LogisticRegression#

class spark_rapids_ml.classification.LogisticRegression(*, featuresCol: Union[str, List[str]] = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction', maxIter: int = 100, regParam: float = 0.0, elasticNetParam: float = 0.0, tol: float = 1e-06, fitIntercept: bool = True, standardization: bool = True, enable_sparse_data_optim: Optional[bool] = None, float32_inputs: bool = True, num_workers: Optional[int] = None, verbose: Union[int, bool] = False, **kwargs: Any)#

LogisticRegression is a machine learning model where the response y is modeled by the sigmoid (or softmax for more than 2 classes) function applied to a linear combination of the features in X. It implements cuML’s GPU accelerated LogisticRegression algorithm based on cuML python library, and it can be used in PySpark Pipeline and PySpark ML meta algorithms like CrossValidator/ TrainValidationSplit/ OneVsRest

This supports multiple types of regularization:

none
L2 (ridge regression)
L1 (lasso)
L2 + L1 (elastic net)

LogisticRegression automatically supports most of the parameters from both LogisticRegression. And it will automatically map pyspark parameters to cuML parameters.

In the case of applying LogisticRegression on sparse vectors, Spark 3.4 or above is required.

Parameters:

featuresCol: str or List[str] (default = “features”)

The feature column names, spark-rapids-ml supports vector, array and columnar as the input.

When the value is a string, the feature columns must be assembled into 1 column with vector or array type.

When the value is a list of strings, the feature columns must be numeric types.

labelCol: (default = “label”)

The label column name.

predictionCol: (default = “prediction”)

The class prediction column name.

probabilityCol: (default = “probability”)

The probability prediction column name.

rawPredictionCol: (default = “rawPrediction”)

The column name for class raw predictions - this is currently set equal to probabilityCol values.

maxIter: (default = 100)

The maximum number of iterations of the underlying L-BFGS algorithm.

regParam: (default = 0.0)

The regularization parameter.

elasticNetParam: (default = 0.0)

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.

tol: (default = 1e-6)

The convergence tolerance.

enable_sparse_data_optim: None or boolean, optional (default=None)

If features column is VectorUDT type, Spark rapids ml relies on this parameter to decide whether to use dense array or sparse array in cuml. If None, use dense array if the first VectorUDT of a dataframe is DenseVector. Use sparse array if it is SparseVector. If False, always uses dense array. This is favorable if the majority of VectorUDT vectors are DenseVector. If True, always uses sparse array. This is favorable if the majority of the VectorUDT vectors are SparseVector. Note this is only supported in spark >= 3.4.

fitIntercept: (default = True)

Whether to fit an intercept term.

standardization: (default = True)

Whether to standardize the training data before fit.

num_workers:

Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU. If not set, spark-rapids-ml tries to infer the number of cuML workers (i.e. GPUs in cluster) from the Spark environment.

verbose:

Logging level.

0 - Disables all log messages.
1 - Enables only critical messages.
2 - Enables all messages up to and including errors.
3 - Enables all messages up to and including warnings.
4 or False - Enables all messages up to and including information messages.
5 or True - Enables all messages up to and including debug messages.
6 - Enables all messages up to and including trace messages.

Examples

>>> from spark_rapids_ml.classification import LogisticRegression
>>> data = [
...     ([1.0, 2.0], 1.0),
...     ([1.0, 3.0], 1.0),
...     ([2.0, 1.0], 0.0),
...     ([3.0, 1.0], 0.0),
... ]
>>> schema = "features array<float>, label float"
>>> df = spark.createDataFrame(data, schema=schema)
>>> df.show()
+----------+-----+
|  features|label|
+----------+-----+
|[1.0, 2.0]|  1.0|
|[1.0, 3.0]|  1.0|
|[2.0, 1.0]|  0.0|
|[3.0, 1.0]|  0.0|
+----------+-----+

>>> lr_estimator = LogisticRegression()
>>> lr_estimator.setFeaturesCol("features")
LogisticRegression_a757215437b0
>>> lr_estimator.setLabelCol("label")
LogisticRegression_a757215437b0
>>> lr_model = lr_estimator.fit(df)
>>> lr_model.coefficients
DenseVector([-0.7148, 0.7148])
>>> lr_model.intercept
-8.543887375367376e-09

