DefaultParamsReader#

class pyspark.ml.util.DefaultParamsReader(cls)[source]#

Specialization of MLReader for Params types

Default MLReader implementation for transformers and estimators that contain basic (json-serializable) params and no data. This will not handle more complex params or types with data (e.g., models with coefficients).

New in version 2.3.0.

Methods

`getAndSetParams`(instance, metadata[, skipParams])	Extract Params from metadata, and set them in the instance.
`isPythonParamsInstance`(metadata)
`load`(path)	Load the ML instance from the input path.
`loadMetadata`(path, sc[, expectedClassName])	Load metadata saved using `DefaultParamsWriter.saveMetadata()`
`loadParamsInstance`(path, sc)	Load a `Params` instance from the given path, and return it.
`session`(sparkSession)	Sets the Spark Session to use for saving/loading.

Attributes

`sc`	Returns the underlying SparkContext.
`sparkSession`	Returns the user-specified Spark Session or the default.

Methods Documentation

static getAndSetParams(instance, metadata, skipParams=None)[source]#: Extract Params from metadata, and set them in the instance.

static isPythonParamsInstance(metadata)[source]#

load(path)[source]#: Load the ML instance from the input path.

static loadMetadata(path, sc, expectedClassName='')[source]#

Load metadata saved using DefaultParamsWriter.saveMetadata()

Parameters

pathstr
scpyspark.SparkContext or pyspark.sql.SparkSession
expectedClassNamestr, optional: If non empty, this is checked against the loaded metadata.

static loadParamsInstance(path, sc)[source]#: Load a Params instance from the given path, and return it. This assumes the instance inherits from MLReadable.

session(sparkSession)#: Sets the Spark Session to use for saving/loading.

Attributes Documentation

sc#: Returns the underlying SparkContext.

sparkSession#: Returns the user-specified Spark Session or the default.