Merge branch '1.1.x' into dev/1.1.0

src/facet/crossfit/_crossfit.py

@@ -101,11 +101,11 @@ class _FitScoreParameters(NamedTuple):
     train_weight: Optional[pd.Series]
 
     # score parameters
-    scorer: Optional[Scorer]
-    score_train_split: bool
-    test_features: Optional[pd.DataFrame]
-    test_target: Union[pd.Series, pd.DataFrame, None]
-    test_weight: Optional[pd.Series]
+    scorer: Optional[Scorer] = None
+    score_train_split: bool = False
+    test_features: Optional[pd.DataFrame] = None
+    test_target: Union[pd.Series, pd.DataFrame, None] = None
+    test_weight: Optional[pd.Series] = None
 
 
 @inheritdoc(match="[see superclass]")
@@ -389,22 +389,15 @@ def _fit_score_queue(
 
         global_fit: Optional[Job[FitResult]]
         if do_fit:
-
-            class _FitModelOnFullData(Job[FitResult]):
-                # noinspection PyMissingOrEmptyDocstring
-                def run(self) -> FitResult:
-                    if sample_weight is None:
-                        pipeline.fit(X=features, y=target, **fit_params)
-                    else:
-                        pipeline.fit(
-                            X=features,
-                            y=target,
-                            sample_weight=sample_weight,
-                            **fit_params,
-                        )
-                    return (pipeline, None)
-
-            global_fit = _FitModelOnFullData()
+            global_fit = _FitModelOnFullData(
+                parameters=_FitScoreParameters(
+                    pipeline=pipeline,
+                    train_features=features,
+                    train_target=target,
+                    train_weight=sample_weight,
+                ),
+                fit_params=fit_params,
+            )
         else:
             global_fit = None
 
@@ -519,13 +512,15 @@ def __len__(self) -> int:
         return self.n_splits_
 
 
-class _FitAndScoreModelForSplit(Job[FitResult]):
+class _BaseFitAndScore(Job[FitResult], metaclass=ABCMeta):
     def __init__(
         self, parameters: _FitScoreParameters, fit_params: Dict[str, Any]
     ) -> None:
         self.parameters = parameters
         self.fit_params = fit_params
 
+
+class _FitAndScoreModelForSplit(_BaseFitAndScore):
     def run(self) -> FitResult:
         """
         Fit and/or score a learner pipeline.
@@ -578,4 +573,26 @@ def run(self) -> FitResult:
         return pipeline if do_fit else None, score
 
 
+class _FitModelOnFullData(_BaseFitAndScore):
+    # noinspection PyMissingOrEmptyDocstring
+    def run(self) -> FitResult:
+        parameters = self.parameters
+        pipeline = parameters.pipeline
+
+        if parameters.train_target is None:
+            pipeline.fit(
+                X=parameters.train_features,
+                y=parameters.train_target,
+                **self.fit_params,
+            )
+        else:
+            pipeline.fit(
+                X=parameters.train_features,
+                y=parameters.train_target,
+                sample_weight=parameters.train_weight,
+                **self.fit_params,
+            )
+        return (pipeline, None)
+
+
 __tracker.validate()