add include_labcodes (#168)

* add include_labcodes

* update black

bofire/surrogates/trainable.py

@@ -51,6 +51,7 @@ def cross_validate(
         experiments: pd.DataFrame,
         folds: int = -1,
         include_X: bool = False,
+        include_labcodes: bool = False,
         hooks: Dict[
             str,
             Callable[
@@ -83,6 +84,8 @@ def cross_validate(
             Tuple[CvResults, CvResults, Dict[str, List[Any]]]: First CvResults object reflects the training data,
                 second CvResults object the test data, dictionary object holds the return values of the applied hooks.
         """
+        if include_labcodes and "labcode" not in experiments.columns:
+            raise ValueError("No labcodes available for the provided experiments.")
 
         if len(self.output_features) > 1:  # type: ignore
             raise NotImplementedError(
@@ -114,6 +117,12 @@ def cross_validate(
             X_test = experiments.iloc[test_index][self.input_features.get_keys()]  # type: ignore
             y_train = experiments.iloc[train_index][self.output_features.get_keys()]  # type: ignore
             y_test = experiments.iloc[test_index][self.output_features.get_keys()]  # type: ignore
+            train_labcodes = (
+                experiments.iloc[train_index]["labcode"] if include_labcodes else None
+            )
+            test_labcodes = (
+                experiments.iloc[test_index]["labcode"] if include_labcodes else None
+            )
             # now fit the model
             self._fit(X_train, y_train)
             # now do the scoring
@@ -127,6 +136,7 @@ def cross_validate(
                     predicted=y_train_pred[key + "_pred"],
                     standard_deviation=y_train_pred[key + "_sd"],
                     X=X_train if include_X else None,
+                    labcodes=train_labcodes,
                 )
             )
             test_results.append(
@@ -136,6 +146,7 @@ def cross_validate(
                     predicted=y_test_pred[key + "_pred"],
                     standard_deviation=y_test_pred[key + "_sd"],
                     X=X_test if include_X else None,
+                    labcodes=test_labcodes,
                 )
             )
             # now call the hooks if available

tests/bofire/surrogates/test_cross_validate.py

@@ -80,8 +80,8 @@ def test_model_cross_validate_descriptor():
         assert len(test_cv.results) == efolds
 
 
-@pytest.mark.parametrize("include_X", [True, False])
-def test_model_cross_validate_include_X(include_X):
+@pytest.mark.parametrize("include_X, include_labcodes", [[True, False], [False, True]])
+def test_model_cross_validate_include_X(include_X, include_labcodes):
     input_features = Inputs(
         features=[
             ContinuousInput(
@@ -93,6 +93,7 @@ def test_model_cross_validate_include_X(include_X):
     )
     output_features = Outputs(features=[ContinuousOutput(key="y")])
     experiments = input_features.sample(n=10)
+    experiments["labcode"] = [str(i) for i in range(10)]
     experiments.eval("y=((x_1**2 + x_2 - 11)**2+(x_1 + x_2**2 -7)**2)", inplace=True)
     experiments["valid_y"] = 1
     model = SingleTaskGPSurrogate(
@@ -101,14 +102,20 @@ def test_model_cross_validate_include_X(include_X):
     )
     model = surrogates.map(model)
     train_cv, test_cv, _ = model.cross_validate(
-        experiments, folds=5, include_X=include_X
+        experiments, folds=5, include_X=include_X, include_labcodes=include_labcodes
     )
     if include_X:
         assert train_cv.results[0].X.shape == (8, 2)
         assert test_cv.results[0].X.shape == (2, 2)
     if include_X is False:
         assert train_cv.results[0].X is None
         assert test_cv.results[0].X is None
+    if include_labcodes:
+        assert train_cv.results[0].labcodes.shape == (8,)
+        assert test_cv.results[0].labcodes.shape == (2,)
+    else:
+        assert train_cv.results[0].labcodes is None
+        assert train_cv.results[0].labcodes is None
 
 
 def test_model_cross_validate_hooks():