GeneralizedLinearRegression¶

class pyspark.ml.regression.GeneralizedLinearRegression(*, labelCol: str = 'label', featuresCol: str = 'features', predictionCol: str = 'prediction', family: str = 'gaussian', link: Optional[str] = None, fitIntercept: bool = True, maxIter: int = 25, tol: float = 1e-06, regParam: float = 0.0, weightCol: Optional[str] = None, solver: str = 'irls', linkPredictionCol: Optional[str] = None, variancePower: float = 0.0, linkPower: Optional[float] = None, offsetCol: Optional[str] = None, aggregationDepth: int = 2)¶

Generalized Linear Regression.

Fit a Generalized Linear Model specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports “gaussian”, “binomial”, “poisson”, “gamma” and “tweedie” as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

“gaussian” -> “identity”, “log”, “inverse”
“binomial” -> “logit”, “probit”, “cloglog”
“poisson” -> “log”, “identity”, “sqrt”
“gamma” -> “inverse”, “identity”, “log”
“tweedie” -> power link function specified through “linkPower”. The default link power in the tweedie family is 1 - variancePower.

Notes

For more information see Wikipedia page on GLM

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     (1.0, Vectors.dense(0.0, 0.0)),
...     (1.0, Vectors.dense(1.0, 2.0)),
...     (2.0, Vectors.dense(0.0, 0.0)),
...     (2.0, Vectors.dense(1.0, 1.0)),], ["label", "features"])
>>> glr = GeneralizedLinearRegression(family="gaussian", link="identity", linkPredictionCol="p")
>>> glr.setRegParam(0.1)
GeneralizedLinearRegression...
>>> glr.getRegParam()
0.1
>>> glr.clear(glr.regParam)
>>> glr.setMaxIter(10)
GeneralizedLinearRegression...
>>> glr.getMaxIter()
10
>>> glr.clear(glr.maxIter)
>>> model = glr.fit(df)
>>> model.setFeaturesCol("features")
GeneralizedLinearRegressionModel...
>>> model.getMaxIter()
25
>>> model.getAggregationDepth()
2
>>> transformed = model.transform(df)
>>> abs(transformed.head().prediction - 1.5) < 0.001
True
>>> abs(transformed.head().p - 1.5) < 0.001
True
>>> model.coefficients
DenseVector([1.5..., -1.0...])
>>> model.numFeatures
2
>>> abs(model.intercept - 1.5) < 0.001
True
>>> glr_path = temp_path + "/glr"
>>> glr.save(glr_path)
>>> glr2 = GeneralizedLinearRegression.load(glr_path)
>>> glr.getFamily() == glr2.getFamily()
True
>>> model_path = temp_path + "/glr_model"
>>> model.save(model_path)
>>> model2 = GeneralizedLinearRegressionModel.load(model_path)
>>> model.intercept == model2.intercept
True
>>> model.coefficients[0] == model2.coefficients[0]
True
>>> model.transform(df).take(1) == model2.transform(df).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.
`getAggregationDepth`()	Gets the value of aggregationDepth or its default value.
`getFamily`()	Gets the value of family or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getFitIntercept`()	Gets the value of fitIntercept or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getLink`()	Gets the value of link or its default value.
`getLinkPower`()	Gets the value of linkPower or its default value.
`getLinkPredictionCol`()	Gets the value of linkPredictionCol or its default value.
`getMaxIter`()	Gets the value of maxIter or its default value.
`getOffsetCol`()	Gets the value of offsetCol 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.
`getRegParam`()	Gets the value of regParam or its default value.
`getSolver`()	Gets the value of solver or its default value.
`getTol`()	Gets the value of tol or its default value.
`getVariancePower`()	Gets the value of variancePower 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.
`setAggregationDepth`(value)	Sets the value of `aggregationDepth`.
`setFamily`(value)	Sets the value of `family`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setFitIntercept`(value)	Sets the value of `fitIntercept`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setLink`(value)	Sets the value of `link`.
`setLinkPower`(value)	Sets the value of `linkPower`.
`setLinkPredictionCol`(value)	Sets the value of `linkPredictionCol`.
`setMaxIter`(value)	Sets the value of `maxIter`.
`setOffsetCol`(value)	Sets the value of `offsetCol`.
`setParams`(self, \*[, labelCol, featuresCol, …])	Sets params for generalized linear regression.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setRegParam`(value)	Sets the value of `regParam`.
`setSolver`(value)	Sets the value of `solver`.
`setTol`(value)	Sets the value of `tol`.
`setVariancePower`(value)	Sets the value of `variancePower`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`aggregationDepth`
`family`
`featuresCol`
`fitIntercept`
`labelCol`
`link`
`linkPower`
`linkPredictionCol`
`maxIter`
`offsetCol`
`params`	Returns all params ordered by name.
`predictionCol`
`regParam`
`solver`
`tol`
`variancePower`
`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.

