Java Code Examples for org.apache.spark.sql.Row#getDouble()

private void assertCorrectness(Dataset<Row> rowDataset, Transformer transformer) {
	List<Row> sparkOutput = rowDataset.collectAsList();
	for (Row row : sparkOutput) {
		List<Object> list = row.getList(0);
		String[] sanitizedAddress = new String[list.size()];
		for (int j = 0; j < sanitizedAddress.length; j++) {
			sanitizedAddress[j] = (String) list.get(j);
		}

		Map<String, Object> data = new HashMap<>();
		data.put("sanitizedAddress", sanitizedAddress);

		double expected = row.getDouble(1);
		transformer.transform(data);
		double actual = (double) data.get("commonFraction");

		assertEquals(expected, actual, 0.01);
	}
}
 

@Override
public void read(Row row, int ordinal) throws StandardException {
	if (row.isNullAt(ordinal))
		setToNull();
	else {
		isNull = false;
		value = row.getDouble(ordinal);
		if (value == Double.POSITIVE_INFINITY ||
		    value == Double.NEGATIVE_INFINITY ||
		    value == Double.NaN)
		    throw StandardException.newException(SQLState.LANG_OUTSIDE_RANGE_FOR_DATATYPE, TypeId.DOUBLE_NAME);
	}
}
 

private static double[] transformRow2DataInstance(Row row, Attributes attributes) throws Exception {

        double[] instance = new double[row.length()];

        for (int i = 0; i < row.length(); i++) {

            Attribute att = attributes.getFullListOfAttributes().get(i);
            StateSpaceType space = att.getStateSpaceType();

            switch (space.getStateSpaceTypeEnum()) {
                case REAL:
                    instance[i] = row.getDouble(i);
                    break;

                case FINITE_SET:
                    String state = row.getString(i);
                    double index = ((FiniteStateSpace) space).getIndexOfState(state);
                    instance[i] = index;
                    break;

                default:
                    // This should never execute
                    throw new Exception("Unrecognized Error");
            }
        }

        return instance;
    }
 

@Test
public void testDecisionTreeRegression() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/regression_test.libsvm");

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    // Train a DecisionTree model.
    DecisionTreeRegressionModel regressionModel = new DecisionTreeRegressor()
            .setFeaturesCol("features").fit(trainingData);

    byte[] exportedModel = ModelExporter.export(regressionModel, null);

    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = regressionModel.transform(testData).select("features", "prediction").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(0);
        double actual = row.getDouble(1);

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put(transformer.getInputKeys().iterator().next(), v.toArray());
        transformer.transform(inputData);
        double predicted = (double) inputData.get(transformer.getOutputKeys().iterator().next());

        System.out.println(actual + ", " + predicted);
        assertEquals(actual, predicted, EPSILON);
    }
}
 

private static Object getPrimitiveValue(Row row, int ord, Type type) {
  if (row.isNullAt(ord)) {
    return null;
  }
  switch (type.typeId()) {
    case BOOLEAN:
      return row.getBoolean(ord);
    case INTEGER:
      return row.getInt(ord);
    case LONG:
      return row.getLong(ord);
    case FLOAT:
      return row.getFloat(ord);
    case DOUBLE:
      return row.getDouble(ord);
    case STRING:
      return row.getString(ord);
    case BINARY:
    case FIXED:
    case UUID:
      return row.get(ord);
    case DATE:
      return row.getDate(ord);
    case TIMESTAMP:
      return row.getTimestamp(ord);
    case DECIMAL:
      return row.getDecimal(ord);
    default:
      throw new IllegalArgumentException("Unhandled type " + type);
  }
}
 

@Test
public void testRandomForestRegression() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/regression_test.libsvm");

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    // Train a RandomForest model.
    RandomForestRegressionModel regressionModel = new RandomForestRegressor()
            .setFeaturesCol("features").fit(trainingData);

    byte[] exportedModel = ModelExporter.export(regressionModel, null);

    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = regressionModel.transform(testData).select("features", "prediction").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(0);
        double actual = row.getDouble(1);

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put(transformer.getInputKeys().iterator().next(), v.toArray());
        transformer.transform(inputData);
        double predicted = (double) inputData.get(transformer.getOutputKeys().iterator().next());

