def test_predict(make_whas500):
whas500 = make_whas500()
model = IPCRidge()
model.fit(whas500.x[:400], whas500.y[:400])
x_test = whas500.x[400:]
y_test = whas500.y[400:]
p = model.predict(x_test)
assert_cindex_almost_equal(y_test['fstat'], y_test['lenfol'], -p,
(0.66925817946226107, 2066, 1021, 0, 1))
assert model.score(x_test, y_test) == 1.0 - 0.66925817946226107
def test_predict(self):
model = IPCRidge()
model.fit(self.x[:400], self.y[:400])
x_test = self.x[400:]
y_test = self.y[400:]
p = model.predict(x_test)
ci = concordance_index_censored(y_test['fstat'], y_test['lenfol'], -p)
self.assertAlmostEqual(ci[0], 0.66925817946226107)
self.assertEqual(ci[1], 2066)
self.assertEqual(ci[2], 1021)
self.assertEqual(ci[3], 0)
self.assertEqual(ci[4], 6)
self.assertEqual(model.score(x_test, y_test), 1.0 - ci[0])
allTarget = np.zeros((2000), dtype=[('indicator', bool), ('time', float)])
for i in range(0, 2000):
if data[i][22] < 0:
allTarget[i]['time'] = data[i][23]
allTarget[i]['indicator'] = False
else:
allTarget[i]['time'] = data[i][22]
allTarget[i]['indicator'] = True
trainingTargetKidney = allTarget[0:800]
testTargetKidney = allTarget[800:1000]
estimator = IPCRidge()
estimator.fit(trainingData, trainingTargetKidney)
prediction = estimator.predict(testData)
estimator = CoxPHSurvivalAnalysis()
estimator.fit(trainingData, trainingTargetStruc)
prediction0 = estimator.predict(testData)
result = concordance_index_censored(testTargetStruc["indicator"],
testTargetStruc["targetValue"], prediction)
result0 = concordance_index_censored(testTargetStruc["indicator"],
testTargetStruc["targetValue"],
prediction0)
print(result)
print(prediction)
print(result0)
print(prediction0)