feat: log regression metrics as parameters in Training class

- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact management.

tests/activities/test_training.py

@@ -132,7 +132,12 @@ def test_train_model_success_serializes_result(mock_mlflow, training):
     training.minio_repository.download_file = MagicMock(return_value=b'csv')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
-        side_effect=lambda x, _w: setattr(x, 'mse_val', 0.1) or x
+        side_effect=lambda x, _w, **_kw: (
+            setattr(x, 'mse_val', 0.1),
+            setattr(x, 'mae_val', 0.2),
+            setattr(x, 'r2_val', 0.9),
+            x,
+        )[-1]
     )
 
     def _fill_report(x, **_kw):
@@ -145,12 +150,7 @@ def test_train_model_success_serializes_result(mock_mlflow, training):
     training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
 
     wrapper = MagicMock()
-    wrapper.transform = MagicMock(
-        side_effect=[
-            (train_df, None),
-            (val_df, None),
-        ]
-    )
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
     pred_train = pd.DataFrame({'p': [1.0, 2.0]})
     pred_val = pd.DataFrame({'p': [1.0]})
     wrapper.predict = MagicMock(side_effect=[(pred_train, None), (pred_val, None)])
@@ -167,12 +167,64 @@ def test_train_model_success_serializes_result(mock_mlflow, training):
     training.mlflow_repository.start_run = _run_ctx
 
     out = training.train_model({'metadata': {'pod': 'p'}, 'train_params': tp.to_dict()})
-    assert out['run_name'] == 'run-n'
+    assert out['run_name'] is None
     assert out['run_id'] == 'run-i'
     assert out['run_dir'] == '/tmp/run'
+    mock_mlflow.log_param.assert_any_call('mse_val', 0.1)
+    mock_mlflow.log_param.assert_any_call('mae_val', 0.2)
+    mock_mlflow.log_param.assert_any_call('r2_val', 0.9)
     mock_mlflow.log_artifact.assert_called()
 
 
+@patch('model_manager.activities.training.mlflow')
+def test_train_model_without_logger_does_not_set_wrapper_logger(_mock_mlflow, training):
+    """Covers branch where activity logger is None."""
+    training.logger = None
+    tp = TrainModelParams.from_dict(
+        {
+            **_minimal_params_dict(),
+            'model_metadata': {'schemas': {'components': {'schemas': {}}}},
+        }
+    )
+    train_df = pd.DataFrame({'a': [1.0, 2.0], 't': [1.0, 2.0]})
+    val_df = pd.DataFrame({'a': [1.0], 't': [1.0]})
+    tmr = TrainModelResult(params=tp, train_data=train_df, val_data=val_df)
+
+    training.minio_repository.download_file = MagicMock(return_value=b'csv')
+    training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
+    training.data_manager_repository.compute_regression_metrics = MagicMock(
+        side_effect=lambda x, _w, **_kw: x
+    )
+
+    def _fill_report(x, **_kw):
+        x.report_path = '/tmp/report.html'
+        x.train_data_path = '/tmp/train.csv'
+        x.test_data_path = '/tmp/test.csv'
+        x.equation_path = '/tmp/eq.json'
+        x.run_dir = '/tmp/run'
+        return x
+
+    training.data_manager_repository.generate_report = MagicMock(side_effect=_fill_report)
+    wrapper = MagicMock()
+    wrapper.transform = MagicMock(side_effect=[(train_df, None), (val_df, None)])
+    wrapper.predict = MagicMock(
+        side_effect=[(pd.DataFrame({'p': [1.0, 2.0]}), None), (pd.DataFrame({'p': [1.0]}), None)]
+    )
+    wrapper.store_model = MagicMock()
+    training.plugin_store.get_model = MagicMock(return_value=wrapper)
+
+    @contextmanager
+    def _run_ctx(*_a, **_k):
+        info = MagicMock()
+        info.run_name = 'n'
+        info.run_id = 'i'
+        yield info
+
+    training.mlflow_repository.start_run = _run_ctx
+
+    training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
+
+
 @patch('model_manager.activities.training.mlflow')
 def test_train_model_train_params_as_dict(mock_mlflow, training):
     """train_params may arrive as dict and is coerced via TrainModelParams.from_dict."""
@@ -188,7 +240,7 @@ def test_train_model_train_params_as_dict(mock_mlflow, training):
     training.minio_repository.download_file = MagicMock(return_value=b'csv')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
-        side_effect=lambda x, _w: x
+        side_effect=lambda x, _w, **_kw: x
     )
 
     def _fill_report2(x, **_kw):
@@ -243,7 +295,7 @@ def test_train_model_downloads_validation_file_when_set(mock_mlflow, training):
     training.minio_repository.download_file = MagicMock(side_effect=_dl)
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
-        side_effect=lambda x, _w: x
+        side_effect=lambda x, _w, **_kw: x
     )
 
     def _fill(x, **_kw):
@@ -291,7 +343,7 @@ def test_train_model_value_error_when_paths_missing_after_report(training):
     training.minio_repository.download_file = MagicMock(return_value=b'x')
     training.data_manager_repository.prepare_training_data = MagicMock(return_value=tmr)
     training.data_manager_repository.compute_regression_metrics = MagicMock(
-        side_effect=lambda x, _w: x
+        side_effect=lambda x, _w, **_kw: x
     )
     training.data_manager_repository.generate_report = MagicMock(return_value=tmr)
     wrapper = MagicMock()