feat(sprint4): completar optimizador heuristico concurrente, integracion Spring Boot, correccion de login y fixes visuales

2026-09-05 02:20:47 -03:00
parent dd93b8c07a
commit 69d545b718
17 changed files with 1021 additions and 91 deletions
@@ -4,6 +4,7 @@ from .reservation_service import ReservationService
 from .user_service import UserService
 from .jwt_service import JWTService
 from .audit_service import AuditService
+from .optimizer_service import OptimizerService, OptimizerJob, OptimizerJobStatus

 __all__ = [
    'GoogleSheetsImporter',
@@ -11,5 +12,8 @@ __all__ = [
    'ReservationService',
    'UserService',
    'JWTService',
-    'AuditService'
+    'AuditService',
+    'OptimizerService',
+    'OptimizerJob',
+    'OptimizerJobStatus'
 ]
@@ -0,0 +1,282 @@
+import time
+import uuid
+import threading
+from datetime import datetime, timedelta
+from enum import Enum
+from typing import List, Dict, Any, Optional
+from flask import current_app
+
+from app.models.genetic_algorithm import (
+    ReservationOptimizer, 
+    GeneticAlgorithm, 
+    ReservationRequest, 
+    Classroom
+)
+from app.models.subject import Commission
+from app.schemas.optimizer_dto import SpringBootSyncPayloadDTO
+
+
+class OptimizerJobStatus(str, Enum):
+    PENDING = "PENDING"
+    RUNNING = "RUNNING"
+    COMPLETED = "COMPLETED"
+    FAILED = "FAILED"
+    CANCELLED = "CANCELLED"
+
+
+class OptimizerJob:
+    """Representa un trabajo asíncrono en la cola del optimizador heurístico."""
+
+    def __init__(self, job_id: str, params: Dict[str, Any]):
+        self.job_id = job_id
+        self.status = OptimizerJobStatus.PENDING
+        self.progress = 0
+        self.generation = 0
+        self.total_generations = params.get("generations", 80)
+        self.fitness_score = 0.0
+        self.total_requests = 0
+        self.assigned_reservations = 0
+        self.reservations: List[Dict[str, Any]] = []
+        self.conflicts: List[Dict[str, Any]] = []
+        self.error: Optional[str] = None
+        self.created_at = datetime.utcnow()
+        self.started_at: Optional[datetime] = None
+        self.completed_at: Optional[datetime] = None
+        self.execution_time_ms: Optional[float] = None
+        self.params = params
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "job_id": self.job_id,
+            "status": self.status.value,
+            "progress": self.progress,
+            "generation": self.generation,
+            "total_generations": self.total_generations,
+            "fitness_score": round(self.fitness_score, 2),
+            "total_requests": self.total_requests,
+            "assigned_reservations": self.assigned_reservations,
+            "reservations": self.reservations,
+            "conflict_count": len(self.conflicts),
+            "conflicts": self.conflicts,
+            "error": self.error,
+            "created_at": self.created_at.isoformat() if self.created_at else None,
+            "started_at": self.started_at.isoformat() if self.started_at else None,
+            "completed_at": self.completed_at.isoformat() if self.completed_at else None,
+            "execution_time_ms": self.execution_time_ms,
+            "params": {k: v for k, v in self.params.items() if k not in ("app",)}
+        }
+
+
+class OptimizerService:
+    """
+    Servicio de alto rendimiento para el Optimizador Heurístico y Cola de Trabajos Asíncronos.
+    Soporta evaluación concurrente multi-hilo y sincronización interoperable con Spring Boot.
+    """
+
+    _jobs: Dict[str, OptimizerJob] = {}
+    _lock = threading.Lock()
+
+    @classmethod
+    def submit_job(
+        cls,
+        commission_ids: List[int],
+        admin_user_id: int,
+        start_date: Optional[datetime] = None,
+        end_date: Optional[datetime] = None,
+        generations: int = 80,
+        population_size: int = 40,
+        workers: int = 4,
+        app = None
+    ) -> OptimizerJob:
+        """
+        Registra y despacha un trabajo de optimización en segundo plano (asíncrono).
+        """
+        job_id = str(uuid.uuid4())
+        params = {
+            "commission_ids": commission_ids,
+            "admin_user_id": admin_user_id,
+            "start_date": start_date,
+            "end_date": end_date,
+            "generations": generations,
+            "population_size": population_size,
+            "workers": workers
+        }
+
+        job = OptimizerJob(job_id, params)
+        with cls._lock:
+            cls._jobs[job_id] = job
+
+        # Si no se pasó app explícitamente, intentar obtener current_app
+        if app is None:
+            try:
+                app = current_app._get_current_object()
+            except Exception:
+                app = None
+
+        thread = threading.Thread(
+            target=cls._run_job_worker,
+            args=(job_id, app),
+            daemon=True,
+            name=f"optimizer-worker-{job_id[:8]}"
+        )
+        thread.start()
+        return job
+
+    @classmethod
+    def _run_job_worker(cls, job_id: str, app):
+        """Worker en segundo plano para ejecutar el algoritmo genético."""
