def cal_comm_mat_UBB(path_str):
'''
200k ratings
calculate the commuting matrix in U-B-*-B style
in fact, only need to calculate BB
'''
print "path str:", path_str
uid_filename = dir_ + 'uids.txt'#users
print 'run cal_comm_mat_samples for 10k users in ', uid_filename
lines = open(uid_filename, 'r').readlines()
uids = [int(l.strip()) for l in lines]
uid2ind = {v:k for k,v in enumerate(uids)}
ind2uid = reverse_map(uid2ind)
bid_filename = dir_ + 'bids.txt'#items
lines = open(bid_filename, 'r').readlines()
bids = [int(l.strip()) for l in lines]
bid2ind = {v:k for k,v in enumerate(bids)}
ind2bid = reverse_map(bid2ind)
upb_filename = dir_ + 'uid_pos_bid.txt'# positive rating
upb = np.loadtxt(upb_filename, dtype=int)
# generate users items adjacency matrix
adj_ub, adj_ub_t = generate_adj_mat(upb, uid2ind, bid2ind)
# print uid2ind[640698], bid2ind[51874]
# print type(adj_ub), adj_ub.toarray()[uid2ind[640698]][bid2ind[51874]], adj_ub.toarray().shape
# print type(adj_ub_t), adj_ub_t.toarray()[bid2ind[51874]][uid2ind[640698]], adj_ub_t.toarray().shape
# generate items object adjacency matrix (cat, state, city, star)
adj_bo, adj_bo_t = get_bo(path_str, bid2ind)
t1 = time.time()
# compute u-> b -> o(cat,city) <- b
comm_res = cal_mat_ubb(path_str, adj_ub, adj_bo, adj_bo_t)
t2 = time.time()
print 'cal res of %s cost %2.f seconds' % (path_str, t2 - t1)
print 'comm_res shape=%s,densit=%s' % (comm_res.shape, comm_res.nnz * 1.0/comm_res.shape[0]/comm_res.shape[1])
K = 500
wfilename = dir_ + 'sim_res/path_count/%s_top%s.res' % (path_str, K)
triplets = get_topK_items(comm_res, ind2uid, ind2bid, topK=K)
save_triplets(wfilename, triplets)
#batch_save_comm_res(path_str, wfilename, comm_res, ind2uid, ind2bid)
t3 = time.time()
print 'save res of %s cost %2.f seconds' % (path_str, t3 - t2)
def output_result(f_output, model, datas, instanse_id, frame_wise=False):
print('predict size:{}'.format(len(datas)))
# mask of sentence len
sents_len = []
for data in datas:
for frame_idx, vector in enumerate(data):
if all(vector == 0.):
sents_len.append(frame_idx)
break
if frame_idx + 1 == len(data):
sents_len.append(frame_idx + 1)
# predict
preds = model.predict(datas)
# transform
preds = np.vectorize(reverse_map(phone2idx).get)(preds)
preds = np.vectorize(phone2char.get)(preds)
# output prediction
import re
print('output:{}'.format(f_output))
with open(f_output, 'w') as out:
_ = out.write('id,phone_sequence\n')
for data_idx, pred in enumerate(preds):
result_str = pred[:sents_len[data_idx]]
# remove peak
for i in range(1, len(result_str) - 1):
if result_str[i - 1] == result_str[
i + 1] and result_str[i] != result_str[i - 1]:
result_str[i] = ''
result_str = ''.join(result_str)
if not frame_wise:
result_str = result_str.strip(phone2char['sil']) # trim sil
result_str = re.sub(r'([a-zA-Z0-9])\1+', r'\1',
result_str) # trim
_ = out.write('{},{}\n'.format(instanse_id[data_idx], result_str))
def load_eids(eid_filename, type_):
lines = open(eid_filename, 'r').readlines()
eids = [int(l.strip()) for l in lines]
eid2ind = {v:k for k,v in enumerate(eids)}
ind2eid = reverse_map(eid2ind)
#logger.info('get %s %s from %s', len(eids), type_, eid_filename)
