def test_predict_log_proba(self, iris_X, iris_y):
rfc = RangerForestClassifier()
rfc.fit(iris_X, iris_y)
pred = rfc.predict_log_proba(iris_X)
assert len(pred) == iris_X.shape[0]
# test with single record
iris_X_record = iris_X[0:1, :]
pred = rfc.predict_log_proba(iris_X_record)
assert len(pred) == 1
def test_sample_fraction(self, iris_X, iris_y):
rfc = RangerForestClassifier(sample_fraction=[0.69])
rfc.fit(iris_X, iris_y)
assert rfc.sample_fraction_ == [0.69]
rfc = RangerForestClassifier(sample_fraction=0.69)
rfc.fit(iris_X, iris_y)
assert rfc.sample_fraction_ == [0.69]
# test with single record
iris_X_record = iris_X[0:1, :]
pred = rfc.predict(iris_X_record)
assert len(pred) == 1
pred = rfc.predict_proba(iris_X_record)
assert len(pred) == 1
pred = rfc.predict_log_proba(iris_X_record)
assert len(pred) == 1