def test_cg_nan2(self):
# test out Nan appearing in CG code (from https://github.com/benfred/implicit/issues/106)
Ciu = random(m=100,
n=100,
density=0.0005,
format='coo',
dtype=np.float32,
random_state=42,
data_rvs=None).T.tocsr()
configs = [{
'use_native': True,
'use_gpu': False
}, {
'use_native': False,
'use_gpu': False
}]
if HAS_CUDA:
configs.append({'use_gpu': True})
for options in configs:
model = AlternatingLeastSquares(factors=32,
regularization=10,
iterations=10,
dtype=np.float32,
**options)
model.show_progress = False
model.fit(Ciu)
self.assertTrue(np.isfinite(model.item_factors).all())
self.assertTrue(np.isfinite(model.user_factors).all())
def test_cg_nan(self):
# test issue with CG code that was causing NaN values in output:
# https://github.com/benfred/implicit/issues/19#issuecomment-283164905
raw = [[0.0, 2.0, 1.5, 1.33333333, 1.25, 1.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 2.0, 1.5, 1.33333333, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 2.0, 1.5, 1.33333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 2.0, 1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 1.5, 1.33333333, 1.25, 1.2],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 1.5, 1.33333333, 1.25],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 1.5, 1.33333333],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 1.5],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
counts = csr_matrix(raw, dtype=np.float64)
for use_native in (True, False):
model = AlternatingLeastSquares(factors=3,
regularization=0.01,
dtype=np.float64,
use_native=use_native,
use_cg=True)
model.show_progress = False
model.fit(counts)
rows, cols = model.item_factors, model.user_factors
self.assertFalse(np.isnan(np.sum(cols)))
self.assertFalse(np.isnan(np.sum(rows)))
def test_explain(self):
counts = csr_matrix(
[[1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 0, 0], [1, 4, 1, 0, 7, 0],
[1, 1, 0, 0, 0, 0], [9, 0, 4, 1, 0, 1], [0, 1, 0, 0, 0, 1],
[0, 0, 2, 0, 1, 1]],
dtype=np.float64)
user_items = counts * 2
item_users = user_items.T
model = AlternatingLeastSquares(factors=4,
regularization=20,
use_native=False,
use_cg=False,
iterations=100)
np.random.seed(23)
model.show_progress = False
model.fit(user_items)
userid = 0
# Assert recommendation is the the same if we recompute user vectors
recs = model.recommend(userid, item_users, N=10)
recalculated_recs = model.recommend(userid,
item_users,
N=10,
recalculate_user=True)
for (item1, score1), (item2, score2) in zip(recs, recalculated_recs):
self.assertEqual(item1, item2)
self.assertAlmostEqual(score1, score2, 4)
# Assert explanation makes sense
top_rec, score = recalculated_recs[0]
score_explained, contributions, W = model.explain(userid,
item_users,
itemid=top_rec)
scores = [s for _, s in contributions]
items = [i for i, _ in contributions]
self.assertAlmostEqual(score, score_explained, 4)
self.assertAlmostEqual(score, sum(scores), 4)
self.assertEqual(scores, sorted(scores, reverse=True),
"Scores not in order")
self.assertEqual([0, 2, 3, 4], sorted(items), "Items not seen by user")
# Assert explanation with precomputed user weights is correct
top_score_explained, top_contributions, W = model.explain(
userid, item_users, itemid=top_rec, user_weights=W, N=2)
top_scores = [s for _, s in top_contributions]
top_items = [i for i, _ in top_contributions]
self.assertEqual(2, len(top_contributions))
self.assertAlmostEqual(score, top_score_explained, 4)
self.assertEqual(scores[:2], top_scores)
self.assertEqual(items[:2], top_items)
def test_factorize(self):
counts = csr_matrix([[1, 1, 0, 1, 0, 0],
[0, 1, 1, 1, 0, 0],
[1, 0, 1, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 1],
[0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1]], dtype=np.float64)
user_items = counts * 2
# try all 8 variants of native/python, cg/cholesky, and
# 64 vs 32 bit factors
options = [(dtype, cg, native, False)
for dtype in (np.float32, np.float64)
for cg in (False, True)
for native in (False, True)]
# also try out GPU support if available
if HAS_CUDA:
options.append((np.float32, False, False, True))
for dtype, use_cg, use_native, use_gpu in options:
try:
model = AlternatingLeastSquares(factors=6,
regularization=0,
dtype=dtype,
use_native=use_native,
use_cg=use_cg,
use_gpu=use_gpu)
np.random.seed(23)
model.show_progress = False
model.fit(user_items)
rows, cols = model.item_factors, model.user_factors
except Exception as e:
self.fail(msg="failed to factorize matrix. Error=%s"
" dtype=%s, cg=%s, native=%s gpu=%s"
% (e, dtype, use_cg, use_native, use_gpu))
reconstructed = rows.dot(cols.T)
for i in range(counts.shape[0]):
for j in range(counts.shape[1]):
self.assertAlmostEqual(counts[i, j], reconstructed[i, j],
delta=0.0001,
msg="failed to reconstruct row=%s, col=%s,"
" value=%.5f, dtype=%s, cg=%s, native=%s gpu=%s"
% (i, j, reconstructed[i, j], dtype, use_cg,
use_native, use_gpu))