You will learn
In this tutorial, you will learn how to quickly integrate the Time Series Change Point Detection SAP Leonardo Machine Learning Functional Services published from the SAP API Business Hub sandbox in a Java program.
Change Point Detection with SAP Leonardo Machine Learning Foundation in Java
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Change Point Detection with SAP Leonardo Machine Learning Foundation in Java
Discover how to implement SAP Leonardo Machine Learning Functional Service in a Java program
Details
The Time Series Change Point Detection service allows you to calculates and returns the probability of the change point for each data point in the provided univariate or multivariate time series.
You will then be able to substitute the Time Series Change Point Detection services with any other SAP Leonardo Machine Learning Functional Services that consumes images content.
For each API exposed in the SAP API Business Hub, you will be able to generate a code snippet which we will use in this tutorial.
This code snippet will be imported in a Java project built with the Eclipse IDE ™.
You can of course substitute the Eclipse IDE ™ with alternate tools and adapt the current tutorial steps.
In this tutorial, you will learn the basics of making API calls against the Machine Learning Functional Services published in SAP API Business Hub.
Step 1: Get Your Sandbox URL
In order to consume the Time Series Change Point Detection Machine Learning Functional Services, you will first need to get the service URI, your API Key and the request and response parameters.
Go to https://api.sap.com/ and click on the Browse tile.
Then you will be able to search for the SAP Leonardo Machine Learning - Functional Services, then click on the package found.
Click on Artifacts, then click on the Time Series Change Point Detection API.
As you can notice the Time Series Change Point Detection API has only one resource (or service): /inference_sync.
Now click on the Generate Code.
Note: the term inference refers to the application phase (scoring) an existing model (as opposed to the training or inception phase) and sync for synchronous.
Now select the Java tab.
Here is a copy of the generated code (which you can use later from here or from the Copy to clipboard button at the bottom):
DataOutputStream dataOut = null;
BufferedReader in =null;
try {
String url = "https://sandbox.api.sap.com/ml/changepointdetection/inference_sync";
URL urlObj = new URL(url);
HttpURLConnection connection = (HttpURLConnection) urlObj.openConnection();
//setting request method
connection.setRequestMethod("POST");
//adding headers
connection.setRequestProperty("content-type","multipart/form-data; boundary=---011000010111000001101001");
connection.setRequestProperty("Accept","application/json");
connection.setRequestProperty("APIKey","<API_KEY>");
connection.setDoInput(true);
//sending POST request
connection.setDoOutput(true);
dataOut = new DataOutputStream(connection.getOutputStream());
dataOut.writeBytes("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"options\"\r\n\r\nstring\r\n-----011000010111000001101001--");
dataOut.flush();
int responseCode = connection.getResponseCode();
in = new BufferedReader(new InputStreamReader(connection.getInputStream()));
String inputLine;
StringBuffer response = new StringBuffer();
while ((inputLine = in.readLine()) != null) {
response.append(inputLine);
}
//printing response
System.out.println(response.toString());
} catch (Exception e) {
//do something with exception
e.printStackTrace();
} finally {
try {
if(dataOut != null) {
dataOut.close();
}
if(in != null) {
in.close();
}
} catch (IOException e) {
//do something with exception
e.printStackTrace();
}
}
As you can notice, the Java import statements are missing along with request expected form data.
Step 2: Get Your API key
When using any of the APIs outside of the SAP API Business Hub, an application key will be needed in every request header of your APIs calls.
To get to your API key, click on the 
icon in the top right corner of the page. Click on the key icon.
The following pop-up should appear. Click on the Copy API Key button and save it in a text editor.
Step 3: Start the Eclipse IDE
Now, let’s start the Eclipse IDE and select your workspace (either the default, a new or an existing one).
If you don’t have the Eclipse IDE installed, you can download the latest version from the following link: Eclipse Downloads
You can pick either the Eclipse IDE for Java EE Developers or the Eclipse IDE for Java Developers.
By default the Java perspective should be Launched. If not, use the menu bar and select Window > Perspective > Open Perspective > Java.
If the Java perspective is not listed, then use the Other… o open it.
You can also close the Welcome Page.
Step 4: Create a New Project in the Eclipse IDE
Using the menu bar, go to File > New > Java Project.
You can name your project the way you want, here we will call it ml-changepointdetection.
Click on Finish.
Note: make sure you pick
JavaSE-1.8as your project Execution Runtime JRE. This should help avoid coding compliance and runtime issues with the provided code.
Step 5: Create a New Java Class
Using the menu bar, go to File > New > Class.
Make sure that your source folder is ml-imageclassification/src.
You can name your Java class the way you want, here we will call it DemoChangePointDetection and put it in a demo package.
Check the public static void main(String[] arg) box in order to get the main function created.
Click on Finish.
