v0.0.15 Wrapped Individual (necessary for wrapping metaheuristic bases)

Author: CameleoGrey

examples/object_oriented/nqueens/score/NQueensScoreCalculator.py

@@ -2,6 +2,7 @@
 
 from greyjack.score_calculation.score_calculators.PlainScoreCalculator import PlainScoreCalculator
 from greyjack.score_calculation.scores.SimpleScore import SimpleScore
+from greyjack.score_calculation.scores.ScoreVariants import ScoreVariants
 import polars as pl
 
 #import os
@@ -14,7 +15,7 @@ def __init__(self):
 
         super().__init__()
 
-        self.score_type = "SimpleScore"
+        self.score_variant = ScoreVariants.SimpleScore
 
         #self.add_constraint("different_rows", self.different_rows)
         #self.add_constraint("different_descending_diagonals", self.different_descending_diagonals)

greyjack/Cargo.lock

@@ -1,6 +1,6 @@
 [package]
 name = "greyjack"
-version = "0.0.13"
+version = "0.0.15"
 edition = "2021"
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
@@ -17,4 +17,10 @@ ndarray = "0.16.1"
 ndarray-rand = "0.15.0"
 ndarray-stats = "0.6.0"
 chrono = "0.4.39"
-polars = { version = "0.46.0", features = ["lazy", "ndarray", "serde", "abs"] }
+polars = { version = "0.46.0", features = ["lazy", "ndarray", "serde", "abs"] }
+
+#[profile.release]
+#lto = true
+#codegen-units = 1
+#debug = true
+#opt-level = 3

greyjack/Cargo.toml

@@ -4,7 +4,7 @@
 import random
 
 from greyjack.agents.base.GJSolution import GJSolution
-from greyjack.agents.base.Individual import Individual
+from greyjack.agents.base.individuals.Individual import Individual
 from pathos.multiprocessing import ProcessPool
 from pathos.threading import ThreadPool
 import multiprocessing
@@ -127,8 +127,9 @@ def solve(self):
             agent_id = agent_publication["agent_id"]
             agent_status = agent_publication["status"]
             local_step = agent_publication["step"]
+            score_variant = agent_publication["score_variant"]
             solution_candidate = agent_publication["candidate"]
-            solution_candidate = Individual.from_list(solution_candidate)
+            solution_candidate = Individual.get_related_individual_type_by_value(score_variant).from_list(solution_candidate)
 
             new_best_flag = False
             if current_best_candidate is None:

greyjack/greyjack/Solver.py

@@ -7,10 +7,10 @@
 import zmq
 import sys
 
-from greyjack.agents.base.Individual import Individual
 from greyjack.agents.termination_strategies import *
 from greyjack.score_calculation.score_requesters import OOPScoreRequester
 from greyjack.score_calculation.score_calculators import PlainScoreCalculator, IncrementalScoreCalculator
+from greyjack.agents.base.individuals.Individual import Individual
 
 current_platform = sys.platform
 
@@ -25,6 +25,8 @@ def __init__(self, migration_rate, migration_frequency, termination_strategy):
         self.agent_id = None
         self.population_size = None
         self.population = None
+        self.individual_type = None
+        self.score_variant = None
         self.agent_top_individual = None
         self.logger = None
         self.logging_level = None
@@ -85,7 +87,10 @@ def _build_cotwin(self):
             domain = self.domain_builder.build_from_domain(self.initial_solution)
 
         self.cotwin = self.cotwin_builder.build_cotwin(domain, is_already_initialized)
-        
+    
+    def _define_individual_type(self):
+        self.score_variant = self.cotwin.score_calculator.score_variant
+        self.individual_type = Individual.get_related_individual_type(self.cotwin.score_calculator.score_variant)
 
     # implements by concrete metaheuristics
     def _build_metaheuristic_base(self):
@@ -106,6 +111,7 @@ def solve(self):
 
         try:
             self._build_cotwin()
+            self._define_individual_type()
             self._build_metaheuristic_base()
             self._build_logger()
             self.init_population()
@@ -167,13 +173,13 @@ def init_population(self):
             scores = self.score_requester.request_score_plain(samples)
 
             for i in range(self.population_size):
-                self.population.append(Individual(samples[i].copy(), scores[i]))
+                self.population.append(self.individual_type(samples[i].copy(), scores[i]))
 
         else:
             generated_sample = self.score_requester.variables_manager.sample_variables()
             deltas = [[(i, val) for i, val in enumerate(generated_sample)]]
             scores = self.score_requester.request_score_incremental(generated_sample, deltas)
-            self.population.append(Individual(generated_sample, scores[0]))
+            self.population.append(self.individual_type(generated_sample, scores[0]))
 
     def step_plain(self):
         new_population = []
@@ -183,7 +189,7 @@ def step_plain(self):
             for score in scores:
                 score.round(self.score_precision)
 
