RandomForestRegressionModel#
- class spark_rapids_ml.regression.RandomForestRegressionModel(n_cols: int, dtype: str, treelite_model: Union[str, List[str]], model_json: Union[List[str], List[List[str]]] = [])#
Model fitted by
RandomForestRegressor
.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 RandomForestRegressionModel
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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.
Gets the value of bootstrap or its default value.
Gets the value of featureSubsetStrategy or its default value.
Gets the value of
featuresCol
orfeaturesCols
Gets the value of featuresCols or its default value.
Gets the value of impurity or its default value.
Gets the value of labelCol or its default value.
Gets the value of maxBins or its default value.
Gets the value of maxDepth or its default value.
Gets the value of minInstancesPerNode 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.
Gets the value of predictionCol or its default value.
getSeed
()Gets the value of seed 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.
predictLeaf
(value)Predict the indices of the leaves corresponding to the feature vector.
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
orfeatureCols
.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
Returns the dictionary of parameters intended for the underlying cuML class.
Estimate the importance of each feature.
Number of trees in ensemble.
Returns the number of features the model was trained on.
Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU.
Returns all params ordered by name.
Full description of model.
Total number of nodes, summed over all trees in the ensemble.
Return the weights for each tree.
Trees in this ensemble.
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() RandomForestRegressionModel #
Return the PySpark ML RandomForestRegressionModel
- 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
- getBootstrap() bool #
Gets the value of bootstrap or its default value.
New in version 3.0.0.
- getFeatureSubsetStrategy() str #
Gets the value of featureSubsetStrategy or its default value.
New in version 1.4.0.
- getFeaturesCol() Union[str, List[str]] #
Gets the value of
featuresCol
orfeaturesCols
- getFeaturesCols() List[str] #
Gets the value of featuresCols or its default value.
- getImpurity() str #
Gets the value of impurity or its default value.
New in version 1.4.0.
- getLabelCol() str #
Gets the value of labelCol or its default value.
- getMaxBins() int #
Gets the value of maxBins or its default value.
- getMaxDepth() int #
Gets the value of maxDepth or its default value.
- getMinInstancesPerNode() int #
Gets the value of minInstancesPerNode 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.
- getPredictionCol() str #
Gets the value of predictionCol or its default value.
- getSeed() int #
Gets the value of seed or its 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.
- classmethod load(path: str) RL #
Reads an ML instance from the input path, a shortcut of read().load(path).
- predictLeaf(value: Vector) float #
Predict the indices of the leaves corresponding to the feature vector.
- save(path: str) None #
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
- setFeaturesCol(value: Union[str, List[str]]) P #
Sets the value of
featuresCol
orfeatureCols
.
- setFeaturesCols(value: List[str]) P #
Sets the value of
featuresCols
.
- 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:
- dataset
pyspark.sql.DataFrame
input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns:
pyspark.sql.DataFrame
transformed dataset
Attributes Documentation
- bootstrap: Param[bool] = Param(parent='undefined', name='bootstrap', doc='Whether bootstrap samples are used when building trees.')#
- cuml_params#
Returns the dictionary of parameters intended for the underlying cuML class.
- featureImportances#
Estimate the importance of each feature.
- featureSubsetStrategy: Param[str] = Param(parent='undefined', name='featureSubsetStrategy', doc="The number of features to consider for splits at each tree node. Supported options: 'auto' (choose automatically for task: If numTrees == 1, set to 'all'. If numTrees > 1 (forest), set to 'sqrt' for classification and to 'onethird' for regression), 'all' (use all features), 'onethird' (use 1/3 of the features), 'sqrt' (use sqrt(number of features)), 'log2' (use log2(number of features)), 'n' (when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features). default = 'auto'")#
- 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.')#
- getNumTrees#
Number of trees in ensemble.
- impurity: Param[str] = Param(parent='undefined', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: variance')#
- labelCol: Param[str] = Param(parent='undefined', name='labelCol', doc='label column name.')#
- maxBins: Param[int] = Param(parent='undefined', name='maxBins', doc='Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.')#
- maxDepth: Param[int] = Param(parent='undefined', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. Must be in range [0, 30].')#
- minInstancesPerNode: Param[int] = Param(parent='undefined', name='minInstancesPerNode', doc='Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.')#
- numFeatures#
Returns the number of features the model was trained on. If unknown, returns -1
- numTrees: Param[int] = Param(parent='undefined', name='numTrees', doc='Number of trees to train (>= 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 typeParam
.
- predictionCol: Param[str] = Param(parent='undefined', name='predictionCol', doc='prediction column name.')#
- seed: Param[int] = Param(parent='undefined', name='seed', doc='random seed.')#
- supportedFeatureSubsetStrategies: List[str] = ['auto', 'all', 'onethird', 'sqrt', 'log2']#
- supportedImpurities: List[str] = ['variance']#
- toDebugString#
Full description of model.
- totalNumNodes#
Total number of nodes, summed over all trees in the ensemble.
- treeWeights#
Return the weights for each tree.
- trees#
Trees in this ensemble. Warning: These have null parent Estimators.