def test_implicit_bpr_train_rec():
algo = BPR(25)
ratings = lktu.ml_pandas.renamed.ratings
algo.fit(ratings)
recs = algo.recommend(100, n=20)
assert len(recs) == 20
def test_implicit_bpr_train_rec():
algo = BPR(25)
assert algo.factors == 25
ratings = lktu.ml_pandas.renamed.ratings
algo.fit(ratings)
recs = algo.recommend(100, n=20)
assert len(recs) == 20
_log.info('serializing implicit model')
mod = pickle.dumps(algo)
_log.info('serialized to %d bytes')
a2 = pickle.loads(mod)
r2 = a2.recommend(100, n=20)
assert len(r2) == 20
assert all(r2 == recs)
def test_implicit_bpr_train_rec():
algo = BPR(25, use_gpu=False)
assert algo.factors == 25
ratings = lktu.ml_test.ratings
algo.fit(ratings)
recs = algo.recommend(100, n=20)
assert len(recs) == 20
preds = algo.predict_for_user(100, [20, 30, 23148010])
assert all(preds.index == [20, 30, 23148010])
assert all(preds.isna() == [False, False, True])
_log.info('serializing implicit model')
mod = pickle.dumps(algo)
_log.info('serialized to %d bytes')
a2 = pickle.loads(mod)
r2 = a2.recommend(100, n=20)
assert len(r2) == 20
assert all(r2 == recs)