def test_consistency(self):
# tests for model's load save consistency.
old_model = svm.SVC()
old_model.fit(self.X, self.y)
fname = _tempfile("model.fs2ml")
old_model.save_model(fname)
new_model = svm.SVC()
new_model.load_model(fname)
self.model_equal(old_model, new_model)
def test_poly_kernel(self):
# Tests polynomial kernel of svc.
X1 = Distribution.linear(pts=50, mean=[8, 20],
covr=[[1.5, 1], [1, 2]], seed=100)
X2 = Distribution.linear(pts=50, mean=[8, 15],
covr=[[1.5, -1], [-1, 2]], seed=100)
X3 = Distribution.linear(pts=50, mean=[15, 20],
covr=[[1.5, 1], [1, 2]], seed=100)
X4 = Distribution.linear(pts=50, mean=[15, 15],
covr=[[1.5, -1], [-1, 2]], seed=100)
X1 = np.vstack((X1, X2))
X2 = np.vstack((X3, X4))
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X2.shape[0])
X_train = np.vstack((X1, X2))
y_train = np.hstack((Y1, Y2))
clf = svm.SVC(kernel='polynomial', const=1, degree=2)
clf.fit(X_train, y_train)
X1 = Distribution.linear(pts=5, mean=[8, 20],
covr=[[1.5, 1], [1, 2]], seed=100)
X2 = Distribution.linear(pts=5, mean=[8, 15],
covr=[[1.5, -1], [-1, 2]], seed=100)
X3 = Distribution.linear(pts=5, mean=[15, 20],
covr=[[1.5, 1], [1, 2]], seed=100)
X4 = Distribution.linear(pts=5, mean=[15, 15],
covr=[[1.5, -1], [-1, 2]], seed=100)
X1 = np.vstack((X1, X2))
X2 = np.vstack((X3, X4))
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X2.shape[0])
X_test = np.vstack((X1, X2))
y_test = np.hstack((Y1, Y2))
predictions, projections = clf.predict(X_test, return_projection=True)
expected_projections = np.array([1.2630574, 1.3302442, 1.502788, 1.2003369, 1.4567516,
1.0555044, 1.434326, 1.4227715, 1.1069533, 1.104987,
-1.6992458, -1.5001097, -1.0005158, -1.8284273, -1.0863144,
-2.238042, -1.2274336, -1.2235101, -2.1250129, -2.0870237]
)
expected_projections = np.array([1.9282368, 4.1053743, 4.449601, 2.8149981, 3.337817,
1.5934888, 4.237419, 3.699658, 3.8548565, 2.8402433,
-6.7378554, -2.9163127, -2.5978136, -4.833237, -4.421687,
-5.2333884, -2.2744238, -3.0598483, -2.4422958, -3.890006],
)
self.assertTrue(np.allclose(projections, expected_projections))
self.assertTrue(np.allclose(predictions, y_test))
def test_rbf_kernel(self):
# Tests RBF kernel of svc.
X1 = Distribution.radial_binary(pts=100,
mean=[0, 0],
st=1,
ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=100,
mean=[0, 0],
st=4,
ed=5,
seed=100)
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X1.shape[0])
X_train = np.vstack((X1, X2))
y_train = np.hstack((Y1, Y2))
clf = svm.SVC(kernel='rbf', gamma=10)
clf.fit(X_train, y_train)
X1 = Distribution.radial_binary(pts=10,
mean=[0, 0],
st=1,
ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=10,
mean=[0, 0],
st=4,
ed=5,
seed=100)
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X2.shape[0])
X_test = np.vstack((X1, X2))
y_test = np.hstack((Y1, Y2))
predictions, projections = clf.predict(X_test, return_projection=True)
expected_projections = np.array([
1.2630574, 1.3302442, 1.502788, 1.2003369, 1.4567516, 1.0555044,
1.434326, 1.4227715, 1.1069533, 1.104987, -1.6992458, -1.5001097,
-1.0005158, -1.8284273, -1.0863144, -2.238042, -1.2274336,
-1.2235101, -2.1250129, -2.0870237
], )
self.assertTrue(np.allclose(projections, expected_projections))
self.assertTrue(np.allclose(predictions, y_test))
def test_linear_kernel(self):
