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#2 Add predict and score support

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Add a test in features notebook
Show max_features in main.py
This commit is contained in:
Ricardo Montañana Gómez
2020-06-14 14:00:21 +02:00
parent f1ee4de37b
commit 502ee72799
4 changed files with 82 additions and 14 deletions
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18
stree/Strees.py
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@@ -205,7 +205,7 @@ class Stree(BaseEstimator, ClassifierMixin):
the hyperplane of the node
:rtype: np.array
"""
return node._clf.decision_function(data)
return node._clf.decision_function(data[:, node._features])
def _min_distance(self, data: np.array, _) -> np.array:
# chooses the lowest distance of every sample
@@ -286,11 +286,14 @@ class Stree(BaseEstimator, ClassifierMixin):
sample_weight = _check_sample_weight(sample_weight, X)
check_classification_targets(y)
# Initialize computed parameters
if self.random_state is not None:
random.seed(self.random_state)
self.classes_, y = np.unique(y, return_inverse=True)
self.n_classes_ = self.classes_.shape[0]
self.n_iter_ = self.max_iter
self.depth_ = 0
self.n_features_ = X.shape[1]
self.n_features_in_ = X.shape[1]
self.max_features_ = self._initialize_max_features()
self.criterion_function_ = getattr(self, f"_{self.criterion}")
self.tree_ = self.train(X, y, sample_weight, 1, "root")
@@ -336,12 +339,12 @@ class Stree(BaseEstimator, ClassifierMixin):
)
# Train the model
clf = self._build_clf()
Xs, indices_subset = self._get_subspace(X)
Xs, features = self._get_subspace(X)
clf.fit(Xs, y, sample_weight=sample_weight)
impurity = self.criterion_function_(y)
node = Snode(clf, X, y, indices_subset, impurity, title)
node = Snode(clf, X, y, features, impurity, title)
self.depth_ = max(depth, self.depth_)
down = self._split_criteria(self._distances(node, Xs), node)
down = self._split_criteria(self._distances(node, X), node)
X_U, X_D = self._split_array(X, down)
y_u, y_d = self._split_array(y, down)
sw_u, sw_d = self._split_array(sample_weight, down)
@@ -439,6 +442,11 @@ class Stree(BaseEstimator, ClassifierMixin):
check_is_fitted(self, ["tree_"])
# Input validation
X = check_array(X)
if X.shape[1] != self.n_features_:
raise ValueError(
f"Expected {self.n_features_} features but got "
f"({X.shape[1]})"
)
# setup prediction & make it happen
indices = np.arange(X.shape[0])
result = (
@@ -548,7 +556,7 @@ class Stree(BaseEstimator, ClassifierMixin):
features = range(dataset.shape[1])
features_sets = list(combinations(features, self.max_features_))
if len(features_sets) > 1:
return features_sets[random.randint(0, len(features_sets))]
return features_sets[random.randint(0, len(features_sets) - 1)]
else:
return features_sets[0]

14
stree/tests/Stree_test.py
View File

@@ -360,3 +360,17 @@ class Stree_test(unittest.TestCase):
clf = Stree(criterion="entropy")
clf.fit(*load_dataset())
self.assertEqual(expected, clf.criterion_function_(y))
def test_predict_feature_dimensions(self):
X = np.random.rand(10, 5)
y = np.random.randint(0, 2, 10)
clf = Stree()
clf.fit(X, y)
with self.assertRaises(ValueError):
clf.predict(X[:, :3])
def test_score_max_features(self):
X, y = load_dataset(self._random_state)
clf = Stree(random_state=self._random_state, max_features=2)
clf.fit(X, y)
self.assertAlmostEqual(0.9426666666666667, clf.score(X, y))