IsotonicRegressionModel¶

class pyspark.ml.regression.IsotonicRegressionModel(java_model=None)[source]¶

Model fitted by IsotonicRegression.

New in version 1.6.0.

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.
`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).
`predict`(value)	Predict label for the given features.
`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`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`boundaries`	Boundaries in increasing order for which predictions are known.
`featureIndex`
`featuresCol`
`isotonic`
`labelCol`
`numFeatures`	Returns the number of features the model was trained on.
`params`	Returns all params ordered by name.
`predictionCol`
`predictions`	Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
`weightCol`

Methods Documentation

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

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:

extradict, optional: Extra parameters to copy to the new instance

Returns:

JavaParams: Copy of this 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

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. Raises an error if neither is set.

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.

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

predict(value)[source]¶: Predict label for the given features.

New in version 3.0.0.

classmethod 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)[source]¶: Sets the value of featureIndex.

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

New in version 3.0.0.

setPredictionCol(value)[source]¶: Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset, params=None)¶

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns:

pyspark.sql.DataFrame: transformed dataset

write()¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

boundaries¶: Boundaries in increasing order for which predictions are known.

New in version 1.6.0.

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.')¶

numFeatures¶: Returns the number of features the model was trained on. If unknown, returns -1

New in version 3.0.0.

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.')¶

predictions¶: Predictions associated with the boundaries at the same index, monotone because of isotonic regression.

New in version 1.6.0.

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.')¶

IsotonicRegression LinearRegression