def training(self, partitionedXtr):
"""
Training a base classifier using K-fold cross-validation. This method is used when the input data are preprocessed and partitioned into k parts
INPUT
partitionedXtr An numpy array contains k sub-arrays, in which each subarray is Bunch datatype:
+ lower: lower bounds
+ upper: upper bounds
+ label: class labels
partitionedXtr should be normalized (if needed) beforehand using this function
OUTPUT
baseClassifier base classifier was validated using K-fold cross-validation
"""
baseClassifier = None
minEr = 2
for k in range(self.numFold):
classifier_tmp = AccelBatchGFMM(self.gamma, self.teta, self.bthres,
self.simil, self.sing, False,
self.oper, False)
classifier_tmp.fit(partitionedXtr[k].lower,
partitionedXtr[k].upper,
partitionedXtr[k].label)
# Create the validation set being the remaining training data
for l in range(self.numFold):
if l == k:
continue
else:
if (k == 0 and l == 1) or (l == 0 and k != 0):
lower_valid = partitionedXtr[l].lower
upper_valid = partitionedXtr[l].upper
label_valid = partitionedXtr[l].label
else:
lower_valid = np.concatenate(
(lower_valid, partitionedXtr[l].lower), axis=0)
upper_valid = np.concatenate(
(upper_valid, partitionedXtr[l].upper), axis=0)
label_valid = np.concatenate(
(label_valid, partitionedXtr[l].label))
# validate the trained model
rest = predict(classifier_tmp.V, classifier_tmp.W,
classifier_tmp.classId, lower_valid, upper_valid,
label_valid, self.gamma, self.oper)
er = rest.summis / len(label_valid)
if er < minEr:
minEr = er
baseClassifier = classifier_tmp
return baseClassifier
def fit(self, Xl_onl, Xu_onl, patClassId_onl, Xl_off, Xu_off,
patClassId_off):
"""
Input data need to be normalized before using this function
Xl_onl Input data lower bounds (rows = objects, columns = features) for online learning
Xu_onl Input data upper bounds (rows = objects, columns = features) for online learning
patClassId_onl Input data class labels (crisp) for online learning
Xl_off Input data lower bounds (rows = objects, columns = features) for agglomerative learning
Xu_off Input data upper bounds (rows = objects, columns = features) for agglomerative learning
patClassId_off Input data class labels (crisp) for agglomerative learning
"""
time_start = time.clock()
# Perform agglomerative learning
aggloClassifier = AccelBatchGFMM(self.gamma,
self.teta_agglo,
bthres=self.bthres,
simil=self.simil,
sing=self.sing,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False)
aggloClassifier.fit(Xl_off, Xu_off, patClassId_off)
self.offClassifier = Bunch(V=aggloClassifier.V,
W=aggloClassifier.W,
classId=aggloClassifier.classId)
# Perform online learning
onlClassifier = OnlineGFMM(self.gamma,
self.teta_onl,
self.teta_onl,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False,
norm_range=[self.loLim, self.hiLim])
onlClassifier.fit(Xl_onl, Xu_onl, patClassId_onl)
self.onlClassifier = Bunch(V=onlClassifier.V,
W=onlClassifier.W,
classId=onlClassifier.classId)
time_end = time.clock()
self.elapsed_training_time = time_end - time_start
return self
def fit(self, Xl_onl, Xu_onl, patClassId_onl, Xl_off, Xu_off, patClassId_off, typeOfAgglo = 1):
"""
The input data need to be normalized before using this function
Xl_onl Input data lower bounds (rows = objects, columns = features) for online learning
Xu_onl Input data upper bounds (rows = objects, columns = features) for online learning
patClassId_onl Input data class labels (crisp) for online learning
Xl_off Input data lower bounds (rows = objects, columns = features) for agglomerative learning
Xu_off Input data upper bounds (rows = objects, columns = features) for agglomerative learning
patClassId_off Input data class labels (crisp) for agglomerative learning
typeOfAgglo The used type of agglomerative learning algorithms
"""
time_start = time.clock()
# Perform agglomerative learning
if typeOfAgglo == 1:
aggloClassifier = AccelBatchGFMM(self.gamma, self.teta_agglo, bthres = self.bthres, simil = self.simil, sing = self.sing, isDraw = self.isDraw, oper = self.oper, isNorm = False)
elif typeOfAgglo == 2:
aggloClassifier = BatchGFMMV2(self.gamma, self.teta_agglo, bthres = self.bthres, simil = self.simil, sing = self.sing, isDraw = self.isDraw, oper = self.oper, isNorm = False)
else:
aggloClassifier = BatchGFMMV1(self.gamma, self.teta_agglo, bthres = self.bthres, simil = self.simil, sing = self.sing, isDraw = self.isDraw, oper = self.oper, isNorm = False)
aggloClassifier.fit(Xl_off, Xu_off, patClassId_off)
