add column "members_count" to species_info

algorithms/neat/population.py

@@ -1,6 +1,10 @@
 """
-contains operations on the population: creating the next generation and population speciation.
+Contains operations on the population: creating the next generation and population speciation.
+These im.....
 """
+
+# TODO: Complete python doc
+
 import jax
 from jax import jit, vmap, Array, numpy as jnp
 
@@ -13,8 +17,8 @@ def update_species(randkey, fitness, species_info, idx2species, center_nodes, ce
     args:
         randkey: random key
         fitness: Array[(pop_size,), float], the fitness of each individual
-        species_keys: Array[(species_size, 3), float], the information of each species
+        species_keys: Array[(species_size, 4), float], the information of each species
-            [species_key, best_score, last_update]
+            [species_key, best_score, last_update, members_count]
         idx2species: Array[(pop_size,), int], map the individual to its species
         center_nodes: Array[(species_size, N, 4), float], the center nodes of each species
         center_cons: Array[(species_size, C, 4), float], the center connections of each species
@@ -68,12 +72,12 @@ def stagnation(species_fitness, species_info, center_nodes, center_cons, generat
 
     def aux_func(idx):
         s_fitness = species_fitness[idx]
-        species_key, best_score, last_update = species_info[idx]
+        species_key, best_score, last_update, members_count = species_info[idx]
         st = (s_fitness <= best_score) & (generation - last_update > jit_config['max_stagnation'])
         last_update = jnp.where(s_fitness > best_score, generation, last_update)
         best_score = jnp.where(s_fitness > best_score, s_fitness, best_score)
         # stagnation condition
-        return st, jnp.array([species_key, best_score, last_update])
+        return st, jnp.array([species_key, best_score, last_update, members_count])
 
     spe_st, species_info = vmap(aux_func)(jnp.arange(species_info.shape[0]))
 
@@ -116,7 +120,6 @@ def cal_spawn_numbers(species_info, jit_config):
 
 
 def create_crossover_pair(randkey, species_info, idx2species, spawn_number, fitness, jit_config):
-
     species_size = species_info.shape[0]
     pop_size = fitness.shape[0]
     s_idx = jnp.arange(species_size)
@@ -257,7 +260,8 @@ def speciate(pop_nodes, pop_cons, species_info, center_nodes, center_cons, gener
         idx = fetch_first(jnp.isnan(i2s))
 
         # assign it to the new species
-        si = si.at[i].set(jnp.array([ck, -jnp.inf, generation]))  # [key, best score, last update generation]
+        # [key, best score, last update generation, members_count]
+        si = si.at[i].set(jnp.array([ck, -jnp.inf, generation, 0]))
         i2s = i2s.at[idx].set(ck)
 
         # update center genomes
@@ -296,6 +300,17 @@ def speciate(pop_nodes, pop_cons, species_info, center_nodes, center_cons, gener
     # if there are still some pop genomes not assigned to any species, add them to the last genome
     # this condition seems to be only happened when the number of species is reached species upper bounds
     idx2specie = jnp.where(idx2specie == I_INT, species_info[-1, 0], idx2specie)
+
+    # update members count
+    def count_members(idx):
+        key = species_info[idx, 0]
+        count = jnp.sum(idx2specie == key)
+        count = jnp.where(jnp.isnan(key), jnp.nan, count)
+        return count
+
+    species_member_counts = vmap(count_members)(jnp.arange(species_size))
+    species_info = species_info.at[:, 3].set(species_member_counts)
+
     return idx2specie, center_nodes, center_cons, species_info
 
 

configs/default_config.ini

@@ -13,7 +13,7 @@ batch_size = 4
 fitness_threshold = 100000
 generation_limit = 1000
 fitness_criterion = "max"
-pop_size = 1500
+pop_size = 150
 
 [genome]
 compatibility_disjoint = 1.0

pipeline.py

@@ -30,8 +30,8 @@ class Pipeline:
         self.best_genome = None
 
         self.pop_nodes, self.pop_cons = neat.initialize_genomes(self.N, self.C, self.config)
-        self.species_info = np.full((self.S, 3), np.nan)
+        self.species_info = np.full((self.S, 4), np.nan)
-        self.species_info[0, :] = 0, -np.inf, 0
+        self.species_info[0, :] = 0, -np.inf, 0, self.P
         self.idx2species = np.zeros(self.P, dtype=np.float32)
         self.center_nodes = np.full((self.S, self.N, 5), np.nan)
         self.center_cons = np.full((self.S, self.C, 4), np.nan)
@@ -128,5 +128,8 @@ class Pipeline:
             self.best_fitness = fitnesses[max_idx]
             self.best_genome = (self.pop_nodes[max_idx], self.pop_cons[max_idx])
 
+        species_sizes = [int(i) for i in self.species_info[:, 3] if i > 0]
+
         print(f"Generation: {self.generation}",
+              f"species: {len(species_sizes)}, {species_sizes}",
               f"fitness: {max_f}, {min_f}, {mean_f}, {std_f}, Cost time: {cost_time}")