def main():
dataset = read_data(DATASET_PATH)
# Get basic statistics of the loaded dataset
# data_statistics(dataset)
global train_x, test_x, train_y, test_y
train_x, test_x, train_y, test_y = split_dataset(dataset, 0.25)
# Train and Test dataset size details
# print("Train_x Shape :: ", train_x.shape)
# print("Train_y Shape :: ", train_y.shape)
# print("Test_x Shape :: ", test_x.shape)
# print("Test_y Shape :: ", test_y.shape)
# print("")
# print("----------Random Forest----------")
# rf = random_forest(train_x, train_y)
# print("Trained model:", rf)
#
# rf_predictions = rf.predict(test_x)
# # print("Train Accuracy :: ", accuracy_score(train_y, rf.predict(train_x)))
# result_statistics(rf_predictions)
start_time_rf_de = time_RF.time()
print("")
print("----------Tuning Random Forest----------")
de_rf_result = list(
de_rf(rf_tuning,
bounds=[(10, 150), (1, 20), (2, 20), (2, 50), (0.01, 1),
(1, 10)]))
print(de_rf_result[-1])
print("")
print("--- %s seconds ---" % (time_RF.time() - start_time_rf_de))
print("")
# print("")
# print("----------Multilayer Perceptron----------")
# mlpn = multilayer_perceptron(train_x, train_y)
# print("Trained model:", mlpn)
#
# mlpn_predictions = mlpn.predict(test_x)
# # print("Train Accuracy :: ", accuracy_score(train_y, mlpn.predict(train_x)))
# result_statistics(mlpn_predictions)
#
start_time_cart_de = time_RF.time()
print("----------Tuning Decision Tree----------")
de_cart_result = list(
de_cart(cart_tuning, bounds=[(0.01, 1), (2, 20), (1, 20), (1, 50)]))
print(de_cart_result[-1])
print("")
print("--- %s seconds ---" % (time_RF.time() - start_time_cart_de))
def main():
start_time = time_RF.time()
dataset = read_data(DATASET_PATH)
# Get basic statistics of the loaded dataset
# data_statistics(dataset)
global train_x, test_x, train_y, test_y
train_x, test_x, train_y, test_y = split_dataset(dataset, 0.25)
# Train and Test dataset size details
print("Train_x Shape :: ", train_x.shape)
print("Train_y Shape :: ", train_y.shape)
print("Test_x Shape :: ", test_x.shape)
print("Test_y Shape :: ", test_y.shape)
print("")
print("----------Random Forest----------")
rf = random_forest(train_x, train_y)
print("Trained model:", rf)
rf_predictions = rf.predict(test_x)
# print("Train Accuracy :: ", accuracy_score(train_y, rf.predict(train_x)))
result_statistics(rf_predictions)
print("")
print("----------Decision Tree----------")
dt = cart(train_x, train_y)
print("Trained model:", dt)
dt_predictions = dt.predict(test_x)
# print("Train Accuracy :: ", accuracy_score(train_y, mlpn.predict(train_x)))
result_statistics(dt_predictions)
# print("")
# print("----------KNN----------")
# knn = KNN(train_x, train_y)
# print("Trained model:", knn)
#
# knn_predictions = knn.predict(test_x)
# # print("Train Accuracy :: ", accuracy_score(train_y, knn.predict(train_x)))
# result_statistics(knn_predictions)
print("")
print("--- %s seconds ---" % (time_RF.time() - start_time))
def main():
# send it all to stderr
mp.log_to_stderr()
# get access to a logger and set its logging level to INFO
logger = mp.get_logger()
logger.setLevel(logging.INFO)
dataset = read_data(DATASET_PATH)
# global train_x, test_x, train_y, test_y
train_x, test_x, train_y, test_y = split_dataset(dataset, 0.25)
# print("--- Testing Sequence DE ---")
# start_time_seq = time.time()
# result_seq = list(de_sequence(fobj, bounds=[(-100, 100)] * 6))
# print(result_seq[-1])
# print("")
# print("--- %s seconds ---" % (time.time() - start_time_seq))
#
# sleep(5)
print("--- Tuning Random Forest with Parallel DE ---")
start_time_rf_tuning_para = time_RF.time()
# result_para = list(de_parallel(fobj, bounds=[(-100, 100)] * 6))
# print(result_para[-1])
# initialization
bounds = [(10, 150), (1, 20), (2, 20), (2, 50), (0.01, 1), (1, 10)]
mut = 0.8
crossp = 0.7
popsize = 60
its = 100
dimensions = len(bounds)
pop = np.random.rand(popsize, dimensions)
# pdb.set_trace()
min_b, max_b = np.asarray(bounds).T
diff = np.fabs(min_b - max_b)
pop_denorm = min_b + pop * diff
# convert from float to integer
pop_denorm_convert = pop_denorm.tolist()
result_list = []
temp_list = []
for index in pop_denorm_convert:
temp_list.append(np.int_(np.round_(index[0])))
temp_list.append(np.int_(np.round_(index[1])))
temp_list.append(np.int_(np.round_(index[2])))
temp_list.append(np.int_(np.round_(index[3])))
temp_list.append(float('%.2f' % index[4]))
temp_list.append(np.int(np.round_(index[5])))
result_list.append(temp_list)
temp_list = []
fitness = np.asarray([
rf_tuning(index[0], index[1], index[2], index[3], index[4], index[5],
train_x, test_x, train_y, test_y) for index in result_list
])
best_idx = np.argmax(fitness)
best = pop_denorm[best_idx]
print("Dimension:", dimensions)
print("pop:", pop)
print("min_b:", min_b)
print("max_b:", max_b)
print("diff:", diff)
print("pop_denorm:", pop_denorm)
print("fitness:", fitness)
print("best_idx:", best_idx)
print("best:", best)
lock = mp.Lock()
# execute loops in each process
processes = []
for x in range(mp.cpu_count()):
processes.append(
mp.Process(target=de_innerloop,
args=(output, its, popsize, pop, mut, dimensions,
crossp, min_b, diff, lock, fitness, best_idx,
best, train_x, test_x, train_y, test_y)))
# Run processes
for p in processes:
p.start()
# Exit the completed processes
# Without join() function call, process will remain idle and won’t terminate
for p in processes:
p.join()
# Get process results from the output queue
results = [output.get() for p in processes]
print(results)
print("")
print("--- %s seconds ---" % (time_RF.time() - start_time_rf_tuning_para))
print("")
