mirror of
https://github.com/Doctorado-ML/STree.git
synced 2025-08-18 17:06:01 +00:00
better solution to the sklearn bagging problem
Add better tests enhance .coveragerc
This commit is contained in:
@@ -41,10 +41,7 @@ class Stree_test(unittest.TestCase):
|
||||
_, count_u = np.unique(y_up, return_counts=True)
|
||||
#
|
||||
for i in unique_y:
|
||||
try:
|
||||
number_down = count_d[i]
|
||||
except IndexError:
|
||||
number_down = 0
|
||||
number_down = count_d[i]
|
||||
try:
|
||||
number_up = count_u[i]
|
||||
except IndexError:
|
||||
@@ -67,25 +64,6 @@ class Stree_test(unittest.TestCase):
|
||||
clf.fit(*load_dataset(self._random_state))
|
||||
self._check_tree(clf.tree_)
|
||||
|
||||
@staticmethod
|
||||
def _find_out(px: np.array, x_original: np.array, y_original) -> list:
|
||||
"""Find the original values of y for a given array of samples
|
||||
|
||||
Arguments:
|
||||
px {np.array} -- array of samples to search for
|
||||
x_original {np.array} -- original dataset
|
||||
y_original {[type]} -- original classes
|
||||
|
||||
Returns:
|
||||
np.array -- classes of the given samples
|
||||
"""
|
||||
res = []
|
||||
for needle in px:
|
||||
for row in range(x_original.shape[0]):
|
||||
if all(x_original[row, :] == needle):
|
||||
res.append(y_original[row])
|
||||
return res
|
||||
|
||||
def test_single_prediction(self):
|
||||
X, y = load_dataset(self._random_state)
|
||||
for kernel in self._kernels:
|
||||
@@ -405,3 +383,34 @@ class Stree_test(unittest.TestCase):
|
||||
clf = Stree(splitter="duck")
|
||||
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())
|
||||
|
Reference in New Issue
Block a user