def test_likelihood_efron(rossi, coef_rossi_coxph_efron):
cph = CoxPHOptimizer(rossi.x.values, rossi.y['arrest'], rossi.y['week'], alpha=0.,
ties="efron")
w = coef_rossi_coxph_efron.loc[rossi.x.columns].values
actual_loss = cph.nlog_likelihood(w)
assert round(abs(658.7477 - rossi.x.shape[0] * actual_loss), 4) == 0
def test_likelihood_breslow(rossi, coef_rossi_coxph_breslow):
cph = CoxPHOptimizer(rossi.x.values, rossi.y['arrest'], rossi.y['week'], alpha=0.,
ties="breslow")
w = coef_rossi_coxph_breslow.loc[rossi.x.columns].values
actual_loss = cph.nlog_likelihood(w)
assert round(abs(659.1206 - rossi.x.shape[0] * actual_loss), 4) == 0
def test_gradient_breslow(rossi):
cph = CoxPHOptimizer(rossi.x.values,
rossi.y['arrest'],
rossi.y['week'],
alpha=numpy.zeros(rossi.x.shape[1]),
ties="breslow")
assert_gradient_correctness(cph)
def test_gradient_efron(rossi):
cph = CoxPHOptimizer(rossi.x.values.astype(float),
rossi.y['arrest'],
rossi.y['week'],
alpha=numpy.zeros(rossi.x.shape[1]),
ties="efron")
assert_gradient_correctness(cph)
def test_likelihood(self):
cph = CoxPHOptimizer(self.x.values,
self.y['arrest'],
self.y['week'],
alpha=0.)
w = pandas.Series({
"fin": -0.37902189,
"age": -0.05724593,
"race": 0.31412977,
"wexp": -0.15111460,
"mar": -0.43278257,
"paro": -0.08498284,
"prio": 0.09111154
})
actual_loss = cph.nlog_likelihood(w.loc[self.x.columns].values)
self.assertAlmostEqual(659.1206, self.x.shape[0] * actual_loss, 4)
def test_likelihood(rossi):
cph = CoxPHOptimizer(rossi.x.values,
rossi.y['arrest'],
rossi.y['week'],
alpha=0.)
w = pandas.Series({
"fin": -0.37902189,
"age": -0.05724593,
"race": 0.31412977,
"wexp": -0.15111460,
"mar": -0.43278257,
"paro": -0.08498284,
"prio": 0.09111154
})
actual_loss = cph.nlog_likelihood(w.loc[rossi.x.columns].values)
assert round(abs(659.1206 - rossi.x.shape[0] * actual_loss), 4) == 0