IsotonicRegression¶

class pyspark.ml.regression.IsotonicRegression(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', weightCol: Optional[str] = None, isotonic: bool = True, featureIndex: int = 0)¶

Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Examples

>>> 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
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True

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.
`getFeatureIndex`()	Gets the value of featureIndex or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getIsotonic`()	Gets the value of isotonic 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.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol 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.
`setFeatureIndex`(value)	Sets the value of `featureIndex`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setIsotonic`(value)	Sets the value of `isotonic`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setParams`(*[, featuresCol, labelCol, …])	setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, weightCol=None, isotonic=True, featureIndex=0): Set the params for IsotonicRegression.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`featureIndex`
`featuresCol`
`isotonic`
`labelCol`
`params`	Returns all params ordered by name.
`predictionCol`
`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) → 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.

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.

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.

getFeatureIndex() → int¶: Gets the value of featureIndex or its default value.

getFeaturesCol() → str¶: Gets the value of featuresCol or its default value.

getIsotonic() → bool¶: Gets the value of isotonic 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.

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

getPredictionCol() → str¶: Gets the value of predictionCol 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.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.

setFeatureIndex(value: int) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of featureIndex.

setFeaturesCol(value: str) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of featuresCol.

setIsotonic(value: bool) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of isotonic.

setLabelCol(value: str) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of labelCol.

setParams(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', weightCol: Optional[str] = None, isotonic: bool = True, featureIndex: int = 0) → pyspark.ml.regression.IsotonicRegression ¶: setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, weightCol=None, isotonic=True, featureIndex=0): Set the params for IsotonicRegression.

setPredictionCol(value: str) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of predictionCol.

setWeightCol(value: str) → pyspark.ml.regression.IsotonicRegression ¶: Sets the value of weightCol.

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

Attributes Documentation

featureIndex = Param(parent='undefined', name='featureIndex', doc='The index of the feature if featuresCol is a vector column, no effect otherwise.')¶

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

isotonic = Param(parent='undefined', name='isotonic', doc='whether the output sequence should be isotonic/increasing (true) orantitonic/decreasing (false).')¶

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

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.')¶

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.')¶

GeneralizedLinearRegressionTrainingSummary

IsotonicRegressionModel