LinearRegressionModel

class pyspark.ml.regression.LinearRegressionModel(java_model: Optional[JavaObject] = None)

Model fitted by LinearRegression.

New in version 1.4.0.

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.
evaluate(dataset)	Evaluates the model on a test dataset.
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.
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).
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.
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 GeneralMLWriter instance for this ML instance.

Attributes

aggregationDepth
coefficients	Model coefficients.
elasticNetParam
epsilon
featuresCol
fitIntercept
hasSummary	Indicates whether a training summary exists for this model instance.
intercept	Model intercept.
labelCol
loss
maxBlockSizeInMB
maxIter
numFeatures	Returns the number of features the model was trained on.
params	Returns all params ordered by name.
predictionCol
regParam
scale	The value by which \(\|y - X'w\|\) is scaled down when loss is “huber”, otherwise 1.0.
solver
standardization
summary	Gets summary (residuals, MSE, r-squared ) of model on training set.
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

evaluate(dataset: pyspark.sql.dataframe.DataFrame) → pyspark.ml.regression.LinearRegressionSummary

Evaluates the model on a test dataset.

New in version 2.0.0.

Parameters

datasetpyspark.sql.DataFrame

Test dataset to evaluate model on, where dataset is an instance of pyspark.sql.DataFrame

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

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.

New in version 2.3.0.

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).

predict(value: T) → float

Predict label for the given features.

New in version 3.0.0.

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.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

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() → pyspark.ml.util.GeneralJavaMLWriter

Returns an GeneralMLWriter instance for this ML instance.

Attributes Documentation

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

coefficients

Model coefficients.

New in version 2.0.0.

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: pyspark.ml.param.Param[float] = 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.')

hasSummary

Indicates whether a training summary exists for this model instance.

New in version 2.1.0.

intercept

Model intercept.

New in version 1.4.0.

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

loss: pyspark.ml.param.Param[str] = 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).')

numFeatures

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

New in version 2.1.0.

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).')

scale

The value by which \(\|y - X'w\|\) is scaled down when loss is “huber”, otherwise 1.0.

New in version 2.3.0.

solver: pyspark.ml.param.Param[str] = 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.')

summary

Gets summary (residuals, MSE, r-squared ) of model on training set. An exception is thrown if trainingSummary is None.

New in version 2.0.0.

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.')

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