org.apache.spark.ml.feature

Class CountVectorizerModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.feature.CountVectorizerModel

All Implemented Interfaces:

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

public class CountVectorizerModel
extends Model<CountVectorizerModel>
implements CountVectorizerParams, MLWritable

Converts a text document to a sparse vector of token counts. param: vocabulary An Array over terms. Only the terms in the vocabulary will be counted.

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
CountVectorizerModel(String[] vocabulary)
CountVectorizerModel(String uid, String[] vocabulary)

Method Summary

Modifier and Type	Method and Description
BooleanParam	binary() Binary toggle to control the output vector values.
CountVectorizerModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	inputCol() Param for input column name.
static CountVectorizerModel	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<CountVectorizerModel>	read()
CountVectorizerModel	setBinary(boolean value)
CountVectorizerModel	setInputCol(String value)
CountVectorizerModel	setMinTF(double value)
CountVectorizerModel	setOutputCol(String value)
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
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.
String[]	vocabulary()
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

hasParent, parent, setParent

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

transform, transform, transform

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

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, 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.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

CountVectorizerModel

public CountVectorizerModel(String uid,
                            String[] vocabulary)

CountVectorizerModel

public CountVectorizerModel(String[] vocabulary)

Method Detail

read

public static MLReader<CountVectorizerModel> read()

load

public static CountVectorizerModel load(String path)

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)

vocabulary

public String[] vocabulary()

setInputCol

public CountVectorizerModel setInputCol(String value)

setOutputCol

public CountVectorizerModel setOutputCol(String value)

setMinTF

public CountVectorizerModel setMinTF(double value)

setBinary

public CountVectorizerModel setBinary(boolean value)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

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 CountVectorizerModel 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 Model<CountVectorizerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object