Making Predictions via a Streaming Perceptron Model

Purpose

This application demonstrates how to configure ESB Streaming Integrator Tooling to perform binary classification using a streaming Perceptron.

Prerequisites

  1. Save this sample.
  2. If there is no syntax error, the following message is shown on the console. 
    * Siddhi App streaming-perceptron-sample successfully deployed.

Executing the Sample

  1. Start the Siddhi application by clicking on 'Run'.
  2. If the Siddhi application starts successfully, the following messages are shown on the console. 
    * streaming-perceptron-sample.siddhi - Started Successfully!

Testing the Sample

Note: The Streaming Perceptron for streaming machine learning needs to be trained prior to perform prediction.

Training phase

Send events through one or more of the following methods.

Send events to ProductionTrainStream, via event simulator
  1. Open the event simulator by clicking on the second icon or pressing Ctrl+Shift+I.
  2. In the Single Simulation tab of the panel, specify the values as follows:
    • Siddhi App Name: streaming-perceptron-sample
    • Stream Name: ProductionTrainStream

  3. In the name and amount fields, enter the following and then click Send to send the event. 

    density: 50.4
emperature: 30.03

  4. Send some more events.

Send events to the simulator HTTP endpoint through the curl command
  1. Open a new terminal and issue the following command: 
    curl -X POST -d '{"streamName": "ProductionTrainStream", "siddhiAppName": "streaming-perceptron-sample","data": [50.4, 30.03, true]}' http://localhost:9390/simulation/single -H 'content-type: text/plain'
  2. If there is no error, the following messages are shown on the terminal: 
    {"status":"OK","message":"Single Event simulation started successfully"}
Publish events with Postman
  1. Install 'Postman' application from Chrome web store.
  2. Launch the application.
  3. Make a 'Post' request to the 'http://localhost:9390/simulation/single' endpoint. Set the Content-Type to 'text/plain' and set the request body in text as follows: 
    {"streamName": "ProductionTrainStream", "siddhiAppName": "streaming-perceptron-sample","data": [50.4, 30.03, true]}
  4. Click 'send'. If there is no error, the following messages are shown on the console: 
    "status": "OK",
"message": "Single Event simulation started successfully"

Testing phase

Send events through one or more of the following methods.

Send events to ProductionInputStream, via event simulator
  1. Open the event simulator by clicking on the second icon or pressing Ctrl+Shift+I.
  2. In the Single Simulation tab of the panel, specify the values as follows:
    • Siddhi App Name: streaming-perceptron-sample
    • Stream Name: ProductionInputStream
  3. In the name and amount fields, enter the following and then click Send to send the event. 
    density: 30.4
emperature: 20.5
  4. Send some more events.
Send events to the simulator HTTP endpoint through the curl command
  1. Open a new terminal and issue the following command: 
    curl -X POST -d '{"streamName": "ProductionInputStream", "siddhiAppName": "streaming-perceptron-sample","data": [30.4, 20.5]}' http://localhost:9390/simulation/single -H 'content-type: text/plain'
  2. If there is no error, the following messages are shown on the terminal: 
    {"status":"OK","message":"Single Event simulation started successfully"}
Publish events with Postman
  1. Install 'Postman' application from Chrome web store.
  2. Launch the application.
  3. Make a 'Post' request to the 'http://localhost:9390/simulation/single' endpoint. Set the Content-Type to 'text/plain' and set the request body in text as follows: 
    {"streamName": "ProductionInputStream", "siddhiAppName": "streaming-perceptron-sample","data": [30.4, 20.5]}
  4. Click 'send'. If there is no error, the following messages are shown on the console: 
    "status": "OK",
"message": "Single Event simulation started successfully"

Viewing the Results

See the output on the terminal: 

INFO {io.siddhi.core.query.processor.stream.LogStreamProcessor} - streaming-perceptron-sample: LOGGER, StreamEvent{ timestamp=1513596699142, beforeWindowData=null, onAfterWindowData=null, outputData=[34.0, 12.0, false, 0.0], type=CURRENT, next=null}

@App:name("streaming-perceptron-sample")
@App:description('Train a streaming Perceptron model to predict whether an item passes quality check.')


define stream ProductionTrainStream (density double, temperature double, qualityCheck_pass bool );

define stream ProductionInputStream (density double, temperature double);

@sink(type='log')
define stream PredictedQCStream (density double, temperature double, prediction bool, confidenceLevel double);

@info(name = 'query-train')
from ProductionTrainStream#streamingml:updatePerceptronClassifier('QCmodel', qualityCheck_pass, 0.1, density, temperature)
select *
insert into trainOutputStream;

@info(name = 'query-predict')
from ProductionInputStream#streamingml:perceptronClassifier('QCmodel', 0.0, 0.5, density, temperature)
select *
insert into PredictedQCStream;
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