如何在Apache Spark的MLlib中将数值和分类特征传递给RandomForestRegressor？

我能够单独处理数值或分类特征，但不知道如何将它们一起实现。

我目前的工作代码如下（仅使用数值特征进行预测）

String[] featureNumericalCols = new String[]{        "squareM",        "timeTimeToPragueCityCenter",};String[] featureStringCols = new String[]{ //未使用        "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");// 将索引器和森林链接到Pipeline中Pipeline pipeline = new Pipeline()    .setStages(new PipelineStage[]{assembler, rf});// 训练模型。这也运行了索引器。PipelineModel model = pipeline.fit(trainingData);// 进行预测。Dataset<Row> predictions = model.transform(testData);

回答：

对于所有人，这里是解决方案：

    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",            "typeIndex",            "floorIndex",            "dispositionIndex"    };    VectorAssembler assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features");    preparedData = assembler.transform(preparedData); //    ... 一些更多的实现细节    RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("price")            .setFeaturesCol("features");    return rf.fit(preparedData);