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See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n------------ generated xml file: /opt/receipts/reports/agent1-9.xml ------------\n=========================== short test summary info ============================\nERROR tests/unit/transformers/test_numerical.py\n========================= 2 warnings, 1 error in 2.41s =========================\n","stderr_tail":"ERROR: found no collectors for /RDT/tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit\n\n","stdout_sha256":"e481ba97041abaf4665ed93adbb48c6291484558c85b6febad832a61a1c06e49","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-9.log","tree_digest":"64636415f703d6f3f7a42dded843d459df0af972f58f9cd797c5ca47b5361a1d","tree_download_s":3.147127291187644,"started_at":"2026-10-03T16:58:29.114588Z","duration_s":3.63211,"timed_out":false,"image_before":"338ce513-5317-4c26-9e08-1c285239e148","image_after":"0eaeae9f-6cd7-4a9e-b039-831e45ac54ba","junit":{"passed":[],"failed":[],"errors":[],"skipped":[],"collection_errors":["tests/unit/transformers/test_numerical.py"],"failure_text":{"tests/unit/transformers/test_numerical.py":"collection failure\n/usr/local/lib/python3.10/site-packages/_pytest/python.py:507: in importtestmodule\n    mod = import_path(\n/usr/local/lib/python3.10/site-packages/_pytest/pathlib.py:587: in import_path\n    importlib.import_module(module_name)\n/usr/local/lib/python3.10/importlib/__init__.py:126: in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n<frozen importlib._bootstrap>:1050: in _gcd_import\n    ???\n<frozen importlib._bootstrap>:1027: in _find_and_load\n    ???\n<frozen importlib._bootstrap>:1006: in _find_and_load_unlocked\n    ???\n<frozen importlib._bootstrap>:688: in _load_unlocked\n    ???\n/usr/local/lib/python3.10/site-packages/_pytest/assertion/rewrite.py:197: in exec_module\n    exec(co, module.__dict__)\ntests/unit/transformers/test_numerical.py:11: in <module>\n    from rdt.transformers.null import NullTransformer\nrdt/__init__.py:20: in <module>\n    from rdt import transformers\nrdt/transformers/__init__.py:19: in <module>\n    from rdt.transformers.numerical import ClusterBasedNormalizer, FloatFormatter, GaussianNormalizer\nE     File \"/RDT/rdt/transformers/numerical.py\", line 596\nE       \"\"\"\nE       ^\nE   SyntaxError: unterminated triple-quoted string literal (detected at line 605)"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
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tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_floats\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_over_maximum\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_under_minimum\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__super_attrs\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__str_distr\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__non_distr\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions_copulas_not_installed\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_instance\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_tuple\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_class\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_error\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_missing_value_generation_from_column\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_catch_warnings\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__copula_transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform_missing_value_generation_is_random\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform_missing_value_generation\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement\n=================== 4 failed, 54 passed, 3 warnings in 3.34s ===================\n","stderr_tail":"","stdout_sha256":"ec6461424675a0229b8ac226c886dece7eaebe4f3be19519eace326244f7251e","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-20.log","tree_digest":"fda18fdb704967a17bbee74a298a60c36cf17cf155aee49d1bbe569251f41758","tree_download_s":3.1317878749687225,"started_at":"2026-10-03T16:59:53.683054Z","duration_s":4.3647350000000005,"timed_out":false,"image_before":"9f7b751d-8026-40af-b768-38fa21a6d4ec","image_after":"59bc7b1c-55e6-4e31-a1e2-782f34840b61","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestFloatFormatter::test___init__super_attrs","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_enforce_min_max_values_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_enforce_min_max_values_true","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_inf","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_zero","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_missing_value_replacement_from_column","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_all_missing_value_replacements","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_less_than_15_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_more_than_15_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_float","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_integer","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_computer_representation","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values_with_nulls","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_learn_rounding_scheme_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_big_numbers_type_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_negative_type_float","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_dtype_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_with_nulls","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_with_nulls_dtype_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_small_numbers","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_zero_decimal_places","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__transform","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_floats","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_over_maximum","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_under_minimum","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__super_attrs","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__str_distr","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__non_distr","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions_copulas_not_installed","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_instance","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_tuple","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_class","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_error","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_missing_value_generation_from_column","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_catch_warnings","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__copula_transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform_missing_value_generation_is_random","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform_missing_value_generation","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random"],"failed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform":"[...]eversed.