def test_warp_precision_max_sampled():
model = LightFM(learning_rate=0.05, max_sampled=1, loss='warp')
# This is equivalent to a no-op pass
# over the training data
model.max_sampled = 0
model.fit_partial(train, epochs=1)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
# The AUC should be no better than random
assert full_train_auc < 0.55
assert full_test_auc < 0.55
def test_warp_precision_max_sampled():
model = LightFM(learning_rate=0.05,
max_sampled=1,
loss='warp')
# This is equivalent to a no-op pass
# over the training data
model.max_sampled = 0
model.fit_partial(train,
epochs=1)
full_train_auc = full_auc(model, train)
full_test_auc = full_auc(model, test)
# The AUC should be no better than random
assert full_train_auc < 0.55
assert full_test_auc < 0.55
