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Diffstat (limited to 'test/test_core.py')
| -rw-r--r-- | test/test_core.py | 279 |
1 files changed, 279 insertions, 0 deletions
diff --git a/test/test_core.py b/test/test_core.py new file mode 100644 index 0000000..4be7df4 --- /dev/null +++ b/test/test_core.py @@ -0,0 +1,279 @@ +# -*- coding: utf-8 -*- + +""" + + Unit tests for the GenSVM class + +""" + +from __future__ import division, print_function + +import numpy as np +import unittest +import warnings + +from sklearn.datasets import load_digits, load_iris +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import maxabs_scale + +from gensvm import GenSVM + + +class GenSVMTestCase(unittest.TestCase): + def test_init_1(self): + """ GENSVM: Sanity check for __init__ """ + clf = GenSVM() + self.assertEqual(clf.p, 1.0) + self.assertEqual(clf.lmd, 1e-5) + self.assertEqual(clf.kappa, 0.0) + self.assertEqual(clf.epsilon, 1e-6) + self.assertEqual(clf.weights, "unit") + self.assertEqual(clf.kernel, "linear") + self.assertEqual(clf.gamma, "auto") + self.assertEqual(clf.coef, 1.0) + self.assertEqual(clf.degree, 2.0) + self.assertEqual(clf.kernel_eigen_cutoff, 1e-8) + self.assertEqual(clf.verbose, 0) + self.assertEqual(clf.random_state, None) + self.assertEqual(clf.max_iter, 1e8) + + def test_init_invalid_p(self): + """ GENSVM: Check raises for invalid p """ + with self.assertRaises(ValueError) as err1: + GenSVM(p=2.6) + exc1 = err1.exception + self.assertEqual( + exc1.args, ("Value for p should be within [1, 2]; got p = 2.6",) + ) + with self.assertRaises(ValueError) as err2: + GenSVM(p=0.3) + exc2 = err2.exception + self.assertEqual( + exc2.args, ("Value for p should be within [1, 2]; got p = 0.3",) + ) + + def test_init_invalid_kappa(self): + """ GENSVM: Check raises for invalid kappa """ + with self.assertRaises(ValueError) as err1: + GenSVM(kappa=-1.0) + exc1 = err1.exception + self.assertEqual( + exc1.args, + ("Value for kappa should be larger " "than -1; got kappa = -1.0",), + ) + + def test_init_invalid_lmd(self): + """ GENSVM: Check raises for invalid lmd """ + with self.assertRaises(ValueError) as err1: + GenSVM(lmd=-3.0) + exc1 = err1.exception + self.assertEqual( + exc1.args, + ("Value for lmd should be larger than 0; got lmd = -3.0",), + ) + + def test_init_invalid_epsilon(self): + """ GENSVM: Check raises for invalid epsilon """ + with self.assertRaises(ValueError) as err1: + GenSVM(epsilon=-1.0) + exc1 = err1.exception + self.assertEqual( + exc1.args, + ( + "Value for epsilon should be larger than 0; got " + "epsilon = -1.0", + ), + ) + + def test_init_invalid_gamma(self): + """ GENSVM: Check raises for invalid gamma """ + with self.assertRaises(ValueError) as err1: + GenSVM(gamma=0.0) + exc1 = err1.exception + self.assertEqual(exc1.args, ("A gamma value of 0.0 is invalid",)) + + def test_init_invalid_weights(self): + """ GENSVM: Check raises for invalid weights """ + with self.assertRaises(ValueError) as err1: + GenSVM(weights="other") + exc1 = err1.exception + self.assertEqual( + exc1.args, + ( + "Unknown weight parameter specified. Should be " + "'unit' or 'group'; got 'other'", + ), + ) + + def test_init_invalid_kernel(self): + """ GENSVM: Check raises for invalid kernel """ + with self.assertRaises(ValueError) as err1: + GenSVM(kernel="other") + exc1 = err1.exception + self.assertEqual( + exc1.args, + ( + "Unknown kernel specified. Should be " + "'linear', 'rbf', 'poly', or 'sigmoid'; got 'other'", + ), + ) + + def test_invalid_y(self): + """ GENSVM: Check raises for invalid y type """ + clf = GenSVM() + X = np.random.random((20, 4)) + y = np.random.random((20,)) + with self.assertRaises(ValueError) as err: + clf.fit(X, y) + exc = err.exception + self.assertEqual( + exc.args, ("Label type not allowed for GenSVM: 'continuous'",) + ) + + def test_invalid_seed_V_linear(self): + """ GENSVM: Test for invalid seed_V shape """ + clf = GenSVM() + X = np.random.random((20, 4)) + y = np.random.randint(1, 4, (20,)) + seed_V = np.random.random((10, 2)) + with self.assertRaises(ValueError) as err: + clf.fit(X, y, seed_V=seed_V) + exc = err.exception + self.assertEqual( + exc.args, ("Seed V must have shape [5, 2], but has shape [10, 2]",) + ) + + def test_invalid_seed_V_nonlinear(self): + """ GENSVM: Test for seed_V for nonlinear kernels """ + clf = GenSVM(kernel="rbf") + X = np.random.random((20, 4)) + y = np.random.randint(1, 4, (20,)) + seed_V = np.random.random((5, 2)) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + clf.fit(X, y, seed_V=seed_V) + self.assertTrue(len(w) == 1) + msg = str(w[0].message) + self.assertEqual( + msg, + ( + "Warm starts are only supported for the linear kernel. " + "The seed_V parameter will be ignored." + ), + ) + + def test_fit_predict_strings(self): + """ GENSVM: Test fit and predict with string targets """ + digits = load_digits(4) + n_samples = len(digits.images) + X = digits.images.reshape(n_samples, -1) + y = digits.target + labels = np.array(["zero", "one", "two", "three"]) + yy = labels[y] + + X_train, X_test, y_train, y_test = train_test_split(X, yy) + clf = GenSVM(epsilon=1e-3) # faster testing + clf.fit(X_train, y_train) + y_pred = clf.predict(X_test) + + pred_set = set(y_pred) + label_set = set(labels) + self.assertTrue(pred_set.issubset(label_set)) + + def test_fit_with_seed(self): + """ GENSVM: Test fit with seeding """ + + # This is based on the unit test for gensvm_train in the C library + + n_obs = 10 + n_var = 3 + n_class = 4 + + X = np.zeros((n_obs, n_var + 1)) + X[0, 0] = 1.0000000000000000 + X[0, 1] = 0.8056271362589000 + X[0, 2] = 0.4874175854113872 + X[0, 3] = 0.4453015882771756 + X[1, 0] = 1.0000000000000000 + X[1, 1] = 0.7940590105180981 + X[1, 2] = 0.1861049005485224 + X[1, 3] = 0.8469394287449229 + X[2, 0] = 1.0000000000000000 + X[2, 1] = 0.0294257611061681 + X[2, 2] = 0.0242717976065267 + X[2, 3] = 0.5039128672814752 + X[3, 0] = 1.0000000000000000 + X[3, 1] = 0.1746563833537603 + X[3, 2] = 0.9135736087631979 + X[3, 3] = 0.5270258081021366 + X[4, 0] = 1.0000000000000000 + X[4, 1] = 0.0022298761599785 + X[4, 2] = 0.3773482059713607 + X[4, 3] = 0.8009654729622842 + X[5, 0] = 1.0000000000000000 + X[5, 1] = 0.6638830667081945 + X[5, 2] = 0.6467607601353914 + X[5, 3] = 0.0434948735457108 + X[6, 0] = 1.0000000000000000 + X[6, 1] = 0.0770493004546461 + X[6, 2] = 0.3699566427075194 + X[6, 3] = 0.7863539761080217 + X[7, 0] = 1.0000000000000000 + X[7, 1] = 0.2685233952731509 + X[7, 2] = 0.8539966432782011 + X[7, 3] = 0.0967159557826836 + X[8, 0] = 1.0000000000000000 + X[8, 1] = 0.1163951898554611 + X[8, 2] = 0.7667861436369238 + X[8, 3] = 0.5031912600213351 + X[9, 0] = 1.0000000000000000 + X[9, 1] = 0.2290251898688216 + X[9, 2] = 0.4401981048538806 + X[9, 3] = 0.0884616753393881 + + X = X[:, 1:] + y = np.array([2, 1, 3, 2, 3, 2, 4, 1, 3, 4]) + + seed_V = np.zeros((n_var + 1, n_class - 1)) + seed_V[0, 0] = 0.8233234072519983 + seed_V[0, 1] = 0.7701104553132680 + seed_V[0, 2] = 0.1102697774064020 + seed_V[1, 0] = 0.7956168453294307 + seed_V[1, 1] = 0.3267543833513200 + seed_V[1, 2] = 0.8659836346403005 + seed_V[2, 0] = 0.5777227081256917 + seed_V[2, 1] = 0.3693175185473680 + seed_V[2, 2] = 0.2728942849022845 + seed_V[3, 0] = 0.4426030703804438 + seed_V[3, 1] = 0.2456426390463990 + seed_V[3, 2] = 0.2665038412777220 + + clf = GenSVM( + p=1.2143, + kappa=0.90298, + lmd=0.00219038, + epsilon=1e-15, + weights="unit", + kernel="linear", + ) + + clf.fit(X, y, seed_V=seed_V) + + V = clf.combined_coef_ + eps = 1e-13 + self.assertTrue(abs(V[0, 0] - -1.1907736868272805) < eps) + self.assertTrue(abs(V[0, 1] - 1.8651287814979396) < eps) + self.assertTrue(abs(V[0, 2] - 1.7250030581662932) < eps) + self.assertTrue(abs(V[1, 0] - 0.7925100058806183) < eps) + self.assertTrue(abs(V[1, 1] - -3.6093428916761665) < eps) + self.assertTrue(abs(V[1, 2] - -1.3394018960329377) < eps) + self.assertTrue(abs(V[2, 0] - 1.5203132433193016) < eps) + self.assertTrue(abs(V[2, 1] - -1.9118604362643852) < eps) + self.assertTrue(abs(V[2, 2] - -1.7939246097629342) < eps) + self.assertTrue(abs(V[3, 0] - 0.0658817457370326) < eps) + self.assertTrue(abs(V[3, 1] - 0.6547924025329720) < eps) + self.assertTrue(abs(V[3, 2] - -0.6773346708737853) < eps) + + +if __name__ == "__main__": + unittest.main() |