public class Pipeline extends Estimator<PipelineModel>
Estimator
or a Transformer
. When fit(org.apache.spark.sql.DataFrame, org.apache.spark.ml.param.ParamMap)
is called, the
stages are executed in order. If a stage is an Estimator
, its Estimator.fit(org.apache.spark.sql.DataFrame, org.apache.spark.ml.param.ParamPair<?>...)
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(org.apache.spark.sql.DataFrame, org.apache.spark.ml.param.ParamPair<?>...)
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.Constructor and Description |
---|
Pipeline() |
Modifier and Type | Method and Description |
---|---|
PipelineModel |
fit(DataFrame dataset,
ParamMap paramMap)
Fits the pipeline to the input dataset with additional parameters.
|
PipelineStage[] |
getStages() |
Pipeline |
setStages(PipelineStage[] value) |
Param<PipelineStage[]> |
stages()
param for pipeline stages
|
StructType |
transformSchema(StructType schema,
ParamMap paramMap)
:: DeveloperApi ::
|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
addOutputColumn, checkInputColumn, explainParams, get, getParam, isSet, paramMap, params, set, set, validate, validate
uid
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public Param<PipelineStage[]> stages()
public Pipeline setStages(PipelineStage[] value)
public PipelineStage[] getStages()
public PipelineModel fit(DataFrame dataset, ParamMap paramMap)
Estimator
, its Estimator.fit(org.apache.spark.sql.DataFrame, org.apache.spark.ml.param.ParamPair<?>...)
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(org.apache.spark.sql.DataFrame, org.apache.spark.ml.param.ParamPair<?>...)
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 output model acts as an identity transformer.
fit
in class Estimator<PipelineModel>
dataset
- input datasetparamMap
- parameter mappublic StructType transformSchema(StructType schema, ParamMap paramMap)
PipelineStage
Derives the output schema from the input schema and parameters. The schema describes the columns and types of the data.
transformSchema
in class PipelineStage
schema
- Input schema to this stageparamMap
- Parameters passed to this stage