print 'get %s %s from %s' %(len(eids), type_, eid_filename)
return eids, eid2ind, ind2eid
def cal_rar(path_str):
aid_filename = dir_ + 'aids.txt'
rid_filename = dir_ + 'rids.txt'
aids = open(aid_filename, 'r').readlines()
aids = [int(r.strip()) for r in aids]
aid2ind = {a: ind for ind, a in enumerate(aids)} #global ind
ind2aid = reverse_map(aid2ind)
rids = open(rid_filename, 'r').readlines()
rids = [int(r.strip()) for r in rids]
rid2ind = {r: ind for ind, r in enumerate(rids)} #global ind
ind2rid = reverse_map(rid2ind)
if 'P' in path_str:
ura_filename = dir_ + 'uid_rid_pos_aid_weight.txt'
elif 'N' in path_str:
ura_filename = dir_ + 'uid_rid_neg_aid_weight.txt'
ura = np.loadtxt(ura_filename, dtype=np.float64)
ra = ura[:, (1, 2, 3)]
ra = [(int(r), int(a), w) for r, a, w in ra]
adj_ra, adj_ra_t = generate_adj_mat(ra, rid2ind, aid2ind, is_weight=True)
t1 = time.time()
RA = adj_ra.toarray()
t2 = time.time()
print 'to dense RA%s cost %.2f seconds' % (RA.shape, t2 - t1)
RAR_csr = cal_rar_block(RA, len(rid2ind), ind2rid, step=20000)
print 'finish cal rar by blocks, cost %.2f minutes' % (
(time.time() - t2) / 60.0)
try:
wfilename = dir_ + 'sim_res/path_count/%s_spa_mat.pickle' % path_str
fw = open(wfilename, 'w+')
pickle.dump(RAR_csr, fw, pickle.HIGHEST_PROTOCOL)
map_filename = dir_ + 'sim_res/path_count/%s_spa_mat_id_map.pickle' % path_str
fw = open(map_filename, 'w+')
pickle.dump(ind2rid, fw, pickle.HIGHEST_PROTOCOL)
print 'finish saving sparse mat in ', wfilename
except Exception as e:
print e
def cal_comm_mat_UBB(path_str):
'''
200k ratings
calculate the commuting matrix in U-B-*-B style
in fact, only need to calculate BB
'''
uid_filename = dir_ + 'uids.txt'
print 'run cal_comm_mat_samples for 10k users in ', uid_filename
lines = open(uid_filename, 'r').readlines()
uids = [int(l.strip()) for l in lines]
uid2ind = {v: k for k, v in enumerate(uids)}
ind2uid = reverse_map(uid2ind)
bid_filename = dir_ + 'bids.txt'
lines = open(bid_filename, 'r').readlines()
bids = [int(l.strip()) for l in lines]
bid2ind = {v: k for k, v in enumerate(bids)}
ind2bid = reverse_map(bid2ind)
upb_filename = dir_ + 'uid_pos_bid.txt'
upb = np.loadtxt(upb_filename, dtype=int)
adj_ub, adj_ub_t = generate_adj_mat(upb, uid2ind, bid2ind)
adj_bo, adj_bo_t = get_bo(path_str, bid2ind)
t1 = time.time()
comm_res = cal_mat_ubb(path_str, adj_ub, adj_bo, adj_bo_t)
t2 = time.time()
print 'cal res of %s cost %2.f seconds' % (path_str, t2 - t1)
print 'comm_res shape=%s,densit=%s' % (comm_res.shape, comm_res.nnz * 1.0 /
comm_res.shape[0] /
comm_res.shape[1])
K = 500
wfilename = dir_ + 'sim_res/path_count/%s_top%s.res' % (path_str, K)
triplets = get_topK_items(comm_res, ind2uid, ind2bid, topK=K)
save_triplets(wfilename, triplets)
#batch_save_comm_res(path_str, wfilename, comm_res, ind2uid, ind2bid)
t3 = time.time()
print 'save res of %s cost %2.f seconds' % (path_str, t3 - t2)
def get_bo(path_str, bid2ind):
#U-pos-B-Cat-B
if 'State' in path_str:
sfilename = dir_ + 'bid_state.txt'
elif 'Cat' in path_str:
sfilename = dir_ + 'bid_cat.txt'
elif 'City' in path_str:
sfilename = dir_ + 'bid_city.txt'
elif 'Star' in path_str:
sfilename = dir_ + 'bid_stars.txt'
