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
Methods Documentation
Clears a param from the param map if it has been explicitly set.
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.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
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.
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.
extra param values
merged param map
Gets the value of inputCol or its default value.
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
Gets the value of outputCol or its default value.
Gets a param by its name.
Checks whether a param has a default value.
Tests whether this instance contains a param with a given (string) name.
Checks whether a param is explicitly set by user or has a default value.
Checks whether a param is explicitly set by user.
Reads an ML instance from the input path, a shortcut of read().load(path).
Returns an MLReader instance for this class.
Save this ML instance to the given path, a shortcut of âwrite().save(path)â.
Sets a parameter in the embedded param map.
Sets the value of inputCol.
Sets the value of outputCol.
Sets params for this Tokenizer.
New in version 1.3.0.
Transforms the input dataset with optional parameters.
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
pyspark.sql.DataFrame
transformed dataset
Returns an MLWriter instance for this ML instance.
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
A unique id for the object.
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