org.apache.spark.ml.feature

Class Word2VecModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<Word2VecModel>

org.apache.spark.ml.feature.Word2VecModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, Word2VecBase, Params, HasInputCol, HasMaxIter, HasOutputCol, HasSeed, HasStepSize, Identifiable, MLWritable

public class Word2VecModel
extends Model<Word2VecModel>
implements Word2VecBase, MLWritable

Model fitted by Word2Vec.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Word2VecModel.Data$
static class	Word2VecModel.Word2VecModelWriter$

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

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method and Description
Word2VecModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Dataset<Row>	findSynonyms(String word, int num) Find "num" number of words closest in similarity to the given word, not including the word itself.
Dataset<Row>	findSynonyms(Vector vec, int num) Find "num" number of words whose vector representation is most similar to the supplied vector.
scala.Tuple2<String,Object>[]	findSynonymsArray(String word, int num) Find "num" number of words closest in similarity to the given word, not including the word itself.
scala.Tuple2<String,Object>[]	findSynonymsArray(Vector vec, int num) Find "num" number of words whose vector representation is most similar to the supplied vector.
Dataset<Row>	getVectors()
Param<String>	inputCol() Param for input column name.
static Word2VecModel	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
IntParam	maxSentenceLength() Sets the maximum length (in words) of each sentence in the input data.
IntParam	minCount() The minimum number of times a token must appear to be included in the word2vec model's vocabulary.
IntParam	numPartitions() Number of partitions for sentences of words.
Param<String>	outputCol() Param for output column name.
static MLReader<Word2VecModel>	read()
LongParam	seed() Param for random seed.
Word2VecModel	setInputCol(String value)
Word2VecModel	setOutputCol(String value)
DoubleParam	stepSize() Param for Step size to be used for each iteration of optimization (&gt; 0).
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transform a sentence column to a vector column to represent the whole sentence.
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	vectorSize() The dimension of the code that you want to transform from words.
IntParam	windowSize() The window size (context words from [-window, window]).
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.Word2VecBase

getMaxSentenceLength, getMinCount, getNumPartitions, getVectorSize, getWindowSize, 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.shared.HasMaxIter

getMaxIter

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

getStepSize

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

getSeed

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

Method Detail

read

public static MLReader<Word2VecModel> read()

load

public static Word2VecModel load(String path)

vectorSize

public final IntParam vectorSize()

Description copied from interface: Word2VecBase

The dimension of the code that you want to transform from words. Default: 100

Specified by:

vectorSize in interface Word2VecBase

Returns:

(undocumented)

windowSize

public final IntParam windowSize()

Description copied from interface: Word2VecBase

The window size (context words from [-window, window]). Default: 5

Specified by:

windowSize in interface Word2VecBase

Returns:

(undocumented)

numPartitions

public final IntParam numPartitions()

Description copied from interface: Word2VecBase

Number of partitions for sentences of words. Default: 1

Specified by:

numPartitions in interface Word2VecBase

Returns:

(undocumented)

minCount

public final IntParam minCount()

Description copied from interface: Word2VecBase

The minimum number of times a token must appear to be included in the word2vec model's vocabulary. Default: 5

Specified by:

minCount in interface Word2VecBase

Returns:

(undocumented)

maxSentenceLength

public final IntParam maxSentenceLength()

Description copied from interface: Word2VecBase

Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to maxSentenceLength size. Default: 1000

Specified by:

maxSentenceLength in interface Word2VecBase

Returns:

(undocumented)

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

Returns:

(undocumented)

stepSize

public DoubleParam stepSize()

Description copied from interface: HasStepSize

Param for Step size to be used for each iteration of optimization (&gt; 0).

Specified by:

stepSize in interface HasStepSize

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

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)

getVectors

public Dataset<Row> getVectors()

findSynonyms

public Dataset<Row> findSynonyms(String word,
                                 int num)

Find "num" number of words closest in similarity to the given word, not including the word itself.

Parameters:

word - (undocumented)

num - (undocumented)

Returns:

a dataframe with columns "word" and "similarity" of the word and the cosine similarities between the synonyms and the given word.

findSynonyms

public Dataset<Row> findSynonyms(Vector vec,
                                 int num)

Find "num" number of words whose vector representation is most similar to the supplied vector. If the supplied vector is the vector representation of a word in the model's vocabulary, that word will be in the results.

Parameters:

vec - (undocumented)

num - (undocumented)

Returns:

a dataframe with columns "word" and "similarity" of the word and the cosine similarities between the synonyms and the given word vector.

findSynonymsArray

public scala.Tuple2<String,Object>[] findSynonymsArray(Vector vec,
                                                       int num)

Find "num" number of words whose vector representation is most similar to the supplied vector. If the supplied vector is the vector representation of a word in the model's vocabulary, that word will be in the results.

Parameters:

vec - (undocumented)

num - (undocumented)

Returns:

an array of the words and the cosine similarities between the synonyms given word vector.

findSynonymsArray

public scala.Tuple2<String,Object>[] findSynonymsArray(String word,
                                                       int num)

Find "num" number of words closest in similarity to the given word, not including the word itself.

Parameters:

word - (undocumented)

num - (undocumented)

Returns:

an array of the words and the cosine similarities between the synonyms given word vector.

setInputCol

public Word2VecModel setInputCol(String value)

setOutputCol

public Word2VecModel setOutputCol(String value)

transform

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

Transform a sentence column to a vector column to represent the whole sentence. The transform is performed by averaging all word vectors it contains.

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 Word2VecModel 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<Word2VecModel>

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