def test_matrix_types():
mattypes = (sp.coo_matrix, sp.lil_matrix, sp.csr_matrix, sp.csc_matrix)
dtypes = (np.int32, np.int64, np.float32, np.float64)
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)
weights = train.tocoo()
user_features = mattype((no_users, no_features), dtype=dtype)
item_features = mattype((no_items, no_features), dtype=dtype)
model = LightFM()
model.fit_partial(
train,
sample_weight=weights,
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_matrix_types():
mattypes = (sp.coo_matrix, sp.lil_matrix, sp.csr_matrix, sp.csc_matrix)
dtypes = (np.int32, np.int64, np.float32, np.float64)
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)
weights = train.tocoo()
user_features = mattype((no_users, no_features), dtype=dtype)
item_features = mattype((no_items, no_features), dtype=dtype)
model = LightFM()
model.fit_partial(
train,
sample_weight=weights,
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_user_supplied_features_accuracy():
model = LightFM(random_state=SEED)
model.fit_partial(
train,
user_features=train_user_features,
item_features=train_item_features,
epochs=10,
)
train_predictions = model.predict(
train.row,
train.col,
user_features=train_user_features,
item_features=train_item_features,
)
test_predictions = model.predict(
test.row,
test.col,
user_features=test_user_features,
item_features=test_item_features,
)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_movielens_accuracy():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_movielens_accuracy():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_hogwild_accuracy():
# Should get comparable accuracy with 2 threads
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10, num_threads=2)
train_predictions = model.predict(train.row, train.col, num_threads=2)
test_predictions = model.predict(test.row, test.col, num_threads=2)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_hogwild_accuracy():
# Should get comparable accuracy with 2 threads
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10, num_threads=2)
train_predictions = model.predict(train.row, train.col, num_threads=2)
test_predictions = model.predict(test.row, test.col, num_threads=2)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_movielens_accuracy_pickle():
model = LightFM(random_state=SEED)
model.fit(train, epochs=10)
model = pickle.loads(pickle.dumps(model))
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_movielens_accuracy_pickle():
model = LightFM(random_state=SEED)
model.fit(train, epochs=10)
model = pickle.loads(pickle.dumps(model))
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_regularization():
# Let's regularize
model = LightFM(
no_components=50, item_alpha=0.0001, user_alpha=0.0001, random_state=SEED
)
model.fit_partial(train, epochs=30)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.80
assert roc_auc_score(test.data, test_predictions) > 0.75
def test_overfitting():
# Let's massivly overfit
model = LightFM(no_components=50, random_state=SEED)
model.fit_partial(train, epochs=30)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
overfit_train = roc_auc_score(train.data, train_predictions)
overfit_test = roc_auc_score(test.data, test_predictions)
assert overfit_train > 0.99
assert overfit_test < 0.75
def test_zeros_negative_accuracy():
# Should get the same accuracy when zeros are used to
# denote negative interactions
train.data[train.data == -1] = 0
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_zeros_negative_accuracy():
# Should get the same accuracy when zeros are used to
# denote negative interactions
train.data[train.data == -1] = 0
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
def test_overfitting():
# Let's massivly overfit
model = LightFM(no_components=50, random_state=SEED)
model.fit_partial(train, epochs=30)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
overfit_train = roc_auc_score(train.data, train_predictions)
overfit_test = roc_auc_score(test.data, test_predictions)
assert overfit_train > 0.99
assert overfit_test < 0.75
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_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_predict_not_fitted():
model = LightFM()
with pytest.raises(ValueError):
model.predict(np.arange(10), np.arange(10))
with pytest.raises(ValueError):
model.predict_rank(1)
with pytest.raises(ValueError):
model.get_user_representations()
with pytest.raises(ValueError):
model.get_item_representations()
def test_predict_not_fitted():
model = LightFM()
with pytest.raises(ValueError):
model.predict(np.arange(10), np.arange(10))
with pytest.raises(ValueError):
model.predict_rank(1)
with pytest.raises(ValueError):
model.get_user_representations()
with pytest.raises(ValueError):
model.get_item_representations()
def test_get_representations():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
num_users, num_items = train.shape
for (item_features, user_features) in (
(None, None),
(
(sp.identity(num_items) + sp.random(num_items, num_items)),
(sp.identity(num_users) + sp.random(num_users, num_users)),
),
):
test_predictions = model.predict(test.row,
test.col,
user_features=user_features,
item_features=item_features)
item_biases, item_latent = model.get_item_representations(
item_features)
user_biases, user_latent = model.get_user_representations(
user_features)
assert item_latent.dtype == np.float32
assert user_latent.dtype == np.float32
predictions = (
(user_latent[test.row] * item_latent[test.col]).sum(axis=1) +
user_biases[test.row] + item_biases[test.col])
assert np.allclose(test_predictions, predictions, atol=0.000001)
def test_movielens_genre_accuracy():
item_features = fetch_movielens(indicator_features=False, genre_features=True)[
"item_features"
]
assert item_features.shape[1] < item_features.shape[0]
model = LightFM(random_state=SEED)
model.fit_partial(train, item_features=item_features, epochs=10)
train_predictions = model.predict(train.row, train.col, item_features=item_features)
test_predictions = model.predict(test.row, test.col, item_features=item_features)
assert roc_auc_score(train.data, train_predictions) > 0.75
assert roc_auc_score(test.data, test_predictions) > 0.69
def test_get_representations():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
num_users, num_items = train.shape
for (item_features, user_features) in (
(None, None),
(
(sp.identity(num_items) + sp.random(num_items, num_items)),
(sp.identity(num_users) + sp.random(num_users, num_users)),
),
):
test_predictions = model.predict(
test.row, test.col, user_features=user_features, item_features=item_features
)
item_biases, item_latent = model.get_item_representations(item_features)
user_biases, user_latent = model.get_user_representations(user_features)
assert item_latent.dtype == np.float32
assert user_latent.dtype == np.float32
predictions = (
(user_latent[test.row] * item_latent[test.col]).sum(axis=1)
+ user_biases[test.row]
+ item_biases[test.col]
)
assert np.allclose(test_predictions, predictions, atol=0.000001)
def test_zero_weights_accuracy():
