FIX Galileo-Galilei#83 - Check input_name when set in PipelineML

CHANGELOG.md

@@ -24,6 +24,7 @@
 - Remove `conda_env` and `model_name` arguments from `MlflowPipelineHook` and add them to `PipelineML` and `pipeline_ml`. This is necessary for incoming hook auto-discovery in future release and it enables having multiple `PipelineML` in the same project ([#58](https://github.com/Galileo-Galilei/kedro-mlflow/pull/58)). This mechanically fixes [#54](https://github.com/Galileo-Galilei/kedro-mlflow/issues/54) by making `conda_env` path absolute for airflow suppport.
 - `flatten_dict_params`, `recursive` and `sep` arguments of the `MlflowNodeHook` are moved to the `mlflow.yml` config file to prepare plugin auto registration. This also modifies the `run.py` template (to remove the args) and the `mlflow.yml` keys to add a `hooks` entry. ([#59](https://github.com/Galileo-Galilei/kedro-mlflow/pull/59))
 - Rename CI workflow to `test` ([#57](https://github.com/Galileo-Galilei/kedro-mlflow/issues/57), [#68](https://github.com/Galileo-Galilei/kedro-mlflow/pull/68))
+- The `input_name` attributes of `PipelineML` is now a python property and makes a check at setting time to prevent setting an invalid value. The check ensures that `input_name` is a valid input of the `inference` pipeline.
 
 
 ### Deprecated

kedro_mlflow/pipeline/pipeline_ml.py

@@ -79,9 +79,17 @@ def __init__(
         self.conda_env = conda_env
         self.model_name = model_name
 
-        self._check_input_name(input_name)
         self.input_name = input_name
 
+    @property
+    def input_name(self) -> str:
+        return self._input_name
+
+    @input_name.setter
+    def input_name(self, name: str) -> None:
+        self._check_input_name(name)
+        self._input_name = name
+
     def extract_pipeline_catalog(self, catalog: DataCatalog) -> DataCatalog:
         sub_catalog = DataCatalog()
         for data_set_name in self.inference.inputs():
@@ -134,10 +142,10 @@ def training(self):
 
     def _check_input_name(self, input_name: str) -> str:
         allowed_names = self.inference.inputs()
-        pp_allowed_names = "\n - ".join(allowed_names)
+        pp_allowed_names = "\n    - ".join(allowed_names)
         if input_name not in allowed_names:
             raise KedroMlflowPipelineMLInputsError(
-                f"input_name='{input_name}' but it must be an input of inference, i.e. one of: {pp_allowed_names}"
+                f"input_name='{input_name}' but it must be an input of 'inference', i.e. one of: \n    - {pp_allowed_names}"
             )
         else:
             free_inputs_set = (

tests/pipeline/test_pipeline_ml.py

@@ -119,31 +119,33 @@ def _get_pipelines(self):
     # Disable logging.config.dictConfig in KedroContext._setup_logging as
     # it changes logging.config and affects other unit tests
     mocker.patch("logging.config.dictConfig")
-
-    return DummyContext(tmp_path.as_posix())
+    dummy_context = DummyContext(tmp_path.as_posix())
+    return dummy_context
 
 
 @pytest.fixture
 def dummy_catalog():
-    return DataCatalog(
+    dummy_catalog = DataCatalog(
         {
             "raw_data": MemoryDataSet(),
             "data": MemoryDataSet(),
             "model": CSVDataSet("fake/path/to/model.csv"),
         }
     )
+    return dummy_catalog
 
 
 @pytest.fixture
 def catalog_with_encoder():
-    return DataCatalog(
+    catalog_with_encoder = DataCatalog(
         {
             "raw_data": MemoryDataSet(),
             "data": MemoryDataSet(),
             "encoder": CSVDataSet("fake/path/to/encoder.csv"),
             "model": CSVDataSet("fake/path/to/model.csv"),
         }
     )
+    return catalog_with_encoder
 
 
 @pytest.mark.parametrize(
@@ -332,3 +334,11 @@ def fake_dec(x):
 
     new_pl = pipeline_ml_with_tag.decorate(fake_dec)
     assert all([fake_dec in node._decorators for node in new_pl.nodes])
+
+
+def test_invalid_input_name(pipeline_ml_with_tag):
+    with pytest.raises(
+        KedroMlflowPipelineMLInputsError,
+        match="input_name='whoops_bad_name' but it must be an input of 'inference'",
+    ):
+        pipeline_ml_with_tag.input_name = "whoops_bad_name"