Tokenizer#
- class pyspark.ml.feature.Tokenizer(*, inputCol=None, outputCol=None)[source]#
A tokenizer that converts the input string to lowercase and then splits it by white spaces.
New in version 1.3.0.
Examples
>>> df = spark.createDataFrame([("a b c",)], ["text"]) >>> tokenizer = Tokenizer(outputCol="words") >>> tokenizer.setInputCol("text") Tokenizer... >>> tokenizer.transform(df).head() Row(text='a b c', words=['a', 'b', 'c']) >>> # Change a parameter. >>> tokenizer.setParams(outputCol="tokens").transform(df).head() Row(text='a b c', tokens=['a', 'b', 'c']) >>> # Temporarily modify a parameter. >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() Row(text='a b c', words=['a', 'b', 'c']) >>> tokenizer.transform(df).head() Row(text='a b c', tokens=['a', 'b', 'c']) >>> # Must use keyword arguments to specify params. >>> tokenizer.setParams("text") Traceback (most recent call last): ... TypeError: Method setParams forces keyword arguments. >>> tokenizerPath = temp_path + "/tokenizer" >>> tokenizer.save(tokenizerPath) >>> loadedTokenizer = Tokenizer.load(tokenizerPath) >>> loadedTokenizer.transform(df).head().tokens == tokenizer.transform(df).head().tokens 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.
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.
Gets the value of inputCol or its default value.
getOrDefault(param)Gets the value of a param in the user-supplied param map or its default value.
Gets the value of outputCol or its default value.
getParam(paramName)Gets a param by its name.
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.setOutputCol(value)Sets the value of
outputCol.setParams(self, \*[, inputCol, outputCol])Sets params for this Tokenizer.
transform(dataset[, params])Transforms the input dataset with optional parameters.
write()Returns an MLWriter instance for this ML instance.
Attributes
Returns all params ordered by name.
Methods Documentation
- 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
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParamsCopy 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
- extradict, optional
extra param values
- Returns
- dict
merged param map
- getInputCol()#
Gets the value of inputCol or its default value.
- 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 the value of outputCol or its default value.
- getParam(paramName)#
Gets a param by its name.
- 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).
- 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.
- setParams(self, \*, inputCol=None, outputCol=None)[source]#
Sets params for this Tokenizer.
New in version 1.3.0.
- transform(dataset, params=None)#
Transforms the input dataset with optional parameters.
New in version 1.3.0.
- Parameters
- dataset
pyspark.sql.DataFrame input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrametransformed dataset
- write()#
Returns an MLWriter instance for this ML instance.
Attributes Documentation
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')#
- 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 typeParam.
- uid#
A unique id for the object.