def test_split_rule(self, iris_X, iris_y, split_rule):
tree = RangerTreeClassifier(split_rule=split_rule)
assert tree.criterion == split_rule
if split_rule not in ["gini", "extratrees", "hellinger"]:
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y)
return
# hellinger can only be used in binary classification
if split_rule == "hellinger":
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y)
iris_2 = [0 if v == 2 else v for v in iris_y]
tree.fit(iris_X, iris_2)
if split_rule == "gini":
assert tree.split_rule_ == 1
elif split_rule == "extratrees":
assert tree.split_rule_ == 5
if split_rule == "hellinger":
assert tree.split_rule_ == 7
if split_rule == "extratrees":
tree = RangerTreeClassifier(
split_rule=split_rule,
respect_categorical_features="partition",
save_memory=True,
)
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y)
else:
tree = RangerTreeClassifier(split_rule=split_rule,
num_random_splits=2)
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y)
def test_sample_fraction(self, iris_X, iris_y):
tree = RangerTreeClassifier(sample_fraction=[0.69])
tree.fit(iris_X, iris_y)
assert tree.sample_fraction_ == [0.69]
tree = RangerTreeClassifier(sample_fraction=0.69)
tree.fit(iris_X, iris_y)
assert tree.sample_fraction_ == [0.69]
# test with single record
iris_X_record = iris_X[0:1, :]
pred = tree.predict(iris_X_record)
assert len(pred) == 1
pred = tree.predict_proba(iris_X_record)
assert len(pred) == 1
pred = tree.predict_log_proba(iris_X_record)
assert len(pred) == 1
def test_init(self):
_ = RangerTreeClassifier()
def test_inbag(self, iris_X, iris_y):
inbag = [[1, 2, 3]]
tree = RangerTreeClassifier(inbag=inbag)
tree.fit(iris_X, iris_y)
# can't use inbag with sample weight
tree = RangerTreeClassifier(inbag=inbag)
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y, sample_weight=[1] * len(iris_y))
# can't use class sampling and inbag
tree = RangerTreeClassifier(inbag=inbag, sample_fraction=[1, 1])
with pytest.raises(ValueError):
tree.fit(iris_X, iris_y)