def main():
#print(sys.argv)
test_set_path = sys.argv[1]
output_file_path = sys.argv[2]
X_test = dataset_manip.load_images(load_directory(test_set_path)) / 255
#model = Model(image_shape = (77, 71, 1), num_classes = 10, model_path = './model_files/model', batch_size = 512, first_run = False)
#dataset_manip.store_predictions(dataset_manip.get_filenames(test_set_path), model.predict(X_test), output_file_path)
ens = Ensemble(input_shape=(77, 71, 1),
num_classes=10,
num_models=11,
batch_size=512,
path='./ensemble_files',
load=True)
dataset_manip.store_predictions(dataset_manip.get_filenames(test_set_path),
ens.predict(X_test), output_file_path)
def main():
# Dataset path
dataset_name = ['credit_card_clients_balanced', 'credit_card_clients']
for data_name in dataset_name:
dataset_path = os.getcwd() + "\\dataset\\" + data_name + ".csv"
dataset = pd.read_csv(dataset_path, encoding='utf-8')
# Datasets columns
data_x = dataset[[
'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11',
'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18', 'X19', 'X20',
'X21', 'X22', 'X23'
]]
data_y = dataset['Y']
# Preprocessing data
min_max_scaler = preprocessing.MinMaxScaler()
X_normalized = min_max_scaler.fit_transform(data_x)
acc_rate = []
reject_rate = []
# Runs to test the model
for i in range(20):
print('---------------- Ensemble -----------------')
print('--- MLP - SVM - KNN - GMM - Naive Bayes ---')
print(i + 1, 'of 20 iterations')
X_train, X_test, y_train, y_test = train_test_split(X_normalized,
data_y,
test_size=0.2)
y_train = np.array(y_train)
y_test = np.array(y_test)
model = Ensemble()
model.train(X_train, y_train, gridSearch=False)
y_hat = model.predict(X_test)
error, reject = model.evaluate(y_hat, y_test)
acc_rate.append(1 - error)
reject_rate.append(reject)
graphics(acc_rate, reject_rate, data_name)
dices = np.zeros((n_files, 134))
errors = np.zeros((n_files, ))
pred_functions = {}
dices_mean = []
for atlas_id in xrange(n_files):
# for atlas_id in xrange(1):
start_time = time.clock()
print "Atlas: {}".format(atlas_id)
# brain_batches = data_gen.generate_single_atlas(atlas_id, None, region_centroids, batch_size, True)
# vx_all, pred_all = net.predict_from_generator(brain_batches, scaler, pred_functions)
vx_all, pred_all = ensemble_net.predict(data_gen, atlas_id, None,
region_centroids, batch_size,
scaler, pred_functions, True)
# Construct the predicted image
img_true = data_gen.atlases[atlas_id][1]
img_pred = create_img_from_pred(vx_all, pred_all, img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
dices_all, errs = ensemble_net.stat_of_all_models(img_true, n_out)
