Changed save_state files to save_<thing>.json and updated gitignore t…

…o reflect it. Also added profiling for update population. Will add mutlithreading.

.gitignore

@@ -5,6 +5,6 @@
 __pycache__/
 *#
 .coverage
-state_*.txt
+save_*.json
 docs/_build
-docs/_static
+docs/_static

gastop/genalg.py

@@ -222,8 +222,8 @@ def progress_monitor(self, current_gen, progress_display, ax1):  # Susan
 
         # pass
 
-    def save_state(self, config, dest_config='state_config.txt',
-                   dest_pop='state_population.txt'):  # Cristian
+    def save_state(self, config, dest_config='save_config.json',
+                   dest_pop='save_population.json'):  # Cristian
         # Save rng_seed for reloading
         config['random_params']['rng_seed'] = np.random.get_state()
 
@@ -239,8 +239,8 @@ def save_state(self, config, dest_config='state_config.txt',
             pop_dumped = json.dumps(population, cls=encoders.PopulationEncoder)
             json.dump(pop_dumped, f)
 
-    def load_state(self, dest_config='state_config.txt',
-                   dest_pop='state_population.txt'):  # Cristian
+    def load_state(self, dest_config='save_config.json',
+                   dest_pop='save_population.json'):  # Cristian
         # Load config data
         with open(dest_config, 'r') as f:
             config_loaded = json.load(f)

