Fixe for approvalprofile generator

pabutools/election/profile/approvalprofile.py

@@ -316,7 +316,7 @@ def get_random_approval_profile(instance: Instance, num_agents: int) -> Approval
 
 def get_all_approval_profiles(
     instance: Instance, num_agents: int
-) -> Generator[Iterable[Project]]:
+) -> Generator[ApprovalProfile]:
     """
     Returns a generator over all the possible profile for a given instance of a given length.
 
@@ -332,7 +332,8 @@ def get_all_approval_profiles(
         Generator[Iterable[:py:class:`~pabutools.election.instance.Project`]]
             Generator over subsets of projects.
     """
-    return product(powerset(instance), repeat=num_agents)
+    for p in product(powerset(instance), repeat=num_agents):
+        yield ApprovalProfile([ApprovalBallot(b) for b in p], instance=instance)
 
 
 class ApprovalMultiProfile(MultiProfile, AbstractApprovalProfile):

pabutools/election/satisfaction/satisfactionmeasure.py

@@ -140,3 +140,26 @@ def total_satisfaction(self, projects: Iterable[Project]) -> Number:
         for sat in self:
             res += sat.sat(projects) * self.multiplicity(sat)
         return res
+
+    @abstractmethod
+    def remove_satisfied(
+        self, sat_bound: dict[AbstractBallot, Number], projects: Iterable[Project]
+    ) -> GroupSatisfactionMeasure:
+        """
+        Returns a new satisfaction profile excluding the satisfaction measurs corresponding to satisfied voters, i.e.,
+        who have met or exceeded their satisfaction bound for a given collection of projects.
+
+        Parameters
+        ----------
+            sat_bound : dict[str, Number]
+                A dictionary of ballot names to numbers, specifying for each ballot the satisfaction bound above which
+                the voter is considered satisfied. Note that the keys are ballot names, and that nothing ensures ballot
+                names to be unique, so be careful here.
+            projects : Iterable[:py:class:`~pabutools.election.instance.Project`]
+                The collection of projects.
+
+        Returns
+        -------
+            :py:class:`~pabutools.election.satisfaction.satisfactionmeasure.GroupSatisfactionMeasure`
+                The new satisfaction profile.
+        """

pabutools/election/satisfaction/satisfactionprofile.py

@@ -5,12 +5,14 @@
 
 from collections import Counter
 from collections.abc import Iterable
+from numbers import Number
 
 from pabutools.election.satisfaction.satisfactionmeasure import (
     SatisfactionMeasure,
     GroupSatisfactionMeasure,
 )
-from pabutools.election.instance import Instance
+from pabutools.election.instance import Instance, Project
+from pabutools.election.ballot.ballot import AbstractBallot
 
 from typing import TYPE_CHECKING
 
@@ -116,6 +118,12 @@ def multiplicity(self, sat: SatisfactionMeasure) -> int:
         """
         return 1
 
+    def remove_satisfied(self, sat_bound: dict[str, Number],
+                         projects: Iterable[Project]) -> SatisfactionProfile:
+        res = SatisfactionProfile((s for s in self if s.sat(projects) < sat_bound[s.ballot.name]), instance=self.instance)
+        res.sat_class = self.sat_class
+        return res
+
     @classmethod
     def _wrap_methods(cls, names):
         def wrap_method_closure(name):
@@ -296,6 +304,12 @@ def extend_from_multiprofile(
     def multiplicity(self, sat: SatisfactionMeasure) -> int:
         return self[sat]
 
+    def remove_satisfied(self, sat_bound: dict[AbstractBallot, Number],
+                         projects: Iterable[Project]) -> SatisfactionMultiProfile:
+        res = SatisfactionMultiProfile({s: m for s, m in self.items() if s.sat(projects) < sat_bound[s.ballot.name]}, instance=self.instance)
+        res.sat_class = self.sat_class
+        return res
+
     @classmethod
     def _wrap_methods(cls, names):
         def wrap_method_closure(name):

pabutools/rules/greedywelfare.py

@@ -4,7 +4,9 @@
 from copy import copy
 from collections.abc import Iterable
 from math import inf
+from numbers import Number
 
