LinearRegressionModel#

class spark_rapids_ml.regression.LinearRegressionModel(coef_: Union[List[float], List[List[float]]], intercept_: Union[float, List[float]], n_cols: int, dtype: str)#

Model fitted by LinearRegression.

Methods

`clear`(param)	Reset a Spark ML Param to its default value, setting matching cuML parameter, if exists.
`copy`([extra])
`cpu`()	Return the PySpark ML LinearRegressionModel
`evaluate`(dataset)	evaluate model on 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.
`getElasticNetParam`()	Gets the value of elasticNetParam or its default value.
`getFeaturesCol`()	Gets the value of `featuresCol` or `featuresCols`
`getFeaturesCols`()	Gets the value of featuresCols 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.
`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.
`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 a single sample
`read`()
`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` or `featureCols`.
`setFeaturesCols`(value)	Sets the value of `featuresCols`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()

Attributes

`coefficients`	Model coefficients.
`cuml_params`	Returns the dictionary of parameters intended for the underlying cuML class.
`elasticNetParam`
`featuresCol`
`featuresCols`
`fitIntercept`
`hasSummary`	Indicates whether a training summary exists for this model instance.
`intercept`	Model intercept.
`labelCol`
`loss`
`maxIter`
`numFeatures`	Returns the number of features the model was trained on.
`num_workers`	Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU.
`params`	Returns all params ordered by name.
`predictionCol`
`regParam`
`scale`	Since "huber" loss is not supported by cuML, just returns the value 1.0 for API compatibility.
`solver`
`standardization`
`tol`

Methods Documentation

clear(param: Param) → None#: Reset a Spark ML Param to its default value, setting matching cuML parameter, if exists.

copy(extra: Optional[ParamMap] = None) → P#

cpu() → LinearRegressionModel#: Return the PySpark ML LinearRegressionModel

evaluate(dataset: DataFrame) → LinearRegressionSummary#: evaluate model on dataset

explainParam(param: Union[str, 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

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

getFeaturesCol() → Union[str, List[str]]#: Gets the value of featuresCol or featuresCols

getFeaturesCols() → List[str]#: Gets the value of featuresCols 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.

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

getOrDefault(param: Union[str, 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) → 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.

hasDefault(param: Union[str, 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, Param[Any]]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, 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 a single sample

classmethod read() → MLReader#

save(path: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setFeaturesCol(value: Union[str, List[str]]) → P#: Sets the value of featuresCol or featureCols.

setFeaturesCols(value: List[str]) → P#: Sets the value of featuresCols.

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

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

transform(dataset: DataFrame, params: Optional[ParamMap] = None) → 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() → MLWriter#

Attributes Documentation

coefficients#: Model coefficients.

cuml_params#: Returns the dictionary of parameters intended for the underlying cuML class.

elasticNetParam: Param[float] = 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.')#

featuresCol: Param[str] = Param(parent='undefined', name='featuresCol', doc='features column name.')#

featuresCols = Param(parent='undefined', name='featuresCols', doc='features column names for multi-column input.')#

fitIntercept: Param[bool] = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')#

hasSummary#: Indicates whether a training summary exists for this model instance.

intercept#: Model intercept.

labelCol: Param[str] = Param(parent='undefined', name='labelCol', doc='label column name.')#

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

maxIter: Param[int] = 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

num_workers#: Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU.

params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol: Param[str] = Param(parent='undefined', name='predictionCol', doc='prediction column name.')#

regParam: Param[float] = Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')#

scale#: Since “huber” loss is not supported by cuML, just returns the value 1.0 for API compatibility.

solver: Param[str] = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: auto, normal, l-bfgs.')#

standardization: Param[bool] = Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')#

tol: Param[float] = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')#