\n    \n        Ouput:\n            - a pandas Series with the reverse transformed data.\n    \n        Side Effects:\n            - ``_reverse_transform_helper`` should be called once with the correct data.\n        \"\"\"\n        # Setup\n        transformer = ClusterBasedNormalizer(max_clusters=3)\n        transformer.output_columns = ['col.normalized', 'col.component']\n        reversed_data = np.array([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n        transformer.null_transformer = Mock()\n        transformer.null_transformer.reverse_transform.return_value = reversed_data\n        transformer._reverse_transform_helper = Mock()\n        transformer._reverse_transform_helper.return_value = reversed_data\n    \n        data = pd.DataFrame({\n            'col1': [-0.069, -0.061, -0.086, -0.086, -0.078, 0.073, 0.057, 0.098, 0.107, 0.057],\n            'col2': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n        })\n    \n        # Run\n>       output = transformer._reverse_transform(data)\n\ntests/unit/transformers/test_numerical.py:1513: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([ 0.01,  0.02, -0.01, -0.01,  0.  ,  0.99,  0.97,  1.02,  1.03,\n        0.97])\n\n    def _reverse_transform(self, data):\n        \"\"\"Convert data back into the original format.\n    \n        Args:\n            data (pd.DataFrame or numpy.ndarray):\n                Data to transform.\n    \n        Returns:\n            pandas.Series.\n        \"\"\"\n        if not isinstance(data, np.ndarray):\n            data = data.to_numpy()\n    \n        recovered_data = self._reverse_transform_helper(data)\n        data = recovered_data\n        if self.null_transformer and self.null_transformer.models_missing_values():\n>           data = np.stack([recovered_data, data[:, -1]], axis=1)\nE           IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed\n\nrdt/transformers/numerical.py:564: IndexError","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col":"[...],\n            max_clusters=3\n        )\n        transformer.output_columns = ['col.normalized', 'col.component']\n        transformer._reverse_transform_helper = Mock()\n        transformer._reverse_transform_helper.return_value = np.array([\n            0.68351419, 0.67292805, 0.66234274, 0.66234274, 0.67292805,\n            0.63579893, 0.62239389, 0.67292805, 0.67292805, 0.62239389\n        ])\n    \n        transformer.null_transformer = NullTransformer(\n            'mean',\n            missing_value_generation='from_column'\n        )\n        transformer.null_transformer.fit(pd.Series([0, np.nan]))\n    \n        data = np.array([\n            [-0.033, -0.046, -0.058, -0.058, -0.046, 0.134, 0.122, -0.046, -0.046, 0.122],\n            [0, 0, 0, 0, 0, 1, 1, 0, 0, 1],\n            [1, 1, 1, 1, 1, 0, 0, 0, 1, 0]\n        ]).transpose()\n    \n        # Run\n>       output = transformer._reverse_transform(data)\n\ntests/unit/transformers/test_numerical.py:1560: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3, missing_value_generation='from_column')\ndata = array([0.68351419, 0.67292805, 0.66234274, 0.66234274, 0.67292805,\n       0.63579893, 0.62239389, 0.67292805, 0.67292805, 0.62239389])\n\n    def _reverse_transform(self, data):\n        \"\"\"Convert data back into the original format.\n    \n        Args:\n            data (pd.DataFrame or numpy.ndarray):\n                Data to transform.\n    \n        Returns:\n            pandas.Series.\n        \"\"\"\n        if not isinstance(data, np.ndarray):\n            data = data.to_numpy()\n    \n        recovered_data = self._reverse_transform_helper(data)\n        data = recovered_data\n        if self.null_transformer and self.null_transformer.models_missing_values():\n>           data = np.stack([recovered_data, data[:, -1]], axis=1)\nE           IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed\n\nrdt/transformers/numerical.py:564: IndexError","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform":"[...]es([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n    \n        # Run\n>       output = transformer._transform(data)\n\ntests/unit/transformers/test_numerical.py:1339: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.02],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 1.02],\n       [ 1.03],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Get the mean and std of selected components\n>       selected_means = means[:, self.valid_component_indicator][np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:524: IndexError","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement":"[...]an, np.nan, 0.97])\n    \n        # Run\n        transformer.null_transformer.fit(data)\n>       output = transformer._transform(data)\n\ntests/unit/transformers/test_numerical.py:1412: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.  ],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 0.  ],\n       [ 0.  ],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Get the mean and std of selected components\n>       selected_means = means[:, self.valid_component_indicator][np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:524: IndexError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-21","seq":21,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-7b568cd532f54c469dd6462d1cb97bbb/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-7b568cd532f54c469dd6462d1cb97bbb/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-21.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform FAILED [100%]\n\n=================================== FAILURES ===================================\n______________ TestClusterBasedNormalizer.test__reverse_transform ______________\n\nself = <tests.unit.transformers.test_numerical.TestClusterBasedNormalizer testMethod=test__reverse_transform>\n\n    def test__reverse_transform(self):\n        \"\"\"Test ``_reverse_transform``.\n    \n        Validate that the method correctly calls ``_reverse_transform_helper`` and produces the\n        appropriate output when passed pandas dataframe.\n    \n        Setup:\n            - create an instance of the ``ClusterBasedNormalizer`` where the ``output_columns``\n            is a list of two columns.\n            - mock the `_reverse_transform_helper` with the appropriate return value.\n    \n        Input:\n            - a dataframe containing the data to be reversed.\n    \n        Ouput:\n            - a pandas Series with the reverse transformed data.\n    \n        Side Effects:\n            - ``_reverse_transform_helper`` should be called once with the correct data.