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_recall_at_k():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
model.fit_partial(test)
for k in (10, 5, 1):
# Without omitting train interactions
recall = evaluation.recall_at_k(model, test, k=k)
expected_mean_recall = _recall_at_k(model, test, k)
assert np.allclose(recall.mean(), expected_mean_recall)
assert len(recall) == (test.getnnz(axis=1) > 0).sum()
assert (len(evaluation.recall_at_k(
model, train, preserve_rows=True)) == test.shape[0])
# With omitting train interactions
recall = evaluation.recall_at_k(model,
test,
k=k,
train_interactions=train)
expected_mean_recall = _recall_at_k(model, test, k, train=train)
assert np.allclose(recall.mean(), expected_mean_recall)
def test_precision_at_k():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
# We want a high precision to catch the k=1 case
model.fit_partial(test)
for k in (10, 5, 1):
# Without omitting train interactions
precision = evaluation.precision_at_k(model, test, k=k)
expected_mean_precision = _precision_at_k(model, test, k)
assert np.allclose(precision.mean(), expected_mean_precision)
assert len(precision) == (test.getnnz(axis=1) > 0).sum()
assert (len(evaluation.precision_at_k(
model, train, preserve_rows=True)) == test.shape[0])
# With omitting train interactions
precision = evaluation.precision_at_k(model,
test,
k=k,
train_interactions=train)
expected_mean_precision = _precision_at_k(model, test, k, train=train)
assert np.allclose(precision.mean(), expected_mean_precision)
def test_recall_at_k():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
model.fit_partial(test)
for k in (10, 5, 1):
# Without omitting train interactions
recall = evaluation.recall_at_k(model, test, k=k)
expected_mean_recall = _recall_at_k(model, test, k)
assert np.allclose(recall.mean(), expected_mean_recall)
assert len(recall) == (test.getnnz(axis=1) > 0).sum()
assert (
len(evaluation.recall_at_k(model, train, preserve_rows=True))
== test.shape[0]
)
# With omitting train interactions
recall = evaluation.recall_at_k(model, test, k=k, train_interactions=train)
expected_mean_recall = _recall_at_k(model, test, k, train=train)
assert np.allclose(recall.mean(), expected_mean_recall)
def test_auc_score():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
model.fit_partial(train)
auc = evaluation.auc_score(model, test, num_threads=2)
expected_auc = np.array(_auc(model, test))
assert auc.shape == expected_auc.shape
assert np.abs(auc.mean() - expected_auc.mean()) < 0.01
assert len(auc) == (test.getnnz(axis=1) > 0).sum()
assert len(evaluation.auc_score(model, train,
preserve_rows=True)) == test.shape[0]
# With omitting train interactions
auc = evaluation.auc_score(model,
test,
train_interactions=train,
num_threads=2)
expected_auc = np.array(_auc(model, test, train))
assert np.abs(auc.mean() - expected_auc.mean()) < 0.01
def test_precision_at_k():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
# We want a high precision to catch the k=1 case
model.fit_partial(test)
for k in (10, 5, 1):
# Without omitting train interactions
precision = evaluation.precision_at_k(model, test, k=k)
expected_mean_precision = _precision_at_k(model, test, k)
assert np.allclose(precision.mean(), expected_mean_precision)
assert len(precision) == (test.getnnz(axis=1) > 0).sum()
assert (
len(evaluation.precision_at_k(model, train, preserve_rows=True))
== test.shape[0]
)
# With omitting train interactions
precision = evaluation.precision_at_k(
model, test, k=k, train_interactions=train
)
expected_mean_precision = _precision_at_k(model, test, k, train=train)
assert np.allclose(precision.mean(), expected_mean_precision)
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_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_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_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_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)
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)
def test_intersections_check():
no_users, no_items = (10, 100)
train, test = _generate_data(no_users, no_items)
model = LightFM(loss="bpr")
model.fit_partial(train)
# check error is raised when train and test have interactions in common
with pytest.raises(ValueError):
evaluation.auc_score(
model, train, train_interactions=train, check_intersections=True
)
with pytest.raises(ValueError):
evaluation.recall_at_k(
model, train, train_interactions=train, check_intersections=True
)
with pytest.raises(ValueError):
evaluation.precision_at_k(
model, train, train_interactions=train, check_intersections=True
)
with pytest.raises(ValueError):
evaluation.reciprocal_rank(
model, train, train_interactions=train, check_intersections=True
)
# check no errors raised when train and test have no interactions in common
evaluation.auc_score(
model, test, train_interactions=train, check_intersections=True
)
evaluation.recall_at_k(
model, test, train_interactions=train, check_intersections=True
)
evaluation.precision_at_k(
model, test, train_interactions=train, check_intersections=True
)
evaluation.reciprocal_rank(
model, test, train_interactions=train, check_intersections=True
)
# check no error is raised when there are intersections but flag is False
evaluation.auc_score(
model, train, train_interactions=train, check_intersections=False
)
evaluation.recall_at_k(
