def setUpClass(self):
index = HnswIndex(self.dim)
for i in xrange(self.data_num):
v = [random.gauss(0, 1) for z in xrange(self.dim)]
index.add_data(v)
index.build(n_threads=12)
index.save(self.model_fname)
def test01_most_similar(self):
set_log_level(1)
model = self.load_text8_model()
index = HnswIndex(model.L0.shape[1])
model.normalize('item')
for f in model.L0:
index.add_data(f)
index.build(n_threads=4)
index.save('n2.bin')
par = ParW2V(model)
model.opt.num_workers = 1
all_keys = model._idmanager.itemids[::][:10000]
start_t = time.time()
[model.most_similar(k, topk=10) for k in all_keys]
naive_elapsed = time.time() - start_t
par.num_workers = 4
start_t = time.time()
par.most_similar(all_keys, topk=10, repr=True)
par_elapsed = time.time() - start_t
start_t = time.time()
par.set_hnsw_index('n2.bin', 'item')
par.most_similar(all_keys, topk=10, repr=True)
ann_elapsed = time.time() - start_t
self.assertTrue(naive_elapsed > par_elapsed * 1.5 > ann_elapsed * 5.0,
msg=f'{naive_elapsed} > {par_elapsed} > {ann_elapsed}')
index.unload()
os.remove('n2.bin')
def example2():
log.set_log_level(log.INFO)
als_option = ALSOption().get_default_option()
data_option = MatrixMarketOptions().get_default_option()
data_option.input.main = '../tests/ext/ml-20m/main'
data_option.input.iid = '../tests/ext/ml-20m/iid'
data_option.data.path = './ml20m.h5py'
data_option.data.use_cache = True
als = ALS(als_option, data_opt=data_option)
als.initialize()
als.train()
als.normalize('item')
als.build_itemid_map()
print(
'Make item recommendation on als.ml20m.par.top10.tsv with Paralell(Thread=4)'
)
par = ParALS(als)
par.num_workers = 4
all_items = als._idmanager.itemids
start_t = time.time()
with open('als.ml20m.par.top10.tsv', 'w') as fout:
for idx in range(0, len(all_items), 128):
topks, _ = par.most_similar(all_items[idx:idx + 128], repr=True)
for q, p in zip(all_items[idx:idx + 128], topks):
fout.write('%s\t%s\n' % (q, '\t'.join(p)))
print('took: %.3f secs' % (time.time() - start_t))
from n2 import HnswIndex
index = HnswIndex(als.Q.shape[1])
for f in als.Q:
index.add_data(f)
index.build(n_threads=4)
index.save('ml20m.n2.index')
index.unload()
print(
'Make item recommendation on als.ml20m.par.top10.tsv with Ann(Thread=1)'
)
par.set_hnsw_index('ml20m.n2.index', 'item')
par.num_workers = 4
start_t = time.time()
with open('als.ml20m.ann.top10.tsv', 'w') as fout:
for idx in range(0, len(all_items), 128):
topks, _ = par.most_similar(all_items[idx:idx + 128], repr=True)
for q, p in zip(all_items[idx:idx + 128], topks):
fout.write('%s\t%s\n' % (q, '\t'.join(p)))
print('took: %.3f secs' % (time.time() - start_t))
class N2(BaseANN):
def __init__(self, m, ef_construction, n_threads, ef_search, metric):
self._m = m
self._m0 = m * 2
self._ef_construction = ef_construction
self._n_threads = n_threads
self._ef_search = ef_search
self._index_name = os.path.join(
INDEX_DIR, "youtube_n2_M%d_efCon%d_n_thread%s" %
(m, ef_construction, n_threads))
self.name = "N2_M%d_efCon%d_n_thread%s_efSearch%d" % (
m, ef_construction, n_threads, ef_search)
self._metric = metric
d = os.path.dirname(self._index_name)
if not os.path.exists(d):
os.makedirs(d)
def fit(self, X):
from n2 import HnswIndex
if self._metric == 'euclidean':
self._n2 = HnswIndex(X.shape[1], 'L2')
else:
self._n2 = HnswIndex(X.shape[1])
if os.path.exists(self._index_name):
logging.debug("Loading index from file")
self._n2.load(self._index_name)
else:
logging.debug("Index file is not exist: {0}".format(
self._index_name))
logging.debug("Start fitting")
for i, x in enumerate(X):
self._n2.add_data(x.tolist())
self._n2.build(m=self._m,
max_m0=self._m0,
ef_construction=self._ef_construction,
n_threads=self._n_threads)
self._n2.save(self._index_name)
def query(self, v, n):