profiling/genalg_update_population.py

@@ -0,0 +1,198 @@
+# Cristian
+import numpy as np
+import timeit
+import sys
+sys.path.append('.')
+
+from gastop import GenAlg, Truss, Evaluator, FitnessFunction, Crossover, Mutator, Selector, utilities
+
+# Parse input paramters from init.txt file
+init_file_path = 'gastop-config/struct_making_test_init.txt'
+config = utilities.init_file_parser(init_file_path)
+
+ga_params = config['ga_params']
+random_params = config['random_params']
+crossover_params = config['crossover_params']
+mutator_params = config['mutator_params']
+selector_params = config['selector_params']
+evaluator_params = config['evaluator_params']
+fitness_params = config['fitness_params']
+
+# properties_df = 0
+evaluator = 0
+# Create a Fitness Function Object
+fitness_function = FitnessFunction('rastrigin', 0)
+
+pop_size = int(1e3)
+num_gens = int(1e1)
+
+# Create GenAlg object and assign random fitness scores
+ga = GenAlg(ga_params, mutator_params, random_params, crossover_params, selector_params,
+            evaluator, fitness_function)
+ga.initialize_population(pop_size)
+for truss in ga.population:
+    truss.fitness_score = np.random.random()
+
+setup ='''
+import numpy as np
+from gastop import GenAlg, Truss, Evaluator, FitnessFunction, Selector, utilities
+from __main__ import method1, method2
+init_file_path = 'gastop-config/struct_making_test_init.txt'
+config = utilities.init_file_parser(init_file_path)
+
+ga_params = config['ga_params']
+random_params = config['random_params']
+crossover_params = config['crossover_params']
+mutator_params = config['mutator_params']
+selector_params = config['selector_params']
+evaluator_params = config['evaluator_params']
+fitness_params = config['fitness_params']
+
+evaluator = 0
+fitness_function = FitnessFunction('rastrigin', 0)
+
+pop_size = int(1e3)
+num_gens = int(1e1)
+
+ga = GenAlg(ga_params, mutator_params, random_params, crossover_params, selector_params,
+            evaluator, fitness_function)
+ga.initialize_population(pop_size)
+for truss in ga.population:
+    truss.fitness_score = np.random.random()
+'''
+
+def method1(ga): # Method 1 performs serial loops for crossover/mutation
+    # Store parameters for readability
+    population = ga.population
+    pop_size = ga.ga_params['pop_size']
+    percent_crossover = ga.ga_params['percent_crossover']
+    percent_mutation = ga.ga_params['percent_mutation']
+    num_elite = ga.ga_params['num_elite']
+
+    # Sort population by fitness score (lowest score = most fit)
+    population.sort(key=lambda x: x.fitness_score)
+
+    # Calculate parents needed for crossover, ensure even number
+    num_crossover = round((pop_size-num_elite)*percent_crossover)
+    if (num_crossover % 2) != 0:  # If odd, increment by 1
+        num_crossover += 1
+    # Calculate parents needed for mutation
+    num_mutation = round((pop_size-num_elite)*percent_mutation)
+    # Calculate remaining number of trusses in next population
+    num_random = pop_size - num_elite - num_crossover - num_mutation
+    if num_random < 0:  # Raise exception if input params are unreasonable
+        raise RuntimeError('Invalid GenAlg parameters.')
+
+    # Instantiate objects
+    selector = Selector(ga.selector_params)
+    crossover = Crossover(ga.crossover_params)
+    mutator = Mutator(ga.mutate_params)
+
+    # Select parents as indices in current population
+    crossover_parents = selector(num_crossover, population)
+    mutation_parents = selector(num_mutation, population)
+
+    # Save most fit trusses as elites
+    pop_elite = population[:num_elite]
+
+    # Portion of new population formed by crossover
+    pop_crossover = []
+    for i in range(0, num_crossover, 2):
+        parentindex1 = crossover_parents[i]
+        parentindex2 = crossover_parents[i+1]
+        parent1 = population[parentindex1]
+        parent2 = population[parentindex2]
+        child1, child2 = crossover(parent1, parent2)
+        pop_crossover.extend((child1, child2))
+
+    # Portion of new population formed by mutation
+    pop_mutation = []
+    for i in range(num_mutation):
+        parentindex = mutation_parents[i]
+        parent = population[parentindex]
+        child = mutator(parent)
+        pop_mutation.append(child)
+
+    # Create new random trusses with remaining spots in generation
+    pop_random = [ga.generate_random(2) for i in range(num_random)]
+
+    # Append separate lists to form new generation
+    population = pop_elite + pop_crossover + pop_mutation + pop_random
+
+    # Update population attribute
+    return population
+
+def method2(ga):
+    # Store parameters for readability
+    population = ga.population
+    pop_size = ga.ga_params['pop_size']
+    percent_crossover = ga.ga_params['percent_crossover']
+    percent_mutation = ga.ga_params['percent_mutation']
+    num_elite = ga.ga_params['num_elite']
+
+    # Sort population by fitness score (lowest score = most fit)
+    population.sort(key=lambda x: x.fitness_score)
+
+    # Calculate parents needed for crossover, ensure even number
+    num_crossover = round((pop_size-num_elite)*percent_crossover)
+    if (num_crossover % 2) != 0:  # If odd, increment by 1
+        num_crossover += 1
+    # Calculate parents needed for mutation
+    num_mutation = round((pop_size-num_elite)*percent_mutation)
+    # Calculate remaining number of trusses in next population
+    num_random = pop_size - num_elite - num_crossover - num_mutation
+    if num_random < 0:  # Raise exception if input params are unreasonable
+        raise RuntimeError('Invalid GenAlg parameters.')
+
+    # Instantiate objects
+    selector = Selector(ga.selector_params)
+    crossover = Crossover(ga.crossover_params)
+    mutator = Mutator(ga.mutate_params)
+
+    # Select parents as indices in current population
+    crossover_parents = selector(num_crossover, population)
+    mutation_parents = selector(num_mutation, population)
+
+    # Save most fit trusses as elites
+    pop_elite = population[:num_elite]
+
+    # Portion of new population formed by crossover
+    pop_crossover = []
+    for i in range(0, num_crossover, 2):
+        parentindex1 = crossover_parents[i]
+        parentindex2 = crossover_parents[i+1]
+        parent1 = population[parentindex1]
+        parent2 = population[parentindex2]
+        child1, child2 = crossover(parent1, parent2)
+        pop_crossover.extend((child1, child2))
+
+    # Portion of new population formed by mutation
+    pop_mutation = []
+    for i in range(num_mutation):
+        parentindex = mutation_parents[i]
+        parent = population[parentindex]
+        child = mutator(parent)
+        pop_mutation.append(child)
+
+    # Create new random trusses with remaining spots in generation
+    pop_random = [ga.generate_random(2) for i in range(num_random)]
+
+    # Append separate lists to form new generation
+    population = pop_elite + pop_crossover + pop_mutation + pop_random
+
+    # Update population attribute
+    return population
+
+pop1 = method1(ga)
+pop2 = method2(ga)
+
+test1 = '''pop1 = method1(ga)'''
+test2 = '''pop2 = method2(ga)'''
+
+ntests = int(1e1)
+
+time1 = timeit.timeit(setup=setup,stmt=test1,number=ntests)
+time2 = timeit.timeit(setup=setup,stmt=test2,number=ntests)
+
+print('method1: ' + str(time1))
+print('method2: ' + str(time2))