LinearRegression¶

class pyspark.ml.regression.LinearRegression(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, regParam: float = 0.0, elasticNetParam: float = 0.0, tol: float = 1e-06, fitIntercept: bool = True, standardization: bool = True, solver: str = 'auto', weightCol: Optional[str] = None, aggregationDepth: int = 2, loss: str = 'squaredError', epsilon: float = 1.35, maxBlockSizeInMB: float = 0.0)¶

Linear regression.

The learning objective is to minimize the specified loss function, with regularization. This supports two kinds of loss:

squaredError (a.k.a squared loss)
huber (a hybrid of squared error for relatively small errors and absolute error for relatively large ones, and we estimate the scale parameter from training data)

This supports multiple types of regularization:

none (a.k.a. ordinary least squares)
L2 (ridge regression)
L1 (Lasso)
L2 + L1 (elastic net)

Notes

Fitting with huber loss only supports none and L2 regularization.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     (1.0, 2.0, Vectors.dense(1.0)),
...     (0.0, 2.0, Vectors.sparse(1, [], []))], ["label", "weight", "features"])
>>> lr = LinearRegression(regParam=0.0, solver="normal", weightCol="weight")
>>> lr.setMaxIter(5)
LinearRegression...
>>> lr.getMaxIter()
5
>>> lr.setRegParam(0.1)
LinearRegression...
>>> lr.getRegParam()
0.1
>>> lr.setRegParam(0.0)
LinearRegression...
>>> model = lr.fit(df)
>>> model.setFeaturesCol("features")
LinearRegressionModel...
>>> model.setPredictionCol("newPrediction")
LinearRegressionModel...
>>> model.getMaxIter()
5
>>> model.getMaxBlockSizeInMB()
0.0
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> abs(model.predict(test0.head().features) - (-1.0)) < 0.001
True
>>> abs(model.transform(test0).head().newPrediction - (-1.0)) < 0.001
True
>>> abs(model.coefficients[0] - 1.0) < 0.001
True
>>> abs(model.intercept - 0.0) < 0.001
True
>>> test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> abs(model.transform(test1).head().newPrediction - 1.0) < 0.001
True
>>> lr.setParams(featuresCol="vector")
LinearRegression...
>>> lr_path = temp_path + "/lr"
>>> lr.save(lr_path)
>>> lr2 = LinearRegression.load(lr_path)
>>> lr2.getMaxIter()
5
>>> model_path = temp_path + "/lr_model"
>>> model.save(model_path)
>>> model2 = LinearRegressionModel.load(model_path)
>>> model.coefficients[0] == model2.coefficients[0]
True
>>> model.intercept == model2.intercept
True
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True
>>> model.numFeatures
1
>>> model.write().format("pmml").save(model_path + "_2")

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.
`getElasticNetParam`()	Gets the value of elasticNetParam or its default value.
`getEpsilon`()	Gets the value of epsilon 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.
`getLoss`()	Gets the value of loss or its default value.
`getMaxBlockSizeInMB`()	Gets the value of maxBlockSizeInMB or its default value.
`getMaxIter`()	Gets the value of maxIter 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.
`getStandardization`()	Gets the value of standardization or its default value.
`getTol`()	Gets the value of tol 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`.
`setElasticNetParam`(value)	Sets the value of `elasticNetParam`.
`setEpsilon`(value)	Sets the value of `epsilon`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setFitIntercept`(value)	Sets the value of `fitIntercept`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setLoss`(value)	Sets the value of `loss`.
`setMaxBlockSizeInMB`(value)	Sets the value of `maxBlockSizeInMB`.
`setMaxIter`(value)	Sets the value of `maxIter`.
`setParams`(self, \*[, featuresCol, labelCol, …])	Sets params for linear regression.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setRegParam`(value)	Sets the value of `regParam`.
`setSolver`(value)	Sets the value of `solver`.
`setStandardization`(value)	Sets the value of `standardization`.
`setTol`(value)	Sets the value of `tol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`aggregationDepth`
`elasticNetParam`
`epsilon`
`featuresCol`
`fitIntercept`
`labelCol`
`loss`
`maxBlockSizeInMB`
`maxIter`
`params`	Returns all params ordered by name.
`predictionCol`
`regParam`
`solver`
`standardization`
`tol`
`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.

getElasticNetParam() → float¶: Gets the value of elasticNetParam or its default value.

getEpsilon() → float¶: Gets the value of epsilon 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.

getLoss() → str¶: Gets the value of loss or its default value.

getMaxBlockSizeInMB() → float¶: Gets the value of maxBlockSizeInMB or its default value.

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

getStandardization() → bool¶: Gets the value of standardization or its default value.

getTol() → float¶: Gets the value of tol 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.LinearRegression ¶: Sets the value of aggregationDepth.

setElasticNetParam(value: float) → pyspark.ml.regression.LinearRegression ¶: Sets the value of elasticNetParam.

setEpsilon(value: float) → pyspark.ml.regression.LinearRegression ¶: Sets the value of epsilon.

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

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

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

setLoss(value: str) → pyspark.ml.regression.LinearRegression ¶: Sets the value of loss.

setMaxBlockSizeInMB(value: float) → pyspark.ml.regression.LinearRegression ¶: Sets the value of maxBlockSizeInMB.

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

setParams(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, standardization=True, solver="auto", weightCol=None, aggregationDepth=2, loss="squaredError", epsilon=1.35, maxBlockSizeInMB=0.0)¶: Sets params for linear regression.

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

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

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

setStandardization(value: bool) → pyspark.ml.regression.LinearRegression ¶: Sets the value of standardization.

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

setWeightCol(value: str) → pyspark.ml.regression.LinearRegression ¶: 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).')¶

elasticNetParam = Param(parent='undefined', name='elasticNetParam', doc='the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.')¶

epsilon = Param(parent='undefined', name='epsilon', doc='The shape parameter to control the amount of robustness. Must be > 1.0. Only valid when loss is huber')¶

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.')¶

loss = Param(parent='undefined', name='loss', doc='The loss function to be optimized. Supported options: squaredError, huber.')¶

maxBlockSizeInMB = Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')¶

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 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: auto, normal, l-bfgs.')¶

standardization = Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')¶

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

IsotonicRegressionModel

LinearRegressionModel