Apache SparkのRandomForestRegressorに数値とカテゴリの機能を渡す方法：JavaのMLlib？

Question

数値とカテゴリの機能をApache SparkのRandomForestRegressorに渡す方法：JavaのMLlib？

数字でもカテゴリでもできますが、一緒に実装する方法はわかりません。そこに誰のための

（予測に使用される唯一の数値機能）を次のように私の作業コードがある

String[] featureNumericalCols = new String[]{ 
     "squareM", 
     "timeTimeToPragueCityCenter", 
}; 
String[] featureStringCols = new String[]{ //not used 
     "type", 
     "floor", 
     "disposition", 
}; 
VectorAssembler assembler = new VectorAssembler().setInputCols(featureNumericalCols).setOutputCol("features"); 
Dataset<Row> numericalData = assembler.transform(data); 
numericalData.show(); 
RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("price") 
     .setFeaturesCol("features"); 
// Chain indexer and forest in a Pipeline 
Pipeline pipeline = new Pipeline() 
    .setStages(new PipelineStage[]{assembler, rf}); 
// Train model. This also runs the indexer. 
PipelineModel model = pipeline.fit(trainingData); 
// Make predictions. 
Dataset<Row> predictions = model.transform(testData); 

Answer 1

、これは解決策である：

StringIndexer typeIndexer = new StringIndexer() 
      .setInputCol("type") 
      .setOutputCol("typeIndex"); 

    preparedData = typeIndexer.fit(preparedData).transform(preparedData); 

    StringIndexer floorIndexer = new StringIndexer() 
      .setInputCol("floor") 
      .setOutputCol("floorIndex"); 

    preparedData = floorIndexer.fit(preparedData).transform(preparedData); 

    StringIndexer dispositionIndexer = new StringIndexer() 
      .setInputCol("disposition") 
      .setOutputCol("dispositionIndex"); 

    preparedData = dispositionIndexer.fit(preparedData).transform(preparedData); 

    String[] featureCols = new String[]{ 
      "squareM", 
      "timeTimeToPragueCityCenter", 
      "floorIndex", 
      "floorIndex", 
      "dispositionIndex" 
    }; 

    VectorAssembler assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features"); 

    preparedData = assembler.transform(preparedData); 

// ... some more impelemtation details 

    RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("price") 
      .setFeaturesCol("features"); 

    return rf.fit(preparedData);