def test_predict_proba(self, iris_X, iris_y):
        rfc = RangerForestClassifier()
        rfc.fit(iris_X, iris_y)
        pred = rfc.predict_proba(iris_X)
        assert len(pred) == iris_X.shape[0]

        # test with single record
        iris_X_record = iris_X[0:1, :]
        pred = rfc.predict_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