Refactor Pruner class to improve pruning logic and enhance clarity by…

… separating L1 and L0 pruning methods

fipe/prune/pruner.py

@@ -6,7 +6,7 @@
 
 from ..feature import FeatureEncoder
 from ..mip import MIP
-from ..typing import BaseEnsemble, MNumber, MProb
+from ..typing import BaseEnsemble, MNumber, MProb, Number
 from .base import BasePruner
 
 
@@ -45,12 +45,12 @@ def __init__(
 
     def build(self) -> None:
         self._add_weight_vars()
-        self._add_objective()
         self._n_samples = 0
 
     def add_samples(self, X: npt.ArrayLike) -> None:
         X = np.asarray(X)
-        classes = self.ensemble.predict(X=X, w=self._weights)
+        w = self._weights
+        classes = self.ensemble.predict(X=X, w=w)
         prob = self.ensemble.predict_proba(X=X)
         n = X.shape[0]
         for i in range(n):
@@ -60,7 +60,9 @@ def prune(self) -> None:
         if self._n_samples == 0:
             msg = "No samples have been added to the pruner."
             raise RuntimeError(msg)
-        self.optimize()
+        self._prune_l1()
+        if self._norm == 0:
+            self._prune_l0()
 
     @property
     def n_samples(self) -> int:
@@ -110,3 +112,19 @@ def _validate_norm(self, norm: int) -> None:
         if norm not in self.VALID_NOMRS:
             msg = "The norm must be either 0 or 1."
             raise ValueError(msg)
+
+    def _prune_l1(self) -> None:
+        w = self._weight_vars
+        self.setObjective(w.sum(), gp.GRB.MINIMIZE)
+        self.optimize()
+
+    def _prune_l0(self) -> None:
+        W = Number(np.sum(self._weight_vars.X))
+        n = self.n_estimators
+        w = self._weight_vars
+        u = self.addMVar(shape=n, vtype=gp.GRB.BINARY, name="u")
+        contrs = self.addConstr(w <= W * u, name="bigM")
+        self.setObjective(u.sum(), gp.GRB.MINIMIZE)
+        self.optimize()
+        self.remove(contrs)
+        self.remove(u)