def test_fit_and_predict_hybrid(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.5, max_iter=50, fit_intercept=True, random_state=0) ssvm.fit(self.x.values, self.y) self.assertAlmostEqual(6.1409367385513729, ssvm.intercept_) expected_coef = numpy.array([ -0.0209254120718, -0.265768317208, -0.154254689136, 0.0800600947891, -0.290121131022, -0.0288851785213, 0.0998004550073, 0.0454100937492, -0.125863947621, 0.0343588337797, -0.000710219364914, 0.0546969104996, -0.5375338235, -0.0137995110308 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(780.52617631863893, rmse)
def test_compare_rbf(self): x, y, _, _ = load_arff_file(WHAS500_FILE, ['fstat', 'lenfol'], '1') kpca = KernelPCA(kernel="rbf") xt = kpca.fit_transform(x) nrsvm = FastSurvivalSVM(optimizer='rbtree', tol=1e-8, max_iter=1000, random_state=0) nrsvm.fit(xt, y) rsvm = FastKernelSurvivalSVM(optimizer='rbtree', kernel="rbf", tol=1e-8, max_iter=1000, random_state=0) rsvm.fit(x, y) pred_nrsvm = nrsvm.predict(kpca.transform(x)) pred_rsvm = rsvm.predict(x) self.assertEqual(len(pred_nrsvm), len(pred_rsvm)) c1 = concordance_index_censored(y['fstat'], y['lenfol'], pred_nrsvm) c2 = concordance_index_censored(y['fstat'], y['lenfol'], pred_rsvm) self.assertAlmostEqual(c1[0], c2[0]) self.assertTupleEqual(c1[1:], c2[1:])
def test_compare_clinical_kernel(self): x_full, y, _, _ = load_arff_file(WHAS500_FILE, ['fstat', 'lenfol'], '1', standardize_numeric=False, to_numeric=False) trans = ClinicalKernelTransform() trans.fit(x_full) x = encode_categorical(standardize(x_full)) kpca = KernelPCA(kernel=trans.pairwise_kernel) xt = kpca.fit_transform(x) nrsvm = FastSurvivalSVM(optimizer='rbtree', tol=1e-8, max_iter=1000, random_state=0) nrsvm.fit(xt, y) rsvm = FastKernelSurvivalSVM(optimizer='rbtree', kernel=trans.pairwise_kernel, tol=1e-8, max_iter=1000, random_state=0) rsvm.fit(x, y) pred_nrsvm = nrsvm.predict(kpca.transform(x)) pred_rsvm = rsvm.predict(x) self.assertEqual(len(pred_nrsvm), len(pred_rsvm)) c1 = concordance_index_censored(y['fstat'], y['lenfol'], pred_nrsvm) c2 = concordance_index_censored(y['fstat'], y['lenfol'], pred_rsvm) self.assertAlmostEqual(c1[0], c2[0]) self.assertTupleEqual(c1[1:], c2[1:])
def test_survival_squared_hinge_loss(self): nrsvm = NaiveSurvivalSVM(loss='squared_hinge', dual=False, tol=1e-8, max_iter=1000, random_state=0) nrsvm.fit(self.x, self.y) rsvm = FastSurvivalSVM(optimizer='avltree', tol=1e-8, max_iter=1000, random_state=0) rsvm.fit(self.x, self.y) assert_array_almost_equal(nrsvm.coef_.ravel(), rsvm.coef_, 3) pred_nrsvm = nrsvm.predict(self.x) pred_rsvm = rsvm.predict(self.x) self.assertEqual(len(pred_nrsvm), len(pred_rsvm)) c1 = concordance_index_censored(self.y['fstat'], self.y['lenfol'], pred_nrsvm) c2 = concordance_index_censored(self.y['fstat'], self.y['lenfol'], pred_rsvm) self.assertAlmostEqual(c1[0], c2[0]) self.assertTupleEqual(c1[1:], c2[1:])
def test_fit_timeit(self): rnd = numpy.random.RandomState(0) idx = rnd.choice(numpy.arange(self.x.shape[0]), replace=False, size=100) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, timeit=3, random_state=0) ssvm.fit(self.x.values[idx, :], self.y[idx]) self.assertTrue('timings' in ssvm.optimizer_result_)
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(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_regression_no_intercept(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.0, max_iter=50, fit_intercept=False, random_state=0) ssvm.fit(self.x.values, self.y) self.assertFalse(hasattr(ssvm, "intercept_")) expected_coef = numpy.array([1.39989875, -1.16903161, -0.40195857, -0.05848903, -0.08421557, 4.11924729, 0.25135451, 1.89067276, -0.25751401, -0.10213143, 1.56333622, 3.10136873, -2.23644848, -0.11620715]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(15838.510668936022, rmse)
