New open-source Machine Studying Framework written in Java

I’m completely happy to announce that the Datumbox Machine Studying Framework is now open sourced below GPL 3.0 and you’ll obtain its code from Github!

What is that this Framework?

The Datumbox Machine Studying Framework is an open-source framework written in Java which permits the speedy growth of Machine Studying fashions and Statistical purposes. It’s the code that at present powers up the Datumbox API. The primary focus of the framework is to incorporate a lot of machine studying algorithms & statistical strategies and be capable to deal with small-medium sized datasets. Regardless that the framework targets to help the event of fashions from varied fields, it additionally gives instruments which might be significantly helpful in Pure Language Processing and Textual content Evaluation purposes.

What sorts of fashions/algorithms are supported?

The framework is split in a number of Layers reminiscent of Machine Studying, Statistics, Arithmetic, Algorithms and Utilities. Every of them gives a sequence of courses which might be used for coaching machine studying fashions. The 2 most vital layers are the Statistics and the Machine Studying layer.

The Statistics layer gives courses for calculating descriptive statistics, performing varied sorts of sampling, estimating CDFs and PDFs from generally used likelihood distributions and performing over 35 parametric and non-parametric checks. Such sorts of courses are normally needed whereas performing explanatory information evaluation, sampling and have choice.

The Machine Studying layer gives courses can be utilized in a lot of issues together with Classification, Regression, Cluster Evaluation, Subject Modeling, Dimensionality Discount, Function Choice, Ensemble Studying and Recommender Programs. Listed below are a few of the supported algorithms: LDA, Max Entropy, Naive Bayes, SVM, Bootstrap Aggregating, Adaboost, Kmeans, Hierarchical Clustering, Dirichlet Course of Combination Fashions, Softmax Regression, Ordinal Regression, Linear Regression, Stepwise Regression, PCA and extra.

Datumbox Framework VS Mahout VS Scikit-Study

Each Mahout and Scikit-Study are nice initiatives and each of them have utterly completely different targets. Mahout helps solely a really restricted variety of algorithms which may be parallelized and thus use Hadoop’s Map-Scale back framework to deal with Large Knowledge. Alternatively Scikit-Study helps a lot of algorithms however it might probably’t deal with enormous quantity of knowledge. Furthermore it’s developed in Python, which is a superb language for prototyping and Scientific Computing however not my private favorite for software program growth.

The Datumbox Framework sits in the midst of the 2 options. It tries to assist a lot of algorithms and it’s written in Java. Which means it may be included simpler into manufacturing code, it might probably simpler be tweaked to cut back reminiscence consumption and it may be utilized in actual time programs. Lastly regardless that at present Datumbox Framework is able to dealing with medium-sized datasets, it’s inside my plans to develop it to deal with large-sized datasets.

How secure is it?

The early variations of the framework (as much as 0.3.x) have been developed in August and September of 2013 they usually have been written in PHP (yeap!). Throughout Could and June 2014 (variations 0.4.x), the framework was rewritten in Java and enhanced with further options. Each branches have been closely examined in business purposes together with the Datumbox API. The present model is 0.5.0 and it appears mature sufficient to be launched as the primary public alpha model of the framework. Having stated that, it is very important notice that some functionalities of the framework are examined extra completely than others. Furthermore since this model is alpha, it’s best to count on drastic modifications on the longer term releases.

Why I wrote it and why I open-source it?

My involvement with Machine Studying and NLP dates again to 2009 once I co-founded WebSEOAnalytics.com. Since then I’ve been creating implementations of assorted machine studying algorithms for varied initiatives and purposes. Sadly a lot of the unique implementations have been very problem-specific they usually might hardly be utilized in another downside. In August 2013 I made a decision to begin Datumbox as a private undertaking and develop a framework that gives the instruments for creating machine studying fashions focusing within the space of NLP and Textual content Classification. My goal was to construct a framework that may be reused on the longer term for creating shortly machine studying fashions, incorporating it in initiatives that require machine studying elements or supply it as a service (Machine Studying as a Service).

And right here I’m now, a number of traces of code later, open-sourcing the undertaking. Why? The sincere reply is that at this level, it’s not inside my plans to undergo a “let’s construct a brand new start-up” journey. On the identical time I felt that protecting the code on my laborious disk in case I would like it on the longer term doesn’t make sense. So the one logical factor to do was to open-source it. 🙂

Documentation?

For those who learn the earlier two paragraphs, it’s best to in all probability seen this coming. For the reason that framework was not developed having in thoughts that I’d share it with others, the documentation is poor/non-existent. Many of the courses and public strategies usually are not correctly commented and there’s no doc describing the structure of the code. Luckily all the category names are self-explanatory and the framework gives JUnit checks for each public technique & algorithm and these can be utilized as examples of use the code. I hope that with the assistance of the neighborhood we’ll construct a correct documentation, so I’m relying on you!

Present Limitations and Future Improvement

As in every bit of software program (and particularly the open-source initiatives in alpha model), the Datumbox Machine Studying Framework comes with its personal distinctive and lovable limitations. Let’s dig into them:

1. Documentation: As talked about earlier, the documentation is poor.
2. No Multithreading: Sadly the framework doesn’t at present assist Multithreading. In fact we must always notice that not all machine studying algorithms may be parallelized.
3. Code Examples: For the reason that framework has simply been revealed, you may’t discover any code examples on the net apart from these offered by the framework within the type of JUnit checks.
4. Code Construction: Making a stable structure for any massive undertaking is at all times difficult, not to mention when you need to take care of Machine Studying algorithms that differ considerably (supervised studying, unsupervised studying, dimensionality discount algorithms and many others).
5. Mannequin Persistence and Massive Knowledge Collections: At the moment the fashions may be skilled and saved both on recordsdata on disk or in MongoDB databases. To have the ability to deal with great amount of knowledge, different options should be investigated. For instance MapDB looks like a superb candidate for storing information and parameters whereas coaching. Furthermore it is very important take away any 3^rd celebration libraries that at present deal with the persistence of the fashions and develop a greater dry and modular resolution.
6. New algorithms/checks/fashions: There are such a lot of nice strategies that aren’t at present supported (particularly for time sequence evaluation).

Sadly all of the above are an excessive amount of work and there’s so little time. That’s the reason in case you are within the undertaking, step ahead and provides me a hand with any of the above. Furthermore I’d love to listen to from individuals who have expertise in open-sourcing medium-large initiatives and will present any tips about handle them. Moreover I’d be grateful to any courageous soul who would dare to look into the code and doc some courses or public strategies. Final however not least when you use the framework for something attention-grabbing, please drop me a line or share it with a weblog put up.

Lastly I wish to thank my love Kyriaki for tolerating me whereas scripting this undertaking, my good friend and super-ninja-Java-developer Eleftherios Bampaletakis for serving to out with vital Java points and also you for getting concerned within the undertaking. I’m trying ahead to your feedback.