model, train, train_interactions=train, check_intersections=False
)
evaluation.precision_at_k(
model, train, train_interactions=train, check_intersections=False
)
evaluation.reciprocal_rank(
model, train, train_interactions=train, check_intersections=False
)
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_warp_stability():
learning_rates = (0.05, 0.1, 0.5)
for lrate in learning_rates:
model = LightFM(learning_rate=lrate, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=10)
assert not np.isnan(model.user_embeddings).any()
assert not np.isnan(model.item_embeddings).any()
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_warp_stability():
learning_rates = (0.05, 0.1, 0.5)
for lrate in learning_rates:
model = LightFM(learning_rate=lrate, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=10)
assert not np.isnan(model.user_embeddings).any()
assert not np.isnan(model.item_embeddings).any()
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_random_state_fixing():
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=2)
model_2 = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model_2.fit_partial(train, epochs=2)
assert np.all(model.user_embeddings == model_2.user_embeddings)
assert np.all(model.item_embeddings == model_2.item_embeddings)
def test_random_state_fixing():
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=2)
model_2 = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model_2.fit_partial(train, epochs=2)
assert np.all(model.user_embeddings == model_2.user_embeddings)
assert np.all(model.item_embeddings == model_2.item_embeddings)
def test_logistic_precision():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, train, test)
assert train_precision > 0.3
assert test_precision > 0.03
assert full_train_auc > 0.79
assert full_test_auc > 0.73
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_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_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_random_state_advanced():
# Check that using the random state
# to seed rand_r in Cython advances
# the random generator state.
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=1)
rng_state = model.random_state.get_state()[1].copy()
model.fit_partial(train, epochs=1)
assert not np.all(rng_state == model.random_state.get_state()[1])
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_logistic_precision():
model = LightFM(random_state=SEED)
model.fit_partial(train, epochs=10)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, train, test
)
assert train_precision > 0.3
assert test_precision > 0.03
assert full_train_auc > 0.79
assert full_test_auc > 0.73
def test_bpr_precision():
model = LightFM(learning_rate=0.05, loss="bpr", random_state=SEED)
model.fit_partial(train, epochs=10)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, train, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.91
assert full_test_auc > 0.87
def test_warp_precision_multithreaded():
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=10, num_threads=4)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, train, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.9
assert full_test_auc > 0.9
def test_random_state_advanced():
# Check that using the random state
# to seed rand_r in Cython advances
# the random generator state.
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=1)
rng_state = model.random_state.get_state()[1].copy()
model.fit_partial(train, epochs=1)
assert not np.all(rng_state == model.random_state.get_state()[1])
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_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_warp_precision_multithreaded():
model = LightFM(learning_rate=0.05, loss="warp", random_state=SEED)
model.fit_partial(train, epochs=10, num_threads=4)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, train, test
)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.9
assert full_test_auc > 0.9
def test_bpr_precision():
model = LightFM(learning_rate=0.05, loss="bpr", random_state=SEED)
model.fit_partial(train, epochs=10)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, train, test
)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.91
assert full_test_auc > 0.87
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_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_warp_precision_max_sampled():
model = LightFM(learning_rate=0.05, max_sampled=1, loss="warp", random_state=SEED)
# This is equivalent to a no-op pass
# over the training data
model.max_sampled = 0
model.fit_partial(train, epochs=1)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, train, test
)
# The AUC should be no better than random
assert full_train_auc < 0.55
assert full_test_auc < 0.55
def test_bpr_precision_high_interaction_values():
model = LightFM(learning_rate=0.05, loss="bpr", random_state=SEED)
_train = train.copy()
_train.data = _train.data * 5
model.fit_partial(_train, epochs=10)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, _train, test)
assert train_precision > 0.31
assert test_precision > 0.04
assert full_train_auc > 0.86
assert full_test_auc > 0.84
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_training_schedules():
model = LightFM(no_components=10,
learning_schedule="adagrad",
random_state=SEED)
model.fit_partial(train, epochs=0)
