class ClassifierExecutor(ABC):
def __init__(self):
self.originalInstances = None
self.instances = None
def loadFeatures(self, filename, filter):
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(filename)
self.originalInstances = data
if filter:
for i in range(0, filter.length):
filter[i].setInputFormat(self.originalInstances)
self.originalInstances = Instance(
javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
self.originalInstances, filter[i]))
self.instances = self.originalInstances
return self.originalInstances.num_attributes() - 1
def loadFeatures(self, filename):
f = Filter()
return self.loadFeatures(filename, f)
def loadFeatures(self):
self.instances = self.originalInstances
def getFeaturesSize(self):
if self.originalInstances is None:
return -1
return self.originalInstances.num_attributes() - 1
@classmethod
@abstractmethod
def execute(self, featureInclusion, k):
pass
@classmethod
@abstractmethod
def execute(self, featureInclusion, kFold, classIndex):
pass
class CHA():
def __init__(self):
self.features = {True, True, True, True}
self.featureSize = 0
self.databaseName = "dataset/segment.arff"
self.runtime = 20
self.limit = 6
self.mr = 0.1
self.KFOLD = 10
self.bestFitness = 0
self.bestFoodSource = None
self.foodSources = set()
self.visitedFoodSources = set()
self.scouts = set()
self.abandoned = set()
self.markedToRemoved = set()
self.neighbors = set()
if self.mr > 0:
self.perturbation = PerturbationStrategy.USE_MR
else:
self.perturbation = PerturbationStrategy.CHANGE_ONE_FEATURE
self.states = 0
self.data = None
def loadFeatures(self, filename, filter):
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(filename)
self.originalInstances = data
if filter:
for i in range(0, filter.length):
filter[i].setInputFormat(self.originalInstances)
self.originalInstances = Instance(
javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
self.originalInstances, filter[i]))
self.instances = self.originalInstances
return self.originalInstances.num_attributes() - 1
def loadFeatures(self):
#self.instances = self.originalInstances
#loader = Loader("weka.core.converters.ArffLoader")
#data = loader.load_file(self.databaseName)
#self.originalInstances = data
#self.instances = Instances.copy_instances(self.originalInstances)
#return self.originalInstances.num_attributes - 1
ds = arff.load(open(self.databaseName, 'r'))
self.data = np.array(ds['data'])
self.featureSize = self.data.shape[1] - 1
return self.data.shape[0]
def executeKFoldClassifier(self, featureInclusion, kFold):
deleteFeatures = 0
for i in range(0, len(featureInclusion)):
if featureInclusion[i]:
self.instances.deleteAttributeAt(i - deleteFeatures)
deleteFeatures += 1
self.instances.setClassIndex(self.instances.numAttributes - 1)
cvParameterSelection = javabridge.make_instance(
"weka/classifiers/meta/CVParameterSelection", "()V")
javabridge.call(cvParameterSelection, "setNumFolds", "(I)V", kFold)
javabridge.call(cvParameterSelection,
"buildClassifier(weka/core/Instances)V",
self.instances)
eval = Evaluation(self.instances)
eval.crossvalidate_model(cvParameterSelection, self.instances, kFold,
random())
return eval.percent_correct()
def executeKFoldClassifier(self, featureInclusion, kFold, classIndex):
deletedFeatures = 0
for i in range(0, len(featureInclusion)):
if featureInclusion[i] == False:
self.instances.deleteAttributeAt(i - deletedFeatures)
deletedFeatures += 1
'''
self.instances.setClassIndex(classIndex)
cvParameterSelection = javabridge.make_instance("Lweka/classifiers/meta/CVParameterSelection","()V")
javabridge.call(cvParameterSelection, "setNumFolds", "(I)V", kFold)
javabridge.call(cvParameterSelection,"buildClassifier(Lweka/core/Instances)V",self.instances)
eval = Evaluation(self.instances)
eval.crossvalidate_model(cvParameterSelection, self.instances, kFold, Random(1))
return eval.percent_correct()'''
def executeFullFeaturesWithNoFilters(self):
print('executeFullFeaturesWithNoFilters')
self.executor.loadFeatures(self.databaseName,
self.replaceMissingValues)
result = self.executor.execute(self.features, self.KFOLD)
print('Full ' + result + '%')
def executeWithNoFilter(self):
print('executeWithNoFilter')
self.executor.loadFeatures(self.databaseName,
self.replaceMissingValues)
# self.featureSelection = FeatureSelection(self.runtime,
# self.limit, self.mr, self.executor)
# self.featureSelection.setExecutor(self.executor)
# self.featureSelection.execute()
self.executeFeatureSelection()
def initializeFoodSource(self):
print('initializeFoodSources')
for i in range(0, self.featureSize):
self.states += 1
features = np.zeros(self.featureSize)
features[i] = True
curFitness = self.calculateFitness(features)
fs = FoodSource(features, curFitness, 1)
