def test_cox_ph_prediction_monotonicity(self, data_pred2):
# Concordance wise, all prediction methods should be monotonic versions
# of one-another, unless numerical factors screw it up.
t = data_pred2['t']
e = data_pred2['E']
X = data_pred2[['x1', 'x2']]
for normalize in [True, False]:
msg = ("Predict methods should get the same concordance" +
" when {}normalizing".format('' if normalize else 'not '))
cf = CoxPHFitter(normalize=normalize)
cf.fit(data_pred2, duration_col='t', event_col='E')
# Base comparison is partial_hazards
ci_ph = concordance_index(t, -cf.predict_partial_hazard(X).values, e)
ci_med = concordance_index(t, cf.predict_median(X).ravel(), e)
assert ci_ph == ci_med, msg
ci_exp = concordance_index(t, cf.predict_expectation(X).ravel(), e)
assert ci_ph == ci_exp, msg
def test_cox_ph_prediction_monotonicity(self, data_pred2):
# Concordance wise, all prediction methods should be monotonic versions
# of one-another, unless numerical factors screw it up.
t = data_pred2['t']
e = data_pred2['E']
X = data_pred2[['x1', 'x2']]
for normalize in [True, False]:
msg = ("Predict methods should get the same concordance" +
" when {}normalizing".format('' if normalize else 'not '))
cf = CoxPHFitter(normalize=normalize)
cf.fit(data_pred2, duration_col='t', event_col='E')
# Base comparison is partial_hazards
ci_ph = concordance_index(t, -cf.predict_partial_hazard(X).values, e)
ci_med = concordance_index(t, cf.predict_median(X).ravel(), e)
assert ci_ph == ci_med, msg
ci_exp = concordance_index(t, cf.predict_expectation(X).ravel(), e)
assert ci_ph == ci_exp, msg
