def test_batch_predict_with_items():
no_components = 2
ds = RandomDataset(density=1.0)
model = LightFM(no_components=no_components)
model.fit_partial(ds.train,
user_features=ds.user_features,
item_features=ds.item_features)
model.batch_setup(item_chunks={0: ds.item_ids},
user_features=ds.user_features,
item_features=ds.item_features)
n_items = 10
item_ids = np.random.choice(ds.item_ids, n_items)
for uid in range(ds.no_users):
original_scores = model.predict(
np.repeat(uid, n_items),
item_ids=item_ids,
user_features=ds.user_features,
item_features=ds.item_features,
)
# Check scores
_, batch_predicted_scores = model.predict_for_user(user_id=uid,
item_ids=item_ids,
top_k=0)
assert_array_almost_equal(original_scores, batch_predicted_scores)
# Check ids
original_ids = item_ids[np.argsort(-original_scores)[:5]]
batch_ids, _ = model.predict_for_user(user_id=uid,
item_ids=item_ids,
top_k=5)
assert_array_equal(original_ids, batch_ids)
def test_predict_for_user_with_items():
no_components = 2
ds = RandomDataset(no_items=5, no_users=2, density=1.)
model = LightFM(no_components=no_components)
model.fit_partial(ds.train,
user_features=ds.user_features,
item_features=ds.item_features)
inference._batch_cleanup()
with pytest.raises(EnvironmentError):
model.predict_for_user(user_id=0, top_k=2, item_ids=np.arange(2))
model.batch_setup(
item_chunks={0: ds.item_ids},
user_features=ds.user_features,
item_features=ds.item_features,
)
for user_id in range(ds.no_users):
scores = model.predict_for_user(
user_id=user_id,
top_k=2,
item_ids=np.arange(2),
)
assert len(scores) == 2
def test_matrix_types():
no_users, no_items = 10, 100
no_features = 20
for mattype in mattypes:
for dtype in dtypes:
train = mattype((no_users, no_items), dtype=dtype)
user_features = mattype((no_users, no_features), dtype=dtype)
item_features = mattype((no_items, no_features), dtype=dtype)
model = LightFM()
model.fit_partial(train,
user_features=user_features,
item_features=item_features)
model.predict(
np.random.randint(0, no_users, 10).astype(np.int32),
np.random.randint(0, no_items, 10).astype(np.int32),
user_features=user_features,
item_features=item_features,
)
model.predict_rank(train,
user_features=user_features,
item_features=item_features)
def test_predict():
no_users, no_items = 10, 100
train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
model = LightFM()
model.fit_partial(train)
for uid in range(no_users):
scores_arr = model.predict(np.repeat(uid, no_items),
np.arange(no_items))
scores_int = model.predict(uid, np.arange(no_items))
assert np.allclose(scores_arr, scores_int)
def test_not_enough_features_fails():
no_users, no_items = 10, 100
no_features = 20
train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
user_features = sp.csr_matrix((no_users - 1, no_features), dtype=np.int32)
item_features = sp.csr_matrix((no_items - 1, no_features), dtype=np.int32)
model = LightFM()
with pytest.raises(Exception):
model.fit_partial(train,
user_features=user_features,
item_features=item_features)
def test_batch_predict_user_recs_per_user_wo_features():
no_components = 2
ds = RandomDataset()
model = LightFM(no_components=no_components)
model.fit_partial(ds.train)
for uid in range(ds.no_users):
rec_item_ids, rec_scores = model.predict_for_user(
user_id=uid,
top_k=5,
item_ids=ds.item_ids,
)
assert len(rec_scores) == 5
assert_array_almost_equal(rec_scores, -1 * np.sort(-1 * rec_scores))
def test_return_self():
no_users, no_items = 10, 100
train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
model = LightFM()
assert model.fit_partial(train) is model
assert model.fit(train) is model
def test_batch_predict():
no_components = 2
ds = RandomDataset(density=1.0)
model = LightFM(no_components=no_components)
model.fit_partial(ds.train,
user_features=ds.user_features,
item_features=ds.item_features)
model.batch_setup(
item_chunks={0: ds.item_ids},
user_features=ds.user_features,
item_features=ds.item_features,
)
user_repr = inference._user_repr
item_repr = inference._item_repr
assert np.sum(user_repr)
assert user_repr.shape == (ds.no_users, no_components)
assert np.sum(item_repr)
assert item_repr.shape == (no_components, ds.no_items)
zeros = 0
for uid in range(ds.no_users):
original_scores = model.predict(
np.repeat(uid, ds.no_items),
np.arange(ds.no_items),
user_features=ds.user_features,
item_features=ds.item_features,
)
# Check scores
_, batch_predicted_scores = model.predict_for_user(
user_id=uid, top_k=0, item_ids=ds.item_ids)
assert_array_almost_equal(original_scores, batch_predicted_scores)
# Check ids
original_ids = np.argsort(-original_scores)[:5]
batch_ids, _ = model.predict_for_user(user_id=uid,
top_k=5,
item_ids=ds.item_ids)
assert np.array_equal(original_ids, batch_ids)
if np.sum(batch_predicted_scores) == 0:
zeros += 1
assert zeros < ds.no_users, 'predictions seems to be all zeros'
def test_predict_ranks():
no_users, no_items = 10, 100
train = sp.rand(no_users, no_items, format='csr', random_state=42)
model = LightFM()
model.fit_partial(train)
# Compute ranks for all items
rank_input = sp.csr_matrix(np.ones((no_users, no_items)))
ranks = model.predict_rank(rank_input, num_threads=2).todense()
assert np.all(ranks.min(axis=1) == 0)
assert np.all(ranks.max(axis=1) == no_items - 1)
for row in range(no_users):
assert np.all(np.sort(ranks[row]) == np.arange(no_items))