self.foodSources.add(fs)
if (curFitness > self.bestFitness):
self.bestFoodSource = fs
self.bestFitness = curFitness
def sendEmployedBees(self):
print('sendEmployedBees')
self.scouts = set()
self.markedToRemoved = set()
self.neighbors = set()
for fs in self.foodSources:
self.sendBee(fs)
# remove all markedToRemoved
for mtr in self.markedToRemoved:
if mtr in self.foodSources:
self.foodSources.remove(mtr)
for n in self.neighbors:
self.foodSources.add(n)
def sendOnlookerBees(self):
print('SendOnlookerBees')
self.markedToRemoved = set()
self.neighbors = set()
min = 0
range = 0
for s in self.foodSources:
if s.getFitness() < min:
min = s.getFitness()
if s.getFitness() > range:
range = s.getFitness()
for fs in self.foodSources:
prob = (fs.getFitness() - min) / range
if random.random() < prob:
self.sendBee(fs)
else:
fs.incrementLimit()
for mtr in self.markedToRemoved:
if mtr in self.foodSources:
self.foodSources.remove(mtr)
for n in self.neighbors:
self.foodSources.add(n)
def sendBee(self, foodSource):
features = foodSource.getFeatureInclusion()
nrFeatures = foodSource.getNrFeatures()
times = 0
modifedFoodSource = None
while 1:
times += 1
if self.perturbation == PerturbationStrategy.CHANGE_ONE_FEATURE:
index = round(random.random() * (self.featureSize - 1))
if features[index] is False:
nrFeatures += 1
features[index] = True
elif self.perturbation == PerturbationStrategy.USE_MR:
for i in range(0, self.featureSize):
if random.random() < self.mr:
if features[i] == False:
nrFeatures += 1
features[i] = True
modifedFoodSource = FoodSource(features)
if (modifedFoodSource not in self.foodSources and \
modifedFoodSource not in self.neighbors and \
modifedFoodSource not in self.abandoned and \
modifedFoodSource not in self.visitedFoodSources) or \
times > self.featureSize:
break
if modifedFoodSource not in self.foodSources or \
modifedFoodSource not in self.neighbors or \
modifedFoodSource not in self.visitedFoodSources or \
modifedFoodSource not in self.abandoned:
self.states += 1
fitness = self.calculateFitness(features)
modifedFoodSource.setFitness(fitness)
modifedFoodSource.setNrFeatures(nrFeatures)
if foodSource.getFitness() > fitness or \
(fitness == foodSource.getFitness() and nrFeatures > foodSource.getNrFeatures()):
foodSource.incrementLimit()
if foodSource.getLimit() >= self.limit:
self.markAbandonsFoodSource(foodSource)
self.createScoutBee()
self.visitedFoodSources.add(modifedFoodSource)
else:
if fitness > self.bestFitness or (
fitness == self.bestFitness
and nrFeatures < self.bestFoodSource.getNrFeatures()):
self.bestFoodSource = FoodSource(
modifedFoodSource.getFeatureInclusion(),
modifedFoodSource.getFitness(),
modifedFoodSource.getNrFeatures())
self.bestFitness = fitness
self.neighbors.add(modifedFoodSource)
return True
def createScoutBee(self):
features = np.zeros(self.featureSize)
nrFeatures = 0
for j in range(0, self.featureSize):
inclusio = bool(random.getrandbits(1))
if inclusio:
nrFeatures += 1
features[j] = inclusio
curFitness = self.calculateFitness(features)
foodSource = FoodSource(features, curFitness, nrFeatures)
if foodSource not in self.foodSources and \
foodSource not in self.neighbors and \
foodSource not in self.abandoned and \
foodSource not in self.visitedFoodSources:
self.states += 1
self.scouts.add(foodSource)
def sendScoutBeesAndRemoveAbandonsFoodSource(self):
#remove abandoned
for abd in self.abandoned:
if abd in self.foodSources:
self.foodSources.remove(abd)
for s in self.scouts:
self.foodSources.add(s)
def markAbandonsFoodSource(self, foodSource):
self.abandoned.add(foodSource)
def calculateFitness(self, featureInclusion):
deletedFeatures = 0
data = self.data
for i in range(0, len(featureInclusion)):
if featureInclusion[i] == False:
data = np.delete(data, np.s_[i - deletedFeatures], 1)
deletedFeatures += 1
rows, cols = data.shape
X = data[:, :cols - 1]
y = data[:, cols - 1:]
y = y.ravel()
kf = KFold(n_splits=10)
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
n = KNeighborsClassifier(n_neighbors=3)
n.fit(X_train, y_train)
score = n.score(X_test, y_test)
return score
def executeFeatureSelection(self):
self.visitedFoodSources = set()
self.states = 0
time = datetime.now()
self.initializeFoodSource()
print('init time: ', datetime.now() - time)
for i in range(0, self.runtime):
self.sendEmployedBees()
self.sendOnlookerBees()
self.sendScoutBeesAndRemoveAbandonsFoodSource()
time = (datetime.now() - time) / 60000
self.logBestSolutionAndTime(time)
self.states = 0
def logBestSolutionAndTime(self, t):
print('Time: ', t)
print('Best ', self.bestFoodSource.getFeatureInclusion())
print('Feature selection End.')
def runCHA(self):
self.loadFeatures()
self.executeFeatureSelection()