"""
Test different data operations.
"""
import dev.util as util
from preprocess.DataIO import load_sequences_csv, load_seq_features_csv, load_event_features_csv
from preprocess.DataOperation import stitching, superposing, aggregating, data_info
# test sequence loading functions
# load event sequences
domain_names = {'seq_id': 'id', 'time': 'time', 'event': 'event'}
database = load_sequences_csv('{}/{}/Linkedin.csv'.format(
util.POPPY_PATH, util.DATA_DIR),
domain_names=domain_names)
data_info(database)
# load sequences' features
domain_dict = {'time': 'numerical', 'option1': 'categorical'}
database = load_seq_features_csv('{}/{}/Linkedin.csv'.format(
util.POPPY_PATH, util.DATA_DIR),
seq_domain='id',
domain_dict=domain_dict,
database=database)
data_info(database)
# load event types' features
database = load_event_features_csv('{}/{}/Linkedin.csv'.format(
util.POPPY_PATH, util.DATA_DIR),
event_domain='event',
domain_dict=domain_dict,
database=database)
if __name__ == '__main__':
# hyper-parameters
memory_size = 400
batch_size = 128
use_cuda = True
use_cuda = use_cuda and torch.cuda.is_available()
seed = 2
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
# load event sequences from csv file
domain_names = {'seq_id': 'seq_id', 'time': 'time', 'event': 'event'}
database = load_sequences_csv('{}/{}/IPTV_DATA.csv'.format(
util.POPPY_PATH, util.DATA_DIR),
domain_names=domain_names,
upperlimit=50000)
data_info(database)
# sample batches from database
trainloader_thinning = DataLoader(ThinningSampler(database=database,
length=memory_size),
batch_size=batch_size,
shuffle=True,
**kwargs)
trainloader_interval = DataLoader(EventSampler(database=database,
memorysize=memory_size),
batch_size=batch_size,
shuffle=True,
**kwargs)
validationloader = DataLoader(FullData(database=database))
