def test_model_performance(self, model):
train_predictions = model.predict(train.row, train.col)
test_predictions = model.predict(test.row, test.col)
trn_pred = roc_auc_score(train.data, train_predictions)
tst_pred = roc_auc_score(test.data, test_predictions)
# Performance is same as before when loaded from disk
loaded_model = LightFM.load(TEST_FILE_PATH)
train_predictions = loaded_model.predict(train.row, train.col)
test_predictions = loaded_model.predict(test.row, test.col)
# Use approximately equal because floating point math may make our summation slightly different.
assert roc_auc_score(train.data, train_predictions) == pytest.approx(
trn_pred, 0.0001)
assert roc_auc_score(test.data, test_predictions) == pytest.approx(
tst_pred, 0.0001)
def test_all_loaded_weights_numpy_arrays(self, model):
# Load a model onto an uninstanciated object
loaded_model = LightFM.load(TEST_FILE_PATH)
for weight_name in model_weights:
assert callable(getattr(loaded_model, weight_name).any)
