🧪 🚧 Introduces Features and descriptors for Instances. Not working yet.

digneapy/core/instance.py

@@ -21,14 +21,15 @@
 
 
 class Instance:
-    __slots__ = ("_vars", "_fit", "_p", "_s", "_desc", "_pscores")
+    __slots__ = ("_vars", "_fit", "_p", "_s", "_features", "_desc", "_pscores")
 
     def __init__(
         self,
         variables: Optional[Iterable] = None,
         fitness: float = 0.0,
         p: float = 0.0,
         s: float = 0.0,
+        features: Optional[tuple[float]] = None,
         descriptor: Optional[tuple[float]] = None,
         portfolio_scores: Optional[tuple[float]] = None,
     ):
@@ -44,6 +45,7 @@ def __init__(
         self._fit = fitness
         self._p = p
         self._s = s
+        self._features = tuple(features) if features else tuple()
         self._pscores = tuple(portfolio_scores) if portfolio_scores else tuple()
         self._desc = tuple(descriptor) if descriptor else tuple()
 
@@ -94,6 +96,14 @@ def fitness(self, f: float):
 
         self._fit = f
 
+    @property
+    def features(self) -> tuple:
+        return self._features
+
+    @features.setter
+    def features(self, features: tuple):
+        self._features = features
+
     @property
     def descriptor(self) -> tuple:
         return self._desc
@@ -111,13 +121,13 @@ def portfolio_scores(self, p: tuple):
         self._pscores = tuple(p)
 
     def __repr__(self):
-        return f"Instance<f={self.fitness},p={self.p},s={self.s},vars={len(self._vars)},descriptor={len(self.descriptor)},performance={len(self.portfolio_scores)}>"
+        return f"Instance<f={self.fitness},p={self.p},s={self.s},vars={len(self._vars)},features={len(self.features)},descriptor={len(self.descriptor)},performance={len(self.portfolio_scores)}>"
 
     def __str__(self):
         descriptor = reprlib.repr(self.descriptor)
         performance = reprlib.repr(self.portfolio_scores)
         performance = performance[performance.find("(") : performance.rfind(")") + 1]
-        return f"Instance(f={self.fitness},p={self.p},s={self.s},descriptor={descriptor},performance={performance})"
+        return f"Instance(f={self.fitness},p={self.p},s={self.s},features={len(self.features)},descriptor={descriptor},performance={performance})"
 
     def __iter__(self):
         return iter(self._vars)
@@ -188,6 +198,7 @@ def to_json(self):
             "p": self.p,
             "portfolio": self.portfolio_scores,
             "variables": self._vars.tolist(),
+            "features": self.features,
             "descriptor": self.descriptor,
         }
         return json.dumps(data, sort_keys=True, indent=4)

digneapy/generators/_eig.py

@@ -208,7 +208,7 @@ def _run(self, verbose: bool = False):
                 p_2 = self.selection(self.population)
                 off = self._reproduce(p_1, p_2)
                 if self._describe_by == "features":
-                    off.descriptor = self.domain.extract_features(off)
+                    off.features = self.domain.extract_features(off)
                 offspring.append(off)
 
             self._evaluate_population(offspring)

digneapy/qd/_desc_strategies.py

@@ -53,7 +53,7 @@ def features_strategy(iterable: Iterable[Instance]) -> np.ndarray:
     Returns:
         np.ndarray: Array of the feature descriptors of each instance
     """
-    return np.asarray([i.descriptor for i in iterable])
+    return np.asarray([i.features for i in iterable])
 
 
 def performance_strategy(iterable: Iterable[Instance]) -> np.ndarray:

digneapy/qd/_novelty_search.py

@@ -19,7 +19,8 @@
 
 from digneapy.archives import Archive
 from digneapy.core import Instance
-from digneapy.qd._desc_strategies import descriptor_strategies, features_strategy
+from digneapy.qd._desc_strategies import (descriptor_strategies,
+                                          features_strategy)
 from digneapy.transformers import SupportsTransform