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)