Examples with SpamMessage io.cdap.cdap.datapipeline.mock.SpamMessage used on opensource projects

Example 1 with SpamMessage

use of io.cdap.cdap.datapipeline.mock.SpamMessage in project cdap by caskdata.

the class DataPipelineTest method testSinglePhaseWithSparkSink.

private void testSinglePhaseWithSparkSink() throws Exception {
    /*
     * source1 ---|
     *            |--> sparksink
     * source2 ---|
     */
    ETLBatchConfig etlConfig = ETLBatchConfig.builder().addStage(new ETLStage("source1", MockSource.getPlugin("messages1", SpamMessage.SCHEMA))).addStage(new ETLStage("source2", MockSource.getPlugin("messages2", SpamMessage.SCHEMA))).addStage(new ETLStage("customsink", new ETLPlugin(NaiveBayesTrainer.PLUGIN_NAME, SparkSink.PLUGIN_TYPE, ImmutableMap.of("fileSetName", "modelFileSet", "path", "output", "fieldToClassify", SpamMessage.TEXT_FIELD, "predictionField", SpamMessage.SPAM_PREDICTION_FIELD), null))).addConnection("source1", "customsink").addConnection("source2", "customsink").build();
    AppRequest<ETLBatchConfig> appRequest = new AppRequest<>(APP_ARTIFACT, etlConfig);
    ApplicationId appId = NamespaceId.DEFAULT.app("SparkSinkApp");
    ApplicationManager appManager = deployApplication(appId, appRequest);
    // set up five spam messages and five non-spam messages to be used for classification
    List<StructuredRecord> messagesToWrite = new ArrayList<>();
    messagesToWrite.add(new SpamMessage("buy our clothes", 1.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("sell your used books to us", 1.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("earn money for free", 1.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("this is definitely not spam", 1.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("you won the lottery", 1.0).toStructuredRecord());
    // write records to source1
    DataSetManager<Table> inputManager = getDataset(NamespaceId.DEFAULT.dataset("messages1"));
    MockSource.writeInput(inputManager, messagesToWrite);
    messagesToWrite.clear();
    messagesToWrite.add(new SpamMessage("how was your day", 0.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("what are you up to", 0.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("this is a genuine message", 0.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("this is an even more genuine message", 0.0).toStructuredRecord());
    messagesToWrite.add(new SpamMessage("could you send me the report", 0.0).toStructuredRecord());
    // write records to source2
    inputManager = getDataset(NamespaceId.DEFAULT.dataset("messages2"));
    MockSource.writeInput(inputManager, messagesToWrite);
    // ingest in some messages to be classified
    DataSetManager<FileSet> fileSetManager = getDataset(NaiveBayesTrainer.TEXTS_TO_CLASSIFY);
    FileSet fileSet = fileSetManager.get();
    try (PrintStream out = new PrintStream(fileSet.getLocation("inputTexts").getOutputStream(), true, "UTF-8")) {
        out.println("how are you doing today");
        out.println("free money money");
        out.println("what are you doing today");
        out.println("genuine report");
    }
    // manually trigger the pipeline
    Map<String, String> runtimeArgs = new HashMap<>();
    FileSetArguments.setInputPath(runtimeArgs, "inputTexts");
    WorkflowManager workflowManager = appManager.getWorkflowManager(SmartWorkflow.NAME);
    workflowManager.start(runtimeArgs);
    workflowManager.waitForRun(ProgramRunStatus.COMPLETED, 5, TimeUnit.MINUTES);
    DataSetManager<KeyValueTable> classifiedTexts = getDataset(NaiveBayesTrainer.CLASSIFIED_TEXTS);
    Assert.assertEquals(0.0d, Bytes.toDouble(classifiedTexts.get().read("how are you doing today")), 0.01d);
    // only 'free money money' should be predicated as spam
    Assert.assertEquals(1.0d, Bytes.toDouble(classifiedTexts.get().read("free money money")), 0.01d);
    Assert.assertEquals(0.0d, Bytes.toDouble(classifiedTexts.get().read("what are you doing today")), 0.01d);
    Assert.assertEquals(0.0d, Bytes.toDouble(classifiedTexts.get().read("genuine report")), 0.01d);
    validateMetric(5, appId, "source1.records.out");
    validateMetric(5, appId, "source2.records.out");
    validateMetric(10, appId, "customsink.records.in");
}