# When very small weights are used
# accuracy should be no better than
# random.
weights = train.copy()
weights.data = np.zeros(train.getnnz(), dtype=np.float32)
for loss in ("logistic", "bpr", "warp"):
model = LightFM(loss=loss, random_state=SEED)
model.fit_partial(train, sample_weight=weights, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert 0.45 < roc_auc_score(train.data, train_predictions) < 0.55
assert 0.45 < roc_auc_score(test.data, test_predictions) < 0.55
def test_zero_weights_accuracy():
# When very small weights are used
# accuracy should be no better than
# random.
weights = train.copy()
weights.data = np.zeros(train.getnnz(), dtype=np.float32)
for loss in ("logistic", "bpr", "warp"):
model = LightFM(loss=loss, random_state=SEED)
model.fit_partial(train, sample_weight=weights, epochs=10)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert 0.45 < roc_auc_score(train.data, train_predictions) < 0.55
assert 0.45 < roc_auc_score(test.data, test_predictions) < 0.55
def test_movielens_both_accuracy():
"""
Accuracy with both genre metadata and item-specific
features shoul be no worse than with just item-specific
features (though more training may be necessary).
"""
item_features = fetch_movielens(indicator_features=True, genre_features=True)[
"item_features"
]
model = LightFM(random_state=SEED)
model.fit_partial(train, item_features=item_features, epochs=15)
train_predictions = model.predict(train.row, train.col, item_features=item_features)
test_predictions = model.predict(test.row, test.col, item_features=item_features)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.75
def test_movielens_excessive_regularization():
for loss in ("logistic", "warp", "bpr", "warp-kos"):
# Should perform poorly with high regularization.
# Check that regularization does not accumulate
# until it reaches infinity.
model = LightFM(
no_components=10,
item_alpha=1.0,
user_alpha=1.0,
loss=loss,
random_state=SEED,
)
model.fit_partial(train, epochs=10, num_threads=4)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) < 0.65
assert roc_auc_score(test.data, test_predictions) < 0.65
def test_movielens_excessive_regularization():
for loss in ("logistic", "warp", "bpr", "warp-kos"):
# Should perform poorly with high regularization.
# Check that regularization does not accumulate
# until it reaches infinity.
model = LightFM(
no_components=10,
item_alpha=1.0,
user_alpha=1.0,
loss=loss,
random_state=SEED,
)
model.fit_partial(train, epochs=10, num_threads=4)
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
assert roc_auc_score(train.data, train_predictions) < 0.65
assert roc_auc_score(test.data, test_predictions) < 0.65
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_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_input_dtypes():
dtypes = (np.int32, np.int64, np.float32, np.float64)
no_users, no_items = (10, 100)
no_features = 20
for dtype in dtypes:
train = sp.coo_matrix((no_users, no_items), dtype=dtype)
user_features = sp.coo_matrix((no_users, no_features), dtype=dtype)
item_features = sp.coo_matrix((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,
)
def test_input_dtypes():
dtypes = (np.int32, np.int64, np.float32, np.float64)
no_users, no_items = (10, 100)
no_features = 20
for dtype in dtypes:
train = sp.coo_matrix((no_users, no_items), dtype=dtype)
user_features = sp.coo_matrix((no_users, no_features), dtype=dtype)
item_features = sp.coo_matrix((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,
)
def test_overflow_predict():
no_users, no_items = (1000, 1000)
train = sp.rand(no_users, no_items, format="csr", random_state=42)
model = LightFM(loss="warp")
model.fit(train)
with pytest.raises((ValueError, OverflowError)):
print(
model.predict(
1231241241231241414,
np.arange(no_items),
user_features=sp.identity(no_users),
))
def test_overflow_predict():
no_users, no_items = (1000, 1000)
train = sp.rand(no_users, no_items, format="csr", random_state=42)
model = LightFM(loss="warp")
model.fit(train)
with pytest.raises((ValueError, OverflowError)):
print(
model.predict(
1231241241231241414,
np.arange(no_items),
user_features=sp.identity(no_users),
)
)
def test_user_supplied_features_accuracy():
model = LightFM(random_state=SEED)
model.fit_partial(
train,
user_features=train_user_features,
item_features=train_item_features,
epochs=10,
)
train_predictions = model.predict(
train.row,
train.col,
user_features=train_user_features,
item_features=train_item_features,
)
test_predictions = model.predict(
test.row,
test.col,
user_features=test_user_features,
item_features=test_item_features,
)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.76
