CountVectorizerModel

class pyspark.ml.feature.CountVectorizerModel(java_model=None)

Model fitted by CountVectorizer.

New in version 1.6.0.

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.
from_vocabulary(vocabulary, inputCol[, …])	Construct the model directly from a vocabulary list of strings, requires an active SparkContext.
getBinary()	Gets the value of binary or its default value.
getInputCol()	Gets the value of inputCol or its default value.
getMaxDF()	Gets the value of maxDF or its default value.
getMinDF()	Gets the value of minDF or its default value.
getMinTF()	Gets the value of minTF 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.
getParam(paramName)	Gets a param by its name.
getVocabSize()	Gets the value of vocabSize or its default value.
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.
setBinary(value)	Sets the value of binary.
setInputCol(value)	Sets the value of inputCol.
setMinTF(value)	Sets the value of minTF.
setOutputCol(value)	Sets the value of outputCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

binary
inputCol
maxDF
minDF
minTF
outputCol
params	Returns all params ordered by name.
vocabSize
vocabulary	An array of terms in the vocabulary.

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

classmethod from_vocabulary(vocabulary, inputCol, outputCol=None, minTF=None, binary=None)

Construct the model directly from a vocabulary list of strings, requires an active SparkContext.

New in version 2.4.0.

getBinary()

Gets the value of binary or its default value.

New in version 2.0.0.

getInputCol()

Gets the value of inputCol or its default value.

getMaxDF()

Gets the value of maxDF or its default value.

New in version 2.4.0.

getMinDF()

Gets the value of minDF or its default value.

New in version 1.6.0.

getMinTF()

Gets the value of minTF or its default value.

New in version 1.6.0.

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.

getVocabSize()

Gets the value of vocabSize or its default value.

New in version 1.6.0.

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.

setBinary(value)

Sets the value of binary.

New in version 2.4.0.

setInputCol(value)

Sets the value of inputCol.

New in version 3.0.0.

setMinTF(value)

Sets the value of minTF.

New in version 2.4.0.

setOutputCol(value)

Sets the value of outputCol.

New in version 3.0.0.

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

binary = Param(parent='undefined', name='binary', doc='Binary toggle to control the output vector values. If True, all nonzero counts (after minTF filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default False')

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

maxDF = Param(parent='undefined', name='maxDF', doc='Specifies the maximum number of different documents a term could appear in to be included in the vocabulary. A term that appears more than the threshold will be ignored. If this is an integer >= 1, this specifies the maximum number of documents the term could appear in; if this is a double in [0,1), then this specifies the maximum fraction of documents the term could appear in. Default (2^63) - 1')

minDF = Param(parent='undefined', name='minDF', doc='Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer >= 1, this specifies the number of documents the term must appear in; if this is a double in [0,1), then this specifies the fraction of documents. Default 1.0')

minTF = Param(parent='undefined', name='minTF', doc="Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer >= 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document's token count). Note that the parameter is only used in transform of CountVectorizerModel and does not affect fitting. Default 1.0")

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.

vocabSize = Param(parent='undefined', name='vocabSize', doc='max size of the vocabulary. Default 1 << 18.')

vocabulary

An array of terms in the vocabulary.

New in version 1.6.0.

CountVectorizer DCT