return self._n2.search_by_vector(v.tolist(), n, self._ef_search)
def __str__(self):
return self.name
class N2(BaseANN):
def __init__(self, m, ef_construction, n_threads, ef_search, metric, batch):
self.name = "N2_M%d_efCon%d_n_thread%s_efSearch%d%s" % (m, ef_construction, n_threads, ef_search,
'_batch' if batch else '')
self._m = m
self._m0 = m * 2
self._ef_construction = ef_construction
self._n_threads = n_threads
self._ef_search = ef_search
self._index_name = os.path.join(CACHE_DIR, "index_n2_%s_M%d_efCon%d_n_thread%s"
% (args.dataset, m, ef_construction, n_threads))
self._metric = metric
def fit(self, X):
if self._metric == 'euclidean':
self._n2 = HnswIndex(X.shape[1], 'L2')
elif self._metric == 'dot':
self._n2 = HnswIndex(X.shape[1], 'dot')
else:
self._n2 = HnswIndex(X.shape[1])
if os.path.exists(self._index_name):
n2_logger.info("Loading index from file")
self._n2.load(self._index_name, use_mmap=False)
return
n2_logger.info("Create Index")
for i, x in enumerate(X):
self._n2.add_data(x)
self._n2.build(m=self._m, max_m0=self._m0, ef_construction=self._ef_construction, n_threads=self._n_threads)
self._n2.save(self._index_name)
def query(self, v, n):
return self._n2.search_by_vector(v, n, self._ef_search)
def batch_query(self, X, n):
self.b_res = self._n2.batch_search_by_vectors(X, n, self._ef_search, self._n_threads)
def get_batch_results(self):
return self.b_res
def __str__(self):
return self.name
from n2 import HnswIndex
import random
f = 3
t = HnswIndex(f) # HnswIndex(f, "L2 or angular")
for i in xrange(1000):
v = [random.gauss(0, 1) for z in xrange(f)]
t.add_data(v)
t.build(m=5, max_m0=10, n_threads=4)
t.save('test.n2')
u = HnswIndex(f, "angular")
u.load('test.n2')
search_id = 1
k = 3
neighbor_ids = u.search_by_id(search_id, k)
print(
"[search_by_id]: Nearest neighborhoods of id {}: {}".format(
search_id,
neighbor_ids))
example_vector_query = [random.gauss(0, 1) for z in xrange(f)]
nns = u.search_by_vector(example_vector_query, k, include_distances=True)
print(
"[search_by_vector]: Nearest neighborhoods of vector {}: {}".format(
example_vector_query,
nns))
def _buffalo(algo_name, database):
repeat = 3
options = {'als': {'num_workers': 4,
'compute_loss_on_training': False,
'd': 32,
'num_iters': 10},
'bpr': {'num_workers': 4,
'compute_loss_on_training': False,
'd': 32,
'num_iters': 100},
}
opt = options[algo_name]
# linear
if algo_name == 'als':
PAR = ParALS
model = BuffaloLib().als(database, return_instance_before_train=True, **opt)
elif algo_name == 'bpr':
PAR = ParBPRMF
model = BuffaloLib().bpr(database, return_instance_before_train=True, **opt)
model.train()
model.build_itemid_map()
model.normalize('item')
# parallel
par = PAR(model)
# ann
index = HnswIndex(model.P.shape[1])
for f in model.P:
index.add_data(f)
index.build(n_threads=4)
index.save('bm_n2.bin')
ann = PAR(model)
ann.set_hnsw_index('bm_n2.bin', 'item')
total_queries = 10000
keys = model._idmanager.itemids[::][:total_queries]
print('Total queries: %s' % len(keys))
results = {}
nn_opts = {'topk': 10}
for p, m in [('S', model), ('P', par), ('A', ann)]:
results[p] = {}
opt = nn_opts.copy()
if not isinstance(m, PAR):
opt['iterable'] = keys
for num_workers in [1, 2, 4]:
if isinstance(m, PAR):
m.num_workers = num_workers
else:
m.opt.num_workers = num_workers
opt['model'] = m
elapsed, memory_usage = _get_elapsed_time('most_similar',
keys,
BuffaloLib(), repeat, **opt)
s = elapsed / len(keys)
results[p][f'S={num_workers}'] = s
results[p][f'E={num_workers}'] = elapsed
results[p][f'M={num_workers}'] = memory_usage['max']
results[p][f'A={num_workers}'] = memory_usage['avg']
results[p][f'B={num_workers}'] = memory_usage['min']
print(f'{p}M={num_workers} {elapsed} {memory_usage}')
return results
