pymultifit submission

[syedalimohsinbukhari]

Submitting Author: Syed Ali Mohsin Bukhari (@syedalimohsinbukhari)
All current maintainers: (@syedalimohsinbukhari)
Package Name: pymultifit
One-Line Description of Package: A python library for fitting data with multiple models.
Repository Link: https://github.com/syedalimohsinbukhari/pyMultiFit
Version submitted: v1.0.3 v1.0.6
EiC: @coatless
Editor: @Batalex
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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pymultifit is built primarily to solve one problem, to fit multiple models (and mixture models) to a given data. Be it multiple Gaussian, Laplacian, or a mixture of such models, this package aims to deal with multi-model data fitting. The package also provides easy-to-use BaseDistribution and BaseFitter classes for respective user-defined functions.

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Apart from scipy, lmfit, and scikit-learn the general purpose scientific packages, there exists PyAutoFit, a Python-based probabilistic programming language built on Bayesian inference. Another notable library is Mixture-Models, which specializes in advanced optimization techniques for fitting various families of mixture models, including Gaussian mixture models and their variants. Both libraries are powerful tools for specific use cases, and I recently came to know about them during my search of existing options.

While these libraries offer robust solutions for hierarchical modeling (PyAutoFit) or a diverse array of pre-defined mixture models (Mixture-Models), pyMultiFit distinguishes itself through its simplicity of use and its focus on simplicity of use. Specifically, it is designed to provide a lightweight and user-friendly framework for fitting multi-model data, including custom mixture models (for example, gaussian + laplace + line). pymultifit also provides easy-to-use base classes that can be modified for any distribution/fitter purposes.

One of the more prominent features of pyMultiFit is the BaseFitter template class that provides custom fitting to any definable function with minimal boilerplate code. All the plotting and boundary functionalities are handled inside the template class so that the user can focus solely on running through multiple models quickly without thinking about how to manage multiple models of the same type or even of different types.

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[coatless]

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Editor comments

I think there's enough novelty behind the multifitter and distribution approaches discussed to move forward with a full review. (For clarity, scipy provides scipy.stats.fit() for a single DV or CV whereas multiple and different supports are given by pymultifit via MixedDataFitter) Moreover, there are ample tutorials and a solid case study of applying the package to solve real-world problems.

My only concern is regarding the accuracy benchmarks throwing RuntimeWarning notices. I think this is leaning into a discussion that appeared in the presubmission intake analysis regarding a re-implementation with numpy.

For example, with arcsine() and beta(a=5, b=80, loc=-3, scale=5) we have:

Beta

https://pymultifit.readthedocs.io/latest/benchmarks/_bm_accuracy.html#beta(a=5,-b=80,-loc=-3,-scale=5)

/home/sarl-ws-5/PycharmProjects/pyMultiFit/src/pymultifit/distributions/utilities_d.py:168: RuntimeWarning: overflow encountered in power
  numerator = y**(alpha - 1) * (1 - y)**(beta - 1)
/home/sarl-ws-5/PycharmProjects/pyMultiFit/src/pymultifit/distributions/utilities_d.py:168: RuntimeWarning: overflow encountered in multiply
  numerator = y**(alpha - 1) * (1 - y)**(beta - 1)

Arcsine

https://pymultifit.readthedocs.io/latest/benchmarks/_bm_accuracy.html#arcsine()

/home/sarl-ws-5/PycharmProjects/pyMultiFit/src/pymultifit/distributions/utilities_d.py:71: RuntimeWarning: invalid value encountered in sqrt
  pdf_ = 1 / (np.pi * np.sqrt(y * (1 - y)))
/home/sarl-ws-5/PycharmProjects/pyMultiFit/src/pymultifit/distributions/utilities_d.py:71: RuntimeWarning: divide by zero encountered in divide
  pdf_ = 1 / (np.pi * np.sqrt(y * (1 - y)))
/home/sarl-ws-5/PycharmProjects/pyMultiFit/benchmarks/functions.py:30: RuntimeWarning: invalid value encountered in subtract
  pdf_abs_diff = np.abs(pdf_custom - pdf_scipy) + EPSILON

[syedalimohsinbukhari]

Hi @coatless

Thanks for getting back. I've been meaning to address those issues for a while but was waiting for a response before I proceeded. I've updated the required functions, and they shouldn't give the same issues now. The only issue now is that in arcsine for x=1 (in the edge case of testing), the pdf gives np.inf in both scenarios and thus the invalid value error.

Cheers.

[coatless]

@syedalimohsinbukhari any reason for not directly using np.arcsin()? I'm not having that issue with the built-in version:

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Could you restore the docstring for beta_pdf_():

syedalimohsinbukhari/pyMultiFit@517e6fe#diff-901f230daf84fc7cccf4819b150e039e69d3b060a1bcc2279a55f84ce2570da6L118

[syedalimohsinbukhari]

Hi @coatless,

I think this image should clear things up. The ArcSineDistribution is not the same as np.arcsin of trigonometry; it is a distribution with pdf $f(y) = \dfrac{1}{\pi\sqrt{y(1-y)}}$ that's why.

For reference, wiki article and scipy docs.

Thanks for the headsup for beta_pdf_; strangely, it is showing up in my local builds but not in recent RTD builds. I'll look into it more.

[syedalimohsinbukhari]

Hi @coatless

Just letting you know that beta_pdf_() docstring is now showing up on RTD servers as well.

https://pymultifit.readthedocs.io/latest/distributions/utilities_d.html#pymultifit.distributions.utilities_d.beta_pdf_

[coatless]

@syedalimohsinbukhari thanks! I'm going to work on getting an editor assigned to move the review forward.

[syedalimohsinbukhari]

@coatless Awesome!! Looking forward to it.

[syedalimohsinbukhari]

Hi @coatless

I'm working on some updates and wanted to ask if it'll be okay to push the next version or should I just keep the version as is since I've already mentioned it for submission.

[coatless]

@syedalimohsinbukhari go for it! We're still working on getting editors.

[coatless]

@syedalimohsinbukhari Thanks for your patience. I've secured an editor to further move the review along.

I am happy to announce that @Batalex will be the editor for your submission.

[syedalimohsinbukhari]

Hi @Batalex.

Welcome, and I'm looking forward to working with you.

[Batalex]

Hello @syedalimohsinbukhari, nice to meet you.
I am excited to be part of pymultifit review, and I'll get started with my search for reviewers.

[syedalimohsinbukhari]

Hi @coatless

I'm working on some updates and wanted to ask if it'll be okay to push the next version or should I just keep the version as is since I've already mentioned it for submission.

@syedalimohsinbukhari go for it! We're still working on getting editors.

Hi, @coatless, @Batalex

As mentioned previously, I've updated the submission version for pymultifit with some streamlining of functionalities without changing much infrastructure. The docs, examples, and tutorails are up to date with this version v1.0.6.

[Batalex]

Hey, thanks for letting us know. My personal policy when it comes to the reviews I lead as the editor is as follows:

• you can work as you please on the code base, there is no need to freeze anything during the review process. This is important because finding reviewers can take a while, and there is no point in asking you to delay the work multiple times for several months
• we'll ask the reviewers to review the version submitted. Since there is no reviewer at this time, feel free to bump to the latest release.
• however, as soon as reviewers are assigned to the review, the version submitted in the issue header will stay the same. Making sure that reviewers' concern made on an older codebase are properly addressed in the newer one will be your job