Merge branch 'main' into footer

algorithm/README.md

@@ -1,23 +1,16 @@
 # Algoritme
 
-Algoritmen baserer seg på MIP-programmering (Mixed Integer Linear Programming).
+**mip_matching** er en pakke for å tildele intervjutider til søkere basert på ledige tider for søkere og komitéer.
+
+Algoritmen baserer seg på MIP-programmering (Mixed Integer Linear Programming). Se [Modellering.md](./src/Modellering.md) for detaljer.
 
 ## Setup Python Venv
 
 ```bash
 cd algorithm
 python -m venv ".venv"
-```
-
-```
 .\.venv\Scripts\activate
 pip install -e .
 pip install -r requirements.txt
+pip install pymongo[srv]
 ```
-
-## TODOs
-
-- [x] Lage funksjon som deler opp fra en komités slot
-- [x] Sette opp begrensningene fra modelleringen
-- [ ] Flikke litt på modelleringen.
-- [ ] Finn ut hvordan man kan preprosessere dataen for å få ned kjøretiden (f. eks ved å lage lister av personer for hver komité.)

algorithm/bridge/fetch_applicants_and_committees.py

@@ -18,7 +18,6 @@ def main():
         application_end = datetime.fromisoformat(period["applicationPeriod"]["end"].replace("Z", "+00:00"))
 
         now = datetime.now(timezone.utc)
-
 
         #or period["name"] == "Juli Opptak"
         if  (application_end < now and period["hasSentInterviewTimes"] == False):
@@ -115,11 +114,13 @@ def format_match_results(match_results: MeetingMatch, applicants: List[dict], pe
         time_interval = result[2]
         start = time_interval.start.isoformat()
         end = time_interval.end.isoformat()
+        room = result[3]
 
         transformed_results[applicant_id]["interviews"].append({
             "start": start,
             "end": end,
-            "committeeName": committee.name
+            "committeeName": committee.name,
+            "room": room
         })
 
     return list(transformed_results.values())
@@ -156,8 +157,8 @@ def create_committee_objects(committee_data: List[dict]) -> set[Committee]:
                 start=datetime.fromisoformat(interval_data['start'].replace("Z", "+00:00")),
                 end=datetime.fromisoformat(interval_data['end'].replace("Z", "+00:00"))
             )
-            capacity = interval_data.get('capacity', 1)  
-            committee.add_interval(interval, capacity)
+            room = interval_data["room"]
+            committee.add_interview_slot(interval, room)
         committees.add(committee)
     return committees
 

algorithm/src/Modellering.md

@@ -1,11 +1,4 @@
-# Modellering av problem gjennom Mixed Integer Linear Programming
-
-## Nyttige ressurser
-
-- https://python-mip.readthedocs.io/en/latest/quickstart.html
-- https://towardsdatascience.com/mixed-integer-linear-programming-1-bc0ef201ee87
-- https://towardsdatascience.com/mixed-integer-linear-programming-formal-definition-and-solution-space-6b3286d54892
-- https://www.gurobi.com/resources/mixed-integer-programming-mip-a-primer-on-the-basics/
+# Modellering av møtetildelingsproblem gjennom Mixed Integer Linear Programming
 
 ## Variabler
 
@@ -19,7 +12,7 @@
 
 `t`
 
-- Timeslot (Må gjøres til intervaller etter hvert)
+- Timeslot
 
 `m(p, k, t)`
 
@@ -55,8 +48,15 @@ For alle `k`:
 
 ## Mål
 
-Maksimere `sum(m(p, k, t))` for alle `p`, `k` og `t`
+Maksimere `sum(m(p, k, t))` for alle `p`, `k` og `t`. Altså: Maksimere antall intervjuer som tildeles.
 
