Java Code Examples for org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator#evaluate()

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaRandomForestClassifierExample")
    .getOrCreate();

  // $example on$
  // Load and parse the data file, converting it to a DataFrame.
  Dataset<Row> data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");

  // Index labels, adding metadata to the label column.
  // Fit on whole dataset to include all labels in index.
  StringIndexerModel labelIndexer = new StringIndexer()
    .setInputCol("label")
    .setOutputCol("indexedLabel")
    .fit(data);
  // Automatically identify categorical features, and index them.
  // Set maxCategories so features with > 4 distinct values are treated as continuous.
  VectorIndexerModel featureIndexer = new VectorIndexer()
    .setInputCol("features")
    .setOutputCol("indexedFeatures")
    .setMaxCategories(4)
    .fit(data);

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

  // Train a RandomForest model.
  RandomForestClassifier rf = new RandomForestClassifier()
    .setLabelCol("indexedLabel")
    .setFeaturesCol("indexedFeatures");

  // Convert indexed labels back to original labels.
  IndexToString labelConverter = new IndexToString()
    .setInputCol("prediction")
    .setOutputCol("predictedLabel")
    .setLabels(labelIndexer.labels());

  // Chain indexers and forest in a Pipeline
  Pipeline pipeline = new Pipeline()
    .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});

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

  // Make predictions.
  Dataset<Row> predictions = model.transform(testData);

  // Select example rows to display.
  predictions.select("predictedLabel", "label", "features").show(5);

  // Select (prediction, true label) and compute test error
  MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
    .setLabelCol("indexedLabel")
    .setPredictionCol("prediction")
    .setMetricName("accuracy");
  double accuracy = evaluator.evaluate(predictions);
  System.out.println("Test Error = " + (1.0 - accuracy));

  RandomForestClassificationModel rfModel = (RandomForestClassificationModel)(model.stages()[2]);
  System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());
  // $example off$

  spark.stop();
}
 

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaNaiveBayesExample")
    .getOrCreate();

  // $example on$
  // Load training data
  Dataset<Row> dataFrame =
    spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
  // Split the data into train and test
  Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
  Dataset<Row> train = splits[0];
  Dataset<Row> test = splits[1];

  // create the trainer and set its parameters
  NaiveBayes nb = new NaiveBayes();

  // train the model
  NaiveBayesModel model = nb.fit(train);

  // Select example rows to display.
  Dataset<Row> predictions = model.transform(test);
  predictions.show();

  // compute accuracy on the test set
  MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
    .setLabelCol("label")
    .setPredictionCol("prediction")
    .setMetricName("accuracy");
  double accuracy = evaluator.evaluate(predictions);
  System.out.println("Test set accuracy = " + accuracy);
  // $example off$

  spark.stop();
}
 

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaGradientBoostedTreeClassifierExample")
    .getOrCreate();

  // $example on$
  // Load and parse the data file, converting it to a DataFrame.
  Dataset<Row> data = spark
    .read()
    .format("libsvm")
    .load("data/mllib/sample_libsvm_data.txt");

  // Index labels, adding metadata to the label column.
  // Fit on whole dataset to include all labels in index.
  StringIndexerModel labelIndexer = new StringIndexer()
    .setInputCol("label")
    .setOutputCol("indexedLabel")
    .fit(data);
  // Automatically identify categorical features, and index them.
  // Set maxCategories so features with > 4 distinct values are treated as continuous.
  VectorIndexerModel featureIndexer = new VectorIndexer()
    .setInputCol("features")
    .setOutputCol("indexedFeatures")
    .setMaxCategories(4)
    .fit(data);

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

  // Train a GBT model.
  GBTClassifier gbt = new GBTClassifier()
    .setLabelCol("indexedLabel")
    .setFeaturesCol("indexedFeatures")
    .setMaxIter(10);

  // Convert indexed labels back to original labels.
  IndexToString labelConverter = new IndexToString()
    .setInputCol("prediction")
    .setOutputCol("predictedLabel")
    .setLabels(labelIndexer.labels());

  // Chain indexers and GBT in a Pipeline.
  Pipeline pipeline = new Pipeline()
    .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});

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

  // Make predictions.
  Dataset<Row> predictions = model.transform(testData);

  // Select example rows to display.
  predictions.select("predictedLabel", "label", "features").show(5);

  // Select (prediction, true label) and compute test error.
  MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
    .setLabelCol("indexedLabel")
    .setPredictionCol("prediction")
    .setMetricName("accuracy");
  double accuracy = evaluator.evaluate(predictions);
  System.out.println("Test Error = " + (1.0 - accuracy));

  GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]);
  System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());
  // $example off$

  spark.stop();
}
 

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaDecisionTreeClassificationExample")
    .getOrCreate();

  // $example on$
  // Load the data stored in LIBSVM format as a DataFrame.
  Dataset<Row> data = spark
    .read()
    .format("libsvm")
    .load("data/mllib/sample_libsvm_data.txt");

  // Index labels, adding metadata to the label column.
  // Fit on whole dataset to include all labels in index.
  StringIndexerModel labelIndexer = new StringIndexer()
    .setInputCol("label")
    .setOutputCol("indexedLabel")
    .fit(data);

