org.apache.spark.ml.feature.IndexToString Java Examples
The following examples show how to use
org.apache.spark.ml.feature.IndexToString.
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Example #1
Source File: IndexToStringConverter.java From jpmml-sparkml with GNU Affero General Public License v3.0 | 5 votes |
@Override public List<Feature> encodeFeatures(SparkMLEncoder encoder){ IndexToString transformer = getTransformer(); DataField dataField = encoder.createDataField(formatName(transformer), OpType.CATEGORICAL, DataType.STRING, Arrays.asList(transformer.getLabels())); return Collections.singletonList(new CategoricalFeature(encoder, dataField)); }
Example #2
Source File: SparkMultiClassClassifier.java From mmtf-spark with Apache License 2.0 | 4 votes |
/** * Dataset must at least contain the following two columns: * label: the class labels * features: feature vector * @param data * @return map with metrics */ public Map<String,String> fit(Dataset<Row> data) { int classCount = (int)data.select(label).distinct().count(); StringIndexerModel labelIndexer = new StringIndexer() .setInputCol(label) .setOutputCol("indexedLabel") .fit(data); // Split the data into training and test sets (30% held out for testing) Dataset<Row>[] splits = data.randomSplit(new double[] {1.0-testFraction, testFraction}, seed); Dataset<Row> trainingData = splits[0]; Dataset<Row> testData = splits[1]; String[] labels = labelIndexer.labels(); System.out.println(); System.out.println("Class\tTrain\tTest"); for (String l: labels) { System.out.println(l + "\t" + trainingData.select(label).filter(label + " = '" + l + "'").count() + "\t" + testData.select(label).filter(label + " = '" + l + "'").count()); } // Set input columns predictor .setLabelCol("indexedLabel") .setFeaturesCol("features"); // 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, predictor, labelConverter}); // Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData); // Make predictions. Dataset<Row> predictions = model.transform(testData).cache(); // Display some sample predictions System.out.println(); System.out.println("Sample predictions: " + predictor.getClass().getSimpleName()); predictions.sample(false, 0.1, seed).show(25); predictions = predictions.withColumnRenamed(label, "stringLabel"); predictions = predictions.withColumnRenamed("indexedLabel", label); // collect metrics Dataset<Row> pred = predictions.select("prediction",label); Map<String,String> metrics = new LinkedHashMap<>(); metrics.put("Method", predictor.getClass().getSimpleName()); if (classCount == 2) { BinaryClassificationMetrics b = new BinaryClassificationMetrics(pred); metrics.put("AUC", Float.toString((float)b.areaUnderROC())); } MulticlassMetrics m = new MulticlassMetrics(pred); metrics.put("F", Float.toString((float)m.weightedFMeasure())); metrics.put("Accuracy", Float.toString((float)m.accuracy())); metrics.put("Precision", Float.toString((float)m.weightedPrecision())); metrics.put("Recall", Float.toString((float)m.weightedRecall())); metrics.put("False Positive Rate", Float.toString((float)m.weightedFalsePositiveRate())); metrics.put("True Positive Rate", Float.toString((float)m.weightedTruePositiveRate())); metrics.put("", "\nConfusion Matrix\n" + Arrays.toString(labels) +"\n" + m.confusionMatrix().toString()); return metrics; }
Example #3
Source File: JavaIndexToStringExample.java From SparkDemo with MIT License | 4 votes |
public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaIndexToStringExample") .getOrCreate(); // $example on$ List<Row> data = Arrays.asList( RowFactory.create(0, "a"), RowFactory.create(1, "b"), RowFactory.create(2, "c"), RowFactory.create(3, "a"), RowFactory.create(4, "a"), RowFactory.create(5, "c") ); StructType schema = new StructType(new StructField[]{ new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), new StructField("category", DataTypes.StringType, false, Metadata.empty()) }); Dataset<Row> df = spark.createDataFrame(data, schema); StringIndexerModel indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") .fit(df); Dataset<Row> indexed = indexer.transform(df); System.out.println("Transformed string column '" + indexer.getInputCol() + "' " + "to indexed column '" + indexer.getOutputCol() + "'"); indexed.show(); StructField inputColSchema = indexed.schema().apply(indexer.getOutputCol()); System.out.println("StringIndexer will store labels in output column metadata: " + Attribute.fromStructField(inputColSchema).toString() + "\n"); IndexToString converter = new IndexToString() .setInputCol("categoryIndex") .setOutputCol("originalCategory"); Dataset<Row> converted = converter.transform(indexed); System.out.println("Transformed indexed column '" + converter.getInputCol() + "' back to " + "original string column '" + converter.getOutputCol() + "' using labels in metadata"); converted.select("id", "categoryIndex", "originalCategory").show(); // $example off$ spark.stop(); }
Example #4
Source File: IndexToStringConverter.java From jpmml-sparkml with GNU Affero General Public License v3.0 | 4 votes |
public IndexToStringConverter(IndexToString transformer){ super(transformer); }