Add ability to blacklist params. also sanitise keys

CHANGELOG.md

@@ -2,6 +2,11 @@
 
 ## [Unreleased]
 
+### Added
+
+-   :sparkles: Implement missing PipelineML filtering functionalities to let kedro display resume hints and avoid breaking kedro-viz ([#377, Calychas](https://github.com/Galileo-Galilei/kedro-mlflow/pull/377), [#601, Calychas](https://github.com/Galileo-Galilei/kedro-mlflow/pull/601))
+-   :sparkles: Sanitize parameters name with unsupported characters to avoid mlflow errors when logging ([#595, pascalwhoop](https://github.com/Galileo-Galilei/kedro-mlflow/pull/595))
+
 ## [0.13.2] - 2024-10-15
 
 ### Fixed

kedro_mlflow/framework/hooks/mlflow_hook.py

@@ -1,3 +1,5 @@
+import os
+import re
 from logging import Logger, getLogger
 from pathlib import Path
 from tempfile import TemporaryDirectory
@@ -303,6 +305,11 @@ def before_node_run(
                     d=params_inputs, recursive=self.recursive, sep=self.sep
                 )
 
+            # sanitize params inputs to avoid mlflow errors
+            params_inputs = {
+                self.sanitize_param_name(k): v for k, v in params_inputs.items()
+            }
+
             # logging parameters based on defined strategy
             for k, v in params_inputs.items():
                 self._log_param(k, v)
@@ -446,5 +453,26 @@ def on_pipeline_error(
             # hence it should not be modified. this is only a safeguard
             switch_catalog_logging(catalog, True)
 
+    def sanitize_param_name(self, name: str) -> str:
+        # regex taken from MLFlow codebase: https://github.com/mlflow/mlflow/blob/e40e782b6fcab473159e6d4fee85bc0fc10f78fd/mlflow/utils/validation.py#L140C1-L148C44
+
+        # for windows colon ':' are not accepted
+        matching_pattern = r"^[/\w.\- ]*$" if is_windows() else r"^[/\w.\- :]*$"
+
+        if re.match(matching_pattern, name):
+            return name
+        else:
+            replacement_pattern = r"[^/\w.\- ]" if is_windows() else r"[^/\w.\- :]"
+            # Replace invalid characters with underscore
+            sanitized_name = re.sub(replacement_pattern, "_", name)
+            self._logger.warning(
+                f"'{name}' is not a valid name for a mlflow paramter. It is renamed as '{sanitized_name}'"
+            )
+            return sanitized_name
+
+
+def is_windows():
+    return os.name == "nt"
+
 
 mlflow_hook = MlflowHook()

tests/framework/hooks/test_hook_log_parameters.py

@@ -1,3 +1,4 @@
+import os
 from pathlib import Path
 from typing import Dict
 
@@ -74,6 +75,136 @@ def dummy_catalog():
     return catalog
 
 
+@pytest.mark.parametrize(
+    "param_name,expected_name",
+    [
+        ("valid_param", "valid_param"),
+        ("valid-param", "valid-param"),
+        ("invalid/param", "invalid/param"),
+        ("invalid.param", "invalid.param"),
+        ("[invalid]$param", "_invalid__param"),
+    ],
+)
+def test_parameter_name_sanitization(
+    kedro_project, dummy_run_params, param_name, expected_name
+):
+    mlflow_tracking_uri = (kedro_project / "mlruns").as_uri()
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+
+    node_inputs = {f"params:{param_name}": "test_value"}
+
+    bootstrap_project(kedro_project)
+    with KedroSession.create(
+        project_path=kedro_project,
+    ) as session:
+        context = session.load_context()
+        mlflow_node_hook = MlflowHook()
+        mlflow_node_hook.after_context_created(context)
+
+        with mlflow.start_run():
+            mlflow_node_hook.before_pipeline_run(
+                run_params=dummy_run_params,
+                pipeline=Pipeline([]),
+                catalog=DataCatalog(),
+            )
+            mlflow_node_hook.before_node_run(
+                node=node(func=lambda x: x, inputs=dict(x="a"), outputs=None),
+                catalog=DataCatalog(),
+                inputs=node_inputs,
+                is_async=False,
+            )
+            run_id = mlflow.active_run().info.run_id
+
+        mlflow_client = MlflowClient(mlflow_tracking_uri)
+        current_run = mlflow_client.get_run(run_id)
+        assert expected_name in current_run.data.params
+        assert current_run.data.params[expected_name] == "test_value"
+
+
+@pytest.mark.skipif(
+    os.name != "nt", reason="Windows does not log params with colon symbol"
+)
+def test_parameter_name_with_colon_sanitization_on_windows(
+    kedro_project, dummy_run_params
+):
+    param_name = "valid:param"
+    expected_name = "valid_param"
+
+    mlflow_tracking_uri = (kedro_project / "mlruns").as_uri()
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+
+    node_inputs = {f"params:{param_name}": "test_value"}
+
+    bootstrap_project(kedro_project)
+    with KedroSession.create(
+        project_path=kedro_project,
+    ) as session:
+        context = session.load_context()
+        mlflow_node_hook = MlflowHook()
+        mlflow_node_hook.after_context_created(context)
+
+        with mlflow.start_run():
+            mlflow_node_hook.before_pipeline_run(
+                run_params=dummy_run_params,
+                pipeline=Pipeline([]),
+                catalog=DataCatalog(),
+            )
+            mlflow_node_hook.before_node_run(
+                node=node(func=lambda x: x, inputs=dict(x="a"), outputs=None),
+                catalog=DataCatalog(),
+                inputs=node_inputs,
+                is_async=False,
+            )
+            run_id = mlflow.active_run().info.run_id
+
+        mlflow_client = MlflowClient(mlflow_tracking_uri)
+        current_run = mlflow_client.get_run(run_id)
+        assert expected_name in current_run.data.params
+        assert current_run.data.params[expected_name] == "test_value"
+
+
+@pytest.mark.skipif(
+    os.name == "nt", reason="Linux and Mac do log params with colon symbol"
+)
+def test_parameter_name_with_colon_sanitization_on_mac_linux(
+    kedro_project, dummy_run_params
+):
+    param_name = "valid:param"
+    expected_name = "valid:param"
+
+    mlflow_tracking_uri = (kedro_project / "mlruns").as_uri()
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+
+    node_inputs = {f"params:{param_name}": "test_value"}
+
+    bootstrap_project(kedro_project)
+    with KedroSession.create(
+        project_path=kedro_project,
+    ) as session:
+        context = session.load_context()
+        mlflow_node_hook = MlflowHook()
+        mlflow_node_hook.after_context_created(context)
+
+        with mlflow.start_run():
+            mlflow_node_hook.before_pipeline_run(
+                run_params=dummy_run_params,
+                pipeline=Pipeline([]),
+                catalog=DataCatalog(),
+            )
+            mlflow_node_hook.before_node_run(
+                node=node(func=lambda x: x, inputs=dict(x="a"), outputs=None),
+                catalog=DataCatalog(),
+                inputs=node_inputs,
+                is_async=False,
+            )
+            run_id = mlflow.active_run().info.run_id
+
+        mlflow_client = MlflowClient(mlflow_tracking_uri)
+        current_run = mlflow_client.get_run(run_id)
+        assert expected_name in current_run.data.params
+        assert current_run.data.params[expected_name] == "test_value"
+
+
 def test_pipeline_run_hook_getting_configs(
     kedro_project,
     dummy_run_params,