============================= test session starts ==============================
platform linux -- Python 3.12.3, pytest-8.3.2, pluggy-1.5.0 -- /home/adas/mljar/mljar-supervised/venv/bin/python3
cachedir: .pytest_cache
rootdir: /home/adas/mljar/mljar-supervised
configfile: pytest.ini
plugins: cov-5.0.0
collecting ... collected 1 item

tests/tests_automl/test_targets.py::AutoMLTargetsTest::test_bin_class_AB_missing_targets FAILED

=================================== FAILURES ===================================
_____________ AutoMLTargetsTest.test_bin_class_AB_missing_targets ______________

self = <tests.tests_automl.test_targets.AutoMLTargetsTest testMethod=test_bin_class_AB_missing_targets>

    def test_bin_class_AB_missing_targets(self):
        X = np.random.rand(self.rows, 3)
        X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)])
        y = pd.Series(
            np.random.permutation(["a", "B"] * int(self.rows / 2)), name="target"
        )
    
        y.iloc[1] = None
        y.iloc[3] = np.NaN
        y.iloc[13] = np.nan
    
        automl = AutoML(
            results_path=self.automl_dir,
            total_time_limit=1,
            algorithms=["Xgboost"],
            train_ensemble=False,
            explain_level=0,
            start_random_models=1,
        )
>       automl.fit(X, y)

tests/tests_automl/test_targets.py:106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
supervised/automl.py:432: in fit
    return self._fit(X, y, sample_weight, cv, sensitive_features)
supervised/base_automl.py:967: in _fit
    X, y, sample_weight, sensitive_features = self._build_dataframe(
supervised/base_automl.py:789: in _build_dataframe
    X, y, sample_weight, sensitive_features = ExcludeRowsMissingTarget.transform(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

X =           f0        f1        f2
0   0.219718  0.578841  0.536912
1   0.382829  0.145900  0.430932
2   0.155678  0.482...  0.258146  0.325605
47  0.425501  0.771115  0.633187
48  0.189009  0.472929  0.558061
49  0.760685  0.277196  0.985700
y = 0        B
1     None
2        a
3      NaN
4        a
5        a
6        B
7        B
8        B
9        B
10      ...  B
42       a
43       a
44       B
45       a
46       B
47       a
48       a
49       a
Name: target, dtype: object
sample_weight = None, sensitive_features = None, warn = True

    @staticmethod
    def transform(
        X=None, y=None, sample_weight=None, sensitive_features=None, warn=False
    ):
        if y is None:
            return X, y, sample_weight, sensitive_features
        y_missing = pd.isnull(y)
        if np.sum(np.array(y_missing)) == 0:
            return X, y, sample_weight, sensitive_features
        logger.debug("Exclude rows with missing target values")
        if warn:
>           warnings.warn(
                "There are samples with missing target values in the data which will be excluded for further analysis"
            )
E           UserWarning: There are samples with missing target values in the data which will be excluded for further analysis

supervised/preprocessing/exclude_missing_target.py:25: UserWarning
=========================== short test summary info ============================
FAILED tests/tests_automl/test_targets.py::AutoMLTargetsTest::test_bin_class_AB_missing_targets
============================== 1 failed in 1.92s ===============================