        System.out.println(actual + ", " + predicted);
        assertEquals(actual, predicted, EPSILON);
    }
}
 

private static Object getPrimitiveValue(Row row, int ord, Type type) {
  if (row.isNullAt(ord)) {
    return null;
  }
  switch (type.typeId()) {
    case BOOLEAN:
      return row.getBoolean(ord);
    case INTEGER:
      return row.getInt(ord);
    case LONG:
      return row.getLong(ord);
    case FLOAT:
      return row.getFloat(ord);
    case DOUBLE:
      return row.getDouble(ord);
    case STRING:
      return row.getString(ord);
    case BINARY:
    case FIXED:
    case UUID:
      return row.get(ord);
    case DATE:
      return row.getDate(ord);
    case TIMESTAMP:
      return row.getTimestamp(ord);
    case DECIMAL:
      return row.getDecimal(ord);
    default:
      throw new IllegalArgumentException("Unhandled type " + type);
  }
}
 

@Test
public void testPipeline() {
    // Prepare training documents, which are labeled.
    StructType schema = createStructType(new StructField[]{
            createStructField("id", LongType, false),
            createStructField("text", StringType, false),
            createStructField("label", DoubleType, false)
    });
    Dataset<Row> trainingData = spark.createDataFrame(Arrays.asList(
            cr(0L, "a b c d e spark", 1.0),
            cr(1L, "b d", 0.0),
            cr(2L, "spark f g h", 1.0),
            cr(3L, "hadoop mapreduce", 0.0)
    ), schema);

    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and LogisticRegression.
    RegexTokenizer tokenizer = new RegexTokenizer()
            .setInputCol("text")
            .setOutputCol("words")
            .setPattern("\\s")
            .setGaps(true)
            .setToLowercase(false);

    HashingTF hashingTF = new HashingTF()
            .setNumFeatures(1000)
            .setInputCol(tokenizer.getOutputCol())
            .setOutputCol("features");
    LogisticRegression lr = new LogisticRegression()
            .setMaxIter(10)
            .setRegParam(0.01);
    Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{tokenizer, hashingTF, lr});

    // Fit the pipeline to training documents.
    PipelineModel sparkPipelineModel = pipeline.fit(trainingData);


    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkPipelineModel);
    System.out.println(new String(exportedModel));

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    //prepare test data
    StructType testSchema = createStructType(new StructField[]{
            createStructField("id", LongType, false),
            createStructField("text", StringType, false),
    });
    Dataset<Row> testData = spark.createDataFrame(Arrays.asList(
            cr(4L, "spark i j k"),
            cr(5L, "l m n"),
            cr(6L, "mapreduce spark"),
            cr(7L, "apache hadoop")
    ), testSchema);

    //verify that predictions for spark pipeline and exported pipeline are the same
    List<Row> predictions = sparkPipelineModel.transform(testData).select("id", "text", "probability", "prediction").collectAsList();
    for (Row r : predictions) {
        System.out.println(r);
        double sparkPipelineOp = r.getDouble(3);
        Map<String, Object> data = new HashMap<String, Object>();
        data.put("text", r.getString(1));
        transformer.transform(data);
        double exportedPipelineOp = (double) data.get("prediction");
        double exportedPipelineProb = (double) data.get("probability");
        assertEquals(sparkPipelineOp, exportedPipelineOp, 0.01);
    }
}
 

@Test
public void testAlgebraicTransform(){
    //get expected Ax + b transform for given data
    double[] axB = axBTranform(this.coeff, this.data);
    // prepare data
    JavaRDD<Row> jrdd = sc.parallelize(Arrays.asList(
            RowFactory.create((data[0])),
            RowFactory.create((data[1])),
            RowFactory.create((data[2]))
    ));

    StructType schema = new StructType(new StructField[]{
            new StructField("trueProb", DataTypes.DoubleType, false, Metadata.empty())
    });

    DataFrame df = sqlContext.createDataFrame(jrdd, schema);

    AlgebraicTransform customSparkModel = new AlgebraicTransform()
            .setInputCol("trueProb")
            .setOutputCol("scaledProb")
            .setCoefficients(coeff);