# Train set exclusions. All ranks should be zero
# if train interactions is dense.
ranks = model.predict_rank(rank_input,
train_interactions=rank_input).todense()
assert np.all(ranks == 0)
# Max rank should be num_items - 1 - number of positives
# in train in that row
ranks = model.predict_rank(rank_input, train_interactions=train).todense()
assert np.all(
np.squeeze(np.array(ranks.max(axis=1))) == no_items - 1 -
np.squeeze(np.array(train.getnnz(axis=1))))
# Make sure ranks are computed pessimistically when
# there are ties (that is, equal predictions for every
# item will assign maximum rank to each).
model.user_embeddings = np.zeros_like(model.user_embeddings)
model.item_embeddings = np.zeros_like(model.item_embeddings)
model.user_biases = np.zeros_like(model.user_biases)
model.item_biases = np.zeros_like(model.item_biases)
ranks = model.predict_rank(rank_input, num_threads=2).todense()
assert np.all(ranks.min(axis=1) == 99)
assert np.all(ranks.max(axis=1) == 99)
# Wrong input dimensions
with pytest.raises(ValueError):
model.predict_rank(sp.csr_matrix((5, 5)), num_threads=2)
def test_full_batch_predict_wo_features():
no_components = 2
top_k = 5
ds = RandomDataset(density=1.0)
model = LightFM(no_components=no_components)
model.fit_partial(ds.train)
user_ids = [0, 1, 2]
# Single process
model.batch_setup({0: ds.item_ids})
recoms = model.batch_predict(
user_ids=user_ids,
chunk_id=0,
top_k=top_k,
)
for user_id in user_ids:
assert user_id in recoms
assert len(recoms[user_id][0]) == top_k
def test_full_batch_predict():
no_components = 2
top_k = 5
ds = RandomDataset()
model = LightFM(no_components=no_components)
model.fit_partial(ds.train,
user_features=ds.user_features,
item_features=ds.item_features)
user_ids = [0, 1, 2]
chunks = {0: ds.item_ids}
# Single process
model.batch_setup(item_chunks=chunks,
user_features=ds.user_features,
item_features=ds.item_features,
n_process=1)
recoms = model.batch_predict(
user_ids=user_ids,
chunk_id=0,
top_k=top_k,
)
for user_id in user_ids:
assert user_id in recoms
assert len(recoms[user_id][0]) == top_k
initial_recoms = recoms
model.batch_cleanup()
model.batch_setup(item_chunks=chunks,
user_features=ds.user_features,
item_features=ds.item_features,
n_process=2)
# Multiple processes
recoms = model.batch_predict(
user_ids=user_ids,
chunk_id=0,
top_k=top_k,
)
for user_id in user_ids:
assert user_id in recoms
assert_array_almost_equal(recoms[user_id], initial_recoms[user_id])
def test_feature_inference_fails():
# On predict if we try to use feature inference and supply
# higher ids than the number of features that were supplied to fit
# we should complain
no_users, no_items = 10, 100
no_features = 20
train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
user_features = sp.csr_matrix((no_users, no_features), dtype=np.int32)
item_features = sp.csr_matrix((no_items, no_features), dtype=np.int32)
model = LightFM()
model.fit_partial(train,
user_features=user_features,
item_features=item_features)
with pytest.raises(ValueError):
model.predict(np.array([no_features], dtype=np.int32),
np.array([no_features], dtype=np.int32))
def test_empty_matrix():
no_users, no_items = 10, 100
train = sp.coo_matrix((no_users, no_items), dtype=np.int32)
model = LightFM()
model.fit_partial(train)