  // Automatically identify categorical features, and index them.
  VectorIndexerModel featureIndexer = new VectorIndexer()
    .setInputCol("features")
    .setOutputCol("indexedFeatures")
    .setMaxCategories(4) // features with > 4 distinct values are treated as continuous.
    .fit(data);

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

  // Train a DecisionTree model.
  DecisionTreeClassifier dt = new DecisionTreeClassifier()
    .setLabelCol("indexedLabel")
    .setFeaturesCol("indexedFeatures");

  // Convert indexed labels back to original labels.
  IndexToString labelConverter = new IndexToString()
    .setInputCol("prediction")
    .setOutputCol("predictedLabel")
    .setLabels(labelIndexer.labels());

  // Chain indexers and tree in a Pipeline.
  Pipeline pipeline = new Pipeline()
    .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});

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

  // Make predictions.
  Dataset<Row> predictions = model.transform(testData);

  // Select example rows to display.
  predictions.select("predictedLabel", "label", "features").show(5);

  // Select (prediction, true label) and compute test error.
  MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
    .setLabelCol("indexedLabel")
    .setPredictionCol("prediction")
    .setMetricName("accuracy");
  double accuracy = evaluator.evaluate(predictions);
  System.out.println("Test Error = " + (1.0 - accuracy));

  DecisionTreeClassificationModel treeModel =
    (DecisionTreeClassificationModel) (model.stages()[2]);
  System.out.println("Learned classification tree model:\n" + treeModel.toDebugString());
  // $example off$

  spark.stop();
}
 

public static void main(String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("JavaOneVsRestExample")
    .getOrCreate();

  // $example on$
  // load data file.
  Dataset<Row> inputData = spark.read().format("libsvm")
    .load("data/mllib/sample_multiclass_classification_data.txt");

  // generate the train/test split.
  Dataset<Row>[] tmp = inputData.randomSplit(new double[]{0.8, 0.2});
  Dataset<Row> train = tmp[0];
  Dataset<Row> test = tmp[1];

  // configure the base classifier.
  LogisticRegression classifier = new LogisticRegression()
    .setMaxIter(10)
    .setTol(1E-6)
    .setFitIntercept(true);

  // instantiate the One Vs Rest Classifier.
  OneVsRest ovr = new OneVsRest().setClassifier(classifier);

  // train the multiclass model.
  OneVsRestModel ovrModel = ovr.fit(train);

  // score the model on test data.
  Dataset<Row> predictions = ovrModel.transform(test)
    .select("prediction", "label");

  // obtain evaluator.
  MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
          .setMetricName("accuracy");

  // compute the classification error on test data.
  double accuracy = evaluator.evaluate(predictions);
  System.out.println("Test Error = " + (1 - accuracy));
  // $example off$

  spark.stop();
}
 

/**
 * Trains a whitespace classifier model and save the resulting pipeline model
 * to an external file. 
 * @param sentences a list of tokenized sentences.
 * @param pipelineModelFileName
 * @param numFeatures
 */
public void train(List<String> sentences, String pipelineModelFileName, int numFeatures) {
	List<WhitespaceContext> contexts = new ArrayList<WhitespaceContext>(sentences.size());
	int id = 0;
	for (String sentence : sentences) {
		sentence = sentence.trim();
		for (int j = 0; j < sentence.length(); j++) {
			char c = sentence.charAt(j);
			if (c == ' ' || c == '_') {
				WhitespaceContext context = new WhitespaceContext();
				context.setId(id++);
				context.setContext(extractContext(sentence, j));
				context.setLabel(c == ' ' ? 0d : 1d);
				contexts.add(context);
			}
		}
	}
	JavaRDD<WhitespaceContext> jrdd = jsc.parallelize(contexts);
	DataFrame df = sqlContext.createDataFrame(jrdd, WhitespaceContext.class);
	df.show(false);
	System.out.println("N = " + df.count());
	df.groupBy("label").count().show();
	
	org.apache.spark.ml.feature.Tokenizer tokenizer = new Tokenizer()
			.setInputCol("context").setOutputCol("words");
	HashingTF hashingTF = new HashingTF().setNumFeatures(numFeatures)
			.setInputCol(tokenizer.getOutputCol()).setOutputCol("features");
	LogisticRegression lr = new LogisticRegression().setMaxIter(100)
			.setRegParam(0.01);
	Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {
			tokenizer, hashingTF, lr });
	model = pipeline.fit(df);
	
	try {
		model.write().overwrite().save(pipelineModelFileName);
	} catch (IOException e) {
		e.printStackTrace();
	}
	
	DataFrame predictions = model.transform(df);
	predictions.show();
	MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator().setMetricName("precision");
	double accuracy = evaluator.evaluate(predictions);
	System.out.println("training accuracy = " + accuracy);
	
	LogisticRegressionModel lrModel = (LogisticRegressionModel) model.stages()[2];
	LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
	double[] objectiveHistory = trainingSummary.objectiveHistory();
	System.out.println("#(iterations) = " + objectiveHistory.length);
	for (double lossPerIteration : objectiveHistory) {
	  System.out.println(lossPerIteration);
	}
	
}