Methods

`clear`(param)	Reset a Spark ML Param to its default value, setting matching cuML parameter, if exists.
`copy`([extra])	Create a copy of the current instance, including its parameters and cuml_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 multiple models to the input dataset for all param maps in a single pass.
`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.
`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.
`getProbabilityCol`()	Gets the value of probabilityCol or its default value.
`getRawPredictionCol`()	Gets the value of rawPredictionCol or its default value.
`getRegParam`()	Gets the value of regParam 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).
`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.
`setElasticNetParam`(value)	Sets the value of `regParam`.
`setFeaturesCol`(value)	Sets the value of `featuresCol` or `featureCols`.
`setFeaturesCols`(value)	Sets the value of `featuresCols`.
`setFitIntercept`(value)	Sets the value of `fitIntercept`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setMaxIter`(value)	Sets the value of `maxIter`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setRegParam`(value)	Sets the value of `regParam`.
`setStandardization`(value)	Sets the value of `standardization`.
`setTol`(value)	Sets the value of `tol`.
`write`()

Attributes

`cuml_params`	Returns the dictionary of parameters intended for the underlying cuML class.
`elasticNetParam`
`enable_sparse_data_optim`
`featuresCol`
`featuresCols`
`fitIntercept`
`labelCol`
`maxIter`
`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`
`probabilityCol`
`rawPredictionCol`
`regParam`
`standardization`
`tol`
`verbose`

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#

Create a copy of the current instance, including its parameters and cuml_params.

This function extends the default copy() method to ensure the cuml_params variable is also copied. The default super().copy() method only handles _paramMap and _defaultParamMap.

Parameters:

extraOptional[ParamMap]: A dictionary or ParamMap containing additional parameters to set in the copied instance. Note ParamMap = Dict[pyspark.ml.param.Param, Any].

Returns:

P: A new instance of the same type as the current object, with parameters and cuml_params copied.

Raises:

TypeError: If any key in the extra dictionary is not an instance of pyspark.ml.param.Param.

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

fit(dataset: DataFrame, params: Optional[Union[ParamMap, List[ParamMap], Tuple[ParamMap]]] = None) → Union[M, List[M]]#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

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: DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, _CumlModel]]#

Fits multiple models to the input dataset for all param maps in a single pass.

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.

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.

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.

getProbabilityCol() → str#: Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str#: Gets the value of rawPredictionCol or its default value.

getRegParam() → float#: Gets the value of regParam 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).

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.

setElasticNetParam(value: float) → LogisticRegression#: Sets the value of regParam.

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

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

setFitIntercept(value: bool) → LogisticRegression#: Sets the value of fitIntercept.

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

setMaxIter(value: int) → LogisticRegression#: Sets the value of maxIter.

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

setProbabilityCol(value: str) → _LogisticRegressionCumlParams#: Sets the value of probabilityCol.

setRawPredictionCol(value: str) → _LogisticRegressionCumlParams#: Sets the value of rawPredictionCol.

setRegParam(value: float) → LogisticRegression#: Sets the value of regParam.

setStandardization(value: bool) → LogisticRegression#: Sets the value of standardization.

setTol(value: float) → LogisticRegression#: Sets the value of tol.

write() → MLWriter#

Attributes Documentation

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.')#

enable_sparse_data_optim = Param(parent='undefined', name='enable_sparse_data_optim', doc='This param activates sparse data optimization for VectorUDT features column. If the param is not included in an Estimator class, Spark rapids ml always converts VectorUDT features column into dense arrays when calling cuml backend. If included, Spark rapids ml will determine whether to create sparse arrays based on the param value: (1) If None, create dense arrays if the first VectorUDT of a dataframe is DenseVector. Create sparse arrays if it is SparseVector.(2) If False, create dense arrays. This is favorable if the majority of vectors are DenseVector.(3) If True, create sparse arrays. This is favorable if the majority of the VectorUDT vectors are SparseVector.')#

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.')#

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

maxIter: Param[int] = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')#

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.')#

probabilityCol: Param[str] = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')#

rawPredictionCol: Param[str] = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')#

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

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).')#

verbose: Param[Union[int, bool]] = Param(parent='undefined', name='verbose', doc='cuml verbosity level (False, True or an integer between 0 and 6).')#