lines = open(sfilename, 'r').readlines()
parts = [l.strip().split() for l in lines]
bos = [(int(b), int(o)) for b,o in parts]
ond2ind = {v:k for k,v in enumerate(set([o for _, o in bos]))}
ind2ond = reverse_map(ond2ind)
adj_bo, adj_bo_t = generate_adj_mat(bos, bid2ind, ond2ind)
return adj_bo, adj_bo_t
def run(path_str, comb='', K=10):
if path_str in ['ratings_only']:
use_topK = False
else:
use_topK = True
sim_filename = dir_ + 'sim_res/path_count/%s.res' % path_str
if path_str == 'ratings_only':
sim_filename = dir_ + 'ratings.txt'
if use_topK:
sim_filename = dir_ + 'sim_res/path_count/%s_top%s.res' % (path_str,
topK)
if comb:
sim_filename = dir_ + 'sim_res/path_count/combs/%s_%s_top%s.res' % (
path_str, comb, topK)
start_time = time.time()
data = np.loadtxt(sim_filename)
uids = set(data[:, 0].flatten())
bids = set(data[:, 1].flatten())
uid2ind = {int(v): k for k, v in enumerate(uids)}
ind2uid = reverse_map(uid2ind)
bid2ind = {int(v): k for k, v in enumerate(bids)}
ind2bid = reverse_map(bid2ind)
data[:, 0] = [uid2ind[int(r)] for r in data[:, 0]]
data[:, 1] = [bid2ind[int(r)] for r in data[:, 1]]
print 'finish load data from %s, cost %.2f seconds, users: %s, items=%s' % (
sim_filename, time.time() - start_time, len(uids), len(bids))
eps, lamb, iters = 10, 10, 500
print 'start generate mf features, (K, eps, reg, iters) = (%s, %s, %s, %s)' % (
K, eps, lamb, iters)
mf = MF(data=data,
train_data=data,
test_data=[],
K=K,
eps=eps,
lamb=lamb,
max_iter=iters,
call_logger=logger)
U, V = mf.run()
start_time = time.time()
wfilename = dir_ + 'mf_features/path_count/%s_user.dat' % (path_str)
rank_dir = dir_ + 'mf_features/path_count/ranks/%s/' % K
if K != 10 and not os.path.isdir(rank_dir):
os.makedirs(rank_dir)
if use_topK:
#wfilename = dir_ + 'mf_features/path_count/%s_top%s_user.dat' % (path_str, topK)
wfilename = dir_ + 'mf_features/path_count/%s_top%s_user.dat' % (
path_str, topK)
else:
wfilename = dir_ + 'mf_features/path_count/%s_user.dat' % (path_str)
fw = open(wfilename, 'w+')
res = []
for ind, fs in enumerate(U):
row = []
row.append(ind2uid[ind])
row.extend(fs.flatten())
res.append('\t'.join([str(t) for t in row]))
fw.write('\n'.join(res))
fw.close()
print 'User-Features: %s saved in %s, cost %.2f seconds' % (
U.shape, wfilename, time.time() - start_time)
start_time = time.time()
wfilename = dir_ + 'mf_features/path_count/%s_item.dat' % (path_str)
if use_topK:
#wfilename = dir_ + 'mf_features/path_count/%s_top%s_item.dat' % (path_str, topK)
wfilename = dir_ + 'mf_features/path_count/%s_top%s_item.dat' % (
path_str, topK)
else:
wfilename = dir_ + 'mf_features/path_count/%s_item.dat' % (path_str)
fw = open(wfilename, 'w+')
res = []
for ind, fs in enumerate(V):
row = []
row.append(ind2bid[ind])
row.extend(fs.flatten())
res.append('\t'.join([str(t) for t in row]))
fw.write('\n'.join(res))
fw.close()
print 'Item-Features: %s saved in %s, cost %.2f seconds' % (
V.shape, wfilename, time.time() - start_time)
def cal_comm_mat_USUB(path_str):
'''
Given meta_structure_str, generate the commuting matrix
e.g. 'user-review-business,t10_aspect-review-user'
'''
uid_filename = dir_ + 'uids.txt'
bid_filename = dir_ + 'bids.txt'