\n        \"\"\"\n        # Setup\n        transformer = ClusterBasedNormalizer(max_clusters=3)\n        transformer.output_columns = ['col.normalized', 'col.component']\n        reversed_data = np.array([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n        transformer.null_transformer = Mock()\n        transformer.null_transformer.reverse_transform.return_value = reversed_data\n        transformer._reverse_transform_helper = Mock()\n        transformer._reverse_transform_helper.return_value = reversed_data\n    \n        data = pd.DataFrame({\n            'col1': [-0.069, -0.061, -0.086, -0.086, -0.078, 0.073, 0.057, 0.098, 0.107, 0.057],\n            'col2': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n        })\n    \n        # Run\n>       output = transformer._reverse_transform(data)\n\ntests/unit/transformers/test_numerical.py:1513: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([ 0.01,  0.02, -0.01, -0.01,  0.  ,  0.99,  0.97,  1.02,  1.03,\n        0.97])\n\n    def _reverse_transform(self, data):\n        \"\"\"Convert data back into the original format.\n    \n        Args:\n            data (pd.DataFrame or numpy.ndarray):\n                Data to transform.\n    \n        Returns:\n            pandas.Series.\n        \"\"\"\n        if not isinstance(data, np.ndarray):\n            data = data.to_numpy()\n    \n        recovered_data = self._reverse_transform_helper(data)\n        data = recovered_data\n        if self.null_transformer and self.null_transformer.models_missing_values():\n>           data = np.stack([recovered_data, data[:, -1]], axis=1)\nE           IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed\n\nrdt/transformers/numerical.py:564: IndexError\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n----------- generated xml file: /opt/receipts/reports/agent1-21.xml ------------\n=========================== short test summary info ============================\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform\n======================== 1 failed, 2 warnings in 3.06s =========================\n","stderr_tail":"","stdout_sha256":"61747293b5263315725e97321d018702eab8946e717fcff2df64a4844b8b9b4a","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-21.log","tree_digest":"fda18fdb704967a17bbee74a298a60c36cf17cf155aee49d1bbe569251f41758","tree_download_s":1.90307795885019,"started_at":"2026-10-03T17:00:07.801074Z","duration_s":4.153938,"timed_out":false,"image_before":"59bc7b1c-55e6-4e31-a1e2-782f34840b61","image_after":"73e26344-1899-459e-9f2b-5d07e2d16f5b","junit":{"passed":[],"failed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform":"[...]eversed.\n    \n        Ouput:\n            - a pandas Series with the reverse transformed data.\n    \n        Side Effects:\n            - ``_reverse_transform_helper`` should be called once with the correct data.\n        \"\"\"\n        # Setup\n        transformer = ClusterBasedNormalizer(max_clusters=3)\n        transformer.output_columns = ['col.normalized', 'col.component']\n        reversed_data = np.array([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n        transformer.null_transformer = Mock()\n        transformer.null_transformer.reverse_transform.return_value = reversed_data\n        transformer._reverse_transform_helper = Mock()\n        transformer._reverse_transform_helper.return_value = reversed_data\n    \n        data = pd.DataFrame({\n            'col1': [-0.069, -0.061, -0.086, -0.086, -0.078, 0.073, 0.057, 0.098, 0.107, 0.057],\n            'col2': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n        })\n    \n        # Run\n>       output = transformer._reverse_transform(data)\n\ntests/unit/transformers/test_numerical.py:1513: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([ 0.01,  0.02, -0.01, -0.01,  0.  ,  0.99,  0.97,  1.02,  1.03,\n        0.97])\n\n    def _reverse_transform(self, data):\n        \"\"\"Convert data back into the original format.\n    \n        Args:\n            data (pd.DataFrame or numpy.ndarray):\n                Data to transform.\n    \n        Returns:\n            pandas.Series.\n        \"\"\"\n        if not isinstance(data, np.ndarray):\n            data = data.to_numpy()\n    \n        recovered_data = self._reverse_transform_helper(data)\n        data = recovered_data\n        if self.null_transformer and self.null_transformer.models_missing_values():\n>           data = np.stack([recovered_data, data[:, -1]], axis=1)\nE           IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed\n\nrdt/transformers/numerical.py:564: IndexError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-22","seq":22,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-88b5218aa02c417fb7c5c88a97c6b1c1/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-88b5218aa02c417fb7c5c88a97c6b1c1/incoming/dded20a1a170a5835670a92d887899ee62e07bdf1376aa9c788a48cf7ffff273 /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"dded20a1a170a5835670a92d887899ee62e07bdf1376aa9c788a48cf7ffff273","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-22.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:00:22.183833Z","duration_s":0.005359,"timed_out":false,"image_before":"73e26344-1899-459e-9f2b-5d07e2d16f5b","image_after":"3b1a3d55-9613-44b4-9f1b-11c8b15d3708","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-23","seq":23,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-7542e4bef83240d8b493f9f9e1a6759b/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-7542e4bef83240d8b493f9f9e1a6759b/incoming/f090221a08c5141e62734eedb9fb7a75a5505c327ba4c67b82fc9358a51a3112 /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"f090221a08c5141e62734eedb9fb7a75a5505c327ba4c67b82fc9358a51a3112","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-23.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:00:26.821804Z","duration_s":0.015947,"timed_out":false,"image_before":"3b1a3d55-9613-44b4-9f1b-11c8b15d3708","image_after":"fdafc79f-e6e4-46be-9039-ff985c63ca31","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-24","seq":24,"branch_id":"agent1","kind":"command","cmd":"git show HEAD:rdt/transformers/numerical.py | sed -n '540,560p'","path":null,"exit_code":0,"stdout_tail":"\n        Returns:\n            pandas.Series.\n        \"\"\"\n        if not isinstance(data, np.ndarray):\n            data = data.to_numpy()\n\n        recovered_data = self._reverse_transform_helper(data)\n        if self.null_transformer and self.null_transformer.models_missing_values():\n            data = np.stack([recovered_data, data[:, -1]], axis=1)  # noqa: PD013\n        else:\n            data = recovered_data\n\n        return super()._reverse_transform(data)\n","stderr_tail":"","stdout_sha256":"50578672eae798bf5d4194ae0f00b300e327d74dd806a232214d51b7ab762ad3","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-24.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:00:33.213731Z","duration_s":0.006444,"timed_out":false,"image_before":"fdafc79f-e6e4-46be-9039-ff985c63ca31","image_after":"23decf5e-adad-4364-8038-41e1391cc56c","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-25","seq":25,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-24827cb81c734056a277e07be7ec9b4a/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-24827cb81c734056a277e07be7ec9b4a/incoming/f090221a08c5141e62734eedb9fb7a75a5505c327ba4c67b82fc9358a51a3112 /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"f090221a08c5141e62734eedb9fb7a75a5505c327ba4c67b82fc9358a51a3112","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-25.