RandomForestClassificationModel#

class spark_rapids_ml.classification.RandomForestClassificationModel(n_cols: int, dtype: str, treelite_model: Union[str, List[str]], model_json: Union[List[str], List[List[str]]], num_classes: int)#

Model fitted by RandomForestClassifier.

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 RandomForestClassificationModel
`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.
`getBootstrap`()	Gets the value of bootstrap or its default value.
`getFeatureSubsetStrategy`()	Gets the value of featureSubsetStrategy or its default value.
`getFeaturesCol`()	Gets the value of `featuresCol` or `featuresCols`
`getFeaturesCols`()	Gets the value of featuresCols or its default value.
`getImpurity`()	Gets the value of impurity or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getMaxBins`()	Gets the value of maxBins or its default value.
`getMaxDepth`()	Gets the value of maxDepth or its default value.
`getMinInstancesPerNode`()	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.
`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.
`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.
`predictProbability`(value)	Predict the probability of each class given the features.
`predictRaw`(value)	Raw prediction for each possible label.
`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`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()

Attributes

`bootstrap`
`cuml_params`	Returns the dictionary of parameters intended for the underlying cuML class.
`featureImportances`	