aid_filename = dir_ + 'aids.txt'
rid_filename = dir_ + 'rids.txt'
upb_filename = dir_ + 'uid_pos_bid.txt'
print 'cal commut mat for %s, filenames: %s, %s, %s' % (path_str, uid_filename, bid_filename, upb_filename)
uids, uid2ind, ind2uid = load_eids(uid_filename, 'user')
bids, bid2ind, ind2bid = load_eids(bid_filename, 'biz')
aids, aid2ind, ind2aid = load_eids(aid_filename, 'aspect')
upb = np.loadtxt(upb_filename, dtype=np.int64)
adj_upb, adj_upb_t = generate_adj_mat(upb, uid2ind, bid2ind)
if 'P' in path_str:
urb_filename = dir_ + 'uid_rid_pos_bid.txt'
ura_filename = dir_ + 'uid_rid_pos_aid.txt'
ind2rid_filename = dir_ + 'sim_res/path_count/%s_spa_mat_id_map.pickle' % path_str
rar_mat_filename = dir_ + 'sim_res/path_count/%s_spa_mat.pickle' % path_str
elif 'N' in path_str:
urb_filename = dir_ + 'uid_rid_neg_bid.txt'
ura_filename = dir_ + 'uid_rid_neg_aid.txt'
ind2rid_filename = dir_ + 'sim_res/path_count/%s_spa_mat_id_map.pickle' % path_str
rar_mat_filename = dir_ + 'sim_res/path_count/%s_spa_mat.pickle' % path_str
f = open(ind2rid_filename, 'r')
ind2rid = pickle.load(f)
rid2ind = reverse_map(ind2rid)
urb = np.loadtxt(urb_filename, dtype=np.int64)
ura = np.loadtxt(ura_filename, dtype=np.int64)
ur = urb[:,(0,1)]
adj_ur, adj_ur_t = generate_adj_mat(ur, uid2ind, rid2ind)
rb = urb[:,(1,2)]
adj_rb, adj_rb_t = generate_adj_mat(rb, rid2ind, bid2ind)
ra = ura[:,(1,2)]
adj_ra, adj_ra_t = generate_adj_mat(ra, rid2ind, aid2ind)
start = time.time()
RBR = adj_rb.dot(adj_rb_t)
print 'RBR(%s), density=%.5f cost %.2f seconds' % (RBR.shape, RBR.nnz * 1.0/RBR.shape[0]/RBR.shape[1], time.time() - start)
start = time.time()
#RAR = adj_ra.dot(adj_ra_t)
f = open(rar_mat_filename, 'r')
RAR = pickle.load(f)
print 'load RAR(%s), density=%.5f cost %.2f seconds' % (RAR.shape, RAR.nnz * 1.0/RAR.shape[0]/RAR.shape[1], time.time() - start)
start = time.time()
RSR = RBR.multiply(RAR)
print 'RSR(%s), density=%.5f cost %.2f seconds' % (RSR.shape, RSR.nnz * 1.0/RSR.shape[0]/RSR.shape[1], time.time() - start)
start = time.time()
URSR = adj_ur.dot(RSR)
print 'URSR(%s), density=%.5f cost %.2f seconds' % (URSR.shape, URSR.nnz * 1.0/URSR.shape[0]/URSR.shape[1], time.time() - start)
start = time.time()
URSRU = URSR.dot(adj_ur_t)
print 'URSRU(%s), density=%.5f cost %.2f seconds' % (URSRU.shape, URSRU.nnz * 1.0/URSRU.shape[0]/URSRU.shape[1], time.time() - start)
start = time.time()
URSRUB = URSRU.dot(adj_upb)
print 'URSRUB(%s), density=%.5f cost %.2f seconds' % (URSRUB.shape, URSRUB.nnz * 1.0/URSRUB.shape[0]/URSRUB.shape[1], time.time() - start)
start = time.time()
K = 500
wfilename = dir_ + 'sim_res/path_count/%s_top%s.res' % (path_str, K)
#wfilename = dir_ + 'sim_res/path_count/%s.res' % path_str
batch_save_comm_res(path_str, wfilename, URSRUB, ind2uid, ind2bid)
print 'finish saving %s %s entries in %s, cost %.2f seconds' % (URSRUB.nnz, path_str, wfilename, time.time() - start)
def cal_comm_mat_UUB(path_str, cikm=False):
'''
calculate commuting matrix for U-*-U-pos-B style
'''
print "path str:", path_str
uid_filename = dir_ + 'uids.txt'
bid_filename = dir_ + 'bids.txt'
upb_filename = dir_ + 'uid_pos_bid.txt'
if not cikm:
rid_filename = dir_ + 'rids.txt'
aid_filename = dir_ + 'aids.txt'
print 'cal commut mat for %s, filenames: %s, %s, %s' % (path_str, uid_filename, bid_filename, upb_filename)
uids, uid2ind, ind2uid = load_eids(uid_filename, 'user')
bids, bid2ind, ind2bid = load_eids(bid_filename, 'biz')
if not cikm:
rids, rid2ind, ind2rid = load_eids(rid_filename, 'review')
aids, aid2ind, ind2aid = load_eids(aid_filename, 'aspect')
upb = np.loadtxt(upb_filename, dtype=np.int64)
adj_upb, adj_upb_t = generate_adj_mat(upb, uid2ind, bid2ind)
if path_str == 'UPBUB':
start = time.time()
UBU = adj_upb.dot(adj_upb_t)
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str in ['UPBCatBUB', 'UPBCityBUB']:
start = time.time()
adj_bo, adj_bo_t = get_bo(path_str, bid2ind)
UBO = adj_upb.dot(adj_bo)
UBU = UBO.dot(UBO.transpose())
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str in ['UNBCatBUB', 'UNBCityBUB']:
unb_filename = dir_ + 'uid_neg_bid.txt'
unb = np.loadtxt(unb_filename, dtype=np.int64)
adj_unb, adj_unb_t = generate_adj_mat(unb, uid2ind, bid2ind)
start = time.time()
adj_bo, adj_bo_t = get_bo(path_str, bid2ind)
UBO = adj_unb.dot(adj_bo)
UBU = UBO.dot(UBO.transpose())
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str == 'UNBUB':
unb_filename = dir_ + 'uid_neg_bid.txt'
unb = np.loadtxt(unb_filename, dtype=np.int64)
adj_unb, adj_unb_t = generate_adj_mat(unb, uid2ind, bid2ind)
start = time.time()
UBU = adj_unb.dot(adj_unb_t)
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str == 'UUB':
social_filename = dir_ + 'user_social.txt'
uu = np.loadtxt(social_filename, dtype=np.int64)
adj_uu, adj_uu_t = generate_adj_mat(uu, uid2ind, uid2ind)
start = time.time()
UBU = adj_uu.copy()
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str == 'UCompUB':
uid_comp_filename = dir_ + 'uid_comp.txt'
uc = np.loadtxt(uid_comp_filename, dtype=np.int64)
cids = set(uc[:,1])
cid2ind = {v:k for k,v in enumerate(cids)}
ind2cnd = reverse_map(cid2ind)
adj_uc, adj_uc_t = generate_adj_mat(uc, uid2ind, cid2ind)
start = time.time()
UBU = adj_uc.dot(adj_uc_t)
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str == 'URPARUB':
urpa_filename = dir_ + 'uid_rid_pos_aid.txt'
urpa = np.loadtxt(urpa_filename)
ur = list(set([(u,r) for u, r in urpa[:,(0,1)]]))# u, r multiple aspects, thus u-r can be duplicate
adj_ur, adj_ur_t = generate_adj_mat(ur, uid2ind, rid2ind)
ra = urpa[:,(1,2)]
adj_ra, adj_ua_t = generate_adj_mat(ra, rid2ind, aid2ind)
start = time.time()
URA = adj_ur.dot(adj_ra)
UBU = URA.dot(URA.transpose())#it should be URARU, here we use UBU for convenience
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
elif path_str == 'URNARUB':
urpa_filename = dir_ + 'uid_rid_neg_aid.txt'
urpa = np.loadtxt(urpa_filename)
ur = list(set([(u,r) for u, r in urpa[:,(0,1)]]))# u, r multiple aspects, thus u-r can be duplicate
adj_ur, adj_ur_t = generate_adj_mat(ur, uid2ind, rid2ind)
ra = urpa[:,(1,2)]
adj_ra, adj_ua_t = generate_adj_mat(ra, rid2ind, aid2ind)
start = time.time()
URA = adj_ur.dot(adj_ra)
UBU = URA.dot(URA.transpose())#it should be URARU, here we use UBU for convenience
print 'UBU(%s), density=%.5f cost %.2f seconds' % (UBU.shape, UBU.nnz * 1.0/UBU.shape[0]/UBU.shape[1], time.time() - start)
