Bayesian Inference Tools in Python

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Deep Learning BayesPy
Overview

BayesPy

Bayesian Inference Tools in Python

Our goal is, given the discrete outcomes of events, estimate the distribution of categories. Using gradient descent we can estimate the parameters of a dirchlet prior from past data that can be combined as a conjugate prior with the multinomial distribution to better estimate the likelihood of seeing an event of a given type in the future.

Conjugate Prior Tools: The main file is ./findDirichletPrior - you pipe in your counts (given in test.csv as an example) and the maximum-likelihood dirichlet comes out.

Some things to try on your terminal: cat test.csv | ./findDirichletPrior.py -- This will find the priors for a test file

./flipCoins .7 1.2 | ./findDirichletPrior.py -- This will generate a data set on the fly using dirichlet parameters .7 1.2 (feel free to change those) -- findDirichletPrior should come up with a good estimate of those numbers using only the coin flips

cat oneDoublesided.csv | ./findDirichletPrior.py -- This is a sample of a case where findDirichletPrior won't give you a great result. This is because every -- coin in the input is fair except two coins: one is double sided heads, and the other tails. -- Dirichlet distributions cannot handle this trimodal data very well, but it'll end up giving a compromise solution

#Using the priors You can test the strength of your prior using the H parameter. Higher values for Beta will give lower probabilities.

python findDirichletPrior.py -H1,4,5 < /dev/null

gammaDistTools is not used. These functions will be used for a future gamma distribution estimations.

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Comments
  • Fixed some bugs?

    Fixed some bugs?

    I set the Beta and W to None when it's not passed in. That didn't seem to have an effect but mine as well make use of the if statements

    I found a few instances were the index variables felt wrong to me. I didn't review the math but they seemed off.

    Please review

    Thanks Adam

    opened by adamilardi 5
  • Add docs, iteration param and bug fix

    Add docs, iteration param and bug fix

    K should be K-1 since that will be last last element of the array. It only noticed it go wrong when you pass in bad input like [0] I added iterations to test out some of the -H param values

    opened by adamilardi 3
  • Logging changes

    Logging changes

    I used the logging module to print debug messages to stderr so you can pipe the final results to a file without parsing

    If you want I can remove the verbose flag also if you like this style.

    opened by adamilardi 0
  • Divide by 0 error

    Divide by 0 error

    I haven't looked into the root cause yet but here is the data that causes it. It would appear to be the columns with all 0s.

    0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0

    python findDirichletPrior.py -K 6

    Loading data all data loaded into memory time to load memory: 0.000438928604126 1 Loss: 4.75135269617 , Priors: [0.6, 0.0, 0.2, 0.0, 0.2, 0.0] , Gradient Size: 75.0 Traceback (most recent call last): File "findDirichletPrior.py", line 94, in priors = DME.findDirichletPriors(uMatrix, vVector, priors, verbose) File "/BayesPy/ConjugatePriorTools/dirichletMultinomialEstimation.py", line 155, in findDirichletPriors trialStep = predictStepUsingHessian(gradient, priors, uMatrix, vVector) File "/BayesPy/ConjugatePriorTools/dirichletMultinomialEstimation.py", line 111, in predictStepUsingHessian return getPredictedStep(totalHConst, totalHDiag, gradient) File "/BayesPy/ConjugatePriorTools/dirichletMultinomialEstimation.py", line 69, in getPredictedStep numSum += gradient[i] / hDiag[i] ZeroDivisionError: float division by zero

    opened by adamilardi 8
Owner
Max Sklar
Machine Learning Enthusiast, Foursquare Engineer, Former Talk Radio Host, Marsbot's Mechanic. @marsbot, @swarmingnow
Max Sklar
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