    //Export this model
    byte[] exportedModel = ModelExporter.export(customSparkModel, df);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    //compare predictions
    Row[] customSparkOutput = customSparkModel.transform(df).select("trueProb", "scaledProb").collect();

    for (int i = 0; i < customSparkOutput.length; i++) {
        Row row= customSparkOutput[i];
        Map<String, Object> mapData = new HashMap<String, Object>();
        mapData.put(transformer.getInputKeys().iterator().next(), row.getDouble(0));
        transformer.transform(mapData);
        double transformedOp = (double) mapData.get(transformer.getOutputKeys().iterator().next());

        double sparkOp = ((double) row.getDouble(1));
        //Check if imported model produces same result as spark output
        assertEquals(transformedOp, sparkOp, 0.000001);
        //check if spark output is correct. This also tests for correctness of AlgebraicTransform
        assertEquals(axB[i], sparkOp, 0.000001);

    }
}
 

@Test
public void testProbabilityTransform(){
    //get expected true probability
    double[] trueProb = getTrueProb(data, this.p1, this.r1);
    // prepare data
    JavaRDD<Row> jrdd = sc.parallelize(Arrays.asList(
            RowFactory.create((data[0])),
            RowFactory.create((data[1])),
            RowFactory.create((data[2]))
    ));

    StructType schema = new StructType(new StructField[]{
            new StructField("probability", DataTypes.DoubleType, false, Metadata.empty())
            //new StructField("probability", new VectorUDT(), false, Metadata.empty()),
    });

    DataFrame df = sqlContext.createDataFrame(jrdd, schema);

    ProbabilityTransformModel customSparkModel = new ProbabilityTransform()
            .setInputCol("probability")
            .setOutputCol("trueProbability")
            .setActualClickProportion(p1)
            .setUnderSampledClickProportion(r1)
            .setProbIndex(idx)
            .fit(df);

    //Export this model
    byte[] exportedModel = ModelExporter.export(customSparkModel, df);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    //compare predictions
    Row[] customSparkOutput = customSparkModel.transform(df).select("probability", "trueProbability").collect();

    for (int i = 0; i < customSparkOutput.length; i++) {
        Row row= customSparkOutput[i];
        Map<String, Object> mapData = new HashMap<String, Object>();
        mapData.put(transformer.getInputKeys().iterator().next(), row.getDouble(0));
        transformer.transform(mapData);
        double transformedOp = (double) mapData.get(transformer.getOutputKeys().iterator().next());

        double sparkOp = ((double) row.getDouble(1));
        //Check if imported model produces same result as spark output
        assertEquals(transformedOp, sparkOp, 0.000001);
        //check if spark output is correct. This also tests for correctness of ProbabilityTransform
        assertEquals(trueProb[i], sparkOp, 0.000001);

    }
}
 

@Test
public void bucketizerTest() {
    double[] validData = {-0.5, -0.3, 0.0, 0.2};
    double[] expectedBuckets = {0.0, 0.0, 1.0, 1.0};
    double[] splits = {-0.5, 0.0, 0.5};

    StructType schema = new StructType(new StructField[]{
            new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
            new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
    });
    List<Row> trainingData = Arrays.asList(
            cr(0, validData[0]),
            cr(1, validData[1]),
            cr(2, validData[2]),
            cr(3, validData[3]));

    Dataset<Row> df = spark.createDataFrame(trainingData, schema);

    Bucketizer sparkModel = new Bucketizer()
            .setInputCol("feature")
            .setOutputCol("result")
            .setSplits(splits);

    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkModel);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    List<Row> sparkOutput = sparkModel.transform(df).orderBy("id").select("id", "feature", "result").collectAsList();

    for (Row r : sparkOutput) {
        double input = r.getDouble(1);
        double sparkOp = r.getDouble(2);

        Map<String, Object> data = new HashMap<String, Object>();
        data.put(sparkModel.getInputCol(), input);
        transformer.transform(data);
        double transformedInput = (double) data.get(sparkModel.getOutputCol());

        assertTrue((transformedInput >= 0) && (transformedInput <= 1));
        assertEquals(transformedInput, sparkOp, 0.01);
        assertEquals(transformedInput, expectedBuckets[r.getInt(0)], 0.01);
    }
}
 

/**
 * Trains a transition classifier on the data frame.
 * @param jsc
 * @param graphs
 * @param featureFrame
 * @param classifierFileName
 * @param numHiddenUnits
 * @return a transition classifier.
 */
public Transformer trainMLP(JavaSparkContext jsc,
		List<DependencyGraph> graphs, FeatureFrame featureFrame,
		String classifierFileName, int numHiddenUnits) {
	// create a SQLContext
	this.sqlContext = new SQLContext(jsc);
	// extract a data frame from these graphs
	DataFrame dataset = toDataFrame(jsc, graphs, featureFrame);
	
