Support test_io_data for MultiDataEval and eval util

src/sensai/evaluation/eval_util.py

@@ -91,12 +91,12 @@ def create_vector_model_cross_validator(data: InputOutputData,
 
 def create_evaluation_util(data: InputOutputData, model: VectorModel = None, is_regression: bool = None,
         evaluator_params: Optional[Union[RegressionEvaluatorParams, ClassificationEvaluatorParams]] = None,
-        cross_validator_params: Optional[Dict[str, Any]] = None) \
+        cross_validator_params: Optional[Dict[str, Any]] = None, test_io_data: Optional[InputOutputData] = None) \
             -> Union["ClassificationModelEvaluation", "RegressionModelEvaluation"]:
     if _is_regression(model, is_regression):
-        return RegressionModelEvaluation(data, evaluator_params=evaluator_params, cross_validator_params=cross_validator_params)
+        return RegressionModelEvaluation(data, evaluator_params=evaluator_params, cross_validator_params=cross_validator_params, test_io_data=test_io_data)
     else:
-        return ClassificationModelEvaluation(data, evaluator_params=evaluator_params, cross_validator_params=cross_validator_params)
+        return ClassificationModelEvaluation(data, evaluator_params=evaluator_params, cross_validator_params=cross_validator_params, test_io_data=test_io_data)
 
 
 def eval_model_via_evaluator(model: TModel, io_data: InputOutputData, test_fraction=0.2,
@@ -576,16 +576,21 @@ class MultiDataModelEvaluation:
     def __init__(self, io_data_dict: Dict[str, InputOutputData], key_name: str = "dataset",
             meta_data_dict: Optional[Dict[str, Dict[str, Any]]] = None,
             evaluator_params: Optional[Union[RegressionEvaluatorParams, ClassificationEvaluatorParams, Dict[str, Any]]] = None,
-            cross_validator_params: Optional[Union[VectorModelCrossValidatorParams, Dict[str, Any]]] = None):
+            cross_validator_params: Optional[Union[VectorModelCrossValidatorParams, Dict[str, Any]]] = None,
+            test_io_data_dict: Optional[Dict[str, InputOutputData]] = None):
         """
         :param io_data_dict: a dictionary mapping from names to the data sets with which to evaluate models
         :param key_name: a name for the key value used in inputOutputDataDict, which will be used as a column name in result data frames
         :param meta_data_dict: a dictionary which maps from a name (same keys as in inputOutputDataDict) to a dictionary, which maps
             from a column name to a value and which is to be used to extend the result data frames containing per-dataset results
         :param evaluator_params: parameters to use for the instantiation of evaluators (relevant if useCrossValidation==False)
         :param cross_validator_params: parameters to use for the instantiation of cross-validators (relevant if useCrossValidation==True)
+        :param test_io_data_dict: a dictionary mapping from names to the test data sets to use for evaluation. Datasets under the same
+            keys as in io_data_dict will be used for evaluation of the models that were trained on the respective io_data_dict.
+            The keys don't need to be the same as io_data_dict, unused keys are ignored, and for missing keys the test_io_data is None.
         """
         self.io_data_dict = io_data_dict
+        self.test_io_data_dict = test_io_data_dict or {}
         self.key_name = key_name
         self.evaluator_params = evaluator_params
         self.cross_validator_params = cross_validator_params
@@ -659,8 +664,9 @@ def compare_models(self,
                 else:
                     raise ValueError("The models have to be either all regression models or all classification, not a mixture")
 
+            test_io_data = self.test_io_data_dict.get(key)
             ev = create_evaluation_util(inputOutputData, is_regression=is_regression, evaluator_params=self.evaluator_params,
-                cross_validator_params=self.cross_validator_params)
+                cross_validator_params=self.cross_validator_params, test_io_data=test_io_data)
 
             if plot_collector is None:
                 plot_collector = ev.eval_stats_plot_collector