OneVsRest

class pyspark.ml.classification.OneVsRest(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', rawPredictionCol: str = 'rawPrediction', classifier: Optional[pyspark.ml.classification.Classifier[CM]] = None, weightCol: Optional[str] = None, parallelism: int = 1)

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

New in version 2.0.0.

Examples

>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> data_path = "data/mllib/sample_multiclass_classification_data.txt"
>>> df = spark.read.format("libsvm").load(data_path)
>>> lr = LogisticRegression(regParam=0.01)
>>> ovr = OneVsRest(classifier=lr)
>>> ovr.getRawPredictionCol()
'rawPrediction'
>>> ovr.setPredictionCol("newPrediction")
OneVsRest...
>>> model = ovr.fit(df)
>>> model.models[0].coefficients
DenseVector([0.5..., -1.0..., 3.4..., 4.2...])
>>> model.models[1].coefficients
DenseVector([-2.1..., 3.1..., -2.6..., -2.3...])
>>> model.models[2].coefficients
DenseVector([0.3..., -3.4..., 1.0..., -1.1...])
>>> [x.intercept for x in model.models]
[-2.7..., -2.5..., -1.3...]
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, 0.0, 1.0, 1.0))]).toDF()
>>> model.transform(test0).head().newPrediction
0.0
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(4, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().newPrediction
2.0
>>> test2 = sc.parallelize([Row(features=Vectors.dense(0.5, 0.4, 0.3, 0.2))]).toDF()
>>> model.transform(test2).head().newPrediction
0.0
>>> model_path = temp_path + "/ovr_model"
>>> model.save(model_path)
>>> model2 = OneVsRestModel.load(model_path)
>>> model2.transform(test0).head().newPrediction
0.0
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True
>>> model.transform(test2).columns
['features', 'rawPrediction', 'newPrediction']

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 a randomly generated 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.
getClassifier()	Gets the value of classifier or its default value.
getFeaturesCol()	Gets the value of featuresCol or its default value.
getLabelCol()	Gets the value of labelCol or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getParallelism()	Gets the value of parallelism or its default value.
getParam(paramName)	Gets a param by its name.
getPredictionCol()	Gets the value of predictionCol or its default value.
getRawPredictionCol()	Gets the value of rawPredictionCol or its default value.
getWeightCol()	Gets the value of weightCol 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.
setClassifier(value)	Sets the value of classifier.
setFeaturesCol(value)	Sets the value of featuresCol.
setLabelCol(value)	Sets the value of labelCol.
setParallelism(value)	Sets the value of parallelism.
setParams(*[, featuresCol, labelCol, …])	setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, rawPredictionCol=”rawPrediction”, classifier=None, weightCol=None, parallelism=1): Sets params for OneVsRest.
setPredictionCol(value)	Sets the value of predictionCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setWeightCol(value)	Sets the value of weightCol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

classifier
featuresCol
labelCol
parallelism
params	Returns all params ordered by name.
predictionCol
rawPredictionCol
weightCol

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) → OneVsRest

Creates a copy of this instance with a randomly generated uid and some extra params. This creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over.

New in version 2.0.0.

Returns

OneVsRest

Copy of this instance

Examples

extradict, optional

Extra parameters to copy to the new 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

:py:class:`Transformer` or a list ofpy:class: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.

getClassifier() → pyspark.ml.classification.Classifier

Gets the value of classifier or its default value.

New in version 2.0.0.

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getLabelCol() → str

Gets the value of labelCol 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.

getParallelism() → int

Gets the value of parallelism or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getWeightCol() → str

Gets the value of weightCol 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.classification.OneVsRestReader

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.

setClassifier(value: pyspark.ml.classification.Classifier[CM]) → pyspark.ml.classification.OneVsRest

Sets the value of classifier.

New in version 2.0.0.

setFeaturesCol(value: str) → pyspark.ml.classification.OneVsRest

Sets the value of featuresCol.

setLabelCol(value: str) → pyspark.ml.classification.OneVsRest

Sets the value of labelCol.

setParallelism(value: int) → pyspark.ml.classification.OneVsRest

Sets the value of parallelism.

setParams(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', rawPredictionCol: str = 'rawPrediction', classifier: Optional[pyspark.ml.classification.Classifier[CM]] = None, weightCol: Optional[str] = None, parallelism: int = 1) → pyspark.ml.classification.OneVsRest

setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, rawPredictionCol=”rawPrediction”, classifier=None, weightCol=None, parallelism=1): Sets params for OneVsRest.

New in version 2.0.0.

setPredictionCol(value: str) → pyspark.ml.classification.OneVsRest

Sets the value of predictionCol.

setRawPredictionCol(value: str) → pyspark.ml.classification.OneVsRest

Sets the value of rawPredictionCol.

setWeightCol(value: str) → pyspark.ml.classification.OneVsRest

Sets the value of weightCol.

write() → pyspark.ml.util.MLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

classifier = Param(parent='undefined', name='classifier', doc='base binary classifier')

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

parallelism = Param(parent='undefined', name='parallelism', doc='the number of threads to use when running parallel algorithms (>= 1).')

params

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

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')

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