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Add sigmoid kernel

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This commit is contained in:
Ricardo Montañana Gómez
2021-04-22 18:09:27 +02:00
parent 3af7864278
commit 881777c38c
3 changed files with 19 additions and 2 deletions
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2
README.md
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@@ -37,7 +37,7 @@ Can be found in
| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** | | | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |
| --- | ------------------ | ------------------------------------------------------ | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --- | ------------------ | ------------------------------------------------------ | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| \* | C | \<float\> | 1.0 | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. | | \* | C | \<float\> | 1.0 | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. |
| \* | kernel | {"linear", "poly", "rbf"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’ or ‘rbf’. | | \* | kernel | {"linear", "poly", "rbf", "sigmoid"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’ or ‘rbf’. |
| \* | max_iter | \<int\> | 1e5 | Hard limit on iterations within solver, or -1 for no limit. | | \* | max_iter | \<int\> | 1e5 | Hard limit on iterations within solver, or -1 for no limit. |
| \* | random_state | \<int\> | None | Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False.<br>Pass an int for reproducible output across multiple function calls | | \* | random_state | \<int\> | None | Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False.<br>Pass an int for reproducible output across multiple function calls |
| | max_depth | \<int\> | None | Specifies the maximum depth of the tree | | | max_depth | \<int\> | None | Specifies the maximum depth of the tree |

4
stree/Strees.py
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@@ -619,7 +619,9 @@ class Stree(BaseEstimator, ClassifierMixin):
f"Maximum depth has to be greater than 1... got (max_depth=\ f"Maximum depth has to be greater than 1... got (max_depth=\
{self.max_depth})" {self.max_depth})"
) )
kernels = ["linear", "rbf", "poly", "sigmoid"]
if self.kernel not in kernels:
raise ValueError(f"Kernel {self.kernel} not in {kernels}")
check_classification_targets(y) check_classification_targets(y)
X, y = check_X_y(X, y) X, y = check_X_y(X, y)
sample_weight = _check_sample_weight( sample_weight = _check_sample_weight(

15
stree/tests/Stree_test.py
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@@ -21,6 +21,21 @@ class Stree_test(unittest.TestCase):
def setUp(cls): def setUp(cls):
os.environ["TESTING"] = "1" os.environ["TESTING"] = "1"
def test_valid_kernels(self):
valid_kernels = ["linear", "rbf", "poly", "sigmoid"]
X, y = load_dataset()
for kernel in valid_kernels:
clf = Stree(kernel=kernel)
clf.fit(X, y)
self.assertIsNotNone(clf.tree_)
def test_bogus_kernel(self):
kernel = "other"
X, y = load_dataset()
clf = Stree(kernel=kernel)
with self.assertRaises(ValueError):
clf.fit(X, y)
def _check_tree(self, node: Snode): def _check_tree(self, node: Snode):
"""Check recursively that the nodes that are not leaves have the """Check recursively that the nodes that are not leaves have the
correct number of labels and its sons have the right number of elements correct number of labels and its sons have the right number of elements