import pickle
import numpy as np
import torch
from torch.utils.data import TensorDataset
from .folded_dataset import FoldedDataset
from options import opt
def get_msda_amazon_datasets(data_file, domain, kfold, feature_num):
print(f'Loading mSDA Preprocessed Multi-Domain Amazon data for {domain} Domain')
dataset = pickle.load(open(data_file, 'rb'))[domain]
lx, ly = dataset['labeled']
if feature_num > 0:
lx = lx[:, : feature_num]
lx = torch.from_numpy(lx.toarray()).float().to(opt.device)
ly = torch.from_numpy(ly).long().to(opt.device)
print(f'{domain} Domain has {len(ly)} labeled instances.')
# if opt.use_cuda:
# lx, ly = lx.cuda(), ly.cuda()
labeled_set = FoldedDataset(TensorDataset, kfold, lx, ly)
ux, uy = dataset['unlabeled']
if feature_num > 0:
ux = ux[:, : feature_num]
ux = torch.from_numpy(ux.toarray()).float().to(opt.device)
uy = torch.from_numpy(uy).long().to(opt.device)
print(f'{domain} Domain has {len(uy)} unlabeled instances.')
# if opt.use_cuda:
# ux, uy = ux.cuda(), uy.cuda()
unlabeled_set = TensorDataset(ux, uy)
return labeled_set, unlabeled_set