 ### Sekundærmål
 
-- [Ikke enda implementert] La det være færrest mulig og minst mulig mellomrom mellom intervjuene for komitéene.
+- La intervjuene klumpe seg rundt klokken 12 og dermed også minske hvor mange hull komitéene får i sin intervjuplan.
+
+## Nyttige ressurser
+
+- https://python-mip.readthedocs.io/en/latest/quickstart.html
+- https://towardsdatascience.com/mixed-integer-linear-programming-1-bc0ef201ee87
+- https://towardsdatascience.com/mixed-integer-linear-programming-formal-definition-and-solution-space-6b3286d54892
+- https://www.gurobi.com/resources/mixed-integer-programming-mip-a-primer-on-the-basics/

algorithm/src/mip_matching/Committee.py

@@ -6,6 +6,8 @@
 
 from typing import Iterator
 
+from mip_matching.types import Room
+
 
 class Committee:
     """
@@ -21,39 +23,40 @@ class Committee:
     """
 
     def __init__(self, name: str, interview_length: timedelta = timedelta(minutes=15)):
-        self.capacities: dict[TimeInterval, int] = dict()
+        self.interview_slots: dict[TimeInterval, set[Room]] = dict()
         self.interview_length: timedelta = interview_length
         self.applicants: set[Applicant] = set()
         self.name = name
 
-    def add_interval(self, interval: TimeInterval, capacity: int = 1) -> None:
-        """Legger til et nytt intervall med gitt kapasitet hvis intervallet 
-        ikke allerede har en kapasitet for denne komitéen.
+    def add_interview_slot(self, interval: TimeInterval, room: Room) -> None:
+        """Legger til et nytt intervall med gitt rom.
         Når intervaller legges til deles det automatisk opp i 
         intervaller med lik lengde som intervjulengder."""
         minimal_intervals = TimeInterval.divide_interval(
             interval=interval, length=self.interview_length)
         for interval in minimal_intervals:
-            if interval not in self.capacities:
-                self.capacities[interval] = capacity
-            else:
-                self.capacities[interval] += capacity
-
-    def add_intervals_with_capacities(self, intervals_with_capacities: dict[TimeInterval, int]):
-        """Legger til flere tidsintervaller samtidig."""
-        for interval, capacity in intervals_with_capacities.items():
-            self.add_interval(interval, capacity)
+            if interval not in self.interview_slots:
+                self.interview_slots[interval] = set()
+            self.interview_slots[interval].add(room)
 
     def get_intervals_and_capacities(self) -> Iterator[tuple[TimeInterval, int]]:
         """Generator som returnerer interval-kapasitet-par."""
-        for interval, capacity in self.capacities.items():
-            yield interval, capacity
+        for interval, rooms in self.interview_slots.items():
+            yield interval, len(rooms)
+
+    def get_capacity(self, interval: TimeInterval) -> int:
+        """Returnerer komitéens kapasitet ved et gitt interval (ferdiginndelt etter lengde)"""
+        return len(self.interview_slots[interval])
 
     def get_intervals(self) -> Iterator[TimeInterval]:
         """Generator som returnerer kun intervallene"""
-        for interval in self.capacities.keys():
+        for interval in self.interview_slots.keys():
             yield interval
 
+    def get_rooms(self, interval: TimeInterval) -> Iterator[Room]:
+        for room in self.interview_slots[interval]:
+            yield room
+
     def _add_applicant(self, applicant: Applicant):
         """Metode brukt for å holde toveis-assosiasjonen."""
         self.applicants.add(applicant)
@@ -62,10 +65,6 @@ def get_applicants(self) -> Iterator[Applicant]:
         for applicant in self.applicants:
             yield applicant
 
-    def get_capacity(self, interval: TimeInterval) -> int:
-        """Returnerer komitéens kapasitet ved et gitt interval (ferdiginndelt etter lengde)"""
-        return self.capacities[interval]
-
     def get_applicant_count(self) -> int:
         return len(self.applicants)
 
@@ -77,4 +76,4 @@ def __repr__(self):
 
 
 if __name__ == "__main__":
-    print("running")
+    print("running")

algorithm/src/mip_matching/TimeInterval.py

@@ -65,7 +65,7 @@ def divide(self, length: timedelta) -> list[TimeInterval]:
         return TimeInterval.divide_interval(self, length)
 
     def is_within_distance(self, other: TimeInterval, distance: timedelta) -> bool:
-        return (self.end <= other.start and self.end + distance > other.start) or (other.end <= self.start and other.end + distance > self.start)
+        return (self.end <= other.start < self.end + distance) or (other.end <= self.start < other.end + distance)
 
