如何将数字和分类功能传递给Apache Spark中的RandomForestRegressor：Java中的MLlib？

Question

如何将数字和分类功能传递给Apache Spark中的RandomForestRegressor：Java中的MLlib？

我能用数字或分类来做，但我不知道如何一起实现它。

我的工作代码如下（仅用于预测的数字特征）

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

对于那里的任何人，这是解决方案：

    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);