IsotonicRegression¶
-
class
pyspark.ml.regression.
IsotonicRegression
(featuresCol='features', labelCol='label', predictionCol='prediction', weightCol=None, isotonic=True, featureIndex=0)[source]¶ Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
>>> from pyspark.ml.linalg import Vectors >>> df = spark.createDataFrame([ ... (1.0, Vectors.dense(1.0)), ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"]) >>> ir = IsotonicRegression() >>> model = ir.fit(df) >>> model.setFeaturesCol("features") IsotonicRegressionModel... >>> model.numFeatures 1 >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"]) >>> model.transform(test0).head().prediction 0.0 >>> model.predict(test0.head().features[model.getFeatureIndex()]) 0.0 >>> model.boundaries DenseVector([0.0, 1.0]) >>> ir_path = temp_path + "/ir" >>> ir.save(ir_path) >>> ir2 = IsotonicRegression.load(ir_path) >>> ir2.getIsotonic() True >>> model_path = temp_path + "/ir_model" >>> model.save(model_path) >>> model2 = IsotonicRegressionModel.load(model_path) >>> model.boundaries == model2.boundaries True >>> model.predictions == model2.predictions True
New in version 1.6.0.
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)¶ 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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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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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getFeatureIndex
()¶ Gets the value of featureIndex or its default value.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getIsotonic
()¶ Gets the value of isotonic 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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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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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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setFeatureIndex
(value)[source]¶ Sets the value of
featureIndex
.
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setFeaturesCol
(value)[source]¶ Sets the value of
featuresCol
.New in version 1.6.0.
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setParams
(featuresCol='features', labelCol='label', predictionCol='prediction', weightCol=None, isotonic=True, featureIndex=0)[source]¶ setParams(self, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, weightCol=None, isotonic=True, featureIndex=0): Set the params for IsotonicRegression.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.New in version 1.6.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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featureIndex
= Param(parent='undefined', name='featureIndex', doc='The index of the feature if featuresCol is a vector column, no effect otherwise.')¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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isotonic
= Param(parent='undefined', name='isotonic', doc='whether the output sequence should be isotonic/increasing (true) orantitonic/decreasing (false).')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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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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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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