def fl_train_dataset_for_client(client, num_days):
dataset_d = get_dataset_from_file(client)
# activities_d = dataset_d.get_activities()[client_round_dict[client] * history_size:
# (client_round_dict[client] + 2) * history_size, :]
not_repeated = dataset_d.get_not_repeated_activity_data()
d = convert_to_one_hot(not_repeated)
train_day_index = get_data_of_days(not_repeated, num_days)
limit_index = int(0.7 * len(d))
if limit_index >= train_day_index:
train_d = d[:train_day_index, :]
else:
train_d = d[:limit_index, :]
data_d, labels_d = build_dataset(train_d)
return data_d, labels_d
def load_pie_data(home_id, num_days):
dataset_d = get_dataset_from_file(home_id)
not_repeated = dataset_d.get_not_repeated_activity_data()
one_hot = convert_to_one_hot(not_repeated)
train_day_index = get_data_of_days(not_repeated, num_days)
activities_d = one_hot[:train_day_index, -NUMBER_OF_ACTIVITIES:]
# print("\n\nClient" + str(i) + "\n")
stats = list()
labels = list()
for i, act in enumerate(LIST_OF_MAPPED_2_ACTIVITIES):
activity_count = int(sum(activities_d[:, i]))
stats.append(activity_count)
labels.append(act)
return stats, labels
def create_local_train_test_dataset(dataset_num, num_days):
dataset_d = get_dataset_from_file(dataset_num)
not_repeated = dataset_d.get_not_repeated_activity_data()
d = convert_to_one_hot(not_repeated)
train_day_index = get_data_of_days(not_repeated, num_days)
test_start_index = int(0.7 * len(d))
if test_start_index >= train_day_index:
train_d = d[:train_day_index, :]
else:
train_d = d[:test_start_index, :]
# raise Exception("Number of days for training passed 70% limit of the train dataset.")
test_d = d[test_start_index:, :]
train_data_d, train_labels_d = build_dataset(train_d)
test_data_d, test_labels_d = build_dataset(test_d)
train_data_shuffled, train_labels_shuffled = sklearn.utils.shuffle(
train_data_d, train_labels_d, random_state=0)
return train_data_shuffled, train_labels_shuffled, test_data_d, test_labels_d
def create_centralized_train_dataset(dataset_nums, num_days):
all_data = np.empty((0, HISTORY_SIZE, TOTAL_NUM_OF_FEATURES))
all_labels = np.empty((0, NUMBER_OF_ACTIVITIES))
print('Loading dataset')
for i in tqdm(dataset_nums):
dataset_d = get_dataset_from_file(i)
not_repeated = dataset_d.get_not_repeated_activity_data()
d = convert_to_one_hot(not_repeated)
train_day_index = get_data_of_days(not_repeated, num_days[i])
limit_index = int(0.7 * len(d))
if limit_index >= train_day_index:
train_d = d[:train_day_index, :]
else:
train_d = d[:limit_index, :]
data_d, labels_d = build_dataset(train_d)
if len(data_d) == 0:
continue
# data_d, labels_d = build_dataset(d)
all_data = np.concatenate(([all_data, data_d]), axis=0)
all_labels = np.concatenate(([all_labels, labels_d]), axis=0)
all_data_shuffled, all_labels_shuffled = sklearn.utils.shuffle(
all_data, all_labels, random_state=0)
return all_data_shuffled, all_labels_shuffled
from settings import LIST_OF_MAPPED_2_ACTIVITIES, NUMBER_OF_ACTIVITIES
from datasets import get_dataset_from_file, convert_to_one_hot, get_data_of_days
def client_activities_stats(client_activities):
for i, act in enumerate(LIST_OF_MAPPED_2_ACTIVITIES):
print(act + "," + str(int(sum(client_activities[:, i]))))
if __name__ == "__main__":
for i in range(121, 122):
dataset_d = get_dataset_from_file(i)
not_repeated = dataset_d.get_not_repeated_activity_data()
one_hot = convert_to_one_hot(not_repeated)
train_day_index = get_data_of_days(not_repeated, 2)
activities_d = one_hot[:train_day_index, -NUMBER_OF_ACTIVITIES:]
print("\n\nClient" + str(i) + "\n")
client_activities_stats(activities_d)