============================= 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 ===============================
