def compute_cfss():
'''
计算shop-shop相似关系矩阵。
Input:
shop_actu：用户对店铺做的动作
Process:
取用户动作表示的shop向量，计算向量点积。
Output:
shop-shop 相似关系，cfss.kv
'''
# shop_actu -> shop-shop关系矩阵，并保存cfss.kv，shop\tshop:weight;
kvg = KVEngine()
kvg.load([full_path('shop_actu.kv')])
# get normialized vectors
shop_users = {}
skeys = kvg.keymatch('S\d+_ACTU')
for skey in skeys:
sid = key_id(skey)
vector = dict([(int(key), float(value))
for (key, value) in kvg.getd(skey).items()
if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x: x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
shop_users[sid] = vector
# similarity calculation
shop_similarity = {}
sids = shop_users.keys()
sids.sort()
l = len(sids)
print "Calculating shop-shop similarity matrix, total %d..." % l
for i in range(l):
if i % 1000 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i + 1, l):
sim = norm_dot_product(shop_users[sids[i]], shop_users[sids[j]])
if abs(sim) < 1e-5:
continue
shop_similarity.setdefault(sids[i], {})[sids[j]] = sim
shop_similarity.setdefault(sids[j], {})[sids[i]] = sim
# save as kvfile
write_kv_dict(shop_similarity, 'S%s_CFSIMS', 'cfss.kv')
def compute_cfgg():
'''
计算goods-goods相似关系矩阵。
Input:
user_actg.kv -> goods_actu.kv：用户对店铺做的动作
Process:
取用户动作表示的goods向量，计算向量点积。
Output:
goods-goods 相似关系，cfss.kv
'''
kvg = KVEngine()
kvg.load([full_path('goods_actu.kv')])
# get normialized vectors
goods_users = {}
gkeys = kvg.keymatch('G\d+_ACTU')
for gkey in gkeys:
gid = key_id(gkey)
vector = dict([(int(key), float(value))
for (key, value) in kvg.getd(gkey).items()
if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x: x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
goods_users[gid] = vector
# similarity calculation
goods_similarity = {}
gids = goods_users.keys()
gids.sort()
l = len(gids)
print "Calculating goods-goods similarity matrix, total %d..." % l
for i in range(l):
if i % 100 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i + 1, l):
sim = norm_dot_product(goods_users[gids[i]], goods_users[gids[j]])
if abs(sim) < 1e-5:
continue
goods_similarity.setdefault(gids[i], {})[gids[j]] = sim
goods_similarity.setdefault(gids[j], {})[gids[i]] = sim
# save as kvfile
write_kv_dict(goods_similarity, 'G%s_CFSIMG', 'cfgg.kv')
def compute_cfss():
'''
计算shop-shop相似关系矩阵。
Input:
shop_actu：用户对店铺做的动作
Process:
取用户动作表示的shop向量，计算向量点积。
Output:
shop-shop 相似关系，cfss.kv
'''
# shop_actu -> shop-shop关系矩阵，并保存cfss.kv，shop\tshop:weight;
kvg = KVEngine()
kvg.load([full_path('shop_actu.kv')])
# get normialized vectors
shop_users = {}
skeys = kvg.keymatch('S\d+_ACTU')
for skey in skeys:
sid = key_id(skey)
vector = dict([(int(key), float(value)) for (key, value) in kvg.getd(skey).items() if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x:x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
shop_users[sid] = vector
# similarity calculation
shop_similarity = {}
sids = shop_users.keys()
sids.sort()
l = len(sids)
print "Calculating shop-shop similarity matrix, total %d..." % l
for i in range(l):
if i % 1000 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i+1, l):
sim = norm_dot_product(shop_users[sids[i]], shop_users[sids[j]])
if abs(sim) < 1e-5:
continue
shop_similarity.setdefault(sids[i], {})[sids[j]] = sim
shop_similarity.setdefault(sids[j], {})[sids[i]] = sim
# save as kvfile
write_kv_dict(shop_similarity, 'S%s_CFSIMS', 'cfss.kv')
def compute_cfgg():
'''
计算goods-goods相似关系矩阵。
Input:
user_actg.kv -> goods_actu.kv：用户对店铺做的动作
Process:
取用户动作表示的goods向量，计算向量点积。
Output:
goods-goods 相似关系，cfss.kv
'''
kvg = KVEngine()
kvg.load([full_path('goods_actu.kv')])
# get normialized vectors
goods_users = {}
gkeys = kvg.keymatch('G\d+_ACTU')
for gkey in gkeys:
gid = key_id(gkey)
vector = dict([(int(key), float(value)) for (key, value) in kvg.getd(gkey).items() if key and value])
# tailor to top 20
items = vector.items()
items.sort(key=lambda x:x[1], reverse=True)
items = items[:20]
vector = dict(items)
normalize(vector)
goods_users[gid] = vector
# similarity calculation
goods_similarity = {}
gids = goods_users.keys()
gids.sort()
l = len(gids)
print "Calculating goods-goods similarity matrix, total %d..." % l
for i in range(l):
if i % 100 == 0:
print "%d" % i
sys.stdout.flush()
for j in range(i+1, l):
sim = norm_dot_product(goods_users[gids[i]], goods_users[gids[j]])
if abs(sim) < 1e-5:
continue
goods_similarity.setdefault(gids[i], {})[gids[j]] = sim
goods_similarity.setdefault(gids[j], {})[gids[i]] = sim
# save as kvfile
write_kv_dict(goods_similarity, 'G%s_CFSIMG', 'cfgg.kv')
