snci/probabilistic-matrix-factorization-in-python

Probabilistic matrix factorization (PMF) in Python

Probabilistic matrix factorization (PMF) in Python

Parameters:

num_feat: Number of latent features,
epsilon: learning rate,
_lambda: L2 regularization,
momentum: momentum of the gradient,
maxepoch: Number of epoch before stop,
num_batches: Number of batches in each epoch (for SGD optimization),
batch_size: Number of training samples used in each batches (for SGD optimization)

Methods:

fit(train_tuple, val_tuple)

Fit the model with train_tuple and evaluate RMSE on both train and validation data.
Input tuple format: (userID, movieID, rating) # 0-index ID is recommended
Output: U and V matrices, RMSE Error on Train and Validation after each epoch.

predict(userID)

Predict rating of all movies for the given user.

set_params(parameter_dict)

Set parameters by providing a parameter dictionary.

Helper function:

def wrap_Parameters(num_feat, epsilon, _lambda, momentum, maxepoch, num_batches, batch_size):

return {"num_feat": num_feat, "epsilon":epsilon, "_lambda":_lambda, "momentum":momentum, "maxepoch":maxepoch, "num_batches":num_batches, "batch_size":batch_size}

Reference:

1. Mnih, A., & Salakhutdinov, R. (2007). Probabilistic matrix factorization. In Advances in neural information processing systems (pp. 1257-1264).
2. Salakhutdinov, R. Probabilistic matrix factorization in Matlab. http://www.cs.toronto.edu/~rsalakhu/code_BPMF/pmf.m.