assert (model.item_embedding_gradients == 1).all()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients == 1).all()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients == 1).all()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients == 1).all()
assert (model.user_bias_momentum == 0).all()
model.fit_partial(train, epochs=1)
assert (model.item_embedding_gradients > 1).any()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients > 1).any()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients > 1).any()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients > 1).any()
assert (model.user_bias_momentum == 0).all()
model = LightFM(no_components=10,
learning_schedule="adadelta",
random_state=SEED)
model.fit_partial(train, epochs=0)
assert (model.item_embedding_gradients == 0).all()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients == 0).all()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients == 0).all()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients == 0).all()
assert (model.user_bias_momentum == 0).all()
model.fit_partial(train, epochs=1)
assert (model.item_embedding_gradients > 0).any()
assert (model.item_embedding_momentum > 0).any()
assert (model.item_bias_gradients > 0).any()
assert (model.item_bias_momentum > 0).any()
assert (model.user_embedding_gradients > 0).any()
assert (model.user_embedding_momentum > 0).any()
assert (model.user_bias_gradients > 0).any()
assert (model.user_bias_momentum > 0).any()
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_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_bpr_precision_high_interaction_values():
model = LightFM(learning_rate=0.05, loss="bpr", random_state=SEED)
_train = train.copy()
_train.data = _train.data * 5
model.fit_partial(_train, epochs=10)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, _train, test
)
assert train_precision > 0.31
assert test_precision > 0.04
assert full_train_auc > 0.86
assert full_test_auc > 0.84
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_warp_precision_adadelta_multithreaded():
model = LightFM(
learning_schedule="adadelta",
rho=0.95,
epsilon=0.000001,
loss="warp",
random_state=SEED,
)
model.fit_partial(train, epochs=10, num_threads=4)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, train, test)
assert train_precision > 0.45
assert test_precision > 0.07
assert full_train_auc > 0.9
assert full_test_auc > 0.9
def test_movielens_accuracy_sample_weights():
# Scaling weights down and learning rate up
# by the same amount should result in
# roughly the same accuracy
scale = 0.5
weights = train.copy()
weights.data = np.ones(train.getnnz(), dtype=np.float32) * scale
for (loss, exp_score) in (("logistic", 0.74), ("bpr", 0.84), ("warp",
0.89)):
model = LightFM(loss=loss, random_state=SEED)
model.learning_rate * 1.0 / scale
model.fit_partial(train, sample_weight=weights, epochs=10)
(train_precision, test_precision, full_train_auc,
full_test_auc) = _get_metrics(model, train, test)
assert full_train_auc > exp_score
def test_movielens_accuracy_sample_weights():
# Scaling weights down and learning rate up
# by the same amount should result in
# roughly the same accuracy
scale = 0.5
weights = train.copy()
weights.data = np.ones(train.getnnz(), dtype=np.float32) * scale
for (loss, exp_score) in (("logistic", 0.74), ("bpr", 0.84), ("warp", 0.89)):
model = LightFM(loss=loss, random_state=SEED)
model.learning_rate * 1.0 / scale
model.fit_partial(train, sample_weight=weights, epochs=10)
(train_precision, test_precision, full_train_auc, full_test_auc) = _get_metrics(
model, train, test
)
assert full_train_auc > exp_score
def test_training_schedules():
model = LightFM(no_components=10, learning_schedule="adagrad", random_state=SEED)
model.fit_partial(train, epochs=0)
assert (model.item_embedding_gradients == 1).all()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients == 1).all()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients == 1).all()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients == 1).all()
assert (model.user_bias_momentum == 0).all()
model.fit_partial(train, epochs=1)
assert (model.item_embedding_gradients > 1).any()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients > 1).any()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients > 1).any()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients > 1).any()
assert (model.user_bias_momentum == 0).all()
model = LightFM(no_components=10, learning_schedule="adadelta", random_state=SEED)
model.fit_partial(train, epochs=0)
assert (model.item_embedding_gradients == 0).all()
assert (model.item_embedding_momentum == 0).all()
assert (model.item_bias_gradients == 0).all()
assert (model.item_bias_momentum == 0).all()
assert (model.user_embedding_gradients == 0).all()
assert (model.user_embedding_momentum == 0).all()
assert (model.user_bias_gradients == 0).all()
assert (model.user_bias_momentum == 0).all()
model.fit_partial(train, epochs=1)
assert (model.item_embedding_gradients > 0).any()
assert (model.item_embedding_momentum > 0).any()
assert (model.item_bias_gradients > 0).any()
assert (model.item_bias_momentum > 0).any()
assert (model.user_embedding_gradients > 0).any()
assert (model.user_embedding_momentum > 0).any()
assert (model.user_bias_gradients > 0).any()
assert (model.user_bias_momentum > 0).any()
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_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_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