-        candidates = [Individual(samples[i].copy(), scores[i]) for i in range(len(samples))]
+        candidates = [self.individual_type(samples[i].copy(), scores[i]) for i in range(len(samples))]
         new_population = self.metaheuristic_base.build_updated_population(self.population, candidates)
 
         self.population = new_population
@@ -246,7 +252,7 @@ def _send_updates_windows(self):
         # assuming that all updates are sorted by agents themselves
         # (individual with id == 0 is best in update-subpopulation)
         migrants = self.population[:migrants_count]
-        migrants = Individual.convert_individuals_to_lists(migrants)
+        migrants = self.individual_type.convert_individuals_to_lists(migrants)
         request = {"agent_id": self.agent_id, 
                    "round_robin_status_dict": self.round_robin_status_dict,
                    "request_type": "put_updates", 
@@ -298,14 +304,14 @@ def _get_updates_windows(self):
         if self.metaheuristic_base.metaheuristic_name == "LSHADE":
             history_migrant = updates_reply["history_archive"]
             if (history_migrant is not None and len(self.history_archive) > 0):
-                history_migrant = Individual.from_list(history_migrant)
+                history_migrant = self.individual_type.from_list(history_migrant)
                 rand_id = self.generator.integers(0, len(self.history_archive), 1)[0]
                 #if updates_reply["history_archive"] < self.history_archive[-1]:
                 if history_migrant < self.history_archive[rand_id]:
                     self.history_archive[rand_id] = history_migrant
 
         migrants = updates_reply["migrants"]
-        migrants = Individual.convert_lists_to_individuals(migrants)
+        migrants = self.individual_type.convert_lists_to_individuals(migrants)
         n_migrants = len(migrants)
 
         # population already sorted after step
@@ -350,7 +356,7 @@ def _send_updates_linux(self):
         # assuming that all updates are sorted by agents themselves
         # (individual with id == 0 is best in update-subpopulation)
         migrants = self.population[:migrants_count]
-        migrants = Individual.convert_individuals_to_lists(migrants)
+        migrants = self.individual_type.convert_individuals_to_lists(migrants)
         request = {"agent_id": self.agent_id, 
                    "round_robin_status_dict": self.round_robin_status_dict,
                    "request_type": "put_updates", 
@@ -388,14 +394,14 @@ def _get_updates_linux(self):
         if self.metaheuristic_base.metaheuristic_name == "LSHADE":
             history_migrant = updates_reply["history_archive"]
             if (history_migrant is not None and len(self.history_archive) > 0):
-                history_migrant = Individual.from_list(history_migrant)
+                history_migrant = self.individual_type.from_list(history_migrant)
                 rand_id = self.generator.integers(0, len(self.history_archive), 1)[0]
                 #if updates_reply["history_archive"] < self.history_archive[-1]:
                 if history_migrant < self.history_archive[rand_id]:
                     self.history_archive[rand_id] = history_migrant
 
         migrants = updates_reply["migrants"]
-        migrants = Individual.convert_lists_to_individuals(migrants)
+        migrants = self.individual_type.convert_lists_to_individuals(migrants)
         n_migrants = len(migrants)
 
         # population already sorted after step
@@ -429,6 +435,7 @@ def send_candidate_to_master_windows(self, step_id):
         agent_publication["status"] = self.agent_status
         agent_publication["candidate"] = self.agent_top_individual.as_list()
         agent_publication["step"] = step_id
+        agent_publication["score_variant"] = self.score_variant
         agent_publication = orjson.dumps( agent_publication )
         self.socket_publisher.send( agent_publication )
 
@@ -438,4 +445,5 @@ def send_candidate_to_master_linux(self, step_id):
         agent_publication["status"] = self.agent_status
         agent_publication["candidate"] = self.agent_top_individual.as_list()
         agent_publication["step"] = step_id
+        agent_publication["score_variant"] = self.score_variant
         self.socket_publisher.send( agent_publication )

greyjack/greyjack/agents/base/Agent.py

@@ -0,0 +1,33 @@
+
+import numpy as np
+from greyjack.score_calculation.scores.ScoreVariants import ScoreVariants
+from greyjack.agents.base.individuals.IndividualSimple import IndividualSimple
+from greyjack.agents.base.individuals.IndividualHardSoft import IndividualHardSoft
+from greyjack.agents.base.individuals.IndividualHardMediumSoft import IndividualHardMediumSoft
+
+
+class Individual:
+
+    def get_related_individual_type(score_variant):
+
+        if score_variant == ScoreVariants.SimpleScore:
+            return IndividualSimple
+        if score_variant == ScoreVariants.HardSoftScore:
+            return IndividualHardSoft
+        if score_variant == ScoreVariants.HardMediumSoftScore:
+            return IndividualHardMediumSoft
+        
+        raise Exception("score_variant unrecognized")
+
+    def get_related_individual_type_by_value(score_variant_value):
+
+        if score_variant_value == ScoreVariants.SimpleScore.value:
+            return IndividualSimple
+        if score_variant_value == ScoreVariants.HardSoftScore.value:
+            return IndividualHardSoft
+        if score_variant_value == ScoreVariants.HardMediumSoftScore.value:
+            return IndividualHardMediumSoft
+        
+        raise Exception("score_variant unrecognized")
+
+

greyjack/greyjack/agents/base/Individual.py

@@ -0,0 +1,3 @@
+
+
+from greyjack.greyjack import IndividualHardMediumSoft

greyjack/greyjack/agents/base/individuals/Individual.py

@@ -0,0 +1,3 @@
+
+
+from greyjack.greyjack import IndividualHardSoft

greyjack/greyjack/agents/base/individuals/IndividualHardMediumSoft.py

@@ -0,0 +1,3 @@
+
+
+from greyjack.greyjack import IndividualSimple