def plot_net():
node_ordering = node_ordering = {
0: 'asia',
1: 'tub',
2: 'smoke',
3: 'bronc',
4: 'lung',
5: 'either',
6: 'dysp',
7: 'xray'
}
Population.init_state(POPULATION_SIZE, DATA.columns, node_ordering)
new = as_net(
np.array([
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,
1, 1, 1, 0, 0, 0
]))
new.plot()
def main(dataset):
# node_ordering = order_based_on_conditional_entropy_sum_on_line(dataset) #un dictionar de forma{i:nume variabila}. variabila[i] poate fi parinte pentru orice j>i
# print(node_ordering)
# node_ordering = {0: 'asia', 1: 'tub', 2: 'bronc', 3: 'xray', 4: 'lung', 5: 'smoke', 6: 'dysp', 7: 'either'}
# node_ordering ={0: 'asia', 1: 'tub', 2: 'smoke', 3: 'bronc', 4: 'lung', 5: 'either', 6: 'dysp', 7: 'xray'} #perfect node ordering
# node_ordering = {0: 'diabetes', 1: 'flatulence', 2: 'upper_pain', 3: 'hepatotoxic', 4: 'anorexia', 5: 'nausea', 6: 'hepatomegaly', 7: 'hepatalgia', 8: 'bleeding', 9: 'vh_amn', 10: 'hospital', 11: 'fat', 12: 'transfusion', 13: 'joints', 14: 'proteins', 15: 'hbsag', 16: 'density', 17: 'le_cells', 18: 'ascites', 19: 'skin', 20: 'alcoholism', 21: 'surgery', 22: 'spiders', 23: 'hcv_anti', 24: 'RHepatitis', 25: 'hbsag_anti', 26: 'choledocholithotomy', 27: 'fatigue', 28: 'hbc_anti', 29: 'THepatitis', 30: 'palms', 31: 'edema', 32: 'pain', 33: 'consciousness', 34: 'pain_ruq', 35: 'obesity', 36: 'alcohol', 37: 'edge', 38: 'jaundice', 39: 'itching', 40: 'gallstones', 41: 'injections', 42: 'spleen', 43: 'sex', 44: 'pressure_ruq', 45: 'Hyperbilirubinemia', 46: 'irregular_liver', 47: 'hbeag', 48: 'encephalopathy', 49: 'ama', 50: 'fibrosis', 51: 'amylase', 52: 'PBC', 53: 'Steatosis', 54: 'urea', 55: 'alt', 56: 'ESR', 57: 'triglycerides', 58: 'inr', 59: 'albumin', 60: 'ChHepatitis', 61: 'phosphatase', 62: 'cholesterol', 63: 'ast', 64: 'carcinoma', 65: 'platelet', 66: 'bilirubin', 67: 'Cirrhosis', 68: 'age', 69: 'ggtp'}
node_ordering = perfect_order_alarm()
Population.init_state(POPULATION_SIZE, dataset.columns, node_ordering)
generation = Population()
generation.random()
generation.load()
print('Population shape:', generation.shape)
no_generation = 0
log = open(LOG_FILE, 'a')
init_writer()
no_same_score = 0
old_fitness = 0
ttt = time.time()
while no_generation < MAX_NO_GENERATION and no_same_score < MAX_NO_GENERATION_SAME_SCORE:
print('Generation: ', no_generation, generation.shape)
t=time.time()
fitness = np.array(get_fitness(generation.population_as_nets))
print(" after fitness computation", time.time() - t)
# t = time.time()
indexes_of_best_fitnesses = np.argsort(fitness)[-ELITIST:]
new_generation = Population(
generation.population_as_array[indexes_of_best_fitnesses],
generation.population_as_nets[indexes_of_best_fitnesses]
)
# print(" after elitism", time.time() - t)
# t = time.time()
log.write(str(np.max(fitness)) + '\n')
log.flush()
print(str(np.max(fitness)) + '\n')
if np.max(fitness) == old_fitness:
no_same_score+=1
else:
no_same_score = 0
old_fitness = np.max(fitness)
no_pairs = int((POPULATION_SIZE - ELITIST) / 2)
for _ in range(no_pairs):
index_p1, index_p2 = select(fitness)
offsprings = crossover(generation.get(index_p1), generation.get(index_p2))
new_generation.add_individs(offsprings)
# print(" after crossover", time.time() - t)
# t=time.time()
for i in range(POPULATION_SIZE):
new_generation.add_individs([mutate(generation.get(i), Population.index_to_feature,dataset)])
print(" after mutation")
generation = copy.deepcopy(new_generation)
no_generation += 1
if no_generation % INTERMEDIAR_SAVE == 0:
fitness = np.array(get_fitness(generation.population_as_nets))
write(fitness, generation)
if no_generation%25 ==0:
checkpoint(generation)
# print(" end generation", time.time() - t)
else:
fitness = np.array(get_fitness(generation.population_as_nets))
write(fitness, generation)
checkpoint(generation)
print('final time: ', time.time() - ttt)