diff options
Diffstat (limited to 'test/test_gridsearch.py')
| -rw-r--r-- | test/test_gridsearch.py | 209 |
1 files changed, 209 insertions, 0 deletions
diff --git a/test/test_gridsearch.py b/test/test_gridsearch.py new file mode 100644 index 0000000..16f4a3f --- /dev/null +++ b/test/test_gridsearch.py @@ -0,0 +1,209 @@ +# -*- coding: utf-8 -*- + +""" + + Unit tests for the grid_search module + +""" + +from __future__ import division, print_function + +import numpy as np +import unittest + +from sklearn.datasets import load_iris, load_digits +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import maxabs_scale + +from gensvm.gridsearch import ( + GenSVMGridSearchCV, + _validate_param_grid, + load_default_grid, +) + + +class GenSVMGridSearchCVTestCase(unittest.TestCase): + def test_validate_param_grid(self): + """ GENSVM_GRID: Test parameter grid validation """ + pg = { + "p": [1, 1.5, 2.0], + "kappa": [-0.9, 1.0], + "lmd": [0.1, 1.0], + "epsilon": [0.01, 0.002], + "gamma": [1.0, 2.0], + "weights": ["unit", "group"], + } + + _validate_param_grid(pg) + + tmp = {k: v for k, v in pg.items()} + tmp["p"] = [0.5, 1.0, 2.0] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + tmp = {k: v for k, v in pg.items()} + tmp["kappa"] = [-1.0, 0.0, 1.0] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + tmp = {k: v for k, v in pg.items()} + tmp["lmd"] = [-1.0, 0.0, 1.0] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + tmp = {k: v for k, v in pg.items()} + tmp["epsilon"] = [-1.0, 0.0, 1.0] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + tmp = {k: v for k, v in pg.items()} + tmp["gamma"] = [-1.0, 0.0, 1.0] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + tmp = {k: v for k, v in pg.items()} + tmp["weights"] = ["unit", "group", "other"] + with self.assertRaises(ValueError): + _validate_param_grid(tmp) + + def test_fit_predict_strings(self): + """ GENSVM_GRID: Test fit and predict with string targets """ + iris = load_iris() + X = iris.data + y = iris.target + labels = iris.target_names + yy = labels[y] + X_train, X_test, y_train, y_test = train_test_split(X, yy) + + pg = { + "p": [1, 1.5, 2.0], + "kappa": [-0.9, 1.0], + "lmd": [0.1, 1.0], + "epsilon": [0.01, 0.002], + "gamma": [1.0, 2.0], + "weights": ["unit", "group"], + } + + clf = GenSVMGridSearchCV(pg) + 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_score(self): + """ GENSVM_GRID: Test fit and score """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + pg = { + "p": [1, 1.5, 2.0], + "kappa": [-0.9, 1.0, 5.0], + "lmd": [pow(2, x) for x in range(-12, 9, 2)], + } + + clf = GenSVMGridSearchCV(pg) + clf.fit(X_train, y_train) + score = clf.score(X_test, y_test) + + # low for safety + self.assertGreaterEqual(score, 0.80) + + def test_refit(self): + """ GENSVM_GRID: Test refit """ + # we use the fact that large regularization parameters usually don't + # give a good fit. + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + pg = {"lmd": [1e-4, 100, 10000]} + + clf = GenSVMGridSearchCV(pg) + clf.fit(X_train, y_train) + + self.assertTrue(hasattr(clf, "best_params_")) + self.assertTrue(clf.best_params_ == {"lmd": 1e-4}) + + def test_multimetric(self): + """ GENSVM_GRID: Test multimetric """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + pg = {"p": [1., 1.5, 2.]} + + clf = GenSVMGridSearchCV( + pg, scoring=["accuracy", "adjusted_rand_score"], refit=False + ) + clf.fit(X_train, y_train) + + self.assertTrue(clf.multimetric_) + self.assertTrue("mean_test_accuracy" in clf.cv_results_) + self.assertTrue("mean_test_adjusted_rand_score" in clf.cv_results_) + + def test_refit_multimetric(self): + """ GENSVM_GRID: Test refit with multimetric """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + pg = {"lmd": [1e-4, 100, 10000]} + + clf = GenSVMGridSearchCV( + pg, scoring=["accuracy", "adjusted_rand_score"], refit="accuracy" + ) + clf.fit(X_train, y_train) + + self.assertTrue(hasattr(clf, "best_params_")) + self.assertTrue(hasattr(clf, "best_estimator_")) + self.assertTrue(hasattr(clf, "best_index_")) + self.assertTrue(hasattr(clf, "best_score_")) + self.assertTrue(clf.best_params_ == {"lmd": 1e-4}) + + def test_params_rbf_kernel(self): + """ GENSVM_GRID: Test best params with RBF kernel """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + pg = {"lmd": [1e-4, 100, 10000], "kernel": ["rbf"]} + + clf = GenSVMGridSearchCV(pg) + clf.fit(X_train, y_train) + + self.assertTrue(hasattr(clf, "best_params_")) + + def test_invalid_y(self): + """ GENSVM_GRID: Check raises for invalid y type """ + pg = {"lmd": [1e-4, 100, 10000], "kernel": ["rbf"]} + clf = GenSVMGridSearchCV(pg) + 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 slowtest_gridsearch_warnings(self): + """ GENSVM_GRID: Check grid search with warnings """ + np.random.seed(123) + X, y = load_digits(4, return_X_y=True) + small = {} + for k in [1, 2, 3]: + tmp = X[y == k, :] + small[k] = tmp[np.random.choice(tmp.shape[0], 20), :] + + Xs = np.vstack((small[1], small[2], small[3])) + ys = np.hstack((np.ones(20), 2 * np.ones(20), 3 * np.ones(20))) + pg = { + "p": [1.0, 2.0], + "lmd": [pow(10, x) for x in range(-4, 1, 2)], + "epsilon": [1e-6], + } + gg = GenSVMGridSearchCV(pg, verbose=True) + gg.fit(Xs, ys) |