	// create a processing pipeline and fit it to the data frame
	Pipeline pipeline = createPipeline();
	PipelineModel pipelineModel = pipeline.fit(dataset);
	DataFrame trainingData = pipelineModel.transform(dataset);
	
	// cache the training data for better performance
	trainingData.cache();
	
	if (verbose) {
		trainingData.show(false);
	}
	
	// compute the number of different labels, which is the maximum element 
	// in the 'label' column.
	trainingData.registerTempTable("dfTable");
	Row row = sqlContext.sql("SELECT MAX(label) as maxValue from dfTable").first();
	int numLabels = (int)row.getDouble(0);
	numLabels++;
	
	int vocabSize = ((CountVectorizerModel)(pipelineModel.stages()[1])).getVocabSize();
	
	// default is a two-layer MLP
	int[] layers = {vocabSize, numLabels};
	// if user specify a hidden layer, use a 3-layer MLP:
	if (numHiddenUnits > 0) {
		layers = new int[3];
		layers[0] = vocabSize;
		layers[1] = numHiddenUnits;
		layers[2] = numLabels;
	}
	MultilayerPerceptronClassifier classifier = new MultilayerPerceptronClassifier()
		.setLayers(layers)
		.setBlockSize(128)
		.setSeed(1234L)
		.setTol((Double)params.getOrDefault(params.getTolerance()))
		.setMaxIter((Integer)params.getOrDefault(params.getMaxIter()));
	MultilayerPerceptronClassificationModel model = classifier.fit(trainingData);
	
	// compute precision on the training data
	//
	DataFrame result = model.transform(trainingData);
	DataFrame predictionAndLabel = result.select("prediction", "label");
	MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator().setMetricName("precision");
	if (verbose) {
		System.out.println("N = " + trainingData.count());
		System.out.println("D = " + vocabSize);
		System.out.println("K = " + numLabels);
		System.out.println("H = " + numHiddenUnits);
		System.out.println("training precision = " + evaluator.evaluate(predictionAndLabel));
	}
	
	// save the trained MLP to a file
	//
	String classifierPath = new Path(classifierFileName, "data").toString();
	jsc.parallelize(Arrays.asList(model), 1).saveAsObjectFile(classifierPath);
	// save the pipeline model to sub-directory "pipelineModel"
	// 
	try {
		String pipelinePath = new Path(classifierFileName, "pipelineModel").toString(); 
		pipelineModel.write().overwrite().save(pipelinePath);
	} catch (IOException e) {
		e.printStackTrace();
	}
	return model;
}
 

@Test
public void testDecisionTreeRegressionWithPipeline() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/regression_test.libsvm");

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    // Train a DecisionTree model.
    DecisionTreeRegressor dt = new DecisionTreeRegressor()
            .setFeaturesCol("features");

    Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{dt});

    // Train model.  This also runs the indexer.
    PipelineModel sparkPipeline = pipeline.fit(trainingData);

    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkPipeline, null);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = sparkPipeline.transform(testData).select("features", "prediction").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(0);
        double actual = row.getDouble(1);

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put(transformer.getInputKeys().iterator().next(), v.toArray());
        transformer.transform(inputData);
        double predicted = (double) inputData.get(transformer.getOutputKeys().iterator().next());

        assertEquals(actual, predicted, EPSILON);
    }
}
 

@Test
public void bucketizerTest() {
    double[] validData = {-0.5, -0.3, 0.0, 0.2};
    double[] expectedBuckets = {0.0, 0.0, 1.0, 1.0};
    double[] splits = {-0.5, 0.0, 0.5};

    StructType schema = new StructType(new StructField[]{
            new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
            new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
    });
    List<Row> trainingData = Arrays.asList(
            cr(0, validData[0]),
            cr(1, validData[1]),
            cr(2, validData[2]),
            cr(3, validData[3]));

    DataFrame df = sqlContext.createDataFrame(trainingData, schema);

    Bucketizer sparkModel = new Bucketizer()
            .setInputCol("feature")
            .setOutputCol("result")
            .setSplits(splits);

    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkModel, df);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = sparkModel.transform(df).orderBy("id").select("id", "feature", "result").collect();

    for (Row r : sparkOutput) {
        double input = r.getDouble(1);
        double sparkOp = r.getDouble(2);

