The contractor specification

examples/contractor_spec.py

@@ -0,0 +1,39 @@
+from random import Random
+
+from sampo.scheduler import GeneticScheduler, TopologicalScheduler, RandomizedTopologicalScheduler, LFTScheduler, \
+    RandomizedLFTScheduler
+from sampo.pipeline.lag_optimization import LagOptimizationStrategy
+from sampo.generator.base import SimpleSynthetic
+from sampo.generator.environment.contractor_by_wg import get_contractor_by_wg, ContractorGenerationMethod
+from sampo.generator import SyntheticGraphType
+from sampo.pipeline import SchedulingPipeline
+from sampo.scheduler.heft.base import HEFTScheduler
+from sampo.schemas.schedule_spec import ScheduleSpec
+
+ss = SimpleSynthetic(rand=231)
+rand = Random()
+size = 100
+
+wg = ss.work_graph(bottom_border=size - 5, top_border=size)
+
+contractors = [get_contractor_by_wg(wg) for _ in range(10)]
+spec = ScheduleSpec()
+
+for node in wg.nodes:
+    if not node.is_inseparable_son():
+        selected_contractor_indices = rand.choices(list(range(len(contractors))),
+                                                   k=rand.randint(1, len(contractors)))
+        spec.assign_contractors(node.id, {contractors[i].id for i in selected_contractor_indices})
+
+
+scheduler = GeneticScheduler(number_of_generation=10)
+
+project = SchedulingPipeline.create() \
+    .wg(wg) \
+    .contractors(contractors) \
+    .lag_optimize(LagOptimizationStrategy.TRUE) \
+    .spec(spec) \
+    .schedule(scheduler, validate=True) \
+    .visualization('2022-01-01')[0] \
+    .shape((14, 14)) \
+    .show_gant_chart()

sampo/backend/default.py

@@ -62,8 +62,8 @@ def _ensure_toolbox_created(self):
             from sampo.scheduler.genetic.utils import init_chromosomes_f, create_toolbox_using_cached_chromosomes
 
             if self._init_schedules:
-                init_chromosomes = init_chromosomes_f(self._wg, self._contractors, self._init_schedules,
-                                                      self._landscape)
+                init_chromosomes = init_chromosomes_f(self._wg, self._contractors, self._spec,
+                                                      self._init_schedules, self._landscape)
             else:
                 init_chromosomes = []
 

sampo/backend/multiproc.py

@@ -140,8 +140,8 @@ def cache_genetic_info(self,
                                    weights, init_schedules, assigned_parent_time, fitness_weights, sgs_type,
                                    only_lft_initialization, is_multiobjective)
         if init_schedules:
-            self._init_chromosomes = init_chromosomes_f(self._wg, self._contractors, init_schedules,
-                                                  self._landscape)
+            self._init_chromosomes = init_chromosomes_f(self._wg, self._contractors, self._spec,
+                                                        init_schedules, self._landscape)
         else:
             self._init_chromosomes = []
         self._pool = None

sampo/generator/base.py

@@ -20,6 +20,9 @@ def __init__(self, rand: int | Random | None = None) -> None:
         else:
             self._rand = Random(rand)
 
+    def get_rand(self):
+        return self._rand
+
     def small_work_graph(self, cluster_name: str | None = 'C1') -> WorkGraph:
         """
         Creates a small graph of works consisting of 30-50 vertices;

sampo/scheduler/generic.py

@@ -99,7 +99,7 @@ def run_with_contractor(contractor: Contractor) -> tuple[Time, Time, list[Worker
                                                                              assigned_parent_time, work_estimator)
                 return c_st, c_ft, workers
 
-            return run_contractor_search(contractors, run_with_contractor)
+            return run_contractor_search(contractors, spec, run_with_contractor)
 
         return optimize_resources_def
 
@@ -126,7 +126,7 @@ def schedule_with_cache(self,
         )
 
         if validate:
-            validate_schedule(schedule, wg, contractors)
+            validate_schedule(schedule, wg, contractors, spec)
 
         return [(schedule, schedule_start_time, timeline, ordered_nodes)]
 

sampo/scheduler/genetic/base.py

@@ -171,14 +171,15 @@ def generate_first_population(wg: WorkGraph,
 
         schedule, _, _, node_order = LFTScheduler(work_estimator=work_estimator).schedule_with_cache(wg, contractors,
                                                                                                      spec,
-                                                                                                     landscape=landscape)[0]
+                                                                                                     landscape=landscape,
+                                                                                                     validate=True)[0]
         init_lft_schedule = (schedule, node_order, spec)
 
