#++++++++++++++++++++++++++
# THE SEARCH
# restrictions:
# - 5000 offsprings/run max*
# - 50 offsprings/generation max*
# - use at least 5 runs for your benchmarks
# * including reproduction
#++++++++++++++++++++++++++
ga1 = GeneticAlgorithm2(ann_op_i, random_state, ps, uls.parametrized_tournament_selection(pressure),
uls.one_point_crossover, p_c, uls.parametrized_ball_mutation(radius), 0.2)
ga2 = GeneticAlgorithm(ann_op_i, random_state, ps, uls.parametrized_tournament_selection(pressure),
uls.two_point_crossover, p_c, uls.parametrized_ball_mutation(radius), 0.1)
sa1 = SimulatedAnnealing(ann_op_i, random_state, ps, uls.parametrized_ball_mutation(radius), control, update_rate)
search_algorithms = [ga1, ga2]
# initialize search algorithms
[algorithm.initialize() for algorithm in search_algorithms]
# execute search
[algorithm.search(n_iterations=n_gen, report=False) for algorithm in search_algorithms]
#++++++++++++++++++++++++++
# TEST
# - test algorithms on unseen data
#++++++++++++++++++++++++++
for algorithm in search_algorithms:
ann_i._set_weights(algorithm.best_solution.representation)
y_pred = ann_i.stimulate_with(X_test, False)
accuracy = accuracy_score(y_test, y_pred)
print("Unseen Accuracy of %s: %.2f" % (algorithm.__class__, accuracy_score(y_test, y_pred)))
len(ga1.population)
],
sep='\t')
print(">>>>>>>>>>>>>>>>>FITNESS>>>>>>>>>>>>>>>>>>>>: ",
str(round(best_solution.fitness, 2)))
print("\n")
best_solution.print_()
print("Training fitness of the best solution: %.2f" % best_solution.fitness)
print("Validation fitness of the best solution: %.2f" % best_solution.fitness)
#++++++++++++++++++++++++++
# TEST
#++++++++++++++++++++++++++
ann_i._set_weights(best_solution.representation)
y_pred = ann_i.stimulate_with(X_test, False)
print("Unseen Accuracy of the best solution: %.2f" %
accuracy_score(y_test, y_pred))
if make_plots:
n_images = 25
images = X_test[0:n_images].reshape((n_images, 8, 8))
f = plt.figure(figsize=(10, 10))
for i in range(n_images):
sub = f.add_subplot(5, 5, i + 1)
sub.imshow(images[i],
cmap=plt.get_cmap("Greens")
if y_pred[i] == y_test[i] else plt.get_cmap("Reds"))
plt.xticks([])
plt.yticks([])
import os
import datetime
import logging
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import utils as uls
from problems.ANNOP import ANNOP
from ANN.ANN import ANN, softmax, sigmoid
from algorithms.genetic_algorithm_Elitism import GeneticAlgorithm
# setup logger
file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "LogFiles/" + (str(datetime.datetime.now().date()) + "-" + str(datetime.datetime.now().hour) + \
"_" + str(datetime.datetime.now().minute) + "_log.csv"))
logging.basicConfig(filename=file_path,
level=logging.DEBUG,
format='%(name)s,%(message)s')
#++++++++++++++++++++++++++
# THE DATA
# restrictions:
# - MNIST digits (8*8)
# - 33% for testing
# - flattened input images
#++++++++++++++++++++++++++
# import data
digits = datasets.load_digits()
flat_images = np.array([image.flatten() for image in digits.images])
print(flat_images.shape)
print(digits.target_names)