Word2Vec

class pyspark.ml.feature.Word2Vec(*, vectorSize: int = 100, minCount: int = 5, numPartitions: int = 1, stepSize: float = 0.025, maxIter: int = 1, seed: Optional[int] = None, inputCol: Optional[str] = None, outputCol: Optional[str] = None, windowSize: int = 5, maxSentenceLength: int = 1000)

Word2Vec trains a model of Map(String, Vector), i.e. transforms a word into a code for further natural language processing or machine learning process.

New in version 1.4.0.

Examples

>>> sent = ("a b " * 100 + "a c " * 10).split(" ")
>>> doc = spark.createDataFrame([(sent,), (sent,)], ["sentence"])
>>> word2Vec = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model")
>>> word2Vec.setMaxIter(10)
Word2Vec...
>>> word2Vec.getMaxIter()
10
>>> word2Vec.clear(word2Vec.maxIter)
>>> model = word2Vec.fit(doc)
>>> model.getMinCount()
5
>>> model.setInputCol("sentence")
Word2VecModel...
>>> model.getVectors().show()
+----+--------------------+
|word|              vector|
+----+--------------------+
|   a|[0.0951...
|   b|[-1.202...
|   c|[0.3015...
+----+--------------------+
...
>>> model.findSynonymsArray("a", 2)
[('b', 0.015859...), ('c', -0.568079...)]
>>> from pyspark.sql.functions import format_number as fmt
>>> model.findSynonyms("a", 2).select("word", fmt("similarity", 5).alias("similarity")).show()
+----+----------+
|word|similarity|
+----+----------+
|   b|   0.01586|
|   c|  -0.56808|
+----+----------+
...
>>> model.transform(doc).head().model
DenseVector([-0.4833, 0.1855, -0.273, -0.0509, -0.4769])
>>> word2vecPath = temp_path + "/word2vec"
>>> word2Vec.save(word2vecPath)
>>> loadedWord2Vec = Word2Vec.load(word2vecPath)
>>> loadedWord2Vec.getVectorSize() == word2Vec.getVectorSize()
True
>>> loadedWord2Vec.getNumPartitions() == word2Vec.getNumPartitions()
True
>>> loadedWord2Vec.getMinCount() == word2Vec.getMinCount()
True
>>> modelPath = temp_path + "/word2vec-model"
>>> model.save(modelPath)
>>> loadedModel = Word2VecModel.load(modelPath)
>>> loadedModel.getVectors().first().word == model.getVectors().first().word
True
>>> loadedModel.getVectors().first().vector == model.getVectors().first().vector
True
>>> loadedModel.transform(doc).take(1) == model.transform(doc).take(1)
True

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.
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.
getInputCol()	Gets the value of inputCol or its default value.
getMaxIter()	Gets the value of maxIter or its default value.
getMaxSentenceLength()	Gets the value of maxSentenceLength or its default value.
getMinCount()	Gets the value of minCount or its default value.
getNumPartitions()	Gets the value of numPartitions 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.
getSeed()	Gets the value of seed or its default value.
getStepSize()	Gets the value of stepSize or its default value.
getVectorSize()	Gets the value of vectorSize or its default value.
getWindowSize()	Gets the value of windowSize 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.
setInputCol(value)	Sets the value of inputCol.
setMaxIter(value)	Sets the value of maxIter.
setMaxSentenceLength(value)	Sets the value of maxSentenceLength.
setMinCount(value)	Sets the value of minCount.
setNumPartitions(value)	Sets the value of numPartitions.
setOutputCol(value)	Sets the value of outputCol.
setParams(self, \*[, minCount, …])	Sets params for this Word2Vec.
setSeed(value)	Sets the value of seed.
setStepSize(value)	Sets the value of stepSize.
setVectorSize(value)	Sets the value of vectorSize.
setWindowSize(value)	Sets the value of windowSize.
write()	Returns an MLWriter instance for this ML instance.

Attributes

inputCol
maxIter
maxSentenceLength
minCount
numPartitions
outputCol
params	Returns all params ordered by name.
seed
stepSize
vectorSize
windowSize

Methods Documentation

clear(param: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

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: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramsdict or list or tuple, optional

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

Transformer or a list of Transformer

fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

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

New in version 2.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset.

paramMapscollections.abc.Sequence

A Sequence of param maps.

Returns

_FitMultipleIterator

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.

getInputCol() → str

Gets the value of inputCol or its default value.

getMaxIter() → int

Gets the value of maxIter or its default value.

getMaxSentenceLength() → int

Gets the value of maxSentenceLength or its default value.

New in version 2.0.0.

getMinCount() → int

Gets the value of minCount or its default value.

New in version 1.4.0.

getNumPartitions() → int

Gets the value of numPartitions or its default value.

New in version 1.4.0.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str

Gets the value of outputCol or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getSeed() → int

Gets the value of seed or its default value.

getStepSize() → float

Gets the value of stepSize or its default value.

getVectorSize() → int

Gets the value of vectorSize or its default value.

New in version 1.4.0.

getWindowSize() → int

Gets the value of windowSize or its default value.

New in version 2.0.0.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setInputCol(value: str) → pyspark.ml.feature.Word2Vec

Sets the value of inputCol.

setMaxIter(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of maxIter.

setMaxSentenceLength(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of maxSentenceLength.

New in version 2.0.0.

setMinCount(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of minCount.

New in version 1.4.0.

setNumPartitions(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of numPartitions.

New in version 1.4.0.

setOutputCol(value: str) → pyspark.ml.feature.Word2Vec

Sets the value of outputCol.

setParams(self, \*, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, inputCol=None, outputCol=None, windowSize=5, maxSentenceLength=1000)

Sets params for this Word2Vec.

New in version 1.4.0.

setSeed(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of seed.

setStepSize(value: float) → pyspark.ml.feature.Word2Vec

Sets the value of stepSize.

New in version 1.4.0.

setVectorSize(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of vectorSize.

New in version 1.4.0.

setWindowSize(value: int) → pyspark.ml.feature.Word2Vec

Sets the value of windowSize.

New in version 2.0.0.

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

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

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')

maxSentenceLength = Param(parent='undefined', name='maxSentenceLength', doc='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.')

minCount = Param(parent='undefined', name='minCount', doc="the minimum number of times a token must appear to be included in the word2vec model's vocabulary")

numPartitions = Param(parent='undefined', name='numPartitions', doc='number of partitions for sentences of words')

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.

seed = Param(parent='undefined', name='seed', doc='random seed.')

stepSize = Param(parent='undefined', name='stepSize', doc='Step size to be used for each iteration of optimization (>= 0).')

vectorSize = Param(parent='undefined', name='vectorSize', doc='the dimension of codes after transforming from words')

windowSize = Param(parent='undefined', name='windowSize', doc='the window size (context words from [-window, window]). Default value is 5')

VectorSlicer Word2VecModel