         def init_k_schedule(scheduler_class, k) -> tuple[Schedule | None, list[GraphNode] | None, ScheduleSpec | None]:
             try:
                 schedule, _, _, node_order = (scheduler_class(work_estimator=work_estimator,
                                                               resource_optimizer=AverageReqResourceOptimizer(k))
-                                              .schedule_with_cache(wg, contractors, spec, landscape=landscape))[0]
+                                              .schedule_with_cache(wg, contractors, spec, landscape=landscape, validate=True))[0]
                 return schedule, node_order, spec
             except NoSufficientContractorError:
                 return None, None, None
@@ -187,7 +188,7 @@ def init_k_schedule(scheduler_class, k) -> tuple[Schedule | None, list[GraphNode
             def init_schedule(scheduler_class) -> tuple[Schedule | None, list[GraphNode] | None, ScheduleSpec | None]:
                 try:
                     schedule, _, _, node_order = (scheduler_class(work_estimator=work_estimator)
-                                                  .schedule_with_cache(wg, contractors, spec, landscape=landscape))[0]
+                                                  .schedule_with_cache(wg, contractors, spec, landscape=landscape, validate=True))[0]
                     return schedule, node_order, spec
                 except NoSufficientContractorError:
                     return None, None, None
@@ -197,7 +198,7 @@ def init_schedule(scheduler_class) -> tuple[Schedule | None, list[GraphNode] | N
                 try:
                     (schedule, _, _, node_order), modified_spec = AverageBinarySearchResourceOptimizingScheduler(
                         scheduler_class(work_estimator=work_estimator)
-                    ).schedule_with_cache(wg, contractors, deadline, spec, landscape=landscape)
+                    ).schedule_with_cache(wg, contractors, deadline, spec, landscape=landscape, validate=True)
                     return schedule, node_order, modified_spec
                 except NoSufficientContractorError:
                     return None, None, None
@@ -309,6 +310,6 @@ def schedule_with_cache(self,
 
         if validate:
             for schedule, *_ in schedules:
-                validate_schedule(schedule, wg, contractors)
+                validate_schedule(schedule, wg, contractors, spec)
 
         return schedules

sampo/scheduler/genetic/converter.py

@@ -19,6 +19,7 @@
 from sampo.schemas.schedule_spec import ScheduleSpec
 from sampo.schemas.time import Time
 from sampo.schemas.time_estimator import WorkTimeEstimator, DefaultWorkEstimator
+from sampo.utilities.collections_util import reverse_dictionary
 from sampo.utilities.linked_list import LinkedList
 
 
@@ -63,16 +64,25 @@ def convert_schedule_to_chromosome(work_id2index: dict[str, int],
     for node in order:
         node_id = node.id
         index = work_id2index[node_id]
-        for resource in schedule[node_id].workers:
-            res_count = resource.count
-            res_index = worker_name2index[resource.name]
-            res_contractor = resource.contractor_id
-            resource_chromosome[index, res_index] = res_count
-            resource_chromosome[index, -1] = contractor2index[res_contractor]
+
+        if schedule[node_id].workers:
+            for resource in schedule[node_id].workers:
+                res_count = resource.count
+                res_index = worker_name2index[resource.name]
+                res_contractor = resource.contractor_id
+
+                resource_chromosome[index, res_index] = res_count
+                resource_chromosome[index, -1] = contractor2index[res_contractor]
+        else:
+            contractor_list = spec[node_id].contractors
+            if not contractor_list:
+                contractor_list = contractor2index.keys()
+            random_contractor = list(contractor_list)[0]
+            resource_chromosome[index, -1] = contractor2index[random_contractor]
 
     resource_border_chromosome = np.copy(contractor_borders)
 
-    return order_chromosome, resource_chromosome, resource_border_chromosome, spec, zone_changes_chromosome
+    return order_chromosome, resource_chromosome, resource_border_chromosome, copy.deepcopy(spec), zone_changes_chromosome
 
