def data_loader(status, shuffle=False, validation=False, num_workers=2):
prepare_data = Preprocessing()
if DATA_TYPE == 'original':
data_X, data_y = prepare_data.original(status)
elif DATA_TYPE == 'features':
data_X, data_y = prepare_data.features(status)
elif DATA_TYPE == 'trans':
data_X, data_y = prepare_data.trans(status)
data = DealDataset(data_X, data_y)
size = data.len
if validation:
train, dev = random_split(data, [int(size*SPLIT_RATE), size-int(size*SPLIT_RATE)])
train, dev = DealDataset(train[:][0],train[:][1]), DealDataset(dev[:][0],dev[:][1])
train_loader = DataLoader(dataset=train,
batch_size=BATCH_SIZE,
shuffle=shuffle,
num_workers=num_workers)
dev_loader = DataLoader(dataset=dev,
batch_size=BATCH_SIZE,
shuffle=shuffle,
num_workers=num_workers)
return train_loader, dev_loader
else:
loader = DataLoader(dataset=data,
batch_size=BATCH_SIZE,
shuffle=shuffle,
num_workers=num_workers)
return loader
import pandas as pd
import numpy as np
from utils.Preprocessing import *
def load_data(file_path):
data = pd.read_csv(file_path, sep="\s+")
return data
# X = load_data("../data/train/X_train.txt")
# y = load_data("../data/train/y_train.txt")
# y = np.asarray(y.values)
# actionA = X.iloc[np.argwhere(y==5)[:,0]]
# print(len(actionA))
a = Preprocessing()
X, Y = a.trans('train')
print(X.shape)
X, Y = a.trans('test')
print(X.shape)