OneVsRest¶

class pyspark.ml.classification.OneVsRest(*, featuresCol='features', labelCol='label', predictionCol='prediction', rawPredictionCol='rawPrediction', classifier=None, weightCol=None, parallelism=1)[source]¶

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)¶: Clears a param from the param map if it has been explicitly set.

copy(extra=None)[source]¶

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)¶: 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

fit(dataset, params=None)¶

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, paramMaps)¶

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()¶: Gets the value of classifier or its default value.

New in version 2.0.0.

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. Raises an error if neither is set.

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.

classmethod load(path)¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()[source]¶: 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)[source]¶: Sets the value of classifier.

New in version 2.0.0.

setFeaturesCol(value)[source]¶: Sets the value of featuresCol.

setLabelCol(value)[source]¶: Sets the value of labelCol.

setParallelism(value)[source]¶: Sets the value of parallelism.

setParams(*, featuresCol='features', labelCol='label', predictionCol='prediction', rawPredictionCol='rawPrediction', classifier=None, weightCol=None, parallelism=1)[source]¶: 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)[source]¶: Sets the value of predictionCol.

setRawPredictionCol(value)[source]¶: Sets the value of rawPredictionCol.

setWeightCol(value)[source]¶: Sets the value of weightCol.

write()[source]¶: 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.')¶

MultilayerPerceptronClassificationTrainingSummary

OneVsRestModel