def test_multiclass(self):
data = load_iris()
Xtr, Xte, Ytr, Yte = train_test_split(data.data,
data.target,
shuffle=True,
train_size=100)
Ktr = (Xtr @ Xtr.T)**2
Kte = (Xte @ Xtr.T)**2
y1 = algorithms.KOMD(kernel='poly', degree=2, coef0=0,
rbf_gamma=1).fit(Xtr, Ytr).predict(Xte)
y2 = algorithms.KOMD(kernel='precomputed').fit(Ktr, Ytr).predict(Kte)
self.assertListEqual(y1.tolist(), y2.tolist())
def test_EasyMKL(self):
self.base_evaluation(algorithms.EasyMKL())
self.base_evaluation(algorithms.EasyMKL(learner=SVC(C=10)))
self.base_evaluation(
algorithms.EasyMKL(learner=algorithms.KOMD(lam=1)))
self.base_evaluation(
algorithms.EasyMKL(solver='libsvm', learner=SVC(C=10)))
def test_PWMK(self):
self.base_evaluation(algorithms.PWMK())
self.base_evaluation(algorithms.PWMK(delta=.6, cv=2))
cv = KFold(n_splits=5, shuffle=True, random_state=42)
self.base_evaluation(
algorithms.PWMK(delta=0, cv=cv, learner=SVC(C=100)))
self.base_evaluation(
algorithms.PWMK(delta=1, learner=algorithms.KOMD(lam=.2)))
def test_fit(self):
K = self.Xtr @ self.Xtr.T
clf = algorithms.KOMD().fit(self.Xtr, self.Ytr + 1)
clf1 = algorithms.KOMD(kernel='precomputed').fit(K, self.Ytr + 1)
params = clf1.get_params()
self.assertTrue(params['lam'] == 0.1)
self.assertTrue(params['kernel'] == 'precomputed')
clf2 = algorithms.KOMD(kernel='rbf',
rbf_gamma=.01).fit(self.Xtr, self.Ytr + 1)
clf3 = algorithms.KOMD(kernel='poly',
degree=2).fit(self.Xtr, self.Ytr + 1)
clf4 = algorithms.KOMD(kernel='linear').fit(self.Xtr, self.Ytr + 1)
y1 = clf1.decision_function(self.Xte @ self.Xtr.T)
y2 = clf4.decision_function(self.Xte)
self.assertListEqual(y1.tolist(), y2.tolist())
y1 = clf1.predict(self.Xte @ self.Xtr.T)
y2 = clf4.predict(self.Xte)
self.assertListEqual(y1.tolist(), y2.tolist())
def test_GRAM(self):
self.base_evaluation(algorithms.GRAM(max_iter=10))
self.base_evaluation(algorithms.GRAM(max_iter=10, learner=SVC(C=10)))
self.base_evaluation(
algorithms.GRAM(max_iter=10, learner=algorithms.KOMD(lam=1)))
def test_AverageMKL(self):
self.base_evaluation(algorithms.AverageMKL())
self.base_evaluation(algorithms.AverageMKL(learner=SVC(C=10)))
self.base_evaluation(
algorithms.AverageMKL(learner=algorithms.KOMD(lam=1)))
def test_CKA(self):
self.base_evaluation(algorithms.CKA(learner=SVC(C=10)))
self.base_evaluation(algorithms.CKA(learner=algorithms.KOMD(lam=1)))
def test_FHeuristic(self):
self.base_evaluation(algorithms.FHeuristic(learner=SVC(C=10)))
self.base_evaluation(
algorithms.FHeuristic(learner=algorithms.KOMD(lam=1)))