self.V = aggloClassifier.V
self.W = aggloClassifier.W
self.classId = aggloClassifier.classId
# Perform online learning
onlClassifier = OnlineGFMM(self.gamma, self.teta_onl, self.teta_onl, isDraw = self.isDraw, oper = self.oper, isNorm = False, norm_range = [self.loLim, self.hiLim], V = self.V, W = self.W, classId = self.classId)
# training for online GFMM
onlClassifier.fit(Xl_onl, Xu_onl, patClassId_onl)
self.V = onlClassifier.V
self.W = onlClassifier.W
self.classId = onlClassifier.classId
time_end = time.clock()
self.elapsed_training_time = time_end - time_start
return self
def running_agglo_2(xTr,
patClassIdTr,
xVal,
patClassIdVal,
teta=0.26,
sigma=0.5,
simil='short'):
accelClassifier = AccelBatchGFMM(gamma=1,
teta=teta,
bthres=sigma,
simil=simil,
sing='max',
isDraw=False,
oper='min',
isNorm=False)
accelClassifier.fit(xTr, xTr, patClassIdTr)
result = accelClassifier.predict(xVal, xVal, patClassIdVal)
err = 1
if result != None:
err = result.summis / len(patClassIdVal)
return err
def training(self, X_tr, X_val, isDeleteContainedHyperbox=True):
"""
Training a base classifier using K-fold cross-validation. This method is used when the input data are preprocessed and partitioned into k parts
INPUT
X_tr An object contains training data with the Bunch datatype, its attributes:
+ lower: lower bounds
+ upper: upper bounds
+ label: class labels
X_val An object contains validation data with the Bunch datatype, its attributes:
+ lower: lower bounds
+ upper: upper bounds
+ label: class labels
X_tr, X_val should be normalized (if needed) beforehand using this function
isDeleteContainedHyperbox Identify if hyperboxes contained in other hyperboxes are discarded or not?
"""
V_train = X_tr.lower
W_train = X_tr.upper
classId_train = X_tr.label
V_val = X_val.lower
W_val = X_val.upper
classId_val = X_val.label
bthres = self.bthres
self.numHyperboxes = 0
N = int(self.numClassifier / 2) + 1
delta_thres = (self.bthres - self.bthres_min) / N
minEr_Tr = 2
minEr_Val = 2
opt_Tr = None
opt_Val = None
for k in range(N):
classifier_Tr = AccelBatchGFMM(self.gamma, self.teta, bthres,
self.simil, self.sing, False,
self.oper, False)
classifier_Tr.fit(V_train, W_train, classId_train)
classifier_Val = AccelBatchGFMM(self.gamma, self.teta, bthres,
self.simil, self.sing, False,
self.oper, False)
classifier_Val.fit(V_val, W_val, classId_val)
rest_Tr = predict(classifier_Tr.V, classifier_Tr.W,
classifier_Tr.classId, V_val, W_val, classId_val,
self.gamma, self.oper)
rest_Val = predict(classifier_Val.V, classifier_Val.W,
classifier_Val.classId, V_train, W_train,
classId_train, self.gamma, self.oper)
err_Tr = rest_Tr.summis / len(classifier_Val.classId)
err_Val = rest_Val.summis / len(classifier_Tr.classId)
if err_Tr < minEr_Tr:
minEr_Tr = err_Tr
opt_Tr = classifier_Tr
if err_Val < minEr_Val:
minEr_Val = err_Val
opt_Val = classifier_Val
V_train = classifier_Tr.V
W_train = classifier_Tr.W
classId_train = classifier_Tr.classId
V_val = classifier_Val.V
W_val = classifier_Val.W
classId_val = classifier_Val.classId
bthres = bthres - delta_thres
self.V = np.concatenate((opt_Tr.V, opt_Val.V), axis=0)
self.W = np.concatenate((opt_Tr.W, opt_Val.W), axis=0)
self.classId = np.concatenate((opt_Tr.classId, opt_Val.classId))
if isDeleteContainedHyperbox == True:
self.removeContainedHyperboxes()
self.overlapResolve()
# training using AGGLO-2
combClassifier = AccelBatchGFMM(self.gamma, self.teta, self.bthres_min,
self.simil, self.sing, False,
self.oper, False)
combClassifier.fit(self.V, self.W, self.classId)
self.V = combClassifier.V
self.W = combClassifier.W
self.classId = combClassifier.classId
self.cardin = combClassifier.cardin
self.clusters = combClassifier.clusters
self.numHyperboxes = len(self.classId)
return self
def fit(self, X_l, X_u, patClassId, typeOfAgglo=1):
"""
Xl Input data lower bounds (rows = objects, columns = features)
Xu Input data upper bounds (rows = objects, columns = features)
patClassId Input data class labels (crisp)
typeOfAgglo Type of agglomerative learning
+ 1: Accelerated agglomerative learning AGGLO-2
+ 2: Full batch learning slower version
+ 3: Full batch learning faster version
"""
if self.isNorm == True:
X_l, X_u = self.dataPreprocessing(X_l, X_u)
time_start = time.clock()
# Perform online learning
onlClassifier = OnlineGFMM(self.gamma,
self.teta_onl,
self.teta_onl,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False,
norm_range=[self.loLim, self.hiLim],