        Map<String, Object> data = new HashMap<String, Object>();
        data.put(sparkModel.getInputCol(), input);
        transformer.transform(data);
        double transformedInput = (double) data.get(sparkModel.getOutputCol());

        assertTrue((transformedInput >= 0) && (transformedInput <= 1));
        assertEquals(transformedInput, sparkOp, EPSILON);
        assertEquals(transformedInput, expectedBuckets[r.getInt(0)], EPSILON);
    }
}
 

@Test
public void testPipeline() {
    // Prepare training documents, which are labeled.
    StructType schema = createStructType(new StructField[]{
            createStructField("id", LongType, false),
            createStructField("text", StringType, false),
            createStructField("label", DoubleType, false)
    });
    DataFrame trainingData = sqlContext.createDataFrame(Arrays.asList(
            cr(0L, "a b c d e spark", 1.0),
            cr(1L, "b d", 0.0),
            cr(2L, "spark f g h", 1.0),
            cr(3L, "hadoop mapreduce", 0.0)
    ), schema);

    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and LogisticRegression.
    RegexTokenizer tokenizer = new RegexTokenizer()
            .setInputCol("text")
            .setOutputCol("words")
            .setPattern("\\s")
            .setGaps(true)
            .setToLowercase(false);

    HashingTF hashingTF = new HashingTF()
            .setNumFeatures(1000)
            .setInputCol(tokenizer.getOutputCol())
            .setOutputCol("features");
    LogisticRegression lr = new LogisticRegression()
            .setMaxIter(10)
            .setRegParam(0.01);
    Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{tokenizer, hashingTF, lr});

    // Fit the pipeline to training documents.
    PipelineModel sparkPipelineModel = pipeline.fit(trainingData);


    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkPipelineModel, trainingData);
    System.out.println(new String(exportedModel));

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    //prepare test data
    StructType testSchema = createStructType(new StructField[]{
            createStructField("id", LongType, false),
            createStructField("text", StringType, false),
    });
    DataFrame testData = sqlContext.createDataFrame(Arrays.asList(
            cr(4L, "spark i j k"),
            cr(5L, "l m n"),
            cr(6L, "mapreduce spark"),
            cr(7L, "apache hadoop")
    ), testSchema);

    //verify that predictions for spark pipeline and exported pipeline are the same
    Row[] predictions = sparkPipelineModel.transform(testData).select("id", "text", "probability", "prediction").collect();
    for (Row r : predictions) {
        System.out.println(r);
        double sparkPipelineOp = r.getDouble(3);
        Map<String, Object> data = new HashMap<String, Object>();
        data.put("text", r.getString(1));
        transformer.transform(data);
        double exportedPipelineOp = (double) data.get("prediction");
        double exportedPipelineProb = (double) data.get("probability");
        assertEquals(sparkPipelineOp, exportedPipelineOp, EPSILON);
    }
}
 

@Test
public void testRandomForestClassification() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/classification_test.libsvm");

    StringIndexerModel stringIndexerModel = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("labelIndex")
            .fit(data);

    data = stringIndexerModel.transform(data);

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    // Train a RandomForest model.
    RandomForestClassificationModel classificationModel = new RandomForestClassifier()
            .setLabelCol("labelIndex")
            .setFeaturesCol("features")
            .setPredictionCol("prediction")
            .setRawPredictionCol("rawPrediction")
            .setProbabilityCol("probability")
            .fit(trainingData);


    byte[] exportedModel = ModelExporter.export(classificationModel, null);

    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = classificationModel.transform(testData).select("features", "prediction", "rawPrediction", "probability").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(0);
        double actual = row.getDouble(1);
        double [] actualProbability = ((Vector) row.get(3)).toArray();
        double[] actualRaw = ((Vector) row.get(2)).toArray();

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put(transformer.getInputKeys().iterator().next(), v.toArray());
        transformer.transform(inputData);
        double predicted = (double) inputData.get("prediction");
        double[] probability = (double[]) inputData.get("probability");
        double[] rawPrediction = (double[]) inputData.get("rawPrediction");

        assertEquals(actual, predicted, EPSILON);
        assertArrayEquals(actualProbability, probability, EPSILON);
        assertArrayEquals(actualRaw, rawPrediction, EPSILON);


    }

}
 

@Test
public void testRandomForestClassificationWithPipeline() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/classification_test.libsvm");

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    StringIndexer indexer = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("labelIndex");

