RegexTokenizer¶

class pyspark.ml.feature.RegexTokenizer(*, minTokenLength: int = 1, gaps: bool = True, pattern: str = '\\s+', inputCol: Optional[str] = None, outputCol: Optional[str] = None, toLowercase: bool = True)¶

A regex based tokenizer that extracts tokens either by using the provided regex pattern (in Java dialect) to split the text (default) or repeatedly matching the regex (if gaps is false). Optional parameters also allow filtering tokens using a minimal length. It returns an array of strings that can be empty.

Examples

>>> df = spark.createDataFrame([("A B  c",)], ["text"])
>>> reTokenizer = RegexTokenizer()
>>> reTokenizer.setInputCol("text")
RegexTokenizer...
>>> reTokenizer.setOutputCol("words")
RegexTokenizer...
>>> reTokenizer.transform(df).head()
Row(text='A B  c', words=['a', 'b', 'c'])
>>> # Change a parameter.
>>> reTokenizer.setParams(outputCol="tokens").transform(df).head()
Row(text='A B  c', tokens=['a', 'b', 'c'])
>>> # Temporarily modify a parameter.
>>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head()
Row(text='A B  c', words=['a', 'b', 'c'])
>>> reTokenizer.transform(df).head()
Row(text='A B  c', tokens=['a', 'b', 'c'])
>>> # Must use keyword arguments to specify params.
>>> reTokenizer.setParams("text")
Traceback (most recent call last):
    ...
TypeError: Method setParams forces keyword arguments.
>>> regexTokenizerPath = temp_path + "/regex-tokenizer"
>>> reTokenizer.save(regexTokenizerPath)
>>> loadedReTokenizer = RegexTokenizer.load(regexTokenizerPath)
>>> loadedReTokenizer.getMinTokenLength() == reTokenizer.getMinTokenLength()
True
>>> loadedReTokenizer.getGaps() == reTokenizer.getGaps()
True
>>> loadedReTokenizer.transform(df).take(1) == reTokenizer.transform(df).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.
`getGaps`()	Gets the value of gaps or its default value.
`getInputCol`()	Gets the value of inputCol or its default value.
`getMinTokenLength`()	Gets the value of minTokenLength 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.
`getPattern`()	Gets the value of pattern or its default value.
`getToLowercase`()	Gets the value of toLowercase 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.
`setGaps`(value)	Sets the value of `gaps`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setMinTokenLength`(value)	Sets the value of `minTokenLength`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, minTokenLength, gaps, …])	Sets params for this RegexTokenizer.
`setPattern`(value)	Sets the value of `pattern`.
`setToLowercase`(value)	Sets the value of `toLowercase`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`gaps`
`inputCol`
`minTokenLength`
`outputCol`
`params`	Returns all params ordered by name.
`pattern`
`toLowercase`

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

getGaps() → bool¶: Gets the value of gaps or its default value.

getInputCol() → str¶: Gets the value of inputCol or its default value.

getMinTokenLength() → int¶: Gets the value of minTokenLength 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.

getPattern() → str¶: Gets the value of pattern or its default value.

getToLowercase() → bool¶: Gets the value of toLowercase 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.

setGaps(value: bool) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of gaps.

setInputCol(value: str) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of inputCol.

setMinTokenLength(value: int) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of minTokenLength.

setOutputCol(value: str) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of outputCol.

setParams(self, \*, minTokenLength=1, gaps=True, pattern="\s+", inputCol=None, outputCol=None, toLowercase=True)¶: Sets params for this RegexTokenizer.

setPattern(value: str) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of pattern.

setToLowercase(value: bool) → pyspark.ml.feature.RegexTokenizer ¶: Sets the value of toLowercase.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

gaps: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='gaps', doc='whether regex splits on gaps (True) or matches tokens (False)')¶

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

minTokenLength: pyspark.ml.param.Param[int] = Param(parent='undefined', name='minTokenLength', doc='minimum token length (>= 0)')¶

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.

pattern: pyspark.ml.param.Param[str] = Param(parent='undefined', name='pattern', doc='regex pattern (Java dialect) used for tokenizing')¶

toLowercase: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='toLowercase', doc='whether to convert all characters to lowercase before tokenizing')¶

RobustScalerModel

RFormula