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Update weak test

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This commit is contained in:
Ricardo Montañana Gómez
2021-05-10 10:56:34 +02:00
parent 5a36c5d29b
commit 34bf539fa3
1 changed files with 25 additions and 22 deletions
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47
stree/tests/Stree_test.py
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@@ -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