IDFModel

class pyspark.ml.feature.IDFModel(java_model=None)[source]

Model fitted by IDF.

New in version 1.4.0.

Methods

Attributes

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

extra – Extra parameters to copy to the new instance

Returns

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

extra – extra param values

Returns

merged param map

getInputCol()

Gets the value of inputCol or its default value.

getMinDocFreq()

Gets the value of minDocFreq or its default value.

New in version 1.4.0.

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.

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.

New in version 3.0.0.

setOutputCol(value)[source]

Sets the value of outputCol.

New in version 3.0.0.

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

docFreq

Returns the document frequency.

New in version 3.0.0.

idf

Returns the IDF vector.

New in version 2.0.0.

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')
minDocFreq = Param(parent='undefined', name='minDocFreq', doc='minimum number of documents in which a term should appear for filtering')
numDocs

Returns number of documents evaluated to compute idf

New in version 3.0.0.

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

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