Remove obsolete methods

miplearn/components/dynamic_common.py

@@ -12,7 +12,7 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Sample
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
 
 logger = logging.getLogger(__name__)
@@ -37,44 +37,6 @@ class DynamicConstraintsComponent(Component):
         self.known_cids: List[str] = []
         self.attr = attr
 
-    def sample_xy_with_cids_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> Tuple[
-        Dict[Hashable, List[List[float]]],
-        Dict[Hashable, List[List[bool]]],
-        Dict[Hashable, List[str]],
-    ]:
-        x: Dict[Hashable, List[List[float]]] = {}
-        y: Dict[Hashable, List[List[bool]]] = {}
-        cids: Dict[Hashable, List[str]] = {}
-        for cid in self.known_cids:
-            category = instance.get_constraint_category(cid)
-            if category is None:
-                continue
-            if category not in x:
-                x[category] = []
-                y[category] = []
-                cids[category] = []
-            assert instance.features.instance is not None
-            assert instance.features.instance.user_features is not None
-            cfeatures = instance.get_constraint_features(cid)
-            assert cfeatures is not None
-            assert isinstance(cfeatures, list)
-            for ci in cfeatures:
-                assert isinstance(ci, float)
-            f = list(instance.features.instance.user_features)
-            f += cfeatures
-            x[category] += [f]
-            cids[category] += [cid]
-            if getattr(sample, self.attr) is not None:
-                if cid in getattr(sample, self.attr):
-                    y[category] += [[False, True]]
-                else:
-                    y[category] += [[True, False]]
-        return x, y, cids
-
     def sample_xy_with_cids(
         self,
         instance: Optional[Instance],
@@ -122,15 +84,6 @@ class DynamicConstraintsComponent(Component):
                         y[category] += [[True, False]]
         return x, y, cids
 
-    @overrides
-    def sample_xy_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> Tuple[Dict, Dict]:
-        x, y, _ = self.sample_xy_with_cids_old(instance, sample)
-        return x, y
-
     @overrides
     def sample_xy(
         self,
@@ -140,29 +93,6 @@ class DynamicConstraintsComponent(Component):
         x, y, _ = self.sample_xy_with_cids(instance, sample)
         return x, y
 
-    def sample_predict_old(
-        self,
-        instance: Instance,
-        sample: TrainingSample,
-    ) -> List[Hashable]:
-        pred: List[Hashable] = []
-        if len(self.known_cids) == 0:
-            logger.info("Classifiers not fitted. Skipping.")
-            return pred
-        x, _, cids = self.sample_xy_with_cids_old(instance, sample)
-        for category in x.keys():
-            assert category in self.classifiers
-            assert category in self.thresholds
-            clf = self.classifiers[category]
-            thr = self.thresholds[category]
-            nx = np.array(x[category])
-            proba = clf.predict_proba(nx)
-            t = thr.predict(nx)
-            for i in range(proba.shape[0]):
-                if proba[i][1] > t[1]:
-                    pred += [cids[category][i]]
-        return pred
-
     def sample_predict(
         self,
         instance: Instance,
@@ -186,20 +116,6 @@ class DynamicConstraintsComponent(Component):
                     pred += [cids[category][i]]
         return pred
 
-    @overrides
-    def fit_old(self, training_instances: List[Instance]) -> None:
-        collected_cids = set()
-        for instance in training_instances:
-            instance.load()
-            for sample in instance.training_data:
-                if getattr(sample, self.attr) is None:
-                    continue
-                collected_cids |= getattr(sample, self.attr)
-            instance.free()
-        self.known_cids.clear()
-        self.known_cids.extend(sorted(collected_cids))
-        super().fit_old(training_instances)
-
     @overrides
     def fit(self, training_instances: List[Instance]) -> None:
         collected_cids = set()