Rename more methods to _old

miplearn/components/component.py

@@ -16,7 +16,7 @@ if TYPE_CHECKING:
 
 
 # noinspection PyMethodMayBeStatic
-class Component:
+class Component(EnforceOverrides):
     """
     A Component is an object which adds functionality to a LearningSolver.
 
@@ -205,7 +205,7 @@ class Component:
         """
         pass
 
-    def xy_instances(
+    def xy_instances_old(
         self,
         instances: List[Instance],
     ) -> Tuple[Dict, Dict]:
@@ -227,11 +227,11 @@ class Component:
             instance.free()
         return x_combined, y_combined
 
-    def fit(
+    def fit_old(
         self,
         training_instances: List[Instance],
     ) -> None:
-        x, y = self.xy_instances(training_instances)
+        x, y = self.xy_instances_old(training_instances)
         for cat in x.keys():
             x[cat] = np.array(x[cat])
             y[cat] = np.array(y[cat])
@@ -294,7 +294,7 @@ class Component:
     ) -> None:
         return
 
-    def evaluate(self, instances: List[Instance]) -> List:
+    def evaluate_old(self, instances: List[Instance]) -> List:
         ev = []
         for instance in instances:
             instance.load()

miplearn/components/dynamic_common.py

@@ -161,7 +161,7 @@ class DynamicConstraintsComponent(Component):
         return pred
 
     @overrides
-    def fit(self, training_instances: List[Instance]) -> None:
+    def fit_old(self, training_instances: List[Instance]) -> None:
         collected_cids = set()
         for instance in training_instances:
             instance.load()
@@ -172,7 +172,7 @@ class DynamicConstraintsComponent(Component):
             instance.free()
         self.known_cids.clear()
         self.known_cids.extend(sorted(collected_cids))
-        super().fit(training_instances)
+        super().fit_old(training_instances)
 
     @overrides
     def fit_xy(

miplearn/components/dynamic_lazy.py

@@ -116,8 +116,8 @@ class DynamicLazyConstraintsComponent(Component):
         return self.dynamic.sample_predict(instance, sample)
 
     @overrides
-    def fit(self, training_instances: List[Instance]) -> None:
-        self.dynamic.fit(training_instances)
+    def fit_old(self, training_instances: List[Instance]) -> None:
+        self.dynamic.fit_old(training_instances)
 
     @overrides
     def fit_xy(

miplearn/components/dynamic_user_cuts.py

@@ -120,8 +120,8 @@ class UserCutsComponent(Component):
         return self.dynamic.sample_predict(instance, sample)
 
     @overrides
-    def fit(self, training_instances: List["Instance"]) -> None:
-        self.dynamic.fit(training_instances)
+    def fit_old(self, training_instances: List["Instance"]) -> None:
+        self.dynamic.fit_old(training_instances)
 
     @overrides
     def fit_xy(

miplearn/components/objective.py

@@ -46,7 +46,7 @@ class ObjectiveValueComponent(Component):
         training_data: TrainingSample,
     ) -> None:
         logger.info("Predicting optimal value...")
-        pred = self.sample_predict(instance, training_data)
+        pred = self.sample_predict_old(instance, training_data)
         for (c, v) in pred.items():
             logger.info(f"Predicted {c.lower()}: %.6e" % v)
             stats[f"Objective: Predicted {c.lower()}"] = v  # type: ignore
@@ -62,7 +62,7 @@ class ObjectiveValueComponent(Component):
                 self.regressors[c] = self.regressor_prototype.clone()
                 self.regressors[c].fit(x[c], y[c])
 
-    def sample_predict(
+    def sample_predict_old(
         self,
         instance: Instance,
         sample: TrainingSample,
@@ -148,7 +148,7 @@ class ObjectiveValueComponent(Component):
             }
 
         result: Dict[Hashable, Dict[str, float]] = {}
-        pred = self.sample_predict(instance, sample)
+        pred = self.sample_predict_old(instance, sample)
         if sample.upper_bound is not None:
             result["Upper bound"] = compare(pred["Upper bound"], sample.upper_bound)
         if sample.lower_bound is not None:

miplearn/solvers/learning.py

@@ -415,7 +415,7 @@ class LearningSolver:
             return
         for component in self.components.values():
             logger.info(f"Fitting {component.__class__.__name__}...")
-            component.fit(training_instances)
+            component.fit_old(training_instances)
 
     def _add_component(self, component: Component) -> None:
         name = component.__class__.__name__