Normalizer¶

class pyspark.ml.feature.Normalizer(*, p=2.0, inputCol=None, outputCol=None)[source]¶

Normalize a vector to have unit norm using the given p-norm.

New in version 1.4.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> svec = Vectors.sparse(4, {1: 4.0, 3: 3.0})
>>> df = spark.createDataFrame([(Vectors.dense([3.0, -4.0]), svec)], ["dense", "sparse"])
>>> normalizer = Normalizer(p=2.0)
>>> normalizer.setInputCol("dense")
Normalizer...
>>> normalizer.setOutputCol("features")
Normalizer...
>>> normalizer.transform(df).head().features
DenseVector([0.6, -0.8])
>>> normalizer.setParams(inputCol="sparse", outputCol="freqs").transform(df).head().freqs
SparseVector(4, {1: 0.8, 3: 0.6})
>>> params = {normalizer.p: 1.0, normalizer.inputCol: "dense", normalizer.outputCol: "vector"}
>>> normalizer.transform(df, params).head().vector
DenseVector([0.4286, -0.5714])
>>> normalizerPath = temp_path + "/normalizer"
>>> normalizer.save(normalizerPath)
>>> loadedNormalizer = Normalizer.load(normalizerPath)
>>> loadedNormalizer.getP() == normalizer.getP()
True
>>> loadedNormalizer.transform(df).take(1) == normalizer.transform(df).take(1)
True

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
`getInputCol`()	Gets the value of inputCol or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getOutputCol`()	Gets the value of outputCol or its default value.
`getP`()	Gets the value of p or its default value.
`getParam`(paramName)	Gets a param by its name.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
`set`(param, value)	Sets a parameter in the embedded param map.
`setInputCol`(value)	Sets the value of `inputCol`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setP`(value)	Sets the value of `p`.
`setParams`(self, \*[, p, inputCol, outputCol])	Sets params for this Normalizer.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`inputCol`
`outputCol`
`p`
`params`	Returns all params ordered by name.

Methods Documentation

clear(param)¶: Clears a param from the param map if it has been explicitly set.

copy(extra=None)¶

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

JavaParams: Copy of this instance

explainParam(param)¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)¶

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extradict, optional: extra param values

Returns

dict: merged param map

getInputCol()¶: Gets the value of inputCol or its default value.

getOrDefault(param)¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()¶: Gets the value of outputCol or its default value.

getP()[source]¶: Gets the value of p or its default value.

New in version 1.4.0.

getParam(paramName)¶: Gets a param by its name.

hasDefault(param)¶: Checks whether a param has a default value.

hasParam(paramName)¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param)¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param)¶: Checks whether a param is explicitly set by user.

classmethod load(path)¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()¶: Returns an MLReader instance for this class.

save(path)¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)¶: Sets a parameter in the embedded param map.

setInputCol(value)[source]¶: Sets the value of inputCol.

setOutputCol(value)[source]¶: Sets the value of outputCol.

setP(value)[source]¶: Sets the value of p.

New in version 1.4.0.

setParams(self, \*, p=2.0, inputCol=None, outputCol=None)[source]¶: Sets params for this Normalizer.

New in version 1.4.0.

transform(dataset, params=None)¶

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write()¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')¶

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')¶

p = Param(parent='undefined', name='p', doc='the p norm value.')¶

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

NGram

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