如何在Apache Spark中向RandomForestRegressor传递数字和分类特性:Java中的MLlib?
如何在Apache Spark中向RandomForestRegressor传递数字和分类特性:Java中的MLlib 我可以用数字或分类来实现它,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如何在Apache Spark中向RandomForestRegressor传递数字和分类特性:Java中的MLlib?,java,apache-spark,machine-learning,regression,random-forest,Java,Apache Spark,Machine Learning,Regression,Random Forest,如何在Apache Spark中向RandomForestRegressor传递数字和分类特性:Java中的MLlib 我可以用数字或分类来实现它,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 Stri
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);
对于任何人来说,这就是解决方案:
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);
你看过吗?这里是一个例子,在感谢你的建议,我看了一下,但似乎我只能给他传递一列setInputCol,但不能传递多个setInputCol