    // Train a DecisionTree model.
    RandomForestClassifier classifier = new RandomForestClassifier()
            .setLabelCol("labelIndex")
            .setFeaturesCol("features")
            .setPredictionCol("prediction")
            .setRawPredictionCol("rawPrediction")
            .setProbabilityCol("probability");


    Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{indexer, classifier});

    // Train model.  This also runs the indexer.
    PipelineModel sparkPipeline = pipeline.fit(trainingData);

    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkPipeline, null);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = sparkPipeline.transform(testData).select("label", "features", "prediction", "rawPrediction", "probability").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(1);
        double actual = row.getDouble(2);
        double [] actualProbability = ((Vector) row.get(4)).toArray();
        double[] actualRaw = ((Vector) row.get(3)).toArray();

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put("features", v.toArray());
        inputData.put("label", row.get(0).toString());
        transformer.transform(inputData);
        double predicted = (double) inputData.get("prediction");
        double[] probability = (double[]) inputData.get("probability");
        double[] rawPrediction = (double[]) inputData.get("rawPrediction");

        assertEquals(actual, predicted, EPSILON);
        assertArrayEquals(actualProbability, probability, EPSILON);
        assertArrayEquals(actualRaw, rawPrediction, EPSILON);
    }
}
 

@Test
public void testDecisionTreeClassificationRawPrediction() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/classification_test.libsvm");

    StringIndexerModel stringIndexerModel = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("labelIndex")
            .fit(data);

    data = stringIndexerModel.transform(data);

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    // Train a DecisionTree model.
    DecisionTreeClassificationModel classificationModel = new DecisionTreeClassifier()
            .setLabelCol("labelIndex")
            .setFeaturesCol("features")
            .setRawPredictionCol("rawPrediction")
            .setPredictionCol("prediction")
            .fit(trainingData);

    byte[] exportedModel = ModelExporter.export(classificationModel, null);

    Transformer transformer = (DecisionTreeTransformer) ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = classificationModel.transform(testData).select("features", "prediction", "rawPrediction").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector inp = (Vector) row.get(0);
        double actual = row.getDouble(1);
        double[] actualRaw = ((Vector) row.get(2)).toArray();

        Map<String, Object> inputData = new HashMap<>();
        inputData.put(transformer.getInputKeys().iterator().next(), inp.toArray());
        transformer.transform(inputData);
        double predicted = (double) inputData.get(transformer.getOutputKeys().iterator().next());
        double[] rawPrediction = (double[]) inputData.get("rawPrediction");

        assertEquals(actual, predicted, EPSILON);
        assertArrayEquals(actualRaw, rawPrediction, EPSILON);
    }
}
 

@Test
public void testDecisionTreeClassificationWithPipeline() {
    // Load the data stored in LIBSVM format as a DataFrame.
    DataFrame data = sqlContext.read().format("libsvm").load("src/test/resources/classification_test.libsvm");

    // Split the data into training and test sets (30% held out for testing)
    DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3});
    DataFrame trainingData = splits[0];
    DataFrame testData = splits[1];

    StringIndexer indexer = new StringIndexer()
            .setInputCol("label")
            .setOutputCol("labelIndex");

    // Train a DecisionTree model.
    DecisionTreeClassifier classificationModel = new DecisionTreeClassifier()
            .setLabelCol("labelIndex")
            .setFeaturesCol("features");

    Pipeline pipeline = new Pipeline()
            .setStages(new PipelineStage[]{indexer, classificationModel});

    // Train model.  This also runs the indexer.
    PipelineModel sparkPipeline = pipeline.fit(trainingData);

    //Export this model
    byte[] exportedModel = ModelExporter.export(sparkPipeline, null);

    //Import and get Transformer
    Transformer transformer = ModelImporter.importAndGetTransformer(exportedModel);

    Row[] sparkOutput = sparkPipeline.transform(testData).select("label", "features", "prediction").collect();

    //compare predictions
    for (Row row : sparkOutput) {
        Vector v = (Vector) row.get(1);
        double actual = row.getDouble(2);

        Map<String, Object> inputData = new HashMap<String, Object>();
        inputData.put("features", v.toArray());
        inputData.put("label", row.get(0).toString());
        transformer.transform(inputData);
        double predicted = (double) inputData.get("prediction");

        assertEquals(actual, predicted, EPSILON);
    }
}
 

@Override
public Object evaluate(Row row) {		
	return row.getDouble(0)/row.getDouble(1);
}