Datumbox Machine Studying Framework 0.7.0 Launched

I’m actually excited to announce that, after a number of months of improvement, the brand new model of Datumbox is out! The 0.7.0 model brings multi-threading help, quick disk-based coaching for datasets that don’t slot in reminiscence, a number of algorithmic enhancements and higher structure. Obtain it now from Github or Maven Central Repository.

The main target of model 0.7.0 is to lastly deliver multi-threading help to the framework and make the disk-based coaching extremely quick. Furthermore it brings a number of algorithmic enhancements in all of the Regression-based algorithms, the Collaborative Filtering mannequin and the N-grams extractor which is utilized in NLP purposes. The structure of the framework has been redesigned to separate the challenge into a number of modules (be aware that the artifactId of the primary library is now datumbox-framework-lib) and to simplify its construction. Lastly the brand new model brings a number of code enhancements, higher documentation within the type of javadocs and improved take a look at protection.

The 0.7.0 model of the framework just isn’t backwards suitable with the 0.6.x department. It is because main redevelopment was crucial as a way to add the brand new options and enhance & simplify the structure of the framework. Under I talk about intimately the brand new options:

Multi-threading help

The brand new framework is a number of instances quicker than the 0.6.x department. This was achieved through the use of threads, by doing heavy profiling on the new spots of the code and by rewriting core elements to allow non-blocking concurrent reads/writes. At the moment threads are being utilized in all of the algorithms that may be parallelized which is almost all of the supported fashions of the framework. The parallel execution is supported each throughout coaching and testing/predicting.

The challenge makes use of tons Java 8 options as a way to cut back the verbosity of the code, enhance readability and modernize the code-base. Notice that though the framework makes heavy use of streams, all duties are executed in their very own ForkJoinPool to make sure that they won’t get caught. The extent of parallelism is managed both by altering programmatically the ConcurrencyConfiguration object or by configuring the datumbox.config.properties file.

Disk-based Coaching

Despite the fact that disk-based coaching (coaching fashions with out loading the info in reminiscence) was potential because the 0.6.0 model, it was so gradual that made the function virtually unusable. In model 0.7.0, the Storage Engine mechanism was redeveloped to allow a hybrid strategy of storing the new/usually accessed data in reminiscence & LRU cache whereas preserving the remaining on disk. This strategy makes the disk-based coaching very quick and it ought to be most well-liked even in circumstances the place the info barely slot in reminiscence (clearly if the info match simply in RAM, the default in-memory coaching ought to be most well-liked). As within the earlier model, the reminiscence storage configuration could be modified programmatically by altering the suitable DatabaseConfiguration objects or by configuring the datumbox.config.properties file.

At this level I wish to level out that this function wouldn’t have been potential with out the superb work carried out by Jan Kotek on MapDB. MapDB is an embeded Java database engine which supplies concurrent Maps backed by disk storage and off-heap-memory. Utilizing his open-source library, I used to be in a position to develop a Storage Engine which permits Datumbox to deal with a number of GB price of coaching information on my laptop computer with out loading them in reminiscence.

Algorithmic Enhancements

The brand new model provides help of L1, L2 and ElasticNet regularization within the SoftMaxRegression (Multinomial Logistic Regresion), OrdinalRegression and NLMS (Linear Regression) fashions. Which means through the use of the identical customary courses one can carry out Ridge Regression, Lasso Regression or make use of Elastic Nets. Furthermore within the new model the Collaborative Filtering algorithm was modified to help extra generic Person-user CF fashions. Lastly the NgramsExtractor algorithm was rewritten to make it in a position to export extra key phrases and supply higher scores.

Framework Structure & Code Enhancements

One other essential replace on the brand new framework is the truth that the challenge was cut up into a number of sub-modules. Under I listing the at the moment supported modules named after their artifactIds:

1. datumbox-framework-common: It accommodates an important interfaces, helper and utility courses, information constructions and mechanisms of the framework. This module doesn’t include any algorithms however it’s the base of the framework.
2. datumbox-framework-core: It consists of the three most important layers of the framework (Machine Studying, Statistics and Arithmetic) together with the utilities layer. This module accommodates all of the algorithms, strategies and statistical checks of the framework.
3. datumbox-framework-applications: It accommodates an inventory of courses that are construct to supply off-the-shelf options for widespread machine studying issues corresponding to Textual content Classification, Knowledge Modelling and many others. All of the courses of the module are constructed on high of the core module.
4. datumbox-framework-lib: That is the Datumbox Machine Studying Framework! Notice that the artifactId of the library modified from “datumbox-framework” to “datumbox-framework-lib” on account of the restructuring.

