Best way to create Model Data in java programming
In the list of configurations you can have Python scripts, Java applications, maven goals etc. We have an ETL sort of Java based product where one can do most of data Preparation steps for machine learning, like data ingestion from JDBC, files, HDFS, No SQL etc., joins and aggregations etc.(which are required for Feature engineering) and now we want to add Analytics capabilities using machine learning/statistical modeling.
Best way to create Model Data in java programming
After making a webservice call and gathering data for your model data, i can think of a few ways to persist the model until the application process terminates:
- save the model as static variable so its in memory and fast to access
- write the model data to a DB. Access the db tables when data is needed. this is slightly slower depending on how much data your trying to get and searches.
- create a singleton dataModel but this is # 1. I could inject it using Dependency injection so it would not be seen as a singleton.
- pass the model data from class to class via parameters. this might be good for testing different model data, etc
This is all i can think of. What is the recommended way in design architecture like MVC to create a model available to the entire application?
Ok, this is definitely opinion based, there are many right answers, but static variables and singletons are very likely wrong. They make testing incredibly difficult, and strongly couple your application to a single implementation.
It also strongly limits your application size to the amount of data that can fit into memory at one time.
2 vs 4 is probably where it will end up a debate between various architectures, and should probably not be discussed here.
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How to fit and score a machine learning models in Java/JVM based application
Could you please guide me on how to create and execute a machine learning models/statistical models (regression, Decision tree, K means clustering, Naive bayes, scorecard/linear/logistic regression etc. and GBM, GLM ) in Java/JVM based application (in production).
We have an ETL sort of Java based product where one can do most of data Preparation steps for machine learning, like data ingestion from JDBC, files, HDFS, No SQL etc., joins and aggregations etc.(which are required for Feature engineering) and now we want to add Analytics capabilities using machine learning/statistical modeling.
Right now, we are using jpmml- evaluator to score the models created in PMML format using R and python (and Knime) but it needs three separate and unconnected steps:- 1- first step for Data preparation in our Java/JVM application and save the sampling data (training and test) data in csv file or in DB, — 2- Create a machine learning Model in R and python (and Knime) and export it in PMML 4.2 format — 3- Import/deploy the PMML in our Java based application and use JPMML evaluator to execute it in production.
I am sure it’s a common problem in machine learning as generally in Production JAVA is preferred over Python or R. Could you suggest what is the better approach(s) to create as well as execute a python/scikit based machine learning Model in JVM based application.
What are your thought to achieve the steps # 2 and #3 more seamlessly in a JVM based application, without compromising performance and usability:-
1- Call a java program which internally calls the python scikit script (under the hood) to create a model in PMML and then use JPMML evaluator. It will pretend to the user that he is in a single JVM based application (better usability). I am not sure what are the limitations and short coming of using PMML as not all features are supported in jpmml-sklearn. 2- Call a java program which internally calls the python script and do the model creation as well as execution in an external python environment and serialized the model and the results in a file/csv or in memory DB (or cache, like hazelcast) from where the parent java application will fetch the results etc.. I researched that I can’t use Jython for executing Sci-kit models. 3- Can I use Jep (Embed Python in Java) to embed Cpython in JVM ? Does anybody tried it for sci-kit models?
Alternatively, I should explore to use Mahout or weka — java based machine learning libraries in my JVM based application. (I need to support both windows and non-windows platforms)
I am also exploring H2Oai which is java based. Does anybody tried it.
I use IntelliJ IDEA with the python plugin. This way I have both java and python code in one and the same project. The data is in the database; the connection is always visible and accessible, independently of whether I have a .java or a .py file currently in the editor. In the list of configurations you can have Python scripts, Java applications, maven goals etc. Therefore I don’t think you have to mix Python and Java code together (by calling Python scripts out of Java). That is completely unnecessary.
My workflow is (everything in IntelliJ IDEA): 1. Prepare the data (usually SQL) 2. Run python script, which applies a pipeline of transformators to the pandas data frame constructed from a certain database table and outputs a PMML. 3. Use the scikit-learn model in your java application.
If you have an ETL with HDFS backend, I would suggest deploying Spark on the cluster and using Spark’s MLib machine learning algorithms. They support the methods you mentioned above.
