The project

This project is using Tribuo from Oracle Lab, and Tribuo is a library for creating Machine Learning (ML) models and for using those models to make predictions on previously unseen data.

there is two sections:

• Train model
• Predicate input data

The Iris Flower classification

Iris dataset includes three species of iris flowers, and our mission is base on the information of these flowers predict which species it has in a new data coming.

• Setosa
• Versicolor
• Virginica

1st Column: Sepal-Length 2nd Column: Sepal-Width 3rd Column: Petal-Length 4th Column: Petal-Width 5th Column: species

So this dataset has 4 features classes: the length and the width of the sepals and petals, in centimeters.

Train the model and store it as a file.

There is a test class for training model, and we can run it a couple of times to make mature model. Then this mature trained model can be store as file on disk, and it will be doable by this maven command.

$ mvn test -Dtest=com.thinksky.tribuo.training.TrainingModelTest

Main job of training a model is doing on TrainingModel, this class has two main public methods.

• train()

  1. Load dataset as a csv file format from ClassLoader as Java resource.
  2. Use TrainTestSplitter from Tribuo lib to separate train and test datasets.
  3. Use LogisticRegressionTrainerto train mode.

• evaluator()

  1. It will get trained model and return LabelEvaluation, and it has lots of useful statistics data of evaluation of model.

Test the trained model

Mostly testing a Java application occurs on uni-test, so we can run our test with JUnit and TrainingModel. TrainingModelTest has a method which it will repeatedly run to have mature model.

trainingTest()

• This method will load the bezdekIris.data file as URL object and get a train moel and evaluate information.

• evaluator object includes useful information

  Class	n	tp	fn	fp	recall	prec	f1
  Iris-versicolor	16	16	0	1	1.000	0.941	0.970
  Iris-virginica	15	14	1	0	0.933	1.000	0.966
  Iris-setosa	14	14	0	0	1.000	1.000	1.000
  Total	45	44	1	1

  • Accuracy 0.978
  • Micro Average 0.978 0.978 0.978
  • Macro Average 0.978 0.980 0.978
  • Balanced Error Rate 0.022

This output lists:

• class, the different classes in the test set
• n, the number of ground truth labels of that class
• tp, the number of true positives (i.e., the number of times the classifier correctly predicted this class)
• fn, the number of false negatives (i.e., the number of times the classifier predicted this class as another class)
• fp, the number of false positives (i.e., the number of times the classifier incorrectly predicted this class when it was another class)
• recall, the true positives divided by the number of ground truth labels (i.e., the fraction of this class that the classifier can detect)
• precision, the true positives divided by the predicted positives (i.e, the fraction of the time that this class is predicted correctly)
• accuracy, the sum of the true positives divided by the total number of test examples
• balanced error rate, the average of the per class error rates

Predict Iris

If we have a mature trained model and store it in a file then it is ready to predict a dataset of new data. We can build the Java application by a maven command.

$ mvn clean install

then it is ready to predict a new dataset by execute java app. The java application will read iris-prediction.data and print the result of prediction on the console.

$ java -jar target/iris-classification-1.0-SNAPSHOT.jar

this is a sample of prediction.

2021-09-12 22:53:22:238 -0700 [main] INFO com.thinksky.tribuo.prediction.Application - Predicted: Setosa
2021-09-12 22:53:22:238 -0700 [main] INFO com.thinksky.tribuo.prediction.Application - Predicted: Versicolor
2021-09-12 22:53:22:238 -0700 [main] INFO com.thinksky.tribuo.prediction.Application - Predicted: Virginica
2021-09-12 22:53:22:238 -0700 [main] INFO com.thinksky.tribuo.prediction.Application - Application ends