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.
>>> 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(test2).columns ['features', 'rawPrediction', 'newPrediction']
New in version 2.0.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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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.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
New in version 2.0.0.
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
extra – extra param values
- Returns
merged param map
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fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – 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
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
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getClassifier
()¶ Gets the value of classifier or its default value.
New in version 2.0.0.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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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.
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getParallelism
()¶ Gets the value of parallelism or its default value.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getRawPredictionCol
()¶ Gets the value of rawPredictionCol or its default value.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setClassifier
(value)[source]¶ Sets the value of
classifier
.New in version 2.0.0.
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setFeaturesCol
(value)[source]¶ Sets the value of
featuresCol
.
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setParallelism
(value)[source]¶ Sets the value of
parallelism
.
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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.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.
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setRawPredictionCol
(value)[source]¶ Sets the value of
rawPredictionCol
.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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classifier
= Param(parent='undefined', name='classifier', doc='base binary classifier')¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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parallelism
= Param(parent='undefined', name='parallelism', doc='the number of threads to use when running parallel algorithms (>= 1).')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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rawPredictionCol
= Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')¶
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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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