machine learning ass-kicking in java part 1

You searched for some way to export your machine learning models so you can use them for evaluating your data and you see that you can export them in PMML format. You actually work in Java ecosystem but not motivated to write neither your PMML library nor an rest api for it. Then I will recommend you LightningScorer, which is a side project of mine.

Let's take you a tour for deploying, and scoring your machine learning models.


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 

http://localhost:8080/

.
Server returns

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

Ok then we are now ready to kick ass.

I'll use apache commons' http get/post methods. First, we'll deploy our machine learning model. Then we will check if it's safe and sound and then use our input values and score it. We will use a decision tree trained with iris data set from UCI machine learning repository. We will send 4 parameters ( sepal length and width and petal length and width) and the model will classify it for us into one of 3 values.

view source

print?

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

02	final String modelId = "test1";

03

04	//http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml

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

06

07	CloseableHttpClient client = HttpClients.createDefault();

08

09	//first we will deploy our pmml file

10	HttpPost deployPost = new HttpPost(url + modelId);

11	MultipartEntityBuilder builder = MultipartEntityBuilder.create();

12	builder.addBinaryBody("model", new File(pmmlFile.getAbsolutePath()), ContentType.APPLICATION_OCTET_STREAM, "model");

13	HttpEntity multipart = builder.build();

14	deployPost.setEntity(multipart);

15

16	CloseableHttpResponse response = client.execute(deployPost);

17	String deployResponse = IOUtils.toString(response.getEntity().getContent(), Charset.forName("UTF-8"));

18	System.out.println(deployResponse);

19	// response is {"data":true,"success":true}

20	deployPost.releaseConnection();

21

22	//now we check the model

23	HttpGet httpGet = new HttpGet(url + "ids");

24	response = client.execute(httpGet);

25	String getAllModelsResponse = IOUtils.toString(response.getEntity().getContent(), Charset.forName("UTF-8"));

26	System.out.println(getAllModelsResponse);

27	// response is {"data":["test1"],"success":true}

28	httpGet.releaseConnection();

29

30	// lets score our deployed mode with parameters below

31	HttpPost scorePost = new HttpPost(url + modelId + "/score");

32	StringEntity params = new StringEntity("{" +

33	"\"fields\":" +

34	"{\"sepal_length\":4.5," +

35	"\"sepal_width\":3.5," +

36	"\"petal_length\":3.5," +

37	"\"petal_width\":1" +

38

"}" +

39

"} ");

40	scorePost.addHeader("content-type", "application/json");

41	scorePost.setEntity(params);

42

43	CloseableHttpResponse response2 = client.execute(scorePost);

44	String scoreResponse = IOUtils.toString(response2.getEntity().getContent(), Charset.forName("UTF-8"));

45	System.out.println(scoreResponse);

46	//response is{"data":{"result":{"class":"Iris-versicolor"}},"success":true}

47	scorePost.releaseConnection();

48

49	client.close();