I am currently using Pyevolve package to solve some Genetic Algorithms problems. I am wondering is there any examples using Pareto ranking in Pyevolve package, since I have multi evaluation functions.
If not exists, could you plz provides some pseudo code of Pareto ranking algorithms. I want to implement it by myself.
Thank you!!
Based on the last release documentation there effectively doesn't seem to be any Pareto ranking package in Pyevolve.
If you want to implement it yourself, you should check NSGA-II which is one most well known and best working algorithm for multi-objective optimization. The original article, containing pseudocode, can be found here : http://sci2s.ugr.es/docencia/doctobio/2002-6-2-DEB-NSGA-II.pdf
If you want to develop multi-objective genetic algorithms in Python and since Pyevolve development is quite moribund, I would recommend you to check out a more versatile framework named DEAP : http://deap.googlecode.com/. The framework already includes everything you need to do multi-objectives GAs, and provides many examples of how this can be done (NSGA-II is already implemented in DEAP). The transition from Pyevolve should be easy as the documentation is quite complete. You can also get in touch with the developers, they answer questions very quickly.
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For research purposes, I often find myself using the very good dense linear algebra packages available in the Python ecosystem. Mostly numpy, scipy and pytorch, which are (if I understand correctly) heavily based on BLAS/Lapack.
Of course, these go a long way, being quite extensive and having in general quite good performance (in terms of both robustness and speed of execution).
However, I often have quite specific needs that are not currently covered by these libraries. For instance, I recently found myself starting to code structure-preserving linear algebra for symplectic and (skew) Hamilton matrices in Cython (which I find to be a good compromise between speed of execution and ease of integration with the bulk of the python code). This process most often consists in rewriting algorithms of the 70-80s based on dated and sometimes painful to decypher research papers, which I would not mind not having to go through.
I'm not the only person doing this on the stack exchange family either! Some links below of people who have questions doing exactly this:
Function to Convert Square Matrix to Upper Hessenberg with Similarity Transformations
It seems to me (from reading the literature from this community, not from first hand acquaintance with the members / experience in the field) that a large part of these algorithms are tested using Matlab, which is prohibitively expensive for me to get my hands on, and which would probably not work great with the rest of my codebase.
Hence my question: where can I find open source exemples of implementations of "research level" dense algebra algorithms that might easily be used in python or copied?
So mixed-effects regression model is used when I believe that there is dependency with a particular group of a feature. I've attached the Wiki link because it explains better than me. (https://en.wikipedia.org/wiki/Mixed_model)
Although I believe that there are many occasions in which we need to consider the mixed-effects, there aren't too many modules that support this.
R has lme4 and Python seems to have a similar module, but they are both statistic driven; they do not use the cost function algorithm such as gradient boosting.
In Machine Learning setting, how would you handle the situation that you need to consider mixed-effects? Are there any other models that can handle longitudinal data with mixed-effects(random-effects)?
(R seems to have a package that supports mixed-effects: https://rd.springer.com/article/10.1007%2Fs10994-011-5258-3
But I am looking for a Python solution.
There are, at least, two ways to handle longitudinal data with mixed-effects in Python:
StatsModel for linear mixed effects;
MERF for mixed effects random forest.
If you go for StatsModel, I'd recommend you to do some of the examples provided here. If you go for MERF, I'd say that the best starting point is here.
I hope it helps!
I am working on a teaching tool for binary decision diagrams in which there is also a feature for variable reordering. Can anyone suggest a suitable library which implements variable reordering while building the tree or some kind of algorithm which implements the same ?
It would be best if I could work with a library like pyeda, buDDy or pycudd because I am already familiar with these libraries.
Thanks and comment if you need any kind of clarification..
Have you looked at dd, by Ioannis Filippidis?
I'm the author of pyeda. Implementing ROBDDs in Python was definitely fun, and can probably have some educational value, but it definitely doesn't do any automatic variable reordering, so if that's a requirement I would recommend looking at dd or the other ones on your list.
My group at University of Maribor is producing BDD Scout ( http://biddy.meolic.com/ ), a tool for visualization of BDDs. Currently, ROBDDs with complemented edges and 0-sup-BDDs with complemented edges are supported. Conversions are supported. Reordering (i.e. variable swapping and sifting algorithm) is supported for both of them. BDD Scout work on GNU/Linux an MS Windows (source and binary packages are available). We hope that our tool one day becomes a good teaching tool but we need some feedback to improve it. Besides the robustness the set of the functionalities is the most critical part to improve. If you will find some time to try it do not hesitate to give us any comments and questions.
Is there any python library with functions to perform fixed or random effects meta-analysis?
I have search through google, pypi and other sources but it seems that the most popular python stats libraries lack this functionality.
It would be great if it also provide graphical solutions to produce funnel plots and forest plots.
Forest plot example:
It thought of something similar to R package rmeta
I've found some people creating their own functions manually, but it isn't a actual library. In addition, metasoft was promising, but it uses python only to convert between formats.
Just to say, it seems the mostly widely used tool is R's metafor, which provides seemingly every possible method used and includes essential plotting functions.
In Python, PythonMeta the backend for a web-based tool PyMeta which offers many of the methods (fixed and random effects, various data types) found in metafor.
This PyMARE project is still under development but does provide various fixed and random effects meta-analysis estimators (this is a spin-off from the rather more mature NiMARE tool for neuroimaging meta-analysis).
statsmodels now also offers some options for meta-analysis and visualization of its results, more information here:
https://www.statsmodels.org/devel/examples/notebooks/generated/metaanalysis1.html
What are the standard tf-idf implementations/api available in python? I've come across the one in nltk. I want to know the other libraries that provide this feature.
there is a package called scikit which calculates tf-idf scores.
you can refer to my answer to this question
Python: tf-idf-cosine: to find document similarity
and also see the question code from this. Thankz.
Try the libraries which implements TF-IDF algorithm in python.
http://code.google.com/p/tfidf/
https://github.com/hrs/python-tf-idf
Unfortunately, questions asking for a tool or library are offtopic on SO. There are lot of machine learning libraries implementing tfidf. Two most comprehensive of them besides mentioned ntlk in my view are sklearn and gensim.