spark MLlib的 pipeline方式

/**
 * :: Experimental ::
 * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
 * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the
 * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will
 * be called on the input dataset to fit a model. Then the model, which is a transformer, will be
 * used to transform the dataset as the input to the next stage. If a stage is a [[Transformer]],
 * its [[Transformer#transform]] method will be called to produce the dataset for the next stage.
 * The fitted model from a [[Pipeline]] is an [[PipelineModel]], which consists of fitted models and
 * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
 * an identity transformer.
 */
@Experimental
class Pipeline(override val uid: String) extends Estimator[PipelineModel] {