def test_invalid_y(self):
""" GENSVM: Check raises for invalid y type """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.random((20, ))
with self.assertRaises(ValueError) as err:
clf.fit(X, y)
exc = err.exception
self.assertEqual(exc.args,
("Label type not allowed for GenSVM: 'continuous'", ))
def test_invalid_seed_V_linear(self):
""" GENSVM: Test for invalid seed_V shape """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20, ))
seed_V = np.random.random((10, 2))
with self.assertRaises(ValueError) as err:
clf.fit(X, y, seed_V=seed_V)
exc = err.exception
self.assertEqual(
exc.args,
("Seed V must have shape [5, 2], but has shape [10, 2]", ))
def test_fit_nonlinear_auto(self):
""" GENSVM: Fit and predict with nonlinear kernel """
data = load_iris()
X = data.data
y = data.target_names[data.target]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=123)
clf = GenSVM(kernel="rbf", max_iter=1000, random_state=123)
clf.fit(X_train, y_train)
pred = clf.predict(X_test, trainX=X_train)
self.assertTrue(
set(pred).issubset(set(["versicolor", "virginica", "setosa"])))
def test_invalid_seed_V_nonlinear(self):
""" GENSVM: Test for seed_V for nonlinear kernels """
clf = GenSVM(kernel="rbf")
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20, ))
seed_V = np.random.random((5, 2))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
clf.fit(X, y, seed_V=seed_V)
self.assertEqual(len(w), 1)
msg = str(w[0].message)
self.assertEqual(
msg,
("Warm starts are only supported for the linear kernel. "
"The seed_V parameter will be ignored."),
)
def test_support_vectors(self):
""" GENSVM: Test getting the support vectors """
X = np.array(
[[-2, 2], [-2, -2], [-1, 0], [1, 0], [2, 2], [2, -2]] # SV # SV
)
y = np.array([0, 0, 0, 1, 1, 1])
clf = GenSVM()
clf.fit(X, y)
SVs = clf.SVs_
self.assertEqual(SVs[0], 0)
self.assertEqual(SVs[1], 0)
self.assertEqual(SVs[2], 1)
self.assertEqual(SVs[3], 1)
self.assertEqual(SVs[4], 0)
self.assertEqual(SVs[5], 0)
self.assertEqual(clf.n_support_, 2)
def test_fit_predict_strings(self):
""" GENSVM: Test fit and predict with string targets """
digits = load_digits(4)
n_samples = len(digits.images)
X = digits.images.reshape(n_samples, -1)
y = digits.target
labels = np.array(["zero", "one", "two", "three"])
yy = labels[y]
X_train, X_test, y_train, y_test = train_test_split(X, yy)
clf = GenSVM(epsilon=1e-3) # faster testing
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
pred_set = set(y_pred)
label_set = set(labels)
self.assertTrue(pred_set.issubset(label_set))
def test_fit_with_weights(self):
""" GENSVM: Test fit with sample weights """
X, y = load_iris(return_X_y=True)
weights = np.random.random((X.shape[0], ))
clf = GenSVM(max_iter=100)
clf.fit(X, y, sample_weight=weights)
# with seeding
V = clf.combined_coef_
weights = np.random.random((X.shape[0], ))
clf.fit(X, y, sample_weight=weights, seed_V=V)
clf = GenSVM(kernel="rbf")
clf.fit(X, y, sample_weight=weights)
def test_fit_with_seed(self):
""" GENSVM: Test fit with seeding """
# This is based on the unit test for gensvm_train in the C library
n_obs = 10
n_var = 3
n_class = 4
X = np.zeros((n_obs, n_var + 1))
X[0, 0] = 1.0000000000000000
X[0, 1] = 0.8056271362589000
X[0, 2] = 0.4874175854113872
X[0, 3] = 0.4453015882771756
X[1, 0] = 1.0000000000000000
X[1, 1] = 0.7940590105180981
X[1, 2] = 0.1861049005485224
X[1, 3] = 0.8469394287449229
X[2, 0] = 1.0000000000000000
X[2, 1] = 0.0294257611061681
X[2, 2] = 0.0242717976065267
X[2, 3] = 0.5039128672814752
X[3, 0] = 1.0000000000000000
X[3, 1] = 0.1746563833537603
X[3, 2] = 0.9135736087631979
X[3, 3] = 0.5270258081021366
X[4, 0] = 1.0000000000000000
X[4, 1] = 0.0022298761599785
X[4, 2] = 0.3773482059713607
X[4, 3] = 0.8009654729622842
X[5, 0] = 1.0000000000000000
X[5, 1] = 0.6638830667081945
X[5, 2] = 0.6467607601353914
X[5, 3] = 0.0434948735457108
X[6, 0] = 1.0000000000000000
X[6, 1] = 0.0770493004546461
X[6, 2] = 0.3699566427075194
X[6, 3] = 0.7863539761080217
X[7, 0] = 1.0000000000000000
X[7, 1] = 0.2685233952731509
X[7, 2] = 0.8539966432782011
X[7, 3] = 0.0967159557826836
X[8, 0] = 1.0000000000000000
X[8, 1] = 0.1163951898554611
X[8, 2] = 0.7667861436369238
X[8, 3] = 0.5031912600213351
X[9, 0] = 1.0000000000000000
X[9, 1] = 0.2290251898688216
X[9, 2] = 0.4401981048538806
X[9, 3] = 0.0884616753393881
X = X[:, 1:]
y = np.array([2, 1, 3, 2, 3, 2, 4, 1, 3, 4])
seed_V = np.zeros((n_var + 1, n_class - 1))
seed_V[0, 0] = 0.8233234072519983
seed_V[0, 1] = 0.7701104553132680
seed_V[0, 2] = 0.1102697774064020
seed_V[1, 0] = 0.7956168453294307
seed_V[1, 1] = 0.3267543833513200
seed_V[1, 2] = 0.8659836346403005
seed_V[2, 0] = 0.5777227081256917
seed_V[2, 1] = 0.3693175185473680
seed_V[2, 2] = 0.2728942849022845
seed_V[3, 0] = 0.4426030703804438
seed_V[3, 1] = 0.2456426390463990
seed_V[3, 2] = 0.2665038412777220
clf = GenSVM(
p=1.2143,
kappa=0.90298,
lmd=0.00219038,
epsilon=1e-15,
weights="unit",
kernel="linear",
)
clf.fit(X, y, seed_V=seed_V)
V = clf.combined_coef_
eps = 1e-7
self.assertLess(abs(V[0, 0] - -1.1907736868272805), eps)
self.assertLess(abs(V[0, 1] - 1.8651287814979396), eps)
self.assertLess(abs(V[0, 2] - 1.7250030581662932), eps)
self.assertLess(abs(V[1, 0] - 0.7925100058806183), eps)
self.assertLess(abs(V[1, 1] - -3.6093428916761665), eps)
self.assertLess(abs(V[1, 2] - -1.3394018960329377), eps)
self.assertLess(abs(V[2, 0] - 1.5203132433193016), eps)
self.assertLess(abs(V[2, 1] - -1.9118604362643852), eps)
self.assertLess(abs(V[2, 2] - -1.7939246097629342), eps)
self.assertLess(abs(V[3, 0] - 0.0658817457370326), eps)
self.assertLess(abs(V[3, 1] - 0.6547924025329720), eps)
self.assertLess(abs(V[3, 2] - -0.6773346708737853), eps)
def single():
wait_for_human()
X, y = load_iris(return_X_y=True)
clf = GenSVM(lmd=1e-10, epsilon=1e-10, p=2, kappa=-0.99, verbose=1)
clf.fit(X, y)