 
 def convert_chromosome_to_schedule(chromosome: ChromosomeType,

sampo/scheduler/genetic/operators.py

@@ -20,7 +20,7 @@
 from sampo.schemas.landscape import LandscapeConfiguration
 from sampo.schemas.resources import Worker
 from sampo.schemas.schedule import Schedule
-from sampo.schemas.schedule_spec import ScheduleSpec
+from sampo.schemas.schedule_spec import ScheduleSpec, WorkSpec
 from sampo.schemas.time import Time
 from sampo.schemas.time_estimator import WorkTimeEstimator, DefaultWorkEstimator
 from sampo.utilities.resource_usage import resources_peaks_sum, resources_costs_sum, resources_sum
@@ -165,6 +165,7 @@ def init_toolbox(wg: WorkGraph,
                  parents: dict[int, set[int]],
                  children: dict[int, set[int]],
                  resources_border: np.ndarray,
+                 contractors_available: np.ndarray,
                  assigned_parent_time: Time = Time(0),
                  fitness_weights: tuple[int | float, ...] = (-1,),
                  work_estimator: WorkTimeEstimator = DefaultWorkEstimator(),
@@ -200,7 +201,8 @@ def init_toolbox(wg: WorkGraph,
     # combined mutation
     toolbox.register('mutate', mutate, order_mutpb=mut_order_pb, res_mutpb=mut_res_pb, zone_mutpb=mut_zone_pb,
                      rand=rand, parents=parents, children=children, resources_border=resources_border,
-                     statuses_available=statuses_available, priorities=priorities)
+                     contractors_available=contractors_available, statuses_available=statuses_available,
+                     priorities=priorities)
     # crossover for order
     toolbox.register('mate_order', mate_scheduling_order, rand=rand, toolbox=toolbox, priorities=priorities)
     # mutation for order
@@ -210,7 +212,7 @@ def init_toolbox(wg: WorkGraph,
     toolbox.register('mate_resources', mate_resources, rand=rand, toolbox=toolbox)
     # mutation for resources
     toolbox.register('mutate_resources', mutate_resources, resources_border=resources_border,
-                     mutpb=mut_res_pb, rand=rand)
+                     contractors_available=contractors_available, mutpb=mut_res_pb, rand=rand)
     # mutation for resource borders
     toolbox.register('mutate_resource_borders', mutate_resource_borders, contractor_borders=contractor_borders,
                      mutpb=mut_res_pb, rand=rand)
@@ -219,7 +221,7 @@ def init_toolbox(wg: WorkGraph,
                      statuses_available=landscape.zone_config.statuses.statuses_available())
 
     toolbox.register('validate', is_chromosome_correct, node_indices=node_indices, parents=parents,
-                     contractor_borders=contractor_borders, index2node=index2node)
+                     contractor_borders=contractor_borders, index2node=index2node, index2contractor=index2contractor_obj)
     toolbox.register('schedule_to_chromosome', convert_schedule_to_chromosome,
                      work_id2index=work_id2index, worker_name2index=worker_name2index,
                      contractor2index=contractor2index, contractor_borders=contractor_borders, spec=spec,
@@ -324,9 +326,6 @@ def randomized_init(is_topological: bool = False) -> ChromosomeType:
             case _:
                 ind = init_chromosomes[generated_type][0]
 
-        if not toolbox.validate(ind):
-            SAMPO.logger.warn('HELP')
-
         ind = toolbox.Individual(ind)
         chromosomes.append(ind)
 
@@ -390,12 +389,13 @@ def select_new_population(population: list[Individual], k: int) -> list[Individu
 
 
 def is_chromosome_correct(ind: Individual, node_indices: list[int], parents: dict[int, set[int]],
-                          contractor_borders: np.ndarray, index2node: dict[int, GraphNode]) -> bool:
+                          contractor_borders: np.ndarray, index2node: dict[int, GraphNode],
+                          index2contractor: dict[int, Contractor]) -> bool:
     """
     Check correctness of works order and contractors borders.
     """
     return is_chromosome_order_correct(ind, parents, index2node) and \
-        is_chromosome_contractors_correct(ind, node_indices, contractor_borders)
+        is_chromosome_contractors_correct(ind, node_indices, contractor_borders, index2node, index2contractor)
 
 
 def is_chromosome_order_correct(ind: Individual, parents: dict[int, set[int]], index2node: dict[int, GraphNode]) -> bool:
@@ -419,13 +419,27 @@ def is_chromosome_order_correct(ind: Individual, parents: dict[int, set[int]], i
 
 
 def is_chromosome_contractors_correct(ind: Individual, work_indices: Iterable[int],
-                                      contractor_borders: np.ndarray) -> bool:
+                                      contractor_borders: np.ndarray,
+                                      index2node: dict[int, GraphNode],
+                                      index2contractor: dict[int, Contractor]) -> bool:
     """
     Checks that assigned contractors can supply assigned workers.
     """
     if not work_indices:
         return True
+
+    order = ind[0]
     resources = ind[1][work_indices]
+
+    # check contractor align with the spec
+    spec: ScheduleSpec = ind[3]
+    contractors = resources[:, -1]
+    for i in range(len(order)):
+        work_index = order[i]
+        work_spec = spec[index2node[work_index].id]
+        if not work_spec.is_contractor_enabled(index2contractor[contractors[work_index]].id):
+            return False
+
     # sort resource part of chromosome by contractor ids
     resources = resources[resources[:, -1].argsort()]
     # get unique contractors and indexes where they start
@@ -579,8 +593,8 @@ def mutate_scheduling_order(ind: Individual, mutpb: float, rand: random.Random,
     """
     order = ind[0]
 