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:00:38.915025Z","duration_s":0.005915,"timed_out":false,"image_before":"23decf5e-adad-4364-8038-41e1391cc56c","image_after":"3ff3865a-9724-40ec-9452-603aae11ad74","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-26","seq":26,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-d7916e87072c4284a6eff83cc465c823/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-d7916e87072c4284a6eff83cc465c823/incoming/dded20a1a170a5835670a92d887899ee62e07bdf1376aa9c788a48cf7ffff273 /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"dded20a1a170a5835670a92d887899ee62e07bdf1376aa9c788a48cf7ffff273","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-26.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:00:44.127968Z","duration_s":0.005204,"timed_out":false,"image_before":"3ff3865a-9724-40ec-9452-603aae11ad74","image_after":"aa4b8ed9-24b8-46cd-85ff-74601851b60a","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-27","seq":27,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-1242540a08a2470d9fb61ac55e6b8bd7/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-1242540a08a2470d9fb61ac55e6b8bd7/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-27.xml","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform PASSED [100%]\n\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-27.xml ------------\n=========================== short test summary info ============================\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform\n======================== 1 passed, 2 warnings in 2.97s =========================\n","stderr_tail":"","stdout_sha256":"67be44bfa6b79673c58326ffad70e64c885c202ab12c7196611432706e15a34d","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-27.log","tree_digest":"eea17b9d82261c6190aae76a6529122543cae8ec0cedf5b7d383b3c0577f0ccc","tree_download_s":2.350452459184453,"started_at":"2026-10-03T17:00:47.055817Z","duration_s":4.090854,"timed_out":false,"image_before":"aa4b8ed9-24b8-46cd-85ff-74601851b60a","image_after":"7fa8ca28-60e3-48d4-a45f-7148c1161e22","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-28","seq":28,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-3eedcd3a66354aeb8783049008853113/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-3eedcd3a66354aeb8783049008853113/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-28.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform FAILED [100%]\n\n=================================== FAILURES ===================================\n__________________ TestClusterBasedNormalizer.test__transform __________________\n\nself = <tests.unit.transformers.test_numerical.TestClusterBasedNormalizer testMethod=test__transform>\n\n    def test__transform(self):\n        \"\"\"Test ``_transform``.\n    \n        Validate that the method produces the appropriate output when given a pandas Series.\n    \n        Setup:\n            - create an instance of the ``ClusterBasedNormalizer`` where:\n                - ``_bgm_transformer`` is mocked with the appropriate ``means_``, ``covariances_``\n                and ``predict_proba.return_value``.\n                - ``valid_component_indicator`` is set to ``np.array([True, True, False])``.\n    \n        Input:\n            - a pandas Series.\n    \n        Ouput:\n            - a numpy array with the transformed data.\n        \"\"\"\n        # Setup\n        random_state = np.random.get_state()\n        np.random.set_state(np.random.RandomState(10).get_state())\n        transformer = ClusterBasedNormalizer(max_clusters=3)\n        transformer._bgm_transformer = Mock()\n    \n        means = np.array([\n            [0.90138867],\n            [0.09169366],\n            [0.499]\n        ])\n        transformer._bgm_transformer.means_ = means\n    \n        covariances = np.array([\n            [[0.09024532]],\n            [[0.08587948]],\n            [[0.27487667]]\n        ])\n        transformer._bgm_transformer.covariances_ = covariances\n    \n        probabilities = np.array([\n            [0.01519528, 0.98480472, 0.],\n            [0.01659093, 0.98340907, 0.],\n            [0.012744, 0.987256, 0.],\n            [0.012744, 0.987256, 0.],\n            [0.01391614, 0.98608386, 0.],\n            [0.99220664, 0.00779336, 0.],\n            [0.99059634, 0.00940366, 0.],\n            [0.9941256, 0.0058744, 0.],\n            [0.99465502, 0.00534498, 0.],\n            [0.99059634, 0.00940366, 0.]\n        ])\n        transformer._bgm_transformer.predict_proba.return_value = probabilities\n    \n        transformer.valid_component_indicator = np.array([True, True, False])\n        transformer.null_transformer = NullTransformer()\n        data = pd.Series([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n    \n        # Run\n>       output = transformer._transform(data)\n\ntests/unit/transformers/test_numerical.py:1339: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.02],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 1.02],\n       [ 1.03],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Get the mean and std of selected components\n>       selected_means = means[:, self.valid_component_indicator][np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:524: IndexError\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n----------- generated xml file: /opt/receipts/reports/agent1-28.xml ------------\n=========================== short test summary info ============================\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\n======================== 1 failed, 2 warnings in 3.10s =========================\n","stderr_tail":"","stdout_sha256":"8d6cadfa6abb8826beddc3e42bb0b327fa01aa9a9477d1a980f5efd5a63494c6","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-28.log","tree_digest":"eea17b9d82261c6190aae76a6529122543cae8ec0cedf5b7d383b3c0577f0ccc","tree_download_s":3.2914118331391364,"started_at":"2026-10-03T17:01:01.061984Z","duration_s":4.172683,"timed_out":false,"image_before":"7fa8ca28-60e3-48d4-a45f-7148c1161e22","image_after":"f08445f3-d852-4e9b-ad23-8a75fe6610fe","junit":{"passed":[],"failed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform":"[...]es([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n    \n        # Run\n>       output = transformer._transform(data)\n\ntests/unit/transformers/test_numerical.py:1339: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.02],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 1.02],\n       [ 1.03],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Get the mean and std of selected components\n>       selected_means = means[:, self.valid_component_indicator][np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:524: IndexError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-29","seq":29,"branch_id":"agent1","kind":"command","cmd":"git show HEAD:rdt/transformers/numerical.py | sed -n '510,530p'","path":null,"exit_code":0,"stdout_tail":"                p=component_prob_t\n            )\n\n        aranged = np.arange(len(data))\n        normalized = normalized_values[aranged, selected_component].reshape([-1, 1])\n        normalized = np.clip(normalized, -.99, .99)\n        normalized = normalized[:, 0]\n        rows = [normalized, selected_component]\n        if self.null_transformer and self.null_transformer.models_missing_values():\n            rows.append(model_missing_values)\n\n        return np.stack(rows, axis=1)  # noqa: PD013\n\n    def _reverse_transform_helper(self, data):\n        normalized = np.clip(data[:, 0], -1, 1)\n        means = self._bgm_transformer.means_.reshape([-1])\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape([-1])\n        selected_component = data[:, 1].astype(int)  # maybe round instead?\n        std_t = stds[self.valid_component_indicator][selected_component]\n        mean_t = means[self.valid_component_indicator][selected_component]\n        reversed_data = normalized * self.STD_MULTIPLIER * std_t + mean_t\n","stderr_tail":"","stdout_sha256":"27d141c7096e9917b651eaac7f6a5dda495bc0fa5122bc4047c015e5bd30a306","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-29.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:01:19.091148Z","duration_s":0.00648,"timed_out":false,"image_before":"f08445f3-d852-4e9b-ad23-8a75fe6610fe","image_after":"122600f3-84b7-48de-9a82-c3517bfb683d","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-30","seq":30,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-8aef50d902a249ac80383f66e41d6e47/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-8aef50d902a249ac80383f66e41d6e47/incoming/717d13c204479e2f2777e32a75fad749f132c7205e96158d63c13e7387e7c670 /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"717d13c204479e2f2777e32a75fad749f132c7205e96158d63c13e7387e7c670","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-30.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:01:26.619152Z","duration_s":0.005098,"timed_out":false,"image_before":"122600f3-84b7-48de-9a82-c3517bfb683d","image_after":"447f14af-1b73-4f70-9e02-f90847f41170","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-31","seq":31,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-1dddce2d45fb4852a37d54ed6d025748/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-1dddce2d45fb4852a37d54ed6d025748/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-31.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform FAILED [100%]\n\n=================================== FAILURES ===================================\n__________________ TestClusterBasedNormalizer.test__transform __________________\n\nself = <tests.unit.transformers.test_numerical.TestClusterBasedNormalizer testMethod=test__transform>\n\n    def test__transform(self):\n        \"\"\"Test ``_transform``.\n    \n        Validate that the method produces the appropriate output when given a pandas Series.\n    \n        Setup:\n            - create an instance of the ``ClusterBasedNormalizer`` where:\n                - ``_bgm_transformer`` is mocked with the appropriate ``means_``, ``covariances_``\n                and ``predict_proba.return_value``.\n                - ``valid_component_indicator`` is set to ``np.array([True, True, False])``.\n    \n        Input:\n            - a pandas Series.\n    \n        Ouput:\n            - a numpy array with the transformed data.\n        \"\"\"\n        # Setup\n        random_state = np.random.get_state()\n        np.random.set_state(np.random.RandomState(10).get_state())\n        transformer = ClusterBasedNormalizer(max_clusters=3)\n        transformer._bgm_transformer = Mock()\n    \n        means = np.array([\n            [0.90138867],\n            [0.09169366],\n            [0.499]\n        ])\n        transformer._bgm_transformer.means_ = means\n    \n        covariances = np.array([\n            [[0.09024532]],\n            [[0.08587948]],\n            [[0.27487667]]\n        ])\n        transformer._bgm_transformer.covariances_ = covariances\n    \n        probabilities = np.array([\n            [0.01519528, 0.98480472, 0.],\n            [0.01659093, 0.98340907, 0.],\n            [0.012744, 0.987256, 0.],\n            [0.012744, 0.987256, 0.],\n            [0.01391614, 0.98608386, 0.],\n            [0.99220664, 0.00779336, 0.],\n            [0.99059634, 0.00940366, 0.],\n            [0.9941256, 0.0058744, 0.],\n            [0.99465502, 0.00534498, 0.],\n            [0.99059634, 0.00940366, 0.]\n        ])\n        transformer._bgm_transformer.predict_proba.return_value = probabilities\n    \n        transformer.valid_component_indicator = np.array([True, True, False])\n        transformer.null_transformer = NullTransformer()\n        data = pd.Series([0.01, 0.02, -0.01, -0.01, 0.0, 0.99, 0.97, 1.02, 1.03, 0.97])\n    \n        # Run\n>       output = transformer._transform(data)\n\ntests/unit/transformers/test_numerical.py:1339: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.02],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 1.02],\n       [ 1.03],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Filter to only valid components\n        valid_means = means[:, self.valid_component_indicator]\n        valid_stds = stds[:, self.valid_component_indicator]\n    \n        # Get the mean and std of selected components for each data point\n>       selected_means = valid_means[np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:528: IndexError\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n----------- generated xml file: /opt/receipts/reports/agent1-31.xml ------------\n=========================== short test summary info ============================\nFAILED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\n======================== 1 failed, 2 warnings in 3.10s =========================\n","stderr_tail":"","stdout_sha256":"4fc5ca03007f836f8c2939f2d6388511bf806c9115eabc61f4e9039da908f2b5","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-31.log","tree_digest":"75e5f66a6f55c3b6e4deb504b395f4c01c9cbe1a28eae1436a4b4a525a934f52","tree_download_s":3.0808934171218425,"started_at":"2026-10-03T17:01:30.822706Z","duration_s":4.185552,"timed_out":false,"image_before":"447f14af-1b73-4f70-9e02-f90847f41170","image_after":"1782a6d5-5705-43c8-864e-2a4d1e3acc44","junit":{"passed":[],"failed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform":"[...]umerical.py:1339: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = ClusterBasedNormalizer(max_clusters=3)\ndata = array([[ 0.01],\n       [ 0.02],\n       [-0.01],\n       [-0.01],\n       [ 0.  ],\n       [ 0.99],\n       [ 0.97],\n       [ 1.02],\n       [ 1.03],\n       [ 0.97]])\n\n    def _transform(self, data):\n        \"\"\"Transform the numerical data.