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DataHandler.py
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DataHandler.py
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import pickle
import numpy as np
from scipy.sparse import csr_matrix, coo_matrix, dok_matrix
from Params import args
import scipy.sparse as sp
from Utils.TimeLogger import log
import torch as t
import torch.utils.data as data
import torch.utils.data as dataloader
class DataHandler:
def __init__(self):
if args.data == 'amazon':
predir = '../data/amazon/'
elif args.data == 'gowalla':
predir = '../data/gowalla/'
elif args.data == 'retail_rocket':
predir = '../data/retail_rocket/'
elif args.data == 'kindle':
predir = '../data/kindle/'
elif args.data == 'tmall':
predir = '../data/tmall/'
self.predir = predir
self.trnfile = predir + 'trnMat.pkl'
self.tstfile = predir + 'tstMat.pkl'
def loadOneFile(self, filename):
with open(filename, 'rb') as fs:
ret = (pickle.load(fs) != 0).astype(np.float32)
if type(ret) != coo_matrix:
ret = sp.coo_matrix(ret)
return ret
def normalizeAdj(self, mat):
degree = np.array(mat.sum(axis=-1))
dInvSqrt = np.reshape(np.power(degree, -0.5), [-1])
dInvSqrt[np.isinf(dInvSqrt)] = 0.0
dInvSqrtMat = sp.diags(dInvSqrt)
return mat.dot(dInvSqrtMat).transpose().dot(dInvSqrtMat).tocoo()
def makeAllOne(self, torchAdj):
idxs = torchAdj._indices()
# print("idxs:", idxs.size()) #torch.Size([2, 2453111])
vals = t.ones_like(torchAdj._values())
# print("vals:", vals.size()) #torch.Size([2453111])
shape = torchAdj.shape
return t.sparse.FloatTensor(idxs, vals, shape).cuda()
def makeTorchAdj(self, mat):
# make ui adj
a = sp.csr_matrix((args.user, args.user))
b = sp.csr_matrix((args.item, args.item))
mat = sp.vstack([sp.hstack([a, mat]), sp.hstack([mat.transpose(), b])])
mat = (mat != 0) * 1.0
mat = (mat + sp.eye(mat.shape[0])) * 1.0
mat = self.normalizeAdj(mat)
# make cuda tensor
idxs = t.from_numpy(np.vstack([mat.row, mat.col]).astype(np.int64))
vals = t.from_numpy(mat.data.astype(np.float32))
shape = t.Size(mat.shape)
return t.sparse.FloatTensor(idxs, vals, shape).cuda()
def filter4SparseTest(self, trnMat, tstMat, usr=True):
stat = np.squeeze(np.array(np.sum(trnMat != 0, axis=-1 if usr else 0)))
idct = (stat >= 30) * (stat < 40)
pckNodeSet = set(np.squeeze(np.argwhere(idct)))
print(len(pckNodeSet))
def filterTst(tstMat, pckNodeSet, usr=True):
row = tstMat.row
col = tstMat.col
data = tstMat.data
newRow = []
newCol = []
newData = []
rowOrCol = row if usr else col
for i, elem in enumerate(rowOrCol):
if elem in pckNodeSet:
newRow.append(row[i])
newCol.append(col[i])
newData.append(data[i])
return coo_matrix((newData, (newRow, newCol)), shape=tstMat.shape)
return filterTst(tstMat, pckNodeSet, usr)
def LoadData(self):
trnMat = self.loadOneFile(self.trnfile)
tstMat = self.loadOneFile(self.tstfile)
self.trnMat = trnMat
# tstMat = self.filter4SparseTest(trnMat, tstMat, False)
# print('Num of tst entries', np.sum(tstMat!=0))
args.user, args.item = trnMat.shape
print("args.user:", args.user)
print("args.item:", args.item)
self.torchBiAdj = self.makeTorchAdj(trnMat)
self.allOneAdj = self.makeAllOne(self.torchBiAdj)
trnData = TrnData(trnMat)
self.trnLoader = dataloader.DataLoader(trnData, batch_size=args.batch, shuffle=True, num_workers=0)
tstData = TstData(tstMat, trnMat)
self.tstLoader = dataloader.DataLoader(tstData, batch_size=args.tstBat, shuffle=False, num_workers=0)
class TrnData(data.Dataset):
def __init__(self, coomat):
self.rows = coomat.row
self.cols = coomat.col
self.dokmat = coomat.todok()
self.negs = np.zeros(len(self.rows)).astype(np.int32)
def negSampling(self):
for i in range(len(self.rows)):
u = self.rows[i]
while True:
iNeg = np.random.randint(args.item)
if (u, iNeg) not in self.dokmat:
break
self.negs[i] = iNeg
def __len__(self):
return len(self.rows)
def __getitem__(self, idx):
return self.rows[idx], self.cols[idx], self.negs[idx]
class TstData(data.Dataset):
def __init__(self, coomat, trnMat):
self.csrmat = (trnMat.tocsr() != 0) * 1.0
tstLocs = [None] * coomat.shape[0]
tstUsrs = set()
for i in range(len(coomat.data)):
row = coomat.row[i]
col = coomat.col[i]
if tstLocs[row] is None:
tstLocs[row] = list()
tstLocs[row].append(col)
tstUsrs.add(row)
tstUsrs = np.array(list(tstUsrs))
self.tstUsrs = tstUsrs
self.tstLocs = tstLocs
def __len__(self):
return len(self.tstUsrs)
def __getitem__(self, idx):
return self.tstUsrs[idx], np.reshape(self.csrmat[self.tstUsrs[idx]].toarray(), [-1])