def test_fit_and_predict_hybrid_no_intercept(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.5, max_iter=50, fit_intercept=False, random_state=0) ssvm.fit(self.x.values, self.y) self.assertFalse(hasattr(ssvm, "intercept_")) expected_coef = numpy.array([0.00669121, -0.2754864, -0.14124808, 0.0748376, -0.2812598, 0.07543884, 0.09845683, 0.08398258, -0.12182314, 0.02637739, 0.03060149, 0.11870598, -0.52688224, -0.01762842]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(1128.4460587629746, rmse)
def test_fit_and_predict_regression(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.0, max_iter=50, fit_intercept=True, random_state=0) ssvm.fit(self.x.values, self.y) self.assertAlmostEqual(6.4160179606675278, ssvm.intercept_) expected_coef = numpy.array( [-0.0730891368237, -0.536630355029, -0.497411603275, 0.269039958377, -0.730559850692, -0.0148443526234, 0.285916578892, 0.165960302339, -0.301749910087, 0.334855938531, 0.0886214732161, 0.0554890272028, -2.12680470014, 0.0421466831393 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(1206.6556186869332, rmse)
def test_fit_and_predict_hybrid(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.5, max_iter=50, fit_intercept=True, random_state=0) ssvm.fit(self.x.values, self.y) self.assertAlmostEqual(6.1409367385513729, ssvm.intercept_) expected_coef = numpy.array( [-0.0209254120718, -0.265768317208, -0.154254689136, 0.0800600947891, -0.290121131022, -0.0288851785213, 0.0998004550073, 0.0454100937492, -0.125863947621, 0.0343588337797, -0.000710219364914, 0.0546969104996, -0.5375338235, -0.0137995110308 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(780.52617631863893, rmse)
def test_fit_and_predict_hybrid_no_intercept(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.5, max_iter=50, fit_intercept=False, random_state=0) ssvm.fit(self.x.values, self.y) self.assertFalse(hasattr(ssvm, "intercept_")) expected_coef = numpy.array([ 0.00669121, -0.2754864, -0.14124808, 0.0748376, -0.2812598, 0.07543884, 0.09845683, 0.08398258, -0.12182314, 0.02637739, 0.03060149, 0.11870598, -0.52688224, -0.01762842 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(1128.4460587629746, rmse)
def test_fit_and_predict_regression_no_intercept(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.0, max_iter=50, fit_intercept=False, random_state=0) ssvm.fit(self.x.values, self.y) self.assertFalse(hasattr(ssvm, "intercept_")) expected_coef = numpy.array([ 1.39989875, -1.16903161, -0.40195857, -0.05848903, -0.08421557, 4.11924729, 0.25135451, 1.89067276, -0.25751401, -0.10213143, 1.56333622, 3.10136873, -2.23644848, -0.11620715 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(15838.510668936022, rmse)
def test_fit_and_predict_regression(self): if self.OPTIMIZER in {'simple', 'PRSVM'}: raise unittest.SkipTest("regression not implemented for " + self.OPTIMIZER) ssvm = FastSurvivalSVM(optimizer=self.OPTIMIZER, rank_ratio=0.0, max_iter=50, fit_intercept=True, random_state=0) ssvm.fit(self.x.values, self.y) self.assertAlmostEqual(6.4160179606675278, ssvm.intercept_) expected_coef = numpy.array([ -0.0730891368237, -0.536630355029, -0.497411603275, 0.269039958377, -0.730559850692, -0.0148443526234, 0.285916578892, 0.165960302339, -0.301749910087, 0.334855938531, 0.0886214732161, 0.0554890272028, -2.12680470014, 0.0421466831393 ]) assert_array_almost_equal(expected_coef, ssvm.coef_) pred = ssvm.predict(self.x.values) rmse = numpy.sqrt(mean_squared_error(self.y['lenfol'], pred)) self.assertAlmostEqual(1206.6556186869332, rmse)