def batch_learning(train_data, test_data, args):
rgwr = GammaGWR()
utils = Utilities()
utils.remove_files(FILES_FOLDER) # Clear the directory for new data.
# Learn the model.
train_dataset, train_dimension, train_labelSet = utils.describe_data(
train_data, args.dataset_name)
rgwr.initNetwork(train_dimension,
numClasses=args.num_classes,
numWeights=args.num_weights)
rgwr.train(train_dataset,
train_labelSet,
maxEpochs=args.epochs,
insertionT=args.threshold,
beta=CONTEXT_BETA,
epsilon_b=LEARNING_RATE_B,
epsilon_n=LEARNING_RATE_N)
# Test the model.
test_dataset, test_dimension, test_labelSet = utils.describe_data(
test_data, args.dataset_name)
rgwr.evaluate_model(rgwr, train_dataset, train_labelSet, mode='train')
rgwr.evaluate_model(rgwr, test_dataset, test_labelSet, mode='test')
utils.save_predicted_metrics(rgwr, '')
def iterative_learning(train_data, test_data, args, category_column):
rgwr = GammaGWR()
utils = Utilities()
utils.remove_files(FILES_FOLDER) # Clear the directory for new data.
train_accuracies = []
test_accuracies = []
mini_batch_size = 5
iterations = 10
all_object_classes = np.unique(train_data[:, category_column])
random.shuffle(all_object_classes)
rgwr.initNetwork(DATA_DIMENSION,
numClasses=args.num_classes,
numWeights=args.num_weights)
for iteration in range(0, iterations):
objects_to_learn = all_object_classes[mini_batch_size *
iteration:mini_batch_size *
iteration + mini_batch_size]
# Learn the model.
train_data_prepared = train_data[np.in1d(
train_data[:, category_column], objects_to_learn)]
train_dataset, train_dimension, train_labelSet = utils.describe_data(
train_data_prepared, args.dataset_name)
rgwr.train(train_dataset,
train_labelSet,
maxEpochs=args.epochs,
insertionT=args.threshold,
beta=CONTEXT_BETA,
epsilon_b=LEARNING_RATE_B,
epsilon_n=LEARNING_RATE_N)
# Test the model.
test_dataset, test_dimension, test_labelSet = utils.describe_data(
test_data, args.dataset_name)
train_accuracy = rgwr.evaluate_model(rgwr,
train_dataset,
train_labelSet,
mode='train')
test_accuracy = rgwr.evaluate_model(rgwr,
test_dataset,
test_labelSet,
mode='test')
train_accuracies.append(train_accuracy)
test_accuracies.append(test_accuracy)
utils.save_predicted_metrics(rgwr, iteration)
if iteration == 0:
number_neurons = pickle.load(
open("./saved_data/num_neurons" + str(iteration) + '.file',
"rb"))
else:
previous_neurons = pickle.load(
open("./saved_data/num_neurons" + str(iteration) + '.file',
"rb"))
number_neurons = np.append(number_neurons, previous_neurons)
with open('./saved_data/test_accuracies.file', "wb") as f:
pickle.dump(test_accuracies, f, pickle.HIGHEST_PROTOCOL)
with open('./saved_data/train_accuracies.file', "wb") as f:
pickle.dump(train_accuracies, f, pickle.HIGHEST_PROTOCOL)
with open("./saved_data/num_neurons.file", "wb") as f:
pickle.dump(number_neurons, f, pickle.HIGHEST_PROTOCOL)
print("Object classes order: ", all_object_classes)
print("Train accuracies: ", train_accuracies)
print("Test accuracies: ", test_accuracies)
else:
fp.append(object_to_learn)
novel_objects_detected += 1
else:
if object_to_learn not in learnt_objects:
fn.append(object_to_learn)
else:
tn.append(object_to_learn)
# Test the model.
print("Assumption. Object is not novel: ", object_to_learn)
test_data_prepared = test_data[np.in1d(
test_data[:, INSTANCE_COLUMN], object_to_learn)]
test_dataSet, test_dimension, test_labelSet = utils.describe_data(
test_data_prepared, args.dataset_name)
test_accuracy = rgwr.evaluate_model(rgwr,
test_dataSet,
test_labelSet,
mode='test')
test_accuracies.append(test_accuracy)
learnt_objects_detected += 1
iteration += 1
# utils.save_predicted_metrics(rgwr, iteration)
print("Test accuracies: ", test_accuracies)
print("Test accuracy: ", sum(test_accuracies) / len(test_accuracies))
print("Probable novel objects: ", novel_objects_detected)
print("Probable known objects: ", learnt_objects_detected)
print("FP: ", fp)
print("TP: ", tp)
print("FN: ", fn)