diff --git a/stree/tests/Stree_test.py b/stree/tests/Stree_test.py index 5581b66..803ad3c 100644 --- a/stree/tests/Stree_test.py +++ b/stree/tests/Stree_test.py @@ -243,27 +243,27 @@ class Stree_test(unittest.TestCase): outcomes = { "Synt": { "max_samples liblinear": 0.9606666666666667, - "max_samples linear": 0.786, - "max_samples rbf": 0.7133333333333334, - "max_samples poly": 0.618, - "max_samples sigmoid": 0.8826666666666667, + "max_samples linear": 0.9486666666666667, + "max_samples rbf": 0.978, + "max_samples poly": 0.96, + "max_samples sigmoid": 0.908, "impurity liblinear": 0.9606666666666667, - "impurity linear": 0.786, - "impurity rbf": 0.7133333333333334, - "impurity poly": 0.618, - "impurity sigmoid": 0.8826666666666667, + "impurity linear": 0.9486666666666667, + "impurity rbf": 0.978, + "impurity poly": 0.96, + "impurity sigmoid": 0.908, }, "Iris": { "max_samples liblinear": 1.0, "max_samples linear": 1.0, - "max_samples rbf": 0.6910112359550562, - "max_samples poly": 0.6966292134831461, - "max_samples sigmoid": 0.6573033707865169, - "impurity liblinear": 1, - "impurity linear": 1, - "impurity rbf": 0.6910112359550562, - "impurity poly": 0.6966292134831461, - "impurity sigmoid": 0.6573033707865169, + "max_samples rbf": 0.7808988764044944, + "max_samples poly": 0.8202247191011236, + "max_samples sigmoid": 0.7528089887640449, + "impurity liblinear": 1.0, + "impurity linear": 1.0, + "impurity rbf": 0.7808988764044944, + "impurity poly": 0.8202247191011236, + "impurity sigmoid": 0.7528089887640449, }, } @@ -274,17 +274,20 @@ class Stree_test(unittest.TestCase): clf = Stree( C=55, max_iter=1e5, - multiclass_strategy="ovr", + multiclass_strategy="ovr" + if kernel == "liblinear" + else "ovo", kernel=kernel, random_state=self._random_state, ) clf.fit(px, py) outcome = outcomes[name][f"{criteria} {kernel}"] - # print( - # f"{name} {criteria} {kernel} {outcome} " - # f"{clf.score(px, py)}" - # ) - self.assertAlmostEqual(outcome, clf.score(px, py)) + # print(f'"{criteria} {kernel}": {clf.score(px, py)},') + self.assertAlmostEqual( + outcome, + clf.score(px, py), + f"{name} - {criteria} - {kernel}", + ) def test_max_features(self): n_features = 16