     @staticmethod
     def divide_interval(interval: TimeInterval, length: timedelta) -> list[TimeInterval]:

algorithm/src/mip_matching/match_meetings.py

@@ -8,6 +8,7 @@
 from datetime import timedelta, time
 from itertools import combinations
 
+from mip_matching.types import Matching, MeetingMatch
 from mip_matching.utils import subtract_time
 
 
@@ -22,60 +23,53 @@
 MAX_SCALE_CLUSTERING_TIME = timedelta(seconds=43200)  # TODO: Rename variable
 
 
-class MeetingMatch(TypedDict):
-    """Type definition of a meeting match object"""
-    solver_status: mip.OptimizationStatus
-    matched_meetings: int
-    total_wanted_meetings: int
-    matchings: list[tuple[Applicant, Committee, TimeInterval]]
-
-
 def match_meetings(applicants: set[Applicant], committees: set[Committee]) -> MeetingMatch:
     """Matches meetings and returns a MeetingMatch-object"""
     model = mip.Model(sense=mip.MAXIMIZE)
 
-    m: dict[tuple[Applicant, Committee, TimeInterval], mip.Var] = {}
+    m: dict[Matching, mip.Var] = {}
 
     # Lager alle maksimeringsvariabler
     for applicant in applicants:
         for committee in applicant.get_committees():
             for interval in applicant.get_fitting_committee_slots(committee):
-                m[(applicant, committee, interval)] = model.add_var(
-                    var_type=mip.BINARY, name=f"({applicant}, {committee}, {interval})")
+                for room in committee.get_rooms(interval):
+                    m[(applicant, committee, interval, room)] = model.add_var(
+                        var_type=mip.BINARY, name=f"({applicant}, {committee}, {interval}, {room})")
 
     # Legger inn begrensninger for at en komité kun kan ha antall møter i et slot lik kapasiteten.
     for committee in committees:
         for interval, capacity in committee.get_intervals_and_capacities():
-            model += mip.xsum(m[(applicant, committee, interval)]
+            model += mip.xsum(m[(applicant, committee, interval, room)]
                               for applicant in committee.get_applicants()
+                              for room in committee.get_rooms(interval)
+                              if (applicant, committee, interval, room) in m
                               # type: ignore
-                              if (applicant, committee, interval) in m) <= capacity
+                              ) <= capacity
 
     # Legger inn begrensninger for at en person kun har ett intervju med hver komité
     for applicant in applicants:
         for committee in applicant.get_committees():
-            model += mip.xsum(m[(applicant, committee, interval)]
+            model += mip.xsum(m[(applicant, committee, interval, room)]
+                              for interval in applicant.get_fitting_committee_slots(committee)
+                              for room in committee.get_rooms(interval)
                               # type: ignore
-                              for interval in applicant.get_fitting_committee_slots(committee)) <= 1
+                              ) <= 1
 
     # Legger inn begrensninger for at en søker ikke kan ha overlappende intervjutider
     # og minst har et buffer mellom hvert intervju som angitt
     for applicant in applicants:
-        potential_interviews: set[tuple[Committee, TimeInterval]] = set()
-        for applicant_candidate, committee, interval in m:
-            if applicant == applicant_candidate:
-                potential_interviews.add((committee, interval))
+        potential_interviews = set(slot for slot in m.keys() if slot[0] == applicant)
 
         for interview_a, interview_b in combinations(potential_interviews, r=2):
-            if interview_a[1].intersects(interview_b[1]) or interview_a[1].is_within_distance(interview_b[1], APPLICANT_BUFFER_LENGTH):
-                model += m[(applicant, *interview_a)] + \
-                    m[(applicant, *interview_b)] <= 1  # type: ignore
+            if interview_a[2].intersects(interview_b[2]) or interview_a[2].is_within_distance(interview_b[2], APPLICANT_BUFFER_LENGTH):
+                model += m[interview_a] + m[interview_b] <= 1  # type: ignore
 