start = time.time()
UBUB = UBU.dot(adj_upb)
print 'UBUB(%s), density=%.5f cost %.2f seconds' % (UBUB.shape, UBUB.nnz * 1.0/UBUB.shape[0]/UBUB.shape[1], time.time() - start)
start = time.time()
K = 500
triplets = get_topK_items(UBUB, ind2uid, ind2bid, topK=K)
wfilename = dir_ + 'sim_res/path_count/%s_top%s.res' % (path_str, K)
save_triplets(wfilename, triplets)
#save_comm_res(path_str, wfilename, UBUB, ind2uid, ind2bid)
print 'finish saving %s %s entries in %s, cost %.2f seconds' % (len(triplets), path_str, wfilename, time.time() - start)
def run(path_str, K=10):
if path_str in ['ratings_only']:
use_topK = False
else:
use_topK = True
sim_filename = os.path.join(data_dir,
'sim_res/path_count/%s.res' % path_str)
if path_str == 'ratings_only':
sim_filename = os.path.join(data_dir, 'tuples/ratings.txt')
elif use_topK:
sim_filename = os.path.join(
data_dir, 'sim_res/path_count/%s_top%s.res' % (path_str, topK))
start_time = time.time()
data = np.loadtxt(sim_filename, dtype=np.str, delimiter="\t")
uids = set(data[:, 0].flatten())
bids = set(data[:, 1].flatten())
# uid2ind = {v: k for k, v in enumerate(uids)}
uid2ind = {int(v): k for k, v in enumerate(uids)}
ind2uid = reverse_map(uid2ind)
# bid2ind = {v: k for k, v in enumerate(bids)}
bid2ind = {int(v): k for k, v in enumerate(bids)}
ind2bid = reverse_map(bid2ind)
data[:, 0] = [uid2ind[int(r)] for r in data[:, 0]]
data[:, 1] = [bid2ind[int(r)] for r in data[:, 1]]
# data[:, 0] = [uid2ind[r] for r in data[:, 0]]
# data[:, 1] = [bid2ind[r] for r in data[:, 1]]
print('finish load data from %s, cost %.2f seconds, users: %s, items=%s' %
(sim_filename, time.time() - start_time, len(uids), len(bids)))
# must convert data type to float
data = data.astype(dtype=np.float)
print("data shape: ", data.shape, data.dtype)
eps, lamb, iters = 10, 10, 500
print(
'start generate mf features, (K, eps, reg, iters) = (%s, %s, %s, %s)' %
(K, eps, lamb, iters))
mf = MF(data=data,
train_data=data,
test_data=[],
K=K,
eps=eps,
lamb=lamb,
max_iter=iters,
call_logger=logger)
U, V = mf.run()
start_time = time.time()
wfilename = os.path.join(data_dir,
'mf_features/path_count/%s_user.dat' % (path_str))
if use_topK:
wfilename = os.path.join(
data_dir,
'mf_features/path_count/%s_top%s_user.dat' % (path_str, topK))
fw = open(wfilename, 'w+')
res = []
for ind, fs in enumerate(U):
row = []
row.append(ind2uid[ind])
row.extend(fs.flatten())
res.append('\t'.join([str(t) for t in row]))
fw.write('\n'.join(res))
fw.close()
print('User-Features: %s saved in %s, cost %.2f seconds' %
(U.shape, wfilename, time.time() - start_time))
start_time = time.time()
wfilename = os.path.join(data_dir,
'mf_features/path_count/%s_item.dat' % (path_str))
if use_topK:
wfilename = os.path.join(
data_dir,
'mf_features/path_count/%s_top%s_item.dat' % (path_str, topK))
fw = open(wfilename, 'w+')
res = []
for ind, fs in enumerate(V):
row = []
row.append(ind2bid[ind])
row.extend(fs.flatten())
res.append('\t'.join([str(t) for t in row]))
fw.write('\n'.join(res))
fw.close()
print('Item-Features: %s saved in %s, cost %.2f seconds' %
(V.shape, wfilename, time.time() - start_time))
