240 lines
7.4 KiB
Python
240 lines
7.4 KiB
Python
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import math
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from model import Model
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from properties import Properties
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class Ensemble(object):
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def __init__(self, ensemble_size):
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self.models = []
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self.size = ensemble_size
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"""
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Update weights for all models in the ensemble.
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"""
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def updateWeight(self, data, isSource):
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for m in self.models:
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m.computeModelWeight(data, isSource, Properties.MAXVAR)
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def reEvalModelWeights(self, data, maxvar):
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for i in range(0, len(self.models)):
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self.models[i].weight = self.models[i].computeModelWeightKLIEP(data, maxvar)
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"""
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Adding a new model to the Ensemble.
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Returns the index of the Ensemble array where the model is added.
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"""
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def __addModelKLIEP(self, model, data, maxvar):
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index = 0
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self.reEvalModelWeights(data, maxvar)
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if len(self.models) < self.size:
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self.models.append(model)
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index = len(self.models)-1
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else:
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#replace least desirable model
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index = self.__getLeastDesirableModelKLIEP()
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if self.models[index].weight < model.weight:
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Properties.logger.info('Least desirable model removed at ' + str(index))
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self.models[index] = model
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else:
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Properties.logger.info('New model was not added as its weight is less than all of the existing models')
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return -1
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return index
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"""
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Adding a new model to the Ensemble.
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Returns the index of the Ensemble array where the model is added.
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"""
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def __addModel(self, model):
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index = 0
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if len(self.models) < self.size:
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self.models.append(model)
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index = len(self.models)-1
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else:
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#replace least desirable model
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index = self.__getLeastDesirableModel()
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Properties.logger.info('Least desirable model removed at ' + str(index))
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self.models[index] = model
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return index
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"""
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Compute the least desirable model to be replaced when the ensemble size has reached its limit.
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Least desirable is one having least target weight
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Returns the array index of the least desired model.
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"""
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def __getLeastDesirableModelKLIEP(self):
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weights = {}
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for i in xrange(len(self.models)):
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weights[i] = self.models[i].weight
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keys = sorted(weights, key=weights.get)
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return keys[0]
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"""
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Compute the least desirable model to be replaced when the ensemble size has reached its limit.
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Least desirable is one having least target weight, but not the largest source weight.
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Returns the array index of the least desired model.
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"""
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def __getLeastDesirableModel(self):
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sweights = {}
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tweights = {}
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for i in xrange(len(self.models)):
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sweights[i] = self.models[i].sweight
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tweights[i] = self.models[i].tweight
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skeys = sorted(sweights, reverse=True, key=sweights.get)
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tkeys = sorted(tweights, key=tweights.get)
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# skeys = sweights.keys()
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# tkeys = tweights.keys()
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for i in xrange(len(skeys)):
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if tkeys[i] == skeys[i]:
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continue
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else:
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return tkeys[i]
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return tkeys[0]
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"""
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Initiate the creation of appropriate model in the ensemble for given target data.
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Also compute weights for the new model based on the current data.
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"""
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def generateNewModelKLIEP(self, srcData, srcLabels, trgData, weightSrcData, svmC, svmGamma, svmKernel):
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model = Model()
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if len(srcData) == 0 or len(trgData) == 0:
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raise Exception('Source or Target stream should have some elements')
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#Create new model
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Properties.logger.info('Target model creation')
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model.trainUsingKLIEPWeights(srcData, srcLabels, weightSrcData, Properties.MAXVAR, svmC, svmGamma, svmKernel)
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#compute source and target weight
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Properties.logger.info('Computing model weights')
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model.weight = model.computeModelWeightKLIEP(trgData, Properties.MAXVAR)
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#update ensemble
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index = self.__addModelKLIEP(model, trgData, Properties.MAXVAR)
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if index != -1:
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Properties.logger.info('Ensemble updated at ' + str(index))
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"""
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Initiate the creation of appropriate model in the ensemble for given source or target data.
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Also compute weights for the new model based on the current data.
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"""
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def generateNewModel(self, sourceData, targetData, isSource, useSvmCVParams, svmDefC, svmDefGamma):
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model = Model()
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if len(sourceData) == 0 or len(targetData) == 0:
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raise Exception('Source or Target stream should have some elements')
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#Create new model
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if isSource:
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Properties.logger.info('Source model creation')
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model.train(sourceData, None, Properties.MAXVAR, useSvmCVParams, svmDefC, svmDefGamma)
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else:
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Properties.logger.info('Target model creation')
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model.train(sourceData, targetData, Properties.MAXVAR, useSvmCVParams, svmDefC, svmDefGamma)
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#compute source and target weight
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Properties.logger.info('Computing model weights')
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model.computeModelWeight(sourceData, True, Properties.MAXVAR)
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model.computeModelWeight(targetData, False, Properties.MAXVAR)
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#update ensemble
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index = self.__addModel(model)
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Properties.logger.info('Ensemble updated at ' + str(index))
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"""
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Get prediction for a given data instance from each model.
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For source data: Ensemble prediction is 1 if maximum weighted vote class label matches true class label, else 0.
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For target data: Ensemble prediction class with max weighted vote class label, and average (for all class) confidence measure.
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"""
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def evaluateEnsembleKLIEP(self, dataInstance):
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confSum = {}
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weightSum = {}
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for m in self.models:
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# test data instance in each model
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predictedClass, confidence = m.test(dataInstance)
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# gather result
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if predictedClass[0] in confSum:
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confSum[predictedClass[0]] += confidence[0]
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weightSum[predictedClass[0]] += m.weight
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else:
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confSum[predictedClass[0]] = confidence[0]
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weightSum[predictedClass[0]] = m.weight
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# get maximum voted class label
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classMax = 0.0
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sorted(confSum, key=confSum.get, reverse=True)
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classMax = confSum.keys()[0]
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return [classMax, confSum[classMax]/len(self.models)]
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"""
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Get prediction for a given data instance from each model.
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For source data: Ensemble prediction is 1 if maximum weighted vote class label matches true class label, else 0.
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For target data: Ensemble prediction class with max weighted vote class label, and average (for all class) confidence measure.
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"""
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def evaluateEnsemble(self, dataInstance, isSource):
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classSum = {}
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for m in self.models:
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#test data instance in each model
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result = m.test([dataInstance], Properties.MAXVAR)
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#gather result
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if isSource:
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if int(result[0][0]) in classSum:
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classSum[int(result[0][0])] += m.sweight
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else:
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classSum[int(result[0][0])] = m.sweight
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else:
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if int(result[0][0]) in classSum:
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classSum[int(result[0][0])] += result[0][1]
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else:
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classSum[int(result[0][0])] = result[0][1]
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#get maximum voted sum class label
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classMax = 0.0
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sumMax = max(classSum.values())
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for i in classSum:
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if classSum[i] == sumMax:
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classMax = i
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if isSource:
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#for source data, check true vs predicted class label
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if classMax == dataInstance[-1]:
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return [1, -1]
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else:
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return [0, -1]
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else:
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# for target data
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return [classMax, sumMax/len(self.models)]
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"""
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Get summary of models in ensemble.
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"""
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def getEnsembleSummary(self):
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summry = '************************* E N S E M B L E S U M M A R Y ************************\n'
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summry += 'Ensemble has currently ' + str(len(self.models)) + ' models.\n'
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for i in xrange(len(self.models)):
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summry += 'Model' + str(i+1) + ': weights<' + str(self.models[i].weight) + '>\n'
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return summry
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