\n    \n        Args:\n            data (pandas.Series):\n                Data to transform.\n    \n        Returns:\n            numpy.ndarray.\n        \"\"\"\n        data = super()._transform(data)\n        if data.ndim > 1:\n            data, model_missing_values = data[:, 0], data[:, 1]\n    \n        data = data.reshape((len(data), 1))\n    \n        # First select components based on probabilities\n        component_probs = self._bgm_transformer.predict_proba(data)\n        component_probs = component_probs[:, self.valid_component_indicator]\n    \n        selected_component = np.zeros(len(data), dtype='int')\n        for i in range(len(data)):\n            component_prob_t = component_probs[i] + 1e-6\n            component_prob_t = component_prob_t / component_prob_t.sum()\n            selected_component[i] = np.random.choice(\n                np.arange(self.valid_component_indicator.sum()),\n                p=component_prob_t\n            )\n    \n        # Then normalize the selected component values\n        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n    \n        # Filter to only valid components\n        valid_means = means[:, self.valid_component_indicator]\n        valid_stds = stds[:, self.valid_component_indicator]\n    \n        # Get the mean and std of selected components for each data point\n>       selected_means = valid_means[np.arange(len(data)), selected_component]\nE       IndexError: index 1 is out of bounds for axis 0 with size 1\n\nrdt/transformers/numerical.py:528: IndexError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-32","seq":32,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-b38b43ac1b474251ba07c36ecb0d3785/bin/busybox sh -c 'mkdir -p /RDT/rdt/transformers && cp /opt/receipts-b38b43ac1b474251ba07c36ecb0d3785/incoming/335d4084754196f2d83cef8d0dbc87fc7ec0cad7658e3f4fbd8982bebb71992c /RDT/rdt/transformers/numerical.py'","path":"rdt/transformers/numerical.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"335d4084754196f2d83cef8d0dbc87fc7ec0cad7658e3f4fbd8982bebb71992c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-32.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T17:01:49.681723Z","duration_s":0.005332,"timed_out":false,"image_before":"1782a6d5-5705-43c8-864e-2a4d1e3acc44","image_after":"a026445d-6fcc-4d54-aab7-40f76bef1d6b","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-33","seq":33,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-a41a4505a7294513b944641aa0e362f3/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-a41a4505a7294513b944641aa0e362f3/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-33.xml","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform PASSED [100%]\n\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-33.xml ------------\n=========================== short test summary info ============================\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\n======================== 1 passed, 2 warnings in 3.03s =========================\n","stderr_tail":"","stdout_sha256":"f56015ee1c72d4aefc583abb314fe8d454323fcc68a27e523705df5d3e38b3c5","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-33.log","tree_digest":"a6ab8697e4a538858af9577f01708f7f5c19e608e6466b2b208af2301da1ef7a","tree_download_s":1.5777035001665354,"started_at":"2026-10-03T17:01:54.170257Z","duration_s":4.12252,"timed_out":false,"image_before":"a026445d-6fcc-4d54-aab7-40f76bef1d6b","image_after":"91baa369-1ff0-48bb-bf39-d7be2411e1cc","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-34","seq":34,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-81483e9a78a94a6eafc3bdf3aaf441f0/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-81483e9a78a94a6eafc3bdf3aaf441f0/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-34.xml","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 10 items\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit PASSED [ 10%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings PASSED [ 20%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement PASSED [ 30%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none PASSED [ 40%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform PASSED [ 50%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper PASSED [ 60%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col PASSED [ 70%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random PASSED [ 80%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform PASSED [ 90%]\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement PASSED [100%]\n\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-34.xml ------------\n=========================== short test summary info ============================\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement\n======================== 10 passed, 2 warnings in 3.02s ========================\n","stderr_tail":"","stdout_sha256":"501784552a118faf65671381d6290e9c7f09feb727c8082699c5514c60970d4c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-34.log","tree_digest":"a6ab8697e4a538858af9577f01708f7f5c19e608e6466b2b208af2301da1ef7a","tree_download_s":2.342001707991585,"started_at":"2026-10-03T17:02:07.222712Z","duration_s":4.124386,"timed_out":false,"image_before":"91baa369-1ff0-48bb-bf39-d7be2411e1cc","image_after":"65785763-49d1-45fa-a668-f438591acc46","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-35","seq":35,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-f1fb7655055040ecbee1066ee483d6a3/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-f1fb7655055040ecbee1066ee483d6a3/receipts_pytest.py tests/unit/transformers/test_numerical.py -x -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-35.xml","path":null,"exit_code":0,"stdout_tail":"heme_true\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_inf\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_decimals\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_zero\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_missing_value_replacement_from_column\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_all_missing_value_replacements\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_less_than_15_decimals\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_more_than_15_decimals\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_float\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_integer\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_computer_representation\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values_with_nulls\nPASSED 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tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_floats\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_over_maximum\nPASSED tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_under_minimum\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__super_attrs\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__str_distr\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__non_distr\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions_copulas_not_installed\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_instance\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_tuple\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_class\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_error\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_missing_value_generation_from_column\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_catch_warnings\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__copula_transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform_missing_value_generation_is_random\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform\nPASSED tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform_missing_value_generation\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement\n======================== 58 passed, 3 warnings in 3.19s ========================\n","stderr_tail":"","stdout_sha256":"fca05f9107d1a1b83344478a807bc468633e510f8016cac8e28b73723097695e","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-35.log","tree_digest":"a6ab8697e4a538858af9577f01708f7f5c19e608e6466b2b208af2301da1ef7a","tree_download_s":2.3919212501496077,"started_at":"2026-10-03T17:02:21.045003Z","duration_s":4.3481440000000005,"timed_out":false,"image_before":"65785763-49d1-45fa-a668-f438591acc46","image_after":"7ce19b2b-7e0a-40a3-a487-3213e665224a","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestFloatFormatter::test___init__super_attrs","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_enforce_min_max_values_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_enforce_min_max_values_true","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_inf","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_learn_rounding_scheme_true_max_zero","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__fit_missing_value_replacement_from_column","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_all_missing_value_replacements","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_less_than_15_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_more_than_15_decimals","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_float","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__learn_rounding_digits_negative_decimals_integer","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_computer_representation","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_enforce_min_max_values_with_nulls","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_learn_rounding_scheme_false","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_big_numbers_type_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_negative_type_float","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_dtype_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_with_nulls","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_none_with_nulls_dtype_int","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_small_numbers","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__reverse_transform_rounding_zero_decimal_places","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__transform","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_floats","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_over_maximum","tests/unit/transformers/test_numerical.py::TestFloatFormatter::test__validate_values_within_bounds_under_minimum","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__super_attrs","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__str_distr","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test___init__non_distr","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions_copulas_not_installed","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_distributions","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_instance","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_tuple","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_class","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__get_univariate_error","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_missing_value_generation_from_column","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__fit_catch_warnings","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__copula_transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__transform_missing_value_generation_is_random","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform","tests/unit/transformers/test_numerical.py::TestGaussianNormalizer::test__reverse_transform_missing_value_generation","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_catch_warnings","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit_missing_value_replacement","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__get_current_random_seed_random_states_is_none","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_helper","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_from_col","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__reverse_transform_missing_value_replacement_missing_value_replacement_random","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform","tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__transform_missing_value_replacement"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-36","seq":36,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-12d2559cb0934085825a8b4fb6742837/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-12d2559cb0934085825a8b4fb6742837/receipts_pytest.py tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-36.xml","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /RDT\nconfigfile: setup.cfg\nplugins: anyio-4.12.1, Faker-40.4.0, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit PASSED [100%]\n\n=============================== warnings summary ===============================\n../usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428\n  /usr/local/lib/python3.10/site-packages/_pytest/config/__init__.py:1428: PytestConfigWarning: Unknown config option: collect_ignore\n  \n    self._warn_or_fail_if_strict(f\"Unknown config option: {key}\\n\")\n\n../usr/local/lib/python3.10/site-packages/copulas/__init__.py:18\n  /usr/local/lib/python3.10/site-packages/copulas/__init__.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n    from pkg_resources import iter_entry_points\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-36.xml ------------\n=========================== short test summary info ============================\nPASSED tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit\n======================== 1 passed, 2 warnings in 2.94s =========================\n","stderr_tail":"","stdout_sha256":"e8920648cd7603c32b6b32d99820192fb3138022d78b916432b0c2a1e73aaace","log_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/logs/agent1/agent1-36.log","tree_digest":"a6ab8697e4a538858af9577f01708f7f5c19e608e6466b2b208af2301da1ef7a","tree_download_s":2.3580296670552343,"started_at":"2026-10-03T17:02:37.500799Z","duration_s":4.0972990000000005,"timed_out":false,"image_before":"7ce19b2b-7e0a-40a3-a487-3213e665224a","image_after":"dd24084a-646e-4d7d-af27-6eb94c510e9f","junit":{"passed":["tests/unit/transformers/test_numerical.py::TestClusterBasedNormalizer::test__fit"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
