def test_sklearn_api():
model = LightFM()
params = model.get_params()
model2 = LightFM(**params)
params2 = model2.get_params()
assert params == params2
model.set_params(**params)
params['invalid_param'] = 666
with pytest.raises(ValueError):
model.set_params(**params)
def test_sklearn_api():
model = LightFM()
params = model.get_params()
model2 = LightFM(**params)
params2 = model2.get_params()
assert params == params2
model.set_params(**params)
params['invalid_param'] = 666
with pytest.raises(ValueError):
model.set_params(**params)
# In[ ]:
alpha = 0.003
epochs = 50
num_components = 32
step = 5
# %%[markdown]
# # > Model
# In[ ]:
model_k10 = LightFM(no_components=num_components,
loss='warp',
k=10,
learning_schedule='adagrad',
user_alpha=alpha,
item_alpha=alpha)
# %%[markdown]
# # > Train
logger.log(str(model_k10.get_params()))
for epoch in tqdm(range(epochs)):
model_k10.fit_partial(train,
epochs=step,
user_features=user_identity,
item_features=item_identity,
num_threads=6,
verbose=True)
mean_precision = precision_at_k(model_k10, train, k=10).mean()
logger.log("Precision k10 : {}".format(mean_precision))
logger.save_model(model_k10,
session_log_path + "models/epoch_{}".format(epoch))
