search

amidst / toolbox

A Java Toolbox for Scalable Probabilistic Machine Learning
http://www.amidsttoolbox.com
Apache License 2.0
119 stars 35 forks source link

Feature to weigh samples differently #87

Open gowthamnatarajan opened 6 years ago

gowthamnatarajan commented 6 years ago

It would be useful to weight different samples differently. I have to under sample parts of the data because they occur too often. It will be good to just give them more weight rather than have the raw data in the file.

andresmasegosa commented 6 years ago

I agree this is something we also considered at some point. It would not be too difficult to code it for maximum likelihood. Do you dare to do it? :) I could help you out. ;)

gowthamnatarajan commented 6 years ago

I can do it for MLE. Can you help? I already made some changes to the sampler.

andresmasegosa commented 6 years ago

Yes, sure! Let me think a bit which would be a simple design. But a first question, do you want to provide specific weights for each instance as an extra attribute in your data set?

gowthamnatarajan commented 6 years ago

Yes, another column with the weights in the ARFF file might be good.

andresmasegosa commented 6 years ago

Hi,

I've been thinking how to do it. I detail my proposal

a) Modify Attributes class to include "WEIGHT" as a special attribute name like "TIME_ID" and "SEQUENCE_ID". When an attribute has this specific name, it will be recognized by AMIDST and will be used to weight the instances when learning.

b) Within "core" module, create a new java class in the learning parametric package (e.g. ParalelMLWeighted). This class should inherit from ParallelMaximulLikelihood. Overwrite the method "double updateModel(DataOnMemory batch)" to account for the weights of the instances. Weight of the instances can be accessed through the special methods created at point (a).

c) Within "core-dynamic" module, create a new java class in the learning parametric package (e.g. ParalelMLWeighted). This class should inherit from ParallelMaximulLikelihood and be based on its counterpart static version created at point b).

I recommend you performing independent pull-requests for each one of these steps.

gowthamnatarajan commented 6 years ago

Will work on this in 2 weeks when we have to scale up.