V=self.V,
W=self.W,
classId=self.classId)
# training for online GFMM
onlClassifier.fit(X_l, X_u, patClassId)
self.V = onlClassifier.V
self.W = onlClassifier.W
self.classId = onlClassifier.classId
# print('No. hyperboxes after online learning:', len(self.classId))
self.num_hyperbox_after_online = len(self.classId)
# Perform agglomerative learning
if typeOfAgglo == 1:
aggloClassifier = AccelBatchGFMM(self.gamma,
self.teta_agglo,
bthres=self.bthres,
simil=self.simil,
sing=self.sing,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False)
elif typeOfAgglo == 2:
aggloClassifier = BatchGFMMV2(self.gamma,
self.teta_agglo,
bthres=self.bthres,
simil=self.simil,
sing=self.sing,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False)
else:
aggloClassifier = BatchGFMMV1(self.gamma,
self.teta_agglo,
bthres=self.bthres,
simil=self.simil,
sing=self.sing,
isDraw=self.isDraw,
oper=self.oper,
isNorm=False)
aggloClassifier.fit(self.V, self.W, self.classId)
self.V = aggloClassifier.V
self.W = aggloClassifier.W
self.classId = aggloClassifier.classId
#print('No. hyperboxes after the agglomerative learning:', len(self.classId))
self.num_hyperbox_after_agglo = len(self.classId)
time_end = time.clock()
self.elapsed_training_time = time_end - time_start
return self
testing_error_long_si_save = np.array([])
numhyperbox_midmax_si_save = np.array([], dtype=np.int64)
training_time_midmax_si_save = np.array([])
testing_error_midmax_si_save = np.array([])
numhyperbox_midmin_si_save = np.array([], dtype=np.int64)
training_time_midmin_si_save = np.array([])
testing_error_midmin_si_save = np.array([])
for simil_thres in np.arange(0.02, 1, 0.02):
simil_save = np.append(simil_save, simil_thres)
accelClassifier = AccelBatchGFMM(gamma=1,
teta=teta,
bthres=simil_thres,
simil='short',
sing='max',
isDraw=False,
oper='min',
isNorm=False)
accelClassifier.fit(Xtr, Xtr, patClassIdTr)
training_time_short_si_save = np.append(
training_time_short_si_save,
accelClassifier.elapsed_training_time)
numhyperbox_short_si_save = np.append(numhyperbox_short_si_save,
len(accelClassifier.classId))
result = accelClassifier.predict(Xtest, Xtest, patClassIdTest)
if result != None:
numTestSample = Xtest.shape[0]
err = result.summis / numTestSample
testing_error_before_prun_online_gfmm_save = np.append(testing_error_before_prun_online_gfmm_save, err)
start_t = time.perf_counter()
olnClassifier.pruning_val(validationData, validationData, validationLabel)
end_t = time.perf_counter()
training_time_online_gfmm_save = np.append(training_time_online_gfmm_save, olnClassifier.elapsed_training_time + (end_t - start_t))
numhyperbox_online_gfmm_save = np.append(numhyperbox_online_gfmm_save, len(olnClassifier.classId))
result = olnClassifier.predict(testingData, testingData, testingLabel)
if result != None:
err = np.round(result.summis / numTestSample * 100, 3)
testing_error_online_gfmm_save = np.append(testing_error_online_gfmm_save, err)
# agglo-2
accelClassifier = AccelBatchGFMM(gamma = 1, teta = tetaAGGLO2, bthres = 0, simil = 'long', sing = 'min', isDraw = False, oper = 'min', isNorm = False)
accelClassifier.fit(trainingData, trainingData, trainingLabel)
numhyperbox_before_prun_accel_agglo_save = np.append(numhyperbox_before_prun_accel_agglo_save, len(accelClassifier.classId))
result = accelClassifier.predict(testingData, testingData, testingLabel)
if result != None:
err = np.round(result.summis / numTestSample * 100, 3)
testing_error_before_prun_accel_agglo_save = np.append(testing_error_before_prun_accel_agglo_save, err)
start_t = time.perf_counter()
accelClassifier.pruning_val(validationData, validationData, validationLabel)
end_t = time.perf_counter()
training_time_accel_agglo_save = np.append(training_time_accel_agglo_save, accelClassifier.elapsed_training_time + (end_t - start_t))
def fit(self,
X_l,
X_u,
patClassId,
typeOfSplitting=1,
isRemoveContainedHyperboxes=True):
"""
Training the ensemble model at decision level. This method is used when the input data are not partitioned into k parts
INPUT
X_l Input data lower bounds (rows = objects, columns = features)
X_u Input data upper bounds (rows = objects, columns = features)
patClassId Input data class labels (crisp)
typeOfSplitting The way of splitting datasets
+ 1: random split on whole dataset - do not care the classes
+ otherwise: random split according to each class label
isRemoveContainedHyperboxes: Identify if hyperboxes contained in other hyperboxes are discarded or not?