+        job = cls.get_job(job_id)
+        if not job:
+            return
+
+        def _execute():
+            t_start = time.perf_counter()
+            with cls._lock:
+                job.status = OptimizerJobStatus.RUNNING
+                job.started_at = datetime.utcnow()
+
+            def progress_callback(gen: int, total_gen: int, pct: int, best_fit: float):
+                with cls._lock:
+                    job.generation = gen
+                    job.total_generations = total_gen
+                    job.progress = pct
+                    job.fitness_score = best_fit
+
+            optimizer = ReservationOptimizer(
+                population_size=job.params.get("population_size", 40),
+                generations=job.params.get("generations", 80),
+                workers=job.params.get("workers", 4)
+            )
+
+            result = optimizer.optimize_schedule(
+                commission_ids=job.params["commission_ids"],
+                admin_user_id=job.params["admin_user_id"],
+                start_date=job.params.get("start_date"),
+                end_date=job.params.get("end_date"),
+                progress_callback=progress_callback
+            )
+
+            t_end = time.perf_counter()
+            exec_time_ms = round((t_end - t_start) * 1000, 2)
+
+            with cls._lock:
+                job.status = OptimizerJobStatus.COMPLETED
+                job.progress = 100
+                job.fitness_score = result.get("fitness_score", 0.0)
+                job.total_requests = result.get("total_requests", 0)
+                job.assigned_reservations = result.get("assigned_reservations", 0)
+                job.reservations = result.get("reservations", [])
+                job.conflicts = result.get("conflicts", [])
+                job.completed_at = datetime.utcnow()
+                job.execution_time_ms = exec_time_ms
+
+        try:
+            if app:
+                with app.app_context():
+                    _execute()
+            else:
+                _execute()
+        except Exception as ex:
+            with cls._lock:
+                job.status = OptimizerJobStatus.FAILED
+                job.error = str(ex)
+                job.completed_at = datetime.utcnow()
+
+    @classmethod
+    def get_job(cls, job_id: str) -> Optional[OptimizerJob]:
+        """Obtiene un trabajo por su identificador único."""
+        with cls._lock:
+            return cls._jobs.get(job_id)
+
+    @classmethod
+    def list_jobs(cls, limit: int = 50) -> List[Dict[str, Any]]:
+        """Lista los trabajos más recientes ordenados por fecha de creación desc."""
+        with cls._lock:
+            sorted_jobs = sorted(cls._jobs.values(), key=lambda j: j.created_at, reverse=True)
+            return [j.to_dict() for j in sorted_jobs[:limit]]
+
+    @classmethod
+    def sync_with_spring_boot(
+        cls,
+        payload: SpringBootSyncPayloadDTO,
+        admin_user_id: int = 1
+    ) -> Dict[str, Any]:
+        """
+        Procesa una carga de optimización interoperable proveniente de microservicios Spring Boot.
+        Garantiza respuesta conforme al contrato JSON del microservicio.
+        """
+        t_start = time.perf_counter()
+
+        # 1. Resolver aulas disponibles
+        if payload.classrooms and len(payload.classrooms) > 0:
+            classrooms = []
+            for c_dto in payload.classrooms:
+                room = Classroom(
+                    room_number=c_dto.roomNumber,
+                    capacity=c_dto.capacity,
+                    is_virtual=c_dto.isVirtual,
+                    is_active=True
+                )
+                room.id = c_dto.classroomId
+                classrooms.append(room)
+        else:
+            classrooms = Classroom.query.filter_by(is_active=True).all()
+
+        # 2. Generar ReservationRequests desde el DTO de Spring Boot
+        requests: List[ReservationRequest] = []
+        for c in payload.commissions:
+            req = ReservationRequest(
+                commission_id=c.commissionId,
+                expected_attendees=c.expectedAttendees,
+                purpose=f"Spring Boot Class: {c.subjectCode} - {c.subjectName}",
+                preferred_start_time=c.preferredStart,
+                preferred_end_time=c.preferredEnd,
+                priority=1,
+                flexibility_hours=2
+            )
+            requests.append(req)
+
+        # 3. Opciones de configuración de algoritmo
+        options = payload.options or {}
+        generations = options.get("generations", 60)
+        pop_size = options.get("populationSize", 30)
+        workers = options.get("workers", 4)
+
+        ga = GeneticAlgorithm(
+            population_size=pop_size,
+            generations=generations,
+            workers=workers
+        )
+
+        start_date = min(c.preferredStart for c in payload.commissions)
+        end_date = max(c.preferredEnd for c in payload.commissions)
+
+        best_individual = ga.optimize_reservations(requests, classrooms, start_date, end_date)
+
+        t_end = time.perf_counter()
+        exec_ms = round((t_end - t_start) * 1000, 2)
+
+        scheduled_assignments = []
+        for gene in best_individual.genes:
+            scheduled_assignments.append({
+                "commissionId": gene.commission_id,
+                "classroomId": gene.classroom_id,
+                "startTime": gene.start_time.isoformat() if gene.start_time else None,
+                "endTime": gene.end_time.isoformat() if gene.end_time else None,
+                "purpose": gene.purpose,
+                "expectedAttendees": gene.expected_attendees,
+                "status": "SCHEDULED"
+            })
+
+        return {
+            "requestId": payload.requestId,
+            "status": "SUCCESS",
+            "academicTerm": payload.academicTerm,
+            "campusCode": payload.campusCode,
+            "totalCommissions": len(payload.commissions),
+            "scheduledAssignments": scheduled_assignments,
+            "conflictCount": len(best_individual.conflicts),
+            "fitnessScore": round(best_individual.fitness, 2),
+            "executionTimeMs": exec_ms,
+            "timestamp": datetime.utcnow().isoformat() + "Z"
+        }