+from pabutools.election import AbstractBallot
 from pabutools.election.profile import AbstractProfile
 
 from pabutools.fractions import frac
@@ -24,6 +26,7 @@ def greedy_utilitarian_scheme(
     budget_allocation: Iterable[Project],
     tie_breaking: TieBreakingRule,
     resoluteness: bool = True,
+    sat_bounds: dict[AbstractBallot, Number] = None,
 ) -> Iterable[Project] | Iterable[Iterable[Project]]:
     """
     The inner algorithm for the greedy rule. It selects projects in rounds, each time selecting a project that
@@ -71,7 +74,8 @@ def aux(inst, prof, sats, allocs, alloc, tie, resolute):
                 new_alloc = copy(alloc) + [project]
                 if project.cost > 0:
                     total_marginal_score = frac(
-                        sats.total_satisfaction(new_alloc) - sats.total_satisfaction(alloc),
+                        sats.total_satisfaction(new_alloc)
+                        - sats.total_satisfaction(alloc),
                         project.cost,
                     )
                 else:
@@ -164,6 +168,7 @@ def satisfaction_density(proj):
                 return frac(total_sat, proj.cost)
             return inf
         return 0
+
     # We sort based on a tuple to ensure ties are broken as intended
     ordered_projects = sorted(
         projects, key=lambda p: (-satisfaction_density(p), projects.index(p))
@@ -233,7 +238,7 @@ def greedy_utilitarian_welfare(
         budget_allocation = []
     if sat_class is None:
         if sat_profile is None:
-            raise ValueError("Satisfaction and sat_profile cannot both be None.")
+            raise ValueError("sat_class and sat_profile cannot both be None.")
     else:
         if sat_profile is None:
             sat_profile = profile.as_sat_profile(sat_class)

pabutools/rules/maxwelfare.py

@@ -150,7 +150,6 @@ def max_additive_utilitarian_welfare(
     else:
         if sat_profile is None:
             sat_profile = profile.as_sat_profile(sat_class=sat_class)
-
     return max_additive_utilitarian_welfare_scheme(
         instance, sat_profile, budget_allocation, resoluteness=resoluteness
     )

tests/test_profile.py

@@ -143,9 +143,9 @@ def test_approval_profile(self):
         new_inst = Instance(
             [Project("p1", 1), Project("p2", 1), Project("p3", 1)], budget_limit=3
         )
-        assert len(set(get_all_approval_profiles(new_inst, 1))) == 8
-        assert len(set(get_all_approval_profiles(new_inst, 2))) == 8 * 8
-        assert len(set(get_all_approval_profiles(new_inst, 3))) == 8 * 8 * 8
+        assert len(list(get_all_approval_profiles(new_inst, 1))) == 8
+        assert len(list(get_all_approval_profiles(new_inst, 2))) == 8 * 8
+        assert len(list(get_all_approval_profiles(new_inst, 3))) == 8 * 8 * 8
 
     def test_app_multiprofile(self):
         projects = [Project("p" + str(i), cost=2) for i in range(10)]

tests/test_rule.py

@@ -154,12 +154,10 @@ def dummy_elections():
     test_election.irr_results_sat[max_additive_utilitarian_welfare][Cost_Sat] = sorted(
         [[p[0], p[2]], [p[2]]]
     )
-    test_election.irr_results_sat[max_additive_utilitarian_welfare][Cardinality_Sat] = sorted(
-        [[p[0], p[2]]]
-    )
-    test_election.irr_results_sat[method_of_equal_shares][Cost_Sat] = sorted(
-        [[]]
-    )
+    test_election.irr_results_sat[max_additive_utilitarian_welfare][
+        Cardinality_Sat
+    ] = sorted([[p[0], p[2]]])
+    test_election.irr_results_sat[method_of_equal_shares][Cost_Sat] = sorted([[]])
     test_election.irr_results_sat[method_of_equal_shares][Cardinality_Sat] = sorted(
         [[p[0]]]
     )