def test_fit_bad_type(self):
A = [[[1 for i in range(5)] for j in range(5)] for k in range(5)]
ys = [1, 1, 1, 1, 1]
test_model = sg.SignalSubgraph()
with self.assertRaises(TypeError):
test_model.fit(A, np.ones(5), 1)
with self.assertRaises(TypeError):
test_model.fit(A, set(ys), 1)
def test_fit_bad_constraints(self):
A = np.ones((5, 5, 5))
ys = np.ones(5)
test_model = sg.SignalSubgraph()
with self.assertRaises(TypeError):
test_model.fit(A, ys, [1])
with self.assertRaises(TypeError):
test_model.fit(A, ys, [1, 1, 1])
def test_fit_bad_len(self):
A = np.ones((3, 3, 3))
test_model = sg.SignalSubgraph()
with self.assertRaises(ValueError):
test_model.fit(A, np.ones((3, 3)), 1)
with self.assertRaises(ValueError):
test_model.fit(A, np.array([0, 1, 2]), 1)
with self.assertRaises(ValueError):
test_model.fit(A, np.ones(2), 1)
def test_construct_contingency(self):
A = np.ones((1, 1, 5))
A[:, :, 1::2] = 0
ys = np.array([1, 0, 1, 0, 0])
test_model = sg.SignalSubgraph()
test_model.fit(A, ys, 1)
test_model._SignalSubgraph__construct_contingency()
cmat = test_model.contmat_
ver = np.array([[[[1, 2], [2, 0]]]], dtype=float)
np.testing.assert_array_equal(cmat, ver)
def test_estimate_subgraph_inc(self):
ys = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
blank = np.ones((10, 10))
blank[1:6, 0] = 0
A = np.ones((10, 10, 10))
for ind in range(10):
if ys[ind] == 1:
A[:, :, ind] = blank
test_model = sg.SignalSubgraph()
estsub = test_model.fit_transform(A, ys, 5)
ver = np.ones((10, 10))
ver[estsub] = 0
np.testing.assert_array_equal(blank, ver)
def test_fit_bad_size(self):
test_model = sg.SignalSubgraph()
with self.assertRaises(ValueError):
test_model.fit(np.ones((5, 5)), np.ones(5), 1)
with self.assertRaises(ValueError):
test_model.fit(np.ones((3, 4, 2)), np.ones(2), 1)
