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.