ParamGridBuilder¶

class pyspark.ml.tuning.ParamGridBuilder¶

Builder for a param grid used in grid search-based model selection.

Examples

>>> from pyspark.ml.classification import LogisticRegression
>>> lr = LogisticRegression()
>>> output = ParamGridBuilder() \
...     .baseOn({lr.labelCol: 'l'}) \
...     .baseOn([lr.predictionCol, 'p']) \
...     .addGrid(lr.regParam, [1.0, 2.0]) \
...     .addGrid(lr.maxIter, [1, 5]) \
...     .build()
>>> expected = [
...     {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
...     {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
...     {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
...     {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
>>> len(output) == len(expected)
True
>>> all([m in expected for m in output])
True

Methods

`addGrid`(param, values)	Sets the given parameters in this grid to fixed values.
`baseOn`(*args)	Sets the given parameters in this grid to fixed values.
`build`()	Builds and returns all combinations of parameters specified by the param grid.

Methods Documentation

addGrid(param: pyspark.ml.param.Param[Any], values: List[Any]) → pyspark.ml.tuning.ParamGridBuilder ¶

Sets the given parameters in this grid to fixed values.

param must be an instance of Param associated with an instance of Params (such as Estimator or Transformer).

baseOn(*args: Union[ParamMap, Tuple[pyspark.ml.param.Param, Any]]) → ParamGridBuilder¶: Sets the given parameters in this grid to fixed values. Accepts either a parameter dictionary or a list of (parameter, value) pairs.

build() → List[ParamMap]¶: Builds and returns all combinations of parameters specified by the param grid.

SummaryBuilder

CrossValidator