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https://github.com/dmmiller612/machine_learning_spring_weka

Weka with spring example
https://github.com/dmmiller612/machine_learning_spring_weka

machine-learning spring weka

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Weka with spring example

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# Machine Learning with Weka and Spring examples



Code url: https://github.com/dmmiller612/Machine_Learning_Spring_Weka



# Instructions



If wanting to run the server locally, instead of just using the Weka models located in /src/main/resources/models, there are a couple of dependencies needed: Maven and Java.



1. This uses java 1.7, but should work with 1.8 as well. For the JRE, `sudo apt-get install default-jre` . For the jdk, `sudo apt-get install default-jdk`.



2. This uses Maven 3.x . To Install maven 3, use `sudo apt-get install maven`.



3. Go to the root of the assignment code repository and type: `mvn clean package` into the command line. Then type `java -jar target/derek-assignment-1-0.1.0.jar`. Once running the jar, all of optimal models will start to run against the test datasets of both the Car Evaluation and Census dataset. This is here just to make it easier to visualize, so that you do not have to use the rest api. If you want to use the rest api, see documentation below.



4. IF three does not work, it is because the plugin did not properly install. Running this command should do the trick inside of the students-filters-master

"mvn install:install-file -Dfile=filters-0.0.1-SNAPSHOT.jar -DgroupId=filters -DartifactId=filters -Dversion=0.0.1-SNAPSHOT -Dpackaging=jar"



# Navigating the Source Code 



### src/main/java/com/derek/ml/controllers 



contains the rest endpoints.



### src/main/java/com/derek/ml/services 



contains all of the logic and configuration of weka models. ClusterService -> k-means and EM, FeatureReductionService -> ICA, PCA, RP, CFS, KNNService -> KNN, NeuralNetworkService -> Neural Network, DecisionTreeService->Decision Trees (boosted an unboosted), SVMService->SVM



### src/main/java/com/derek/ml/models 



DTO passing layers



# Navigating the Resources 



### src/main/resources/csv 



Contains all of the initial csv files used (Arffs are only used for the models, however)



### src/main/resources/arffs 



Contains all of the arffs used. car_train.arff and car_test.arff are the training and testing instances for the car evaluation dataset. census.arff and censusTest.arff are the training and testing instances for the Census dataset. 



### src/main/resources/models 



Contains several models used for the supervised learning analysis. If you don’t want to run the code locally, you can just use these models against the training and test arffs listed above.



# Using the Rest API (Optional)



I thought I would just add this to show the code I used for experimentation with Weka. I used the api, so that I could do multiple concurrent requests.



Universal Query parameters: fileName : {Car, Census, CarBin, CensusBin}, testType : {CrossValidation, TestData, Train}



# Cluster



Endpoints: /kMeans and /em

Query Params => clusters : int, distances : {Euclidean, Manhatten}, iterations: int, featureSelection: {ICA, PCA, RP, CFS};



# Feature Reduction



Endpoints: /featureReduction/pca /featureReduction/ica /featureReduction/rp /featureReduction/cfs



# Decision Trees



Endpoint: /decisiontree



Query Params => minNumObj : int, boost : boolean, confidence : String, treeType : {ID3, J48}



Example Requests:

http://localhost:8080/decisiontree?fileName=Car&testType=TestData&minNumObj=2&confidence=.25

http://localhost:8080/decisiontree?fileName=Census&testType=CrossValidation&minNumObj=2&confidence=.25

http://localhost:8080/decisiontree?fileName=Car&testType=CrossValidation&minNumObj=2&confidence=.25&boost=true //with boosting



Using incremental testing example:

http://localhost:8080/decisiontree/test?fileName=Car&testType=TestData&minNumObj=2&confidence=.25&boost=true



# KNN



Endpoint: /knn



Query Params => k : int, boost : boolean, treeTypes {BallTree, CoverTree, Linear}, useFeatureSelection : boolean (applies only to Census file)



Examples : 

http://localhost:8080/knn?fileName=Car&testType=TestData&k=3

http://localhost:8080/knn?fileName=Census&testType=TestData&k=5&featureSelection=true

http://localhost:8080/knn?fileName=Census&testType=TestData&k=5&boost=true



Using incremental testing example:

http://localhost:8080/knn/test?fileName=Census&testType=TestData&k=5



# ANN



Endpoint: /neuralnetwork



Query Params => hiddenLayers : int, epochRate : int, featureSelection : boolean (applies only to Census file)



Examples:

http://localhost:8080/neuralnetwork?fileName=Car&testType=TestData&hiddenLayers=10&epochRate=500

http://localhost:8080/neuralnetwork?fileName=Census&testType=TestData&hiddenLayers=5&epochRate=500&featureSelection=true



Using incremental testing example:

http://localhost:8080/neuralnetwork/test?fileName=Car&testType=TestData&hiddenLayers=10&epochRate=500



# SVM



Endpoint: /svm



Query Params => kernelType : {Polynomial, RBF, Sigmoid, Linear}



Examples:

http://localhost:8080/svm?fileName=Car&testType=TestData&kernelType=Polynomial

http://localhost:8080/svm?fileName=Census&testType=TestData&kernelType=RBF

http://localhost:8080/svm?fileName=Census&testType=TestData&kernelType=Sigmoid

http://localhost:8080/svm?fileName=Census&testType=TestData&kernelType=Linear



# MODELS



The model names contain the parameters that were used, fileName, and algorithm name.



Decision Tree Naming Convention: decisionTree + minNumObj + Boosted + confidence + fileName + .model

Example: decisionTree-minNumObj=100-Boosted=false-C=0.25-file=Census.model



KNN Naming Convention: KNearestNeighbor + k + fileName + .model

Example : KNearestNeighbor-k=20-fileName=Car.model



ANN Naming Convention: ANN + hiddenLayers + epochRate + FileName + (Optional) featureSelection + .model

Example : ANN-hiddenLayers=10-epochRate=250-FileName=Census.model



SVM Naming Convention: SVM + kernelType + FileName + .model

Example : SVM-KernelType=Linear-FileName=Car.model