Along with the above modules, we’ve got the “datumbox-framework” dad or mum module which is not the Java library however merely teams collectively all of the sub-modules beneath the identical challenge. So as to use the brand new framework on Maven initiatives add in your pom.xml the next traces:

<dependencies>
   ...
   <dependency>
       <groupId>com.datumboxgroupId>
       <artifactId>datumbox-framework-libartifactId>
       <model>0.7.0model>
   dependency>
   ...
dependencies>

The brand new model brings main modifications on the construction of framework, the interfaces and inheritance with most important objective to simplify and enhance its structure. One of many breaking modifications that have been launched on the brand new framework is the deprecation of the previous Dataset class (which was used to retailer all of the coaching and testing information within the framework) and the introduction of the Dataframe class. The Dataframe class implements the Assortment interface, permits the modification and deletion of data and permits the processing of the data in parallel. One other essential change is the truth that the BaseMLrecommender, which is the bottom class for all Recommender System algorithms, now inherits from BaseMLmodel.

Along with the above modifications the framework consists of some code enhancements and bug fixes: A serialVersionUID is added in each serializable class, the Exceptions and error messages have been improved and so do the javadocs documentation and the test-coverage. For extra details about the updates of the brand new model take a look on the Changelog.

Datumbox 0.7.0 has accomplished a number of essential milestones of the initially proposed roadmap. The event of the framework will proceed within the following months to cowl the next targets:

1. Entry the Framework through Console or Python: The framework ought to turn into extra accessible to non-Java builders. To realize this it ought to present entry to the algorithms through the command line or by providing an API in different languages like Python.
2. New Machine Studying algorithms: Because the structure of the framework turns into extra mature, will probably be simpler to extend the variety of supported algorithms and embrace fashions corresponding to Combination of Gaussians, Gaussian Processes, k-NN, Resolution Bushes, Random Forests, Issue Evaluation, SVD, Factorization Machines, Synthetic Neural Networks and many others.
3. Extra Storage Engines: Extra choices ought to be provided to the customers of the framework to retailer their fashions and prepare their algorithms with out loading all the info in reminiscence. Furthermore higher instruments ought to be offered to those that wish to transfer a mannequin from one storage engine to the opposite.
4. Enhance Documentation, Take a look at protection & Code examples: Despite the fact that the javadocs and take a look at protection enhance in every launch, the documentation of the framework remains to be poor. Subsequent variations ought to present a greater documentation, higher test-coverage and extra examples on the way to use the supported algorithms.

On condition that I’ve a full-time job, I anticipate that the event of the framework will proceed on the similar charge, releasing a brand new model each 4-6 months. If you need to suggest a brand new milestone be happy to open a difficulty on the official Github repository. Final however not least, when you use the challenge please take into account contributing. It doesn’t matter in case you are a ninja Java Developer, a rock-star Knowledge Scientist or an influence consumer of the library; I can use all the assistance I can get so be happy to get in contact with me.

As soon as once more I wish to thank my pal and colleague Eleftherios Bampaletakis for serving to me enhance the structure of the framework, his suggestions was invaluable. Additionally I wish to thank Jan Kotek for providing free consulting on the way to use effectively MapDB and for open-sourcing such a tremendous product. Furthermore numerous because of ej-technologies GmbH and JetBrains for offering licenses for his or her superb instruments JProfiler and IntelliJ IDEA; they each provide superb merchandise that helped loads the event of the framework. Final however not least, I’ll wish to thank the love of my life, Kyriaki, for supporting and placing up with me whereas writing the challenge.

Don’t neglect to clone the code of Datumbox v0.7.0 from Github. The library is accessible additionally on Maven Central Repository. Additionally take a look on the Detailed Set up Information and on the Code Examples to seek out out extra on the way to use the framework.

I’m wanting ahead to your feedback and suggestions. Pull requests are all the time welcome! 🙂