org.apache.spark.ml.feature

Class CountVectorizer

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<CountVectorizerModel>

org.apache.spark.ml.feature.CountVectorizer

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, CountVectorizerParams, Params, HasInputCol, HasOutputCol, DefaultParamsWritable, Identifiable, MLWritable

public class CountVectorizer
extends Estimator<CountVectorizerModel>
implements CountVectorizerParams, DefaultParamsWritable

Extracts a vocabulary from document collections and generates a CountVectorizerModel.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
CountVectorizer()
CountVectorizer(String uid)

Method Summary

Modifier and Type	Method and Description
BooleanParam	binary() Binary toggle to control the output vector values.
CountVectorizer	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
CountVectorizerModel	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	inputCol() Param for input column name.
static CountVectorizer	load(String path)
DoubleParam	maxDF() Specifies the maximum number of different documents a term could appear in to be included in the vocabulary.
DoubleParam	minDF() Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.
DoubleParam	minTF() Filter to ignore rare words in a document.
Param<String>	outputCol() Param for output column name.
static MLReader<T>	read()
CountVectorizer	setBinary(boolean value)
CountVectorizer	setInputCol(String value)
CountVectorizer	setMaxDF(double value)
CountVectorizer	setMinDF(double value)
CountVectorizer	setMinTF(double value)
CountVectorizer	setOutputCol(String value)
CountVectorizer	setVocabSize(int value)
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
IntParam	vocabSize() Max size of the vocabulary.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.feature.CountVectorizerParams

getBinary, getMaxDF, getMinDF, getMinTF, getVocabSize, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol

getInputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol

getOutputCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

CountVectorizer

public CountVectorizer(String uid)

CountVectorizer

public CountVectorizer()

Method Detail

load

public static CountVectorizer load(String path)

read

public static MLReader<T> read()

vocabSize

public IntParam vocabSize()

Description copied from interface: CountVectorizerParams

Max size of the vocabulary. CountVectorizer will build a vocabulary that only considers the top vocabSize terms ordered by term frequency across the corpus.

Default: 2^18^

Specified by:

vocabSize in interface CountVectorizerParams

Returns:

(undocumented)

minDF

public DoubleParam minDF()

Description copied from interface: CountVectorizerParams

Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 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

Specified by:

minDF in interface CountVectorizerParams

Returns:

(undocumented)

maxDF

public DoubleParam maxDF()

Description copied from interface: CountVectorizerParams

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 greater than or equal to 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

Specified by:

maxDF in interface CountVectorizerParams

Returns:

(undocumented)

minTF

public DoubleParam minTF()

Description copied from interface: CountVectorizerParams

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 greater than or equal to 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

Specified by:

minTF in interface CountVectorizerParams

Returns:

(undocumented)

binary

public BooleanParam binary()

Description copied from interface: CountVectorizerParams

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

Specified by:

binary in interface CountVectorizerParams

Returns:

(undocumented)

outputCol

public final Param<String> outputCol()

Description copied from interface: HasOutputCol

Param for output column name.

Specified by:

outputCol in interface HasOutputCol

Returns:

(undocumented)

inputCol

public final Param<String> inputCol()

Description copied from interface: HasInputCol

Param for input column name.

Specified by:

inputCol in interface HasInputCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setInputCol

public CountVectorizer setInputCol(String value)

setOutputCol

public CountVectorizer setOutputCol(String value)

setVocabSize

public CountVectorizer setVocabSize(int value)

setMinDF

public CountVectorizer setMinDF(double value)

setMaxDF

public CountVectorizer setMaxDF(double value)

setMinTF

public CountVectorizer setMinTF(double value)

setBinary

public CountVectorizer setBinary(boolean value)

fit

public CountVectorizerModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<CountVectorizerModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public CountVectorizer copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<CountVectorizerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)