def radius(X_train, X_test, y_train, y_test, string, valor):
if (string == "prob"):
clf = RadiusNeighborsClassifier(radius=valor,
weights='distance',
n_jobs=-1)
clf.fit(X_train, y_train.values.ravel())
return clf.predict_proba(X_test)
clf = RadiusNeighborsClassifier(radius=valor,
weights='distance',
n_jobs=-1)
clf.fit(X_train, y_train.values.ravel())
#pickles.criarModelo(clf,"Rocchio "+string)
y_predito = clf.predict(X_test)
micro = f1_score(y_test, y_predito, average='micro')
macro = f1_score(y_test, y_predito, average='macro')
#f1_individual = f1_score(y_test,y_predito,average=None)
#salvar_dados.salvar(y_test,y_predito,micro, macro, f1_individual," Knn "+string)
print("O f1Score micro do RadiusKnn ", string, " com ", valor,
" de raio é: ", micro)
print("O f1Score macro do RadiusKnn ", string, " com ", valor,
" de raio é: ", macro)
def _transductive_classifier(self, X_train, y_train, test_instance):
clf = RadiusNeighborsClassifier(radius=self.epsilon,
weights='distance').fit(
X_train, y_train)
predict_set = clf.radius_neighbors(test_instance.reshape(1, -1))[1]
predict_set = list(predict_set[0])
if len(predict_set) > 0:
X_train_local, y_train_local = X_train[predict_set], y_train[
predict_set]
if np.min(y_train_local) == np.max(y_train_local):
prediction = y_train_local[0]
else:
clf = self._fit(X_train_local, y_train_local)
if np.max(clf.predict_proba(test_instance.reshape(
1, -1))) < self.threshold_rejection:
prediction = self.new_classes
else:
prediction = clf.predict(test_instance.reshape(1, -1))[0]
else:
prediction = self.new_classes
return prediction
def signal_proba(model, X):
return RadiusNeighborsClassifier.predict_proba(model, X)[:, 1]
