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Complete source comments (#22)

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* Add Hyperparameters description to README
Comment get_subspace method
Add environment info for binder (runtime.txt)

* Complete source comments
Change docstring type to numpy
update hyperameters table and explanation

* Update Jupyter notebooks
This commit is contained in:
Ricardo Montañana Gómez
2021-01-19 10:44:59 +01:00
committed by GitHub
parent e4ac5075e5
commit 3bdac9bd60
10 changed files with 958 additions and 875 deletions
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85
stree/tests/Stree_test.py
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@@ -26,8 +26,10 @@ class Stree_test(unittest.TestCase):
correct number of labels and its sons have the right number of elements
in their dataset
Arguments:
node {Snode} -- node to check
Parameters
----------
node : Snode
node to check
"""
if node.is_leaf():
return
@@ -320,43 +322,6 @@ class Stree_test(unittest.TestCase):
with self.assertRaises(ValueError):
clf.fit(*load_dataset())
def test_weights_removing_class(self):
# This patch solves an stderr message from sklearn svm lib
# "WARNING: class label x specified in weight is not found"
X = np.array(
[
[0.1, 0.1],
[0.1, 0.2],
[0.2, 0.1],
[5, 6],
[8, 9],
[6, 7],
[0.2, 0.2],
]
)
y = np.array([0, 0, 0, 1, 1, 1, 0])
epsilon = 1e-5
weights = [1, 1, 1, 0, 0, 0, 1]
weights = np.array(weights, dtype="float64")
weights_epsilon = [x + epsilon for x in weights]
weights_no_zero = np.array([1, 1, 1, 0, 0, 2, 1])
original = weights_no_zero.copy()
clf = Stree()
clf.fit(X, y)
node = clf.train(
X,
y,
weights,
1,
"test",
)
# if a class is lost with zero weights the patch adds epsilon
self.assertListEqual(weights.tolist(), weights_epsilon)
self.assertListEqual(node._sample_weight.tolist(), weights_epsilon)
# zero weights are ok when they don't erase a class
_ = clf.train(X, y, weights_no_zero, 1, "test")
self.assertListEqual(weights_no_zero.tolist(), original.tolist())
def test_multiclass_classifier_integrity(self):
"""Checks if the multiclass operation is done right"""
X, y = load_iris(return_X_y=True)
@@ -442,3 +407,45 @@ class Stree_test(unittest.TestCase):
self.assertEqual(0.9533333333333334, clf.fit(X, y).score(X, y))
X, y = load_wine(return_X_y=True)
self.assertEqual(0.9550561797752809, clf.fit(X, y).score(X, y))
def test_zero_all_sample_weights(self):
X, y = load_dataset(self._random_state)
with self.assertRaises(ValueError):
Stree().fit(X, y, np.zeros(len(y)))
def test_weights_removing_class(self):
# This patch solves an stderr message from sklearn svm lib
# "WARNING: class label x specified in weight is not found"
X = np.array(
[
[0.1, 0.1],
[0.1, 0.2],
[0.2, 0.1],
[5, 6],
[8, 9],
[6, 7],
[0.2, 0.2],
]
)
y = np.array([0, 0, 0, 1, 1, 1, 0])
epsilon = 1e-5
weights = [1, 1, 1, 0, 0, 0, 1]
weights = np.array(weights, dtype="float64")
weights_epsilon = [x + epsilon for x in weights]
weights_no_zero = np.array([1, 1, 1, 0, 0, 2, 1])
original = weights_no_zero.copy()
clf = Stree()
clf.fit(X, y)
node = clf.train(
X,
y,
weights,
1,
"test",
)
# if a class is lost with zero weights the patch adds epsilon
self.assertListEqual(weights.tolist(), weights_epsilon)
self.assertListEqual(node._sample_weight.tolist(), weights_epsilon)
# zero weights are ok when they don't erase a class
_ = clf.train(X, y, weights_no_zero, 1, "test")
self.assertListEqual(weights_no_zero.tolist(), original.tolist())