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About Sezin Karli

Sezin Karli
Mathematics Engineer & Computer Scientist with a passion for software development. Avid learner for new technologies. Currently working as Senior Software Engineer at Sahibinden.com.

Machine Learning in Java, part 2

Welcome to the second part of the tutorial for scoring your PMML files using  LightningScorer, which is a side project of mine.

Let’s find out how additional parameters work.

The initial steps are similar to the first part of the tutorial.

Get your local copy first

git clone https://github.com/sezinkarli/lightningscorer.git

and build it with maven

mvn clean install

and start it by going to your target folder

java -jar lightningscorer-uberjar-1.0.jar

Now lets make sure our server is up and running by going to



Server returns

"data": "I have come here to chew bubblegum and kick ass...",
"success": true

Ok then we are now ready to kick ass, again.

I’ll use apache commons’ http get/post methods. First, we’ll deploy our machine learning model with an additional parameter. Then we will check if it’s working and then use our input values and score it.  After the scoring we will use our additional parameter.

final String url = "http://localhost:8080/model/";
        final String modelId = "test2";

        File pmmlFile = new File("/tmp/ElNinoPolReg.xml");

        CloseableHttpClient client = HttpClients.createDefault();

        // deployment
        // notice that I give a variance value as an additional parameter that I will use later
        HttpPost deployPost = new HttpPost(url + modelId + "?variance=3.25");
        MultipartEntityBuilder builder = MultipartEntityBuilder.create();
        builder.addBinaryBody("model", new File(pmmlFile.getAbsolutePath()), ContentType.APPLICATION_OCTET_STREAM, "model");
        HttpEntity multipart = builder.build();

        CloseableHttpResponse response = client.execute(deployPost);
        String deployResponse = IOUtils.toString(response.getEntity().getContent(), Charset.forName("UTF-8"));
        // {"data":true,"success":true}

        // check deployed model
        HttpGet httpGet = new HttpGet(url + "ids");

        response = client.execute(httpGet);
        String getAllModelsResponse = IOUtils.toString(response.getEntity().getContent(), Charset.forName("UTF-8"));
        // {"data":["test1"],"success":true}

        //score deployed model
        HttpPost scorePost = new HttpPost(url + modelId + "/score");
        StringEntity params = new StringEntity("{" +
                "\"fields\":" +
                "{\"latitude\":2.5," +
                "\"longitude\":11.4," +
                "\"zon_winds\":3.5," +
                "\"mer_winds\":3," +
                "\"humidity\":31.2," +
                "\"s_s_temp\":25.21" +
                "}" +
                "} ");
        scorePost.addHeader("content-type", "application/json");

        CloseableHttpResponse response2 = client.execute(scorePost);
        String scoreResponse = IOUtils.toString(response2.getEntity().getContent(), Charset.forName("UTF-8"));
        // {"data":{"result":{"airtemp":29.788226026392735}},"success":true}

        HttpGet additionalParamGet = new HttpGet(url + modelId + "/additional");
        CloseableHttpResponse response3 = client.execute(additionalParamGet);
        String additionalParamResponse = IOUtils.toString(response3.getEntity().getContent(), Charset.forName("UTF-8"));
        // {"data":{"variance":"3.25"},"success":true}

        // Then you can use the variance value with your result in airtemp to calculate an interval for your score

Published on Java Code Geeks with permission by Sezin Karli, partner at our JCG program. See the original article here: machine learning ass-kicking in java part 2

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