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{"receipt_id":"agent1-39","seq":39,"branch_id":"agent1","kind":"diff","cmd":"compute patch in executor staging agent1-72ae0e10294541db8acc4a64af722fcb: /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false add -A && /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false diff --cached --binary --no-ext-diff --no-textconv --no-renames 3c2a06f93bdfd6a5c7b8ffcbd5dfddfa1dd03192","path":null,"exit_code":0,"stdout_tail":"diff --git a/rdt/transformers/numerical.py b/rdt/transformers/numerical.py\nindex d0edb29..7a27367 100644\n--- a/rdt/transformers/numerical.py\n+++ b/rdt/transformers/numerical.py\n@@ -382,10 +382,16 @@ class GaussianNormalizer(FloatFormatter):\n class ClusterBasedNormalizer(FloatFormatter):\n     \"\"\"Transformer for numerical data using a Bayesian Gaussian Mixture Model.\n \n-    This transformation takes a numerical value and transforms it using a Bayesian GMM\n-    model. It generates two outputs, a discrete value which indicates the selected\n-    'component' of the GMM and a continuous value which represents the normalized value\n-    based on the mean and std of the selected component.\n+    During fitting, this transformer models the data using a Bayesian Gaussian Mixture\n+    (BGMM). Each BGMM component represents a cluster of similar data points.\n+\n+    For transforming new data:\n+    1. Assign each sample to a BGMM component based on posterior probabilities\n+    2. Normalize the assigned component's mean and standard deviation to [-1, 1] range\n+\n+    The ``_transform`` method returns a 2-column array where:\n+    - First column: normalized values in range [-1, 1] (float)\n+    - Second column: selected component indices (int)\n \n     Args:\n         model_missing_values (bool):\n@@ -426,6 +432,9 @@ class ClusterBasedNormalizer(FloatFormatter):\n             than the ``weight_threshold``, it's indicated with True, otherwise it's set to False.\n     \"\"\"\n \n+    # Multiplier used to convert component standard deviations to a normalized range.\n+    # A value of 4 ensures that approximately 95% of values from each component\n+    # fall within the [-1, 1] range after normalization.\n     STD_MULTIPLIER = 4\n     _bgm_transformer = None\n     valid_component_indicator = None\n@@ -463,7 +472,6 @@ class ClusterBasedNormalizer(FloatFormatter):\n             n_components=self.max_clusters,\n             weight_concentration_prior_type='dirichlet_process',\n             weight_concentration_prior=0.001,\n-            n_init=1,\n             random_state=self._get_current_random_seed()\n         )\n \n@@ -472,6 +480,7 @@ class ClusterBasedNormalizer(FloatFormatter):\n         if data.ndim > 1:\n             data = data[:, 0]\n \n+        # Ignore warnings during BGMM fitting to avoid convergence warnings\n         with warnings.catch_warnings():\n             warnings.simplefilter('ignore')\n             self._bgm_transformer.fit(data.reshape(-1, 1))\n@@ -493,11 +502,8 @@ class ClusterBasedNormalizer(FloatFormatter):\n             data, model_missing_values = data[:, 0], data[:, 1]\n \n         data = data.reshape((len(data), 1))\n-        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n-\n-        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n-        normalized_values = (data - means) / (self.STD_MULTIPLIER * stds)\n-        normalized_values = normalized_values[:, self.valid_component_indicator]\n+        \n+        # First select components based on probabilities\n         component_probs = self._bgm_transformer.predict_proba(data)\n         component_probs = component_probs[:, self.valid_component_indicator]\n \n@@ -510,15 +516,27 @@ class ClusterBasedNormalizer(FloatFormatter):\n                 p=component_prob_t\n             )\n \n+        # Then normalize the selected component values\n+        means = self._bgm_transformer.means_.reshape((1, self.max_clusters))\n+        stds = np.sqrt(self._bgm_transformer.covariances_).reshape((1, self.max_clusters))\n+        \n+        # Compute normalized values for all components first\n+        normalized_values = (data - means) / (self.STD_MULTIPLIER * stds)\n+        # Filter to only valid components\n+        normalized_values = normalized_values[:, self.valid_component_indicator]\n+        \n+        # Get the normalized values for selected components\n         aranged = np.arange(len(data))\n-        normalized = normalized_values[aranged, selected_component].reshape([-1, 1])\n-        normalized = np.clip(normalized, -.99, .99)\n-        normalized = normalized[:, 0]\n+        selected_normalized = normalized_values[aranged, selected_component]\n+        \n+        # Normalize the data using selected component parameters\n+        normalized = np.clip(selected_normalized, -.99, .99)\n+        \n         rows = [normalized, selected_component]\n         if self.null_transformer and self.null_transformer.models_missing_values():\n             rows.append(model_missing_values)\n \n-        return np.stack(rows, axis=1)  # noqa: PD013\n+        return np.stack(rows, axis=1)\n \n     def _reverse_transform_helper(self, data):\n         normalized = np.clip(data[:, 0], -1, 1)\n@@ -546,8 +564,8 @@ class ClusterBasedNormalizer(FloatFormatter):\n \n         recovered_data = self._reverse_transform_helper(data)\n         if self.null_transformer and self.null_transformer.models_missing_values():\n-            data = np.stack([recovered_data, data[:, -1]], axis=1)  # noqa: PD013\n+            data = np.stack([recovered_data, data[:, -1]], axis=1)\n         else:\n             data = recovered_data\n \n-        return super()._reverse_transform(data)\n+        return super()._reverse_transform(data)\n\\ No newline at end of file\n","stderr_tail":"","stdout_sha256":"1a3f141795ae581aff3ab77632988c5f9eddcd6b28953ed50604c0221f969452","log_path":null,"tree_digest":"a6ab8697e4a538858af9577f01708f7f5c19e608e6466b2b208af2301da1ef7a","tree_download_s":2.38351675006561,"started_at":"2026-10-03T17:03:07.214327Z","duration_s":0.030470832949504256,"timed_out":false,"image_before":"90bf3df7-9337-42d4-9aa9-b1fe68f80dc5","image_after":"90bf3df7-9337-42d4-9aa9-b1fe68f80dc5","junit":null,"diff":{"files":[{"path":"rdt/transformers/numerical.py","added":34,"deleted":16,"category":"source","status":"modified"}],"patch_sha256":"1a3f141795ae581aff3ab77632988c5f9eddcd6b28953ed50604c0221f969452","patch_path":"~/dev/nebius-hackathon/pilot/v2-P/sdv-dev__rdt-704/runs/pilot-sdv-dev__rdt-704-4f5a9115a2/artifacts/agent1/agent1-39.patch"},"patch_sha256":"1a3f141795ae581aff3ab77632988c5f9eddcd6b28953ed50604c0221f969452","dropped_paths":[],"snapshot_sha":"3c2a06f93bdfd6a5c7b8ffcbd5dfddfa1dd03192"}