# Tests linear kernel of svc.
X1 = Distribution.linear(pts=100,
mean=[8, 10],
covr=[[1.5, 1], [1, 1.5]],
seed=100)
X2 = Distribution.linear(pts=100,
mean=[9, 5],
covr=[[1.5, 1], [1, 1.5]],
seed=100)
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X2.shape[0])
X_train = np.vstack((X1, X2))
y_train = np.hstack((Y1, Y2))
clf_lin = svm.SVC(kernel='linear')
clf_lin.fit(X_train, y_train)
X1 = Distribution.linear(pts=10,
mean=[8, 10],
covr=[[1.5, 1], [1, 1.5]],
seed=100)
X2 = Distribution.linear(pts=10,
mean=[9, 5],
covr=[[1.5, 1], [1, 1.5]],
seed=100)
Y1 = np.ones(X1.shape[0])
Y2 = -np.ones(X2.shape[0])
X_test = np.vstack((X1, X2))
y_test = np.hstack((Y1, Y2))
predictions, projections = clf_lin.predict(X_test,
return_projection=True)
expected_projections = np.array([
5.2844825, 2.8846788, 3.898558, 2.4527097, 4.271367, 4.6425023,
5.170607, 3.3408344, 5.3939104, 2.779106, -2.909471, -5.3092747,
-4.2953954, -5.7412434, -3.9225864, -3.551451, -3.0233462,
-4.853119, -2.8000426, -5.4148474
])
self.assertTrue(np.allclose(projections, expected_projections))
self.assertTrue(np.allclose(predictions, y_test))
def test_multiclass(self):
X1 = Distribution.radial_binary(pts=10, mean=[0, 0], st=1, ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=10, mean=[0, 0], st=4, ed=5,
seed=100)
X3 = Distribution.radial_binary(pts=10, mean=[0, 0], st=6, ed=7,
seed=100)
X4 = Distribution.radial_binary(pts=10, mean=[0, 0], st=8, ed=9,
seed=100)
Y1 = -np.ones(X1.shape[0])
Y2 = np.ones(X2.shape[0])
Y3 = 2 * np.ones(X3.shape[0])
Y4 = 3000 * np.ones(X4.shape[0])
X_train = np.vstack((X1, X2, X3, X4))
y_train = np.hstack((Y1, Y2, Y3, Y4))
clf = svm.SVC(kernel='rbf', gamma=10)
clf.fit(X_train, y_train)
X1 = Distribution.radial_binary(pts=10, mean=[0, 0], st=1, ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=10, mean=[0, 0], st=4, ed=5,
seed=100)
X3 = Distribution.radial_binary(pts=10, mean=[0, 0], st=6, ed=7,
seed=100)
X4 = Distribution.radial_binary(pts=10, mean=[0, 0], st=8, ed=9,
seed=100)
X_test = np.vstack((X1, X2, X3, X4))
_, projections = clf.predict(X_test, return_projection=True)
expected_projections = np.array(
[1.23564788, 1.15519477, 1.32441802, 1.04496554, 1.29740627, 0.,
1.25561797, 1.22925452, 0., 1.11920321, 0.2991908, 0.23818634,
0.55359011, 0.29655677, 0., 0.59992803, 0.52733203, 0.30456398,
0.6027897, 0.33755249, 0., 0.04997651, 0.12099712, 0.12276944,
0., 0.19631702, 0.11836214, 0.06221966, 0.24539362, 0.,
1.00000106, 1.0000021, 1.00000092, 1.19952335, 1.00000283, 1.17741522,
1.40596479, 1.60945299, 1.41534644, 1.27928235]
)
self.assertTrue(np.allclose(projections, expected_projections))
def test_multiclass(self):
X1 = Distribution.radial_binary(pts=10, mean=[0, 0], st=1, ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=10, mean=[0, 0], st=4, ed=5,
seed=100)
X3 = Distribution.radial_binary(pts=10, mean=[0, 0], st=6, ed=7,
seed=100)
X4 = Distribution.radial_binary(pts=10, mean=[0, 0], st=8, ed=9,
seed=100)
Y1 = -np.ones(X1.shape[0])
Y2 = np.ones(X2.shape[0])
Y3 = 2 * np.ones(X3.shape[0])
Y4 = 3000 * np.ones(X4.shape[0])
X_train = np.vstack((X1, X2, X3, X4))
y_train = np.hstack((Y1, Y2, Y3, Y4))
clf = svm.SVC(kernel='rbf', gamma=10)
clf.fit(X_train, y_train)
X1 = Distribution.radial_binary(pts=10, mean=[0, 0], st=1, ed=2,
seed=100)
X2 = Distribution.radial_binary(pts=10, mean=[0, 0], st=4, ed=5,
seed=100)
X3 = Distribution.radial_binary(pts=10, mean=[0, 0], st=6, ed=7,
seed=100)
X4 = Distribution.radial_binary(pts=10, mean=[0, 0], st=8, ed=9,
seed=100)
X_test = np.vstack((X1, X2, X3, X4))
_, projections = clf.predict(X_test, return_projection=True)
expected_projections = np.array([1.2356423, 1.1552206, 1.3244298, 1.044992, 1.2974104,
1.0000014, 1.2556306, 1.2292726, 1.0000055, 1.1191963,
0.29907486, 0.23834696, 0.5536205, 0.29669908, 0.,
0.59981525, 0.52742934, 0.30471632, 0.60282767, 0.33748698,
0., 0.04998485, 0.12111297, 0.12295954, 0.,
0.19644868, 0.11843737, 0.06227912, 0.24561723, 0.,
1.0000006, 0.99999857, 0.99999964, 1.1995112, 0.999999,
1.177406, 1.4059596, 1.6094441, 1.4153401, 1.2792733],
)
self.assertTrue(np.allclose(projections.numpy(), expected_projections))
def test_not_fitted(self):
# ensure that ModelNotFittedError is raised when predict is called
# before predict.
model = svm.SVC()
with self.assertRaises(ModelNotFittedError):
model.predict(self.X)
