Exemple #1
0
def learnModel(n_users, m_items, regU, regI, regJ,
               learningRate, R, features, epochs, numberOfIterations):
    """Learns a model with the BPRMF algorithm, actually just calls mf.

        n_users             --  The highest internal assigned User ID
        m_items             --  The highest internal assigned Item ID
        regU                --  Regularization for the user vector
        regI                --  Regularization for the positive item
        regJ                --  Regularization for the negative item
        learningRate        --  The learning rate
        R                   --  A dict of the form UserID -> (ItemId, Rating)
        features            --  Number of features of the items and users
        epochs              --  Number of epochs the model should be learned
        numberOfIterations  --  Number of iterations in each epoch

    Returns:
        W   --  User Features
        H   --  Item Features

    Based on:
    Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and
    Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from
    implicit feedback. In Proceedings of the Twenty-Fifth Conference on
    Uncertainty in Artificial Intelligence (UAI '09).
    AUAI Press, Arlington, Virginia, United States, 452-461.
    """
    import mf
    return mf.learnModel(n_users, m_items, regU, regI, regJ, learningRate, R,
                         features, epochs, numberOfIterations, logLoss,
                         dLogLoss)
Exemple #2
0
def learnMF(r, trainingDict, reg, ler, lossF, dlossF):
    W, H = mf.learnModel(
        r.getMaxUid(), r.getMaxIid(),
        reg, reg, reg, ler, trainingDict,
        features, EPOCHS,
        r.numberOfTransactions,
        lossF, dlossF)
#    np.savez_compressed(
#        "BPRMFModelFile", W=W, H=H)
    return W, H
Exemple #3
0
def learnModel(n_users, m_items, regU, regI, regJ,
               learningRate, R, features, epochs, numberOfIterations):
    """Learns a model with the RankMFX algorithm, actually just calls mf.

        n_users             --  The highest internal assigned User ID
        m_items             --  The highest internal assigned Item ID
        regU                --  Regularization for the user vector
        regI                --  Regularization for the positive item
        regJ                --  Regularization for the negative item
        learningRate        --  The learning rate
        R                   --  A dict of the form UserID -> (ItemId, Rating)
        features            --  Number of features of the items and users
        epochs              --  Number of epochs the model should be learned
        numberOfIterations  --  Number of iterations in each epoch

    Returns:
        W   --  User Features
        H   --  Item Features
    """
    import mf
    return mf.learnModel(n_users, m_items, regU, regI, regJ, learningRate, R,
                         features, epochs, numberOfIterations, hingeLoss,
                         dHingeLoss)