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.

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.

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.

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.

getMinCount() → int¶: Gets the value of minCount or its default value.

getNumPartitions() → int¶: Gets the value of numPartitions or its default value.

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.

getWindowSize() → int¶: Gets the value of windowSize or its default value.

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.

setMinCount(value: int) → pyspark.ml.feature.Word2Vec ¶: Sets the value of minCount.

setNumPartitions(value: int) → pyspark.ml.feature.Word2Vec ¶: Sets the value of numPartitions.

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.

setSeed(value: int) → pyspark.ml.feature.Word2Vec ¶: Sets the value of seed.

setStepSize(value: float) → pyspark.ml.feature.Word2Vec ¶: Sets the value of stepSize.

setVectorSize(value: int) → pyspark.ml.feature.Word2Vec ¶: Sets the value of vectorSize.

setWindowSize(value: int) → pyspark.ml.feature.Word2Vec ¶: Sets the value of windowSize.

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