QUESTION:lightFM,predict()

Hi,Maciej! I would like to ask how predict() is calculated? I think it should be like this, like user 1 for item 1:

user_biases = model.get_user_representations()[0]
user_embeddings = model.get_user_representations()[1]
item_biases = model.get_item_representations()[0]
item_embeddings = model.get_item_representations()[1]
user1 = user_embeddings[0]
item1 = item_embeddings[0]
score = u1.dot(i1.T)
ub1 = user_biases[0]
ib1 = item_biases[0]
result = score + ub1 + ib1

However, the calculation result is inconsistent with predict(1,[1]), please answer the wrong place, thank you for your reply!

[FankLi]

Not all of the values match exactly. How could that be?

# find the best model
model_bpr_ult = model_bpr[np.argmax(bpr_map_test)]

# reformulate the interaction matrix. 
user_biases = model_bpr_ult.get_user_representations()[0]
user_embeddings = model_bpr_ult.get_user_representations()[1]
item_biases = model_bpr_ult.get_item_representations()[0]
item_embeddings = model_bpr_ult.get_item_representations()[1]
user1 = user_embeddings[0]
item1 = item_embeddings
score = np.matmul(user1, item1.transpose())
ub1 = user_biases[0]
ib1 = item_biases

# test on user_1
result = score + ub1 + ib1
predict = model_bpr_ult.predict(0, list(range(len(item1))))

Counter(np.argsort(result.round(3)) == np.argsort(predict.round(3)))

Counter({False: 340, True: 13201})

for example: There are 943’s user and 1682’2 item in movielens100k， model.predict（943,[1]） or model.predict（1,[1682]） will error. So, model.predict（942,[1]） is user943 to item0.

[maciejkula]

This passes, using your code from above, fixing variables u1 -> user1 and i1 to item1:

    import numpy as np
    from lightfm import LightFM, datasets
    data = datasets.fetch_movielens()

    model = LightFM(learning_rate=0.05, loss="bpr", random_state=SEED)
    model.fit_partial(data['train'], epochs=10)

    user_biases = model.get_user_representations()[0]
    user_embeddings = model.get_user_representations()[1]
    item_biases = model.get_item_representations()[0]
    item_embeddings = model.get_item_representations()[1]
    user1 = user_embeddings[1]
    item1 = item_embeddings[1]
    score = user1.dot(item1.T)
    ub1 = user_biases[1]
    ib1 = item_biases[1]

    result = score + ub1 + ib1
    predict = model.predict(1, [1])

    assert np.allclose(result, predict[0])