def BRkNNa(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
num_neighbours):
x_train = lil_matrix(dataset_train_x).toarray()
y_train = lil_matrix(dataset_train_y).toarray()
x_test = lil_matrix(dataset_test_x).toarray()
classifier = skadapt.BRkNNaClassifier(k=num_neighbours)
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
text = "BRkNNa w/ k=" + str(num_neighbours)
Metrics_Accuracy(text, predictions, dataset_test_y)
def BRkNNa(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
num_neighbours):
x_train = lil_matrix(dataset_train_x).toarray()
y_train = lil_matrix(dataset_train_y).toarray()
x_test = lil_matrix(dataset_test_x).toarray()
classifier = skadapt.BRkNNaClassifier(k=num_neighbours)
start_time = time.time()
classifier.fit(x_train, y_train)
stop_time = time.time()
time_lapsed = stop_time - start_time
predictions = classifier.predict(x_test)
text = "BRkNNa w/ k=" + str(num_neighbours)
Metrics_Accuracy(text, predictions, dataset_test_y)
print("Execution time: {}s".format(time_lapsed))
def FindBestK(classif, dataset_train_x, dataset_train_y):
rangefloatv = [round(x * 0.1, 1) for x in range(5, 11)]
parameters = {'k': range(1, 20), 's': rangefloatv}
if type(classif) == type(
skadapt.BRkNNaClassifier()) or type(classif) == type(
skadapt.BRkNNbClassifier()):
parameters = {'k': range(1, 20)}
clf = GridSearchCV(classif,
parameters,
scoring=make_scorer(metrics.hamming_loss,
greater_is_better=False),
n_jobs=2)
clf.fit(
lil_matrix(dataset_train_x).toarray(),
lil_matrix(dataset_train_y).toarray())
print(clf.best_params_)
return clf.best_params_
def Util_ClassifierMethods(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y):
#BR
Util_Title("Binary Relevance")
base_classif = GaussianNB()
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.BinaryRelevance(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.BinaryRelevance(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#CC
Util_Title("Classifier Chain")
base_classif = GaussianNB()
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.ClassifierChain(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.ClassifierChain(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#LP
Util_Title("Label Powerset")
base_classif = GaussianNB()
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.LabelPowerset(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.LabelPowerset(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#MLkNN
Util_Title("MLkNN")
dict_res = FindBestK(skadapt.MLkNN(), dataset_train_x, dataset_train_y)
MLkNN(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'], dict_res['s'])
#MLARAM
Util_Title("MLARAM")
dict_res = FindBestVT(dataset_train_x, dataset_train_y)
MLARAM(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['vigilance'], dict_res['threshold'])
#BRkNNa
Util_Title("BRkNNa")
dict_res = FindBestK(skadapt.BRkNNaClassifier(), dataset_train_x,
dataset_train_y)
BRkNNa(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'])
#BRkNNb
Util_Title("BRkNNb")
dict_res = FindBestK(skadapt.BRkNNbClassifier(), dataset_train_x,
dataset_train_y)
BRkNNb(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'])
#RAkELD
Util_Title("RAkELd")
dict_res = GridSearchCV_baseRakel(RakelD(), dataset_train_x,
dataset_train_y)
RAkELd(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['base_classifier'], dict_res['labelset_size'])
#RAkELo
Util_Title("RAkELo")
dict_res = GridSearchCV_baseRakel(RakelO(), dataset_train_x,
dataset_train_y)
RAkELO(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['base_classifier'], dict_res['labelset_size'],
dict_res['model_count'])
#MLTSVM
Util_Title("MLTSVM")
dict_res = FindCKParam(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y)
TwinMLSVM(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['c_k'], dict_res['sor_omega'])
def Util_ClassifierMethodsBookmarks(train_x, y_train, test_x, y_test):
#Scale negatives for BR/ CC and LP for MultinomialNB
x_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
train_x_scaledb = x_scaler.fit_transform(train_x)
test_x_scaledb = x_scaler.fit_transform(test_x)
#BR
Util_Title("Binary Relevance")
base_classif = GaussianNB()
BinaryRelevance(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.BinaryRelevance(), train_x_scaledb,
y_train)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
BinaryRelevance(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.BinaryRelevance(), train_x_scaledb,
y_train)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
BinaryRelevance(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "MNB tuned")
#CC
Util_Title("Classifier Chain")
base_classif = GaussianNB()
ClassifierChain(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.ClassifierChain(), train_x_scaledb,
y_train)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
ClassifierChain(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.ClassifierChain(), train_x_scaledb,
y_train)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
ClassifierChain(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "MNB tuned")
#LP
Util_Title("Label Powerset")
base_classif = GaussianNB()
LabelPowerset(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.LabelPowerset(), train_x_scaledb,
y_train)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
LabelPowerset(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.LabelPowerset(), train_x_scaledb,
y_train)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
LabelPowerset(train_x_scaledb, y_train, test_x_scaledb, y_test,
base_classif, "MNB tuned")
#RAkELo
Util_Title("RAkELo")
lbs_size = 3
mod_count = 2 * y_train.shape[1] #2 * of labels
print(mod_count)
RAkELO(train_x, y_train, test_x, y_test,
LinearSVC(max_iter=500, verbose=1), lbs_size, mod_count)
#RAkELd
lbs_size = 3
RAkELd(train_x, y_train, test_x, y_test, LinearSVC(verbose=2), lbs_size)
#MLkNN
base_classif = skadapt.MLkNN()
k = 10
s = 1
MLkNN(train_x, y_train, test_x, y_test, k, s)
#MLARAM
v = 0.95
t = 0.05
dict_res = FindBestVT(train_x, y_train)
MLARAM(train_x, y_train, test_x, y_test, dict_res['vigilance'],
dict_res['threshold'])
#BRkNNa
dict_res = FindBestK(skadapt.BRkNNaClassifier(), train_x, y_train)
BRkNNa(train_x, y_train, test_x, y_test, dict_res['k'])
#BRkNNb
dict_res = FindBestK(skadapt.BRkNNbClassifier(), train_x, y_train)
BRkNNb(train_x, y_train, test_x, y_test, dict_res['k'])
#MLTSVM
#Test for 0
#TwinMLSVM(train_x,y_train,test_x,y_test,0,1)
#Test for 0.125
TwinMLSVM(train_x, y_train, test_x, y_test, 0.125, 1)