-    priority_groups_count = len(set(priorities))
-    mutpb_for_priority_group = mutpb #/ priority_groups_count
+    # priority_groups_count = len(set(priorities))
+    mutpb_for_priority_group = mutpb  # / priority_groups_count
 
     # priorities of tasks with same order-index should be the same (if chromosome is valid)
     cur_priority = priorities[order[0]]
@@ -634,15 +648,17 @@ def mate_resources(ind1: Individual, ind2: Individual, rand: random.Random,
 
 
 def mutate_resources(ind: Individual, mutpb: float, rand: random.Random,
-                     resources_border: np.ndarray) -> Individual:
+                     resources_border: np.ndarray,
+                     contractors_available: np.ndarray) -> Individual:
     """
     Mutation function for resources.
     It changes selected numbers of workers in random work in a certain interval for this work.
 
     :param ind: the individual to be mutated
-    :param resources_border: low and up borders of resources amounts
     :param mutpb: probability of gene mutation
     :param rand: the rand object used for randomized operations
+    :param resources_border: low and up borders of resources amounts
+    :param contractors_available: mask of contractors available to do tasks
 
     :return: mutated individual
     """
@@ -654,9 +670,22 @@ def mutate_resources(ind: Individual, mutpb: float, rand: random.Random,
         mask = np.array([rand.random() < mutpb for _ in range(num_works)])
         if mask.any():
             # generate new contractors in the number of received True values of mask
-            new_contractors = np.array([rand.randint(0, num_contractors - 1) for _ in range(mask.sum())])
+
+            # [rand.randint(0, num_contractors - 1) for _ in range(mask.sum())]
+            new_contractors_list = []
+
+            # TODO Rewrite to numpy functions if heavy
+            for task, task_selected in enumerate(mask):
+                if not task_selected:
+                    continue
+                contractors_to_select = np.where(contractors_available[task] == 1)
+                new_contractors_list.append(rand.choices(contractors_to_select[0], k=1)[0])
+
+            new_contractors = np.array(new_contractors_list)
+
             # obtain a new mask of correspondence
             # between the borders of the received contractors and the assigned resources
+
             contractor_mask = (res[mask, :-1] <= ind[2][new_contractors]).all(axis=1)
             # update contractors by received mask
             new_contractors = new_contractors[contractor_mask]
@@ -720,9 +749,9 @@ def mate(ind1: Individual, ind2: Individual, optimize_resources: bool,
     return toolbox.Individual(child1), toolbox.Individual(child2)
 
 
-def mutate(ind: Individual, resources_border: np.ndarray, parents: dict[int, set[int]],
-           children: dict[int, set[int]], statuses_available: int, priorities: np.ndarray,
-           order_mutpb: float, res_mutpb: float, zone_mutpb: float,
+def mutate(ind: Individual, resources_border: np.ndarray, contractors_available: np.ndarray,
+           parents: dict[int, set[int]], children: dict[int, set[int]], statuses_available: int,
+           priorities: np.ndarray, order_mutpb: float, res_mutpb: float, zone_mutpb: float,
            rand: random.Random) -> Individual:
     """
     Combined mutation function of mutation for order, mutation for resources and mutation for zones.
@@ -740,7 +769,7 @@ def mutate(ind: Individual, resources_border: np.ndarray, parents: dict[int, set
     :return: mutated individual
     """
     mutant = mutate_scheduling_order(ind, order_mutpb, rand, priorities, parents, children)
-    mutant = mutate_resources(mutant, res_mutpb, rand, resources_border)
+    mutant = mutate_resources(mutant, res_mutpb, rand, resources_border, contractors_available)
     # TODO Make better mutation for zones and uncomment this
     # mutant = mutate_for_zones(mutant, statuses_available, zone_mutpb, rand)
 

sampo/scheduler/genetic/schedule_builder.py

@@ -41,7 +41,7 @@ def create_toolbox(wg: WorkGraph,
 
     worker_pool, index2node, index2zone, work_id2index, worker_name2index, index2contractor_obj, \
         worker_pool_indices, contractor2index, contractor_borders, node_indices, priorities, parents, children, \
-        resources_border = prepare_optimized_data_structures(wg, contractors, landscape)
+        resources_border, contractors_available = prepare_optimized_data_structures(wg, contractors, landscape, spec)
 
     init_chromosomes: dict[str, tuple[ChromosomeType, float, ScheduleSpec]] = \
         {name: (convert_schedule_to_chromosome(work_id2index, worker_name2index,
@@ -79,6 +79,7 @@ def create_toolbox(wg: WorkGraph,
                         parents,
                         children,
                         resources_border,
+                        contractors_available,
                         assigned_parent_time,
                         fitness_weights,
                         work_estimator,