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.
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