Do you mind giving some context as to what the size (rows, columns, type) of the data that you plan to work with? Java would not be my recommended goto-language for ML but Scala compiles to JVM bytecode and has a similar syntax to java (in addition to having a Java API).
If you’re producing a proof-of-concept, then Java is fine but if you’re planning on working with big data, it doesn’t really scale well.
I have found a decent solution for my problem. I am using H2O.ai developed in Java for scalable machine learning using open source. It offers APIs in Java (Restful API), Python, R and Scala. It has best of class algorithms for classification, Regression, Clustering etc. and seamlessly integrates with Apache Hadoop and Spark (sparkling-water) as well, if someone has Spark cluster. It also offers a deep learning algorithm which is based on a multi-layer feedforward artificial neural network. I am using Java binding API/Rest API and sometimes the low-level H2o API (for h2o 3 nodes cluster management).
I come across another java based alternative, called Smile — Statistical Machine Intelligence and Learning Engine which provides regression, classification, clustering, association rule mining, feature selection etc. Does anybody have more feedback on these or similar Java based ML library?
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How do I use Blender models in Java?
Just a general question really?
Let’s say I am making a game and have made a character model in Blender. How would I use this model in Java?
Would I import it somehow?
Generally when making models in blender you export the model in a format which allows you to later import it in the game engine of your choice, which format you use differ in requirements.
The export-import cycle is often referred to as the «Asset Pipeline», and you generally want to keep it as simple and automated as possible since it is something you or your artists will perform on a regular basis.
So if we look at a few specific graphics engines and platforms;
- OGRE3D (or Ogre4J) supports it’s own plain-text format (.scene, .mesh.xml, .material.xml) in order to load scenes, models and materials. It also has support for armature animations among other things, there is also some support for loading .blend-files directly. See their documentation for blender.
- JmonkeyEngine has support for loading both OGRE3D .scene’s and .blend’s directly. It also has it’s own binary j3o format which these can be converted into when you want to package the game. For specific examples, see their tutorials.
There are multiple formats you can take into consideration when deciding how you want to use your model. When it is imported however, the game engine of choice represents it in an internal structure which usually allows you to be decoupled from the exact format of choice.
Picking which to use is and should not be written in stone since requirements might change and if done properly it should not have a considerable effect on the project. This is also something you should take into consideration if you are writing your own engine.
Here’s a better idea: Use an existing Java 3D library (dzzd I highly recommend) and load in your model using the library’s built in functions. Then, instead of just working with the data, you can actually display it. From Blender, it’s a simple matter of exporting as 3DS.
There are input/output scripts available for Blender that will help you.
Blend2Java, for example, is a set of Python scripts for use with Blender that will export to Java XML, which can be decoded with the standard java.beans.XMLDecoder class.
There’s a good overview of how to do this at http://blend2java.sourceforge.net/blend2java-howto.html
Yet another solution: Java .Blend provides you with a type-safe Java API to all data in a Blender file. It supports even creating new Blender files from within Java 😉
How to fit and score a machine learning models in, right now, we are using jpmml- evaluator to score the models created in pmml format using r and python (and knime) but it needs three separate and …
How can I write GMM (Gaussian Mixture Model) in Java?
How can I write a GMM (Gaussian mixture model) in Java? There are some implementations in MATLAB, but I am looking for some documentation about it and example code in Java.
PS: If it is possible, how can I adopt the Weka implementation in my code?
PS 2: I found http://www.lix.polytechnique.fr/~nielsen/MEF/ Where is the source code of GMM inside it so I can adopt it at my code?
PS 3: Other code found is: http://www.dii.unisi.it/~freno/JProGraM.html, but it still has the problem how to adopt it. I should give the List of my input and get all the classified elements list from the algorithm.
Weka is Java data mining software that can do GMMs. It also has a nice GUI where you can do some preliminary modeling before doing everything in Java.
I’m the author of jMEF 🙂 jMEF will allow you to create, process and manage mixtures of exponential families including of course GMMs. You basically have to create a mixture model based on the Gaussian distribution. I’ve written a bunch of tutorials to help understanding how to use it properly. It’s pretty straightforward. The good thing is that you can estimate your mixture from samples using the EM algorithm.
You can use «matlab builder JA» to get «.JAR» file and source code from your «implementations in MATLAB». Like that, you can incorporate the result into your Java application.
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