     # Legger til sekundærmål om at man ønsker å sentrere intervjuer rundt CLUSTERING_TIME_BASELINE
     clustering_objectives = []
 
     for name, variable in m.items():
-        applicant, committee, interval = name
+        applicant, committee, interval, room = name
         if interval.start.time() < CLUSTERING_TIME_BASELINE:
             relative_distance_from_baseline = subtract_time(CLUSTERING_TIME_BASELINE,
                                                             interval.end.time()) / MAX_SCALE_CLUSTERING_TIME
@@ -86,8 +80,8 @@ def match_meetings(applicants: set[Applicant], committees: set[Committee]) -> Me
         clustering_objectives.append(
             CLUSTERING_WEIGHT * relative_distance_from_baseline * variable)  # type: ignore
 
-        # Setter mål til å være maksimering av antall møter
-        # med sekundærmål om å samle intervjuene rundt CLUSTERING_TIME_BASELINE
+    # Setter mål til å være maksimering av antall møter
+    # med sekundærmål om å samle intervjuene rundt CLUSTERING_TIME_BASELINE
     model.objective = mip.maximize(
         mip.xsum(m.values()) + mip.xsum(clustering_objectives))
 
@@ -101,7 +95,6 @@ def match_meetings(applicants: set[Applicant], committees: set[Committee]) -> Me
         if variable.x:
             antall_matchede_møter += 1
             matchings.append(name)
-            print(f"{name}")
 
     antall_ønskede_møter = sum(
         len(applicant.get_committees()) for applicant in applicants)

algorithm/src/mip_matching/types.py

@@ -0,0 +1,22 @@
+"""
+Typealiaser
+"""
+
+from typing import TypedDict, TYPE_CHECKING
+import mip
+if TYPE_CHECKING:
+    # Unngår cyclic import
+    from mip_matching.Applicant import Applicant
+    from mip_matching.Committee import Committee
+    from mip_matching.TimeInterval import TimeInterval
+
+
+type Room = str
+type Matching = tuple[Applicant, Committee, TimeInterval, Room]
+
+class MeetingMatch(TypedDict):
+    """Type definition of a meeting match object"""
+    solver_status: mip.OptimizationStatus
+    matched_meetings: int
+    total_wanted_meetings: int
+    matchings: list[Matching]

algorithm/tests/CommitteeTest.py

@@ -1,21 +1,14 @@
-from __future__ import annotations
-from datetime import datetime, timedelta
-import unittest
-from mip_matching.TimeInterval import TimeInterval
-from mip_matching.Committee import Committee
+# from __future__ import annotations
+# from datetime import datetime, timedelta
+# import unittest
+# from mip_matching.TimeInterval import TimeInterval
+# from mip_matching.Committee import Committee
 
 
-class ApplicantTest(unittest.TestCase):
-    def setUp(self) -> None:
-        self.committee = Committee(
-            "TestKom", interview_length=timedelta(minutes=30))
-        self.committee.add_intervals_with_capacities({
-            TimeInterval(datetime(2024, 8, 24, 8, 0), datetime(2024, 8, 24, 9, 30)): 1,
-            TimeInterval(datetime(2024, 8, 24, 8, 30), datetime(2024, 8, 24, 9, 30)): 1
-        })
+# class ApplicantTest(unittest.TestCase):
+#     def setUp(self) -> None:
+#         self.committee = Committee(
+#             "TestKom", interview_length=timedelta(minutes=30))
+
 
-    def test_capacity_stacking(self) -> None:
-        self.assertEqual(1, self.committee.get_capacity(
-            TimeInterval(datetime(2024, 8, 24, 8, 0), datetime(2024, 8, 24, 8, 30))))
-        self.assertEqual(2, self.committee.get_capacity(
-            TimeInterval(datetime(2024, 8, 24, 8, 30), datetime(2024, 8, 24, 9, 0))))
+