sparknlp.annotator.Word2VecApproach#

class sparknlp.annotator.Word2VecApproach[source]#

Bases: sparknlp.common.AnnotatorApproach, sparknlp.common.HasStorageRef, sparknlp.common.HasEnableCachingProperties

Trains a Word2Vec model that creates vector representations of words in a text corpus.

The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.

We use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a hierarchical softmax method to train the model. The variable names in the implementation match the original C implementation.

For instantiated/pretrained models, see Word2VecModel.

For available pretrained models please see the Models Hub.

Input Annotation types	Output Annotation type
TOKEN	WORD_EMBEDDINGS

Parameters

vectorSize

The dimension of codes after transforming from words (> 0), by default 100

windowSize

The window size (context words from [-window, window]) (> 0), by default 5

numPartitions

Number of partitions for sentences of words (> 0), by default 1

minCount

The minimum number of times a token must appear to be included in the word2vec model’s vocabulary (>= 0), by default 1

maxSentenceLength

The window size (Maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks up to the size (> 0), by default 1000

stepSize

Step size (learning rate) to be used for each iteration of optimization (> 0), by default 0.025

maxIter

Maximum number of iterations (>= 0), by default 1

seed

Random seed, by default 44

References

For the original C implementation, see https://code.google.com/p/word2vec/

For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> embeddings = Word2VecApproach() \
...     .setInputCols(["token"]) \
...     .setOutputCol("embeddings")
>>> pipeline = Pipeline() \
...     .setStages([
...       documentAssembler,
...       tokenizer,
...       embeddings
...     ])
>>> path = "sherlockholmes.txt"
>>> dataset = spark.read.text(path).toDF("text")
>>> pipelineModel = pipeline.fit(dataset)

Methods

__init__()
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.
fit(dataset[, params])	Fits a model to the input dataset with optional parameters.
fitMultiple(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
getEnableCaching()	Gets whether to enable caching DataFrames or RDDs during the training
getInputCols()	Gets current column names of input annotations.
getLazyAnnotator()	Gets whether Annotator should be evaluated lazily in a RecursivePipeline.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()	Gets output column name of annotations.
getParam(paramName)	Gets a param by its name.
getParamValue(paramName)	Gets the value of a parameter.
getStorageRef()	Gets unique reference name for identification.
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.
setEnableCaching(value)	Sets whether to enable caching DataFrames or RDDs during the training
setInputCols(*value)	Sets column names of input annotations.
setLazyAnnotator(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setMaxIter(numIterations)	Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.
setMaxSentenceLength(maxSentenceLength)	Maximum length (in words) of each sentence in the input data.
setMinCount(minCount)	Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).
setNumPartitions(numPartitions)	Sets number of partitions (default: 1).
setOutputCol(value)	Sets output column name of annotations.
setParamValue(paramName)	Sets the value of a parameter.
setSeed(seed)	Sets random seed.
setStepSize(stepSize)	Sets initial learning rate (default: 0.025).
setStorageRef(value)	Sets unique reference name for identification.
setVectorSize(vectorSize)	Sets vector size (default: 100).
setWindowSize(windowSize)	Sets window size (default: 5).
write()	Returns an MLWriter instance for this ML instance.

Attributes

enableCaching
getter_attrs
inputCols
lazyAnnotator
maxIter
maxSentenceLength
minCount
numPartitions
outputCol
params	Returns all params ordered by name.
seed
stepSize
storageRef
vectorSize
windowSize

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

fit(dataset, params=None)#

Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

Parameters

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

• paramMaps – A Sequence of param maps.

Returns

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

getEnableCaching()#

Gets whether to enable caching DataFrames or RDDs during the training

Returns

bool

Whether to enable caching DataFrames or RDDs during the training

getInputCols()#

Gets current column names of input annotations.

getLazyAnnotator()#

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

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 output column name of annotations.

getParam(paramName)#

Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters

paramNamestr

Name of the parameter

getStorageRef()#

Gets unique reference name for identification.

Returns

str

Unique reference name for identification

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).

property params#

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

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.

setEnableCaching(value)#

Sets whether to enable caching DataFrames or RDDs during the training

Parameters

valuebool

Whether to enable caching DataFrames or RDDs during the training

setInputCols(*value)#

Sets column names of input annotations.

Parameters

*valuestr

Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters

valuebool

Whether Annotator should be evaluated lazily in a RecursivePipeline

setMaxIter(numIterations)[source]#

Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

setMaxSentenceLength(maxSentenceLength)[source]#

Maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks up to the size (> 0)

setMinCount(minCount)[source]#

Sets minCount, the minimum number of times a token must appear to be included in the word2vec model’s vocabulary (default: 5).

setNumPartitions(numPartitions)[source]#

Sets number of partitions (default: 1). Use a small number for accuracy.

setOutputCol(value)#

Sets output column name of annotations.

Parameters

valuestr

Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters

paramNamestr

Name of the parameter

setSeed(seed)[source]#

Sets random seed.

setStepSize(stepSize)[source]#

Sets initial learning rate (default: 0.025).

setStorageRef(value)#

Sets unique reference name for identification.

Parameters

valuestr

Unique reference name for identification

setVectorSize(vectorSize)[source]#

Sets vector size (default: 100).

setWindowSize(windowSize)[source]#

Sets window size (default: 5).

uid#

A unique id for the object.

write()#

Returns an MLWriter instance for this ML instance.