: ' + str(w) + ', Value of Threshold: ' + str(threshold))
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return estimatedChangePoint
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