StringIndexer¶

class pyspark.ml.feature.StringIndexer(*, inputCol: Optional[str] = None, outputCol: Optional[str] = None, inputCols: Optional[List[str]] = None, outputCols: Optional[List[str]] = None, handleInvalid: str = 'error', stringOrderType: str = 'frequencyDesc')[source]¶

A label indexer that maps a string column of labels to an ML column of label indices. If the input column is numeric, we cast it to string and index the string values. The indices are in [0, numLabels). By default, this is ordered by label frequencies so the most frequent label gets index 0. The ordering behavior is controlled by setting stringOrderType. Its default value is ‘frequencyDesc’.

New in version 1.4.0.

Examples

>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed",
...     stringOrderType="frequencyDesc")
>>> stringIndexer.setHandleInvalid("error")
StringIndexer...
>>> model = stringIndexer.fit(stringIndDf)
>>> model.setHandleInvalid("error")
StringIndexerModel...
>>> td = model.transform(stringIndDf)
>>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
...     key=lambda x: x[0])
[(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)]
>>> inverter = IndexToString(inputCol="indexed", outputCol="label2", labels=model.labels)
>>> itd = inverter.transform(td)
>>> sorted(set([(i[0], str(i[1])) for i in itd.select(itd.id, itd.label2).collect()]),
...     key=lambda x: x[0])
[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'a'), (4, 'a'), (5, 'c')]
>>> stringIndexerPath = temp_path + "/string-indexer"
>>> stringIndexer.save(stringIndexerPath)
>>> loadedIndexer = StringIndexer.load(stringIndexerPath)
>>> loadedIndexer.getHandleInvalid() == stringIndexer.getHandleInvalid()
True
>>> modelPath = temp_path + "/string-indexer-model"
>>> model.save(modelPath)
>>> loadedModel = StringIndexerModel.load(modelPath)
>>> loadedModel.labels == model.labels
True
>>> indexToStringPath = temp_path + "/index-to-string"
>>> inverter.save(indexToStringPath)
>>> loadedInverter = IndexToString.load(indexToStringPath)
>>> loadedInverter.getLabels() == inverter.getLabels()
True
>>> loadedModel.transform(stringIndDf).take(1) == model.transform(stringIndDf).take(1)
True
>>> stringIndexer.getStringOrderType()
'frequencyDesc'
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed", handleInvalid="error",
...     stringOrderType="alphabetDesc")
>>> model = stringIndexer.fit(stringIndDf)
>>> td = model.transform(stringIndDf)
>>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
...     key=lambda x: x[0])
[(0, 2.0), (1, 1.0), (2, 0.0), (3, 2.0), (4, 2.0), (5, 0.0)]
>>> fromlabelsModel = StringIndexerModel.from_labels(["a", "b", "c"],
...     inputCol="label", outputCol="indexed", handleInvalid="error")
>>> result = fromlabelsModel.transform(stringIndDf)
>>> sorted(set([(i[0], i[1]) for i in result.select(result.id, result.indexed).collect()]),
...     key=lambda x: x[0])
[(0, 0.0), (1, 1.0), (2, 2.0), (3, 0.0), (4, 0.0), (5, 2.0)]
>>> testData = sc.parallelize([Row(id=0, label1="a", label2="e"),
...                            Row(id=1, label1="b", label2="f"),
...                            Row(id=2, label1="c", label2="e"),
...                            Row(id=3, label1="a", label2="f"),
...                            Row(id=4, label1="a", label2="f"),
...                            Row(id=5, label1="c", label2="f")], 3)
>>> multiRowDf = spark.createDataFrame(testData)
>>> inputs = ["label1", "label2"]
>>> outputs = ["index1", "index2"]
>>> stringIndexer = StringIndexer(inputCols=inputs, outputCols=outputs)
>>> model = stringIndexer.fit(multiRowDf)
>>> result = model.transform(multiRowDf)
>>> sorted(set([(i[0], i[1], i[2]) for i in result.select(result.id, result.index1,
...     result.index2).collect()]), key=lambda x: x[0])
[(0, 0.0, 1.0), (1, 2.0, 0.0), (2, 1.0, 1.0), (3, 0.0, 0.0), (4, 0.0, 0.0), (5, 1.0, 0.0)]
>>> fromlabelsModel = StringIndexerModel.from_arrays_of_labels([["a", "b", "c"], ["e", "f"]],
...     inputCols=inputs, outputCols=outputs)
>>> result = fromlabelsModel.transform(multiRowDf)
>>> sorted(set([(i[0], i[1], i[2]) for i in result.select(result.id, result.index1,
...     result.index2).collect()]), key=lambda x: x[0])
[(0, 0.0, 0.0), (1, 1.0, 1.0), (2, 2.0, 0.0), (3, 0.0, 1.0), (4, 0.0, 1.0), (5, 2.0, 1.0)]

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.
`getHandleInvalid`()	Gets the value of handleInvalid or its default value.
`getInputCol`()	Gets the value of inputCol or its default value.
`getInputCols`()	Gets the value of inputCols 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.
`getOutputCols`()	Gets the value of outputCols or its default value.
`getParam`(paramName)	Gets a param by its name.
`getStringOrderType`()	Gets the value of `stringOrderType` or its default value ‘frequencyDesc’.
`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.
`setHandleInvalid`(value)	Sets the value of `handleInvalid`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setInputCols`(value)	Sets the value of `inputCols`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setOutputCols`(value)	Sets the value of `outputCols`.
`setParams`(self, \*[, inputCol, outputCol, …])	Sets params for this StringIndexer.
`setStringOrderType`(value)	Sets the value of `stringOrderType`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`handleInvalid`
`inputCol`
`inputCols`
`outputCol`
`outputCols`
`params`	Returns all params ordered by name.
`stringOrderType`

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.

getHandleInvalid() → str¶: Gets the value of handleInvalid or its default value.

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

getInputCols() → List[str]¶: Gets the value of inputCols 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.

getOutputCols() → List[str]¶: Gets the value of outputCols or its default value.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getStringOrderType() → str¶: Gets the value of stringOrderType or its default value ‘frequencyDesc’.

New in version 2.3.0.

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.

setHandleInvalid(value: str) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of handleInvalid.

setInputCol(value: str) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of inputCol.

setInputCols(value: List[str]) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of inputCols.

New in version 3.0.0.

setOutputCol(value: str) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of outputCol.

setOutputCols(value: List[str]) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of outputCols.

New in version 3.0.0.

setParams(self, \*, inputCol=None, outputCol=None, inputCols=None, outputCols=None, handleInvalid="error", stringOrderType="frequencyDesc")[source]¶: Sets params for this StringIndexer.

New in version 1.4.0.

setStringOrderType(value: str) → pyspark.ml.feature.StringIndexer [source]¶: Sets the value of stringOrderType.

New in version 2.3.0.

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

Attributes Documentation

handleInvalid = Param(parent='undefined', name='handleInvalid', doc="how to handle invalid data (unseen or NULL values) in features and label column of string type. Options are 'skip' (filter out rows with invalid data), error (throw an error), or 'keep' (put invalid data in a special additional bucket, at index numLabels).")¶

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

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

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')¶

outputCols = Param(parent='undefined', name='outputCols', doc='output column names.')¶

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

stringOrderType = Param(parent='undefined', name='stringOrderType', doc='How to order labels of string column. The first label after ordering is assigned an index of 0. Supported options: frequencyDesc, frequencyAsc, alphabetDesc, alphabetAsc. Default is frequencyDesc. In case of equal frequency when under frequencyDesc/Asc, the strings are further sorted alphabetically')¶

StopWordsRemover

StringIndexerModel