def test_survival_squared_hinge_loss(self):
x, y = self.get_data_without_ties()
nrsvm = NaiveSurvivalSVM(loss='squared_hinge',
dual=False,
tol=8e-7,
max_iter=1000,
random_state=0)
nrsvm.fit(x, y)
rsvm = FastSurvivalSVM(optimizer='avltree',
tol=8e-7,
max_iter=1000,
random_state=0)
rsvm.fit(x, y)
assert_array_almost_equal(nrsvm.coef_.ravel(), rsvm.coef_, 3)
pred_nrsvm = nrsvm.predict(x)
pred_rsvm = rsvm.predict(x)
self.assertEqual(len(pred_nrsvm), len(pred_rsvm))
c1 = concordance_index_censored(y['fstat'], y['lenfol'], pred_nrsvm)
c2 = concordance_index_censored(y['fstat'], y['lenfol'], pred_rsvm)
self.assertAlmostEqual(c1[0], c2[0])
self.assertTupleEqual(c1[1:], c2[1:])
def test_survival_squared_hinge_loss(whas500_without_ties):
x, y = whas500_without_ties
nrsvm = NaiveSurvivalSVM(loss='squared_hinge',
dual=False,
tol=8e-7,
max_iter=1000,
random_state=0)
nrsvm.fit(x, y)
rsvm = FastSurvivalSVM(optimizer='avltree',
tol=8e-7,
max_iter=1000,
random_state=0)
rsvm.fit(x, y)
assert_array_almost_equal(nrsvm.coef_.ravel(), rsvm.coef_, 3)
pred_nrsvm = nrsvm.predict(x)
pred_rsvm = rsvm.predict(x)
assert len(pred_nrsvm) == len(pred_rsvm)
expected_cindex = concordance_index_censored(y['fstat'], y['lenfol'],
pred_nrsvm)
assert_cindex_almost_equal(y['fstat'], y['lenfol'], pred_rsvm,
expected_cindex)
def test_fit_uncomparable(whas500_uncomparable):
ssvm = NaiveSurvivalSVM(loss='squared_hinge',
dual=False,
tol=1e-8,
max_iter=1000,
random_state=0)
with pytest.raises(NoComparablePairException):
ssvm.fit(whas500_uncomparable.x, whas500_uncomparable.y)
def test_fit_with_ties(whas500_with_ties):
x, y = whas500_with_ties
nrsvm = NaiveSurvivalSVM(loss='squared_hinge', dual=False, tol=1e-8, max_iter=1000, random_state=0)
nrsvm.fit(x, y)
assert nrsvm.coef_.shape == (1, 14)
cindex = nrsvm.score(x, y)
assert round(abs(cindex - 0.7760582309811175), 7) == 0
def test_fit_with_ties(self):
x, y = self.get_data_with_ties()
nrsvm = NaiveSurvivalSVM(loss='squared_hinge',
dual=False,
tol=1e-8,
max_iter=1000,
random_state=0)
nrsvm.fit(x, y)
self.assertTupleEqual(nrsvm.coef_.shape, (1, 14))
cindex = nrsvm.score(x, y)
self.assertAlmostEqual(cindex, 0.7760582309811175)