getAggregationDepth() → int¶: Gets the value of aggregationDepth or its default value.

getFamily() → str¶: Gets the value of family or its default value.

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

getFitIntercept() → bool¶: Gets the value of fitIntercept or its default value.

getLabelCol() → str¶: Gets the value of labelCol or its default value.

getLink() → str¶: Gets the value of link or its default value.

getLinkPower() → float¶: Gets the value of linkPower or its default value.

getLinkPredictionCol() → str¶: Gets the value of linkPredictionCol or its default value.

getMaxIter() → int¶: Gets the value of maxIter or its default value.

getOffsetCol() → str¶: Gets the value of offsetCol 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.

getRegParam() → float¶: Gets the value of regParam or its default value.

getSolver() → str¶: Gets the value of solver or its default value.

getTol() → float¶: Gets the value of tol or its default value.

getVariancePower() → float¶: Gets the value of variancePower 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.

setAggregationDepth(value: int) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of aggregationDepth.

setFamily(value: str) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of family.

setFeaturesCol(value: str) → P¶: Sets the value of featuresCol.

setFitIntercept(value: bool) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of fitIntercept.

setLabelCol(value: str) → P¶: Sets the value of labelCol.

setLink(value: str) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of link.

setLinkPower(value: float) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of linkPower.

setLinkPredictionCol(value: str) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of linkPredictionCol.

setMaxIter(value: int) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of maxIter.

setOffsetCol(value: str) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of offsetCol.

setParams(self, \*, labelCol="label", featuresCol="features", predictionCol="prediction", family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)¶: Sets params for generalized linear regression.

setPredictionCol(value: str) → P¶: Sets the value of predictionCol.

setRegParam(value: float) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of regParam.

setSolver(value: str) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of solver.

setTol(value: float) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of tol.

setVariancePower(value: float) → pyspark.ml.regression.GeneralizedLinearRegression ¶: Sets the value of variancePower.

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

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

Attributes Documentation

aggregationDepth = Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')¶

family = Param(parent='undefined', name='family', doc='The name of family which is a description of the error distribution to be used in the model. Supported options: gaussian (default), binomial, poisson, gamma and tweedie.')¶

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

fitIntercept = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')¶

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

link = Param(parent='undefined', name='link', doc='The name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: identity, log, inverse, logit, probit, cloglog and sqrt.')¶

linkPower = Param(parent='undefined', name='linkPower', doc='The index in the power link function. Only applicable to the Tweedie family.')¶

linkPredictionCol = Param(parent='undefined', name='linkPredictionCol', doc='link prediction (linear predictor) column name')¶

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶

offsetCol = Param(parent='undefined', name='offsetCol', doc='The offset column name. If this is not set or empty, we treat all instance offsets as 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.')¶

regParam = Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')¶

solver = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: irls.')¶

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶

variancePower = Param(parent='undefined', name='variancePower', doc='The power in the variance function of the Tweedie distribution which characterizes the relationship between the variance and mean of the distribution. Only applicable for the Tweedie family. Supported values: 0 and [1, Inf).')¶

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.')¶

GBTRegressionModel

GeneralizedLinearRegressionModel