def test_fit_and_predict_ranking(self):
ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, random_state=0)
ssvm.fit(self.x.values, self.y)
self.assertFalse(hasattr(ssvm, "intercept_"))
expected_coef = numpy.array([-0.02066177, -0.26449933, -0.15205399, 0.0794547, -0.28840498, -0.02864288,
0.09901995, 0.04505302, -0.12512215, 0.03341365, -0.00110442, 0.05446756,
-0.53009875, -0.01394175])
assert_array_almost_equal(expected_coef, ssvm.coef_)
self.assertEquals(self.x.shape[1], ssvm.coef_.shape[0])
c = ssvm.score(self.x.values, self.y)
self.assertAlmostEqual(0.7860650174985695, c, 6)
def test_fit_and_predict_ranking(make_whas500, optimizer_any):
whas500 = make_whas500(to_numeric=True)
ssvm = FastSurvivalSVM(optimizer=optimizer_any, random_state=0)
ssvm.fit(whas500.x, whas500.y)
assert not hasattr(ssvm, "intercept_")
expected_coef = numpy.array([-0.02066177, -0.26449933, -0.15205399, 0.0794547, -0.28840498, -0.02864288,
0.09901995, 0.04505302, -0.12512215, 0.03341365, -0.00110442, 0.05446756,
-0.53009875, -0.01394175])
assert_array_almost_equal(expected_coef, ssvm.coef_)
assert whas500.x.shape[1] == ssvm.coef_.shape[0]
c = ssvm.score(whas500.x, whas500.y)
assert round(abs(0.7860650174985695 - c), 6) == 0