Example 2 with SpamMessage

use of io.cdap.cdap.datapipeline.mock.SpamMessage in project cdap by caskdata.

the class DataPipelineTest method testSinglePhaseWithSparkCompute.

private void testSinglePhaseWithSparkCompute() throws Exception {
    /*
     * source --> sparkcompute --> sink
     */
    String classifiedTextsTable = "classifiedTextTable";
    ETLBatchConfig etlConfig = ETLBatchConfig.builder().addStage(new ETLStage("source", MockSource.getPlugin(NaiveBayesTrainer.TEXTS_TO_CLASSIFY_SOURCE, SpamMessage.SCHEMA))).addStage(new ETLStage("sparkcompute", new ETLPlugin(NaiveBayesClassifier.PLUGIN_NAME, SparkCompute.PLUGIN_TYPE, ImmutableMap.of("fileSetName", "modelFileSet", "path", "output", "fieldToClassify", SpamMessage.TEXT_FIELD, "fieldToSet", SpamMessage.SPAM_PREDICTION_FIELD), null))).addStage(new ETLStage("sink", MockSink.getPlugin(classifiedTextsTable))).addConnection("source", "sparkcompute").addConnection("sparkcompute", "sink").build();
    AppRequest<ETLBatchConfig> appRequest = new AppRequest<>(APP_ARTIFACT, etlConfig);
    ApplicationId appId = NamespaceId.DEFAULT.app("SparkComputeApp");
    ApplicationManager appManager = deployApplication(appId, appRequest);
    // write some some messages to be classified
    List<StructuredRecord> messagesToWrite = new ArrayList<>();
    messagesToWrite.add(new SpamMessage("how are you doing today").toStructuredRecord());
    messagesToWrite.add(new SpamMessage("free money money").toStructuredRecord());
    messagesToWrite.add(new SpamMessage("what are you doing today").toStructuredRecord());
    messagesToWrite.add(new SpamMessage("genuine report").toStructuredRecord());
    DataSetManager<Table> inputManager = getDataset(NamespaceId.DEFAULT.dataset(NaiveBayesTrainer.TEXTS_TO_CLASSIFY_SOURCE));
    MockSource.writeInput(inputManager, messagesToWrite);
    // manually trigger the pipeline
    WorkflowManager workflowManager = appManager.getWorkflowManager(SmartWorkflow.NAME);
    workflowManager.start();
    workflowManager.waitForRun(ProgramRunStatus.COMPLETED, 5, TimeUnit.MINUTES);
    DataSetManager<Table> classifiedTexts = getDataset(classifiedTextsTable);
    List<StructuredRecord> structuredRecords = MockSink.readOutput(classifiedTexts);
    Set<SpamMessage> results = new HashSet<>();
    for (StructuredRecord structuredRecord : structuredRecords) {
        results.add(SpamMessage.fromStructuredRecord(structuredRecord));
    }
    Set<SpamMessage> expected = new HashSet<>();
    expected.add(new SpamMessage("how are you doing today", 0.0));
    // only 'free money money' should be predicated as spam
    expected.add(new SpamMessage("free money money", 1.0));
    expected.add(new SpamMessage("what are you doing today", 0.0));
    expected.add(new SpamMessage("genuine report", 0.0));
    Assert.assertEquals(expected, results);
    validateMetric(4, appId, "source.records.out");
    validateMetric(4, appId, "sparkcompute.records.in");
    validateMetric(4, appId, "sink.records.in");
}