The default code in your newly created class should be something this:
package demo;
public class DemoChangePointDetection {
public static void main(String[] args) {
// TODO Auto-generated method stub
}
}
You can now paste the generated code that was collected during step 1 and copy it in the body of the main function.
Make sure you replace the <API_KEY> token in the code by your API key (collected during step 2).
You can now Organize imports by using either the CTRL+SHIFT+O keyboard shortcut, the Source > Organize imports from the menu bar or by using a right-click in your code.
The following import will be added to your class:
import java.io.BufferedReader;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;
Save your code.
Step 6: Process the input parameters as Form Data
Now can modify the code snippet to process the input series and send it as form data.
Replace the code below in the main function (around line 34):
//sending POST request
connection.setDoOutput(true);
dataOut = new DataOutputStream(connection.getOutputStream());
dataOut.writeBytes("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"options\"\r\n\r\nstring\r\n-----011000010111000001101001--");
dataOut.flush();
int responseCode = connection.getResponseCode();
in = new BufferedReader(new InputStreamReader(connection.getInputStream()));
String inputLine;
StringBuffer response = new StringBuffer();
while ((inputLine = in.readLine()) != null) {
response.append(inputLine);
}
//printing response
System.out.println(response.toString());
by
// sending POST request
connection.setDoOutput(true);
// read the input file name from user input
Scanner scanner = new Scanner(System.in);
// Reading from System.in
String default_data = "93969.262,76604.444,50000.000,17364.818,-17364.818,-50000.000,-76604.444,-93969.262,-100000.000,-93969.262,-76604.444,-50000.000,-17364.818,17364.818,50000.000,76604.444,93969.262,100000.000";
System.out.println("What is your series data : (example :" + default_data + ")");
String data = scanner.nextLine();
if (data == null || data.length() == 0) {
data = default_data;
}
String default_separator = ",";
System.out.println("What is your value separator (default: \"" + default_separator + "\") : ");
String separator = scanner.nextLine();
if (separator == null || separator.length() > 1 || separator.length() == 0) {
separator = default_separator;
}
String default_series_separator = ":";
System.out.println("What is your series separator (default: \"" + default_series_separator + "\") : ");
String series_separator = scanner.nextLine();
if (series_separator == null || series_separator.length() > 1 || series_separator.length() == 0) {
series_separator = default_series_separator;
}
scanner.close();
// prepare the constant for the form data
String LINE_FEED = "\r\n";
String SEPARATOR = "--";
String BOUNDARY = "------Boundary" + new BigInteger(128, new SecureRandom()).toString(32);
// set the form content as multipart
connection.setRequestProperty("Content-Type", "multipart/form-data; boundary=" + BOUNDARY);
// write the form data content
dataOut = new DataOutputStream(connection.getOutputStream());
dataOut.writeBytes(SEPARATOR + BOUNDARY + LINE_FEED);
dataOut.writeBytes("Content-Disposition: form-data; name=\"options\"" + LINE_FEED);
dataOut.writeBytes(LINE_FEED);
dataOut.writeBytes(
"{ \"separator\" : \"" + separator + "\", \"series_separator\" : \"" + series_separator + "\" }");
dataOut.writeBytes(LINE_FEED);
dataOut.writeBytes(SEPARATOR + BOUNDARY + LINE_FEED);
dataOut.writeBytes("Content-Disposition: form-data; name=\"texts\"" + LINE_FEED);
dataOut.writeBytes(LINE_FEED);
dataOut.writeBytes(data);
// finish the form content
dataOut.writeBytes(LINE_FEED);
dataOut.writeBytes(SEPARATOR + BOUNDARY + SEPARATOR + LINE_FEED);
dataOut.flush();
dataOut.close();
int responseCode = connection.getResponseCode();
if (responseCode != 200) {
in = new BufferedReader(new InputStreamReader(connection.getErrorStream()));
} else {
in = new BufferedReader(new InputStreamReader(connection.getInputStream()));
}
String inputLine;
StringBuffer response = new StringBuffer();
while ((inputLine = in.readLine()) != null) {
response.append(inputLine);
}
// printing response
System.out.println(response.toString());
You can now Organize imports by using either the CTRL+SHIFT+O keyboard shortcut, the Source > Organize imports from the menu bar or by using a right-click in your code.
The following import will be added to your class:
import java.math.BigInteger;
import java.security.SecureRandom;
import java.util.Scanner;
Save your code.
You can now run the code by either pressing ALT + SHIFT + X , J or by clicking on the Run 
button.
Enter the relevant parameters as detailed in the console.
You can use your favorite spreadsheet tool to generate your series of data or use the default input provided.
Step 7: Solution
Step 8: Validation
Next Steps
- Select your next tutorial from these SAP Leonardo Machine Learning groups: SAP API Business Hub, Java or SAPUI5
- Select a tutorial from the Tutorial Navigator or the Tutorial Catalog