"""
X_l, X_u = self.dataPreprocessing(X_l, X_u)
time_start = time.clock()
for i in range(self.numClassifier):
if typeOfSplitting == 1:
partitionedXtr = splitDatasetRndToKPart(
X_l, X_u, patClassId, self.numFold)
else:
partitionedXtr = splitDatasetRndClassBasedToKPart(
X_l, X_u, patClassId, self.numFold)
predictor = self.training(partitionedXtr)
if i == 0:
self.V = predictor.V
self.W = predictor.W
self.classId = predictor.classId
self.cardin = predictor.cardin
self.clusters = predictor.clusters
else:
self.V = np.concatenate((self.V, predictor.V), axis=0)
self.W = np.concatenate((self.W, predictor.W), axis=0)
self.classId = np.concatenate(
(self.classId, predictor.classId))
self.cardin = np.concatenate((self.cardin, predictor.cardin))
self.clusters = np.concatenate(
(self.clusters, predictor.clusters))
if isRemoveContainedHyperboxes == True:
self.removeContainedHyperboxes()
self.overlapResolve()
# training using AGGLO-2
combClassifier = AccelBatchGFMM(self.gamma, self.teta, self.bthres,
self.simil, self.sing, False,
self.oper, False)
combClassifier.fit(self.V, self.W, self.classId)
self.V = combClassifier.V
self.W = combClassifier.W
self.classId = combClassifier.classId
self.cardin = combClassifier.cardin
self.clusters = combClassifier.clusters
self.numHyperboxes = len(self.classId)
time_end = time.clock()
self.elapsed_training_time = time_end - time_start
return self
def training(self, X_tr, X_val):
"""
Training a base classifier using K-fold cross-validation. This method is used when the input data are preprocessed and partitioned into k parts
INPUT
X_tr An object contains training data with the Bunch datatype, its attributes:
+ lower: lower bounds
+ upper: upper bounds
+ label: class labels
X_val An object contains validation data with the Bunch datatype, its attributes:
+ lower: lower bounds
+ upper: upper bounds
+ label: class labels
X_tr, X_val should be normalized (if needed) beforehand using this function
"""
V_train = X_tr.lower
W_train = X_tr.upper
classId_train = X_tr.label
V_val = X_val.lower
W_val = X_val.upper
classId_val = X_val.label
delta_thres = (self.bthres - self.bthres_min) / self.numClassifier
bthres = self.bthres
self.numHyperboxes = 0
for k in range(self.numClassifier):
classifier_Tr = AccelBatchGFMM(self.gamma, self.teta, bthres,
self.simil, self.sing, False,
self.oper, False)
classifier_Tr.fit(V_train, W_train, classId_train)
classifier_Val = AccelBatchGFMM(self.gamma, self.teta, bthres,
self.simil, self.sing, False,
self.oper, False)
classifier_Val.fit(V_val, W_val, classId_val)
rest_Tr = predict(classifier_Tr.V, classifier_Tr.W,
classifier_Tr.classId, V_val, W_val, classId_val,
self.gamma, self.oper)
rest_Val = predict(classifier_Val.V, classifier_Val.W,
classifier_Val.classId, V_train, W_train,
classId_train, self.gamma, self.oper)
err_Tr = rest_Tr.summis / len(classifier_Val.classId)
err_Val = rest_Val.summis / len(classifier_Tr.classId)
if err_Tr < err_Val:
self.baseClassifiers[k] = classifier_Tr
else:
self.baseClassifiers[k] = classifier_Val
self.numHyperboxes = self.numHyperboxes + len(
self.baseClassifiers[k].classId)
V_train = classifier_Tr.V
W_train = classifier_Tr.W
classId_train = classifier_Tr.classId
V_val = classifier_Val.V
W_val = classifier_Val.W
classId_val = classifier_Val.classId
bthres = bthres - delta_thres
return self.baseClassifiers