Build and test model

model.py

def model():
    '''computes p@k and map@k evaluation metrics and saves model'''
    sparse_item_user = load_npz("sparse_item_user.npz")

    train, test = train_test_split(sparse_item_user, train_percentage=0.8)

    model = implicit.als.AlternatingLeastSquares(factors=100, 
        regularization=0.1, iterations=20, calculate_training_loss=False)
    model.fit(train)

    with open('model.sav', 'wb') as pickle_out:
        pickle.dump(model, pickle_out)

    train, test = train.T.tocsr(), test.T.tocsr()
    
    p_at_k = precision_at_k(model, train_user_items=train, 
        test_user_items=test, K=10)
    m_at_k = mean_average_precision_at_k(model, train, test, K=10)

    return p_at_k, m_at_k