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Fix depth sometimes is wrong (#26) (#29)

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Add a test to the tests set
Add depth to node description
Fix iterator and str test due to this addon
This commit is contained in:
Ricardo Montañana Gómez
2021-03-23 14:08:53 +01:00
committed by GitHub
parent f438124057
commit 460c63a6d0
2 changed files with 38 additions and 17 deletions
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12
stree/Strees.py
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@@ -635,6 +635,7 @@ class Stree(BaseEstimator, ClassifierMixin):
X = X[~indices_zero, :] X = X[~indices_zero, :]
y = y[~indices_zero] y = y[~indices_zero]
sample_weight = sample_weight[~indices_zero] sample_weight = sample_weight[~indices_zero]
self.depth_ = max(depth, self.depth_)
if np.unique(y).shape[0] == 1: if np.unique(y).shape[0] == 1:
# only 1 class => pure dataset # only 1 class => pure dataset
return Snode( return Snode(
@@ -652,7 +653,6 @@ class Stree(BaseEstimator, ClassifierMixin):
clf.fit(Xs, y, sample_weight=sample_weight) clf.fit(Xs, y, sample_weight=sample_weight)
impurity = self.splitter_.partition_impurity(y) impurity = self.splitter_.partition_impurity(y)
node = Snode(clf, X, y, features, impurity, title, sample_weight) node = Snode(clf, X, y, features, impurity, title, sample_weight)
self.depth_ = max(depth, self.depth_)
self.splitter_.partition(X, node, True) self.splitter_.partition(X, node, True)
X_U, X_D = self.splitter_.part(X) X_U, X_D = self.splitter_.part(X)
y_u, y_d = self.splitter_.part(y) y_u, y_d = self.splitter_.part(y)
@@ -668,8 +668,14 @@ class Stree(BaseEstimator, ClassifierMixin):
title=title + ", <cgaf>", title=title + ", <cgaf>",
weight=sample_weight, weight=sample_weight,
) )
node.set_up(self.train(X_U, y_u, sw_u, depth + 1, title + " - Up")) node.set_up(
node.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + " - Down")) self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
)
node.set_down(
self.train(
X_D, y_d, sw_d, depth + 1, title + f" - Down({depth+1})"
)
)
return node return node
def _build_predictor(self): def _build_predictor(self):

43
stree/tests/Stree_test.py
View File

@@ -103,20 +103,20 @@ class Stree_test(unittest.TestCase):
def test_iterator_and_str(self): def test_iterator_and_str(self):
"""Check preorder iterator""" """Check preorder iterator"""
expected = [ expected = [
"root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), arr" "root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), "
"ay([750, 750]))", "array([750, 750]))",
"root - Down, <cgaf> - Leaf class=0 belief= 0.928297 impurity=0.37" "root - Down(2), <cgaf> - Leaf class=0 belief= 0.928297 impurity="
"22 counts=(array([0, 1]), array([725, 56]))", "0.3722 counts=(array([0, 1]), array([725, 56]))",
"root - Up feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, 1])" "root - Up(2) feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, "
", array([ 25, 694]))", "1]), array([ 25, 694]))",
"root - Up - Down feaures=(0, 1, 2) impurity=0.8454 counts=(array(" "root - Up(2) - Down(3) feaures=(0, 1, 2) impurity=0.8454 counts="
"[0, 1]), array([8, 3]))", "(array([0, 1]), array([8, 3]))",
"root - Up - Down - Down, <pure> - Leaf class=0 belief= 1.000000 i" "root - Up(2) - Down(3) - Down(4), <pure> - Leaf class=0 belief= "
"mpurity=0.0000 counts=(array([0]), array([7]))", "1.000000 impurity=0.0000 counts=(array([0]), array([7]))",
"root - Up - Down - Up, <cgaf> - Leaf class=1 belief= 0.750000 imp" "root - Up(2) - Down(3) - Up(4), <cgaf> - Leaf class=1 belief= "
"urity=0.8113 counts=(array([0, 1]), array([1, 3]))", "0.750000 impurity=0.8113 counts=(array([0, 1]), array([1, 3]))",
"root - Up - Up, <cgaf> - Leaf class=1 belief= 0.975989 impurity=0" "root - Up(2) - Up(3), <cgaf> - Leaf class=1 belief= 0.975989 "
".1634 counts=(array([0, 1]), array([ 17, 691]))", "impurity=0.1634 counts=(array([0, 1]), array([ 17, 691]))",
] ]
computed = [] computed = []
expected_string = "" expected_string = ""
@@ -439,3 +439,18 @@ class Stree_test(unittest.TestCase):
self.assertEqual(model1.score(X, y), 1) self.assertEqual(model1.score(X, y), 1)
self.assertAlmostEqual(model2.score(X, y), 0.66666667) self.assertAlmostEqual(model2.score(X, y), 0.66666667)
self.assertEqual(model2.score(X, y, w), 1) self.assertEqual(model2.score(X, y, w), 1)
def test_depth(self):
X, y = load_dataset(
random_state=self._random_state,
n_classes=3,
n_features=5,
n_samples=1500,
)
clf = Stree(random_state=self._random_state)
clf.fit(X, y)
self.assertEqual(6, clf.depth_)
X, y = load_wine(return_X_y=True)
clf = Stree(random_state=self._random_state)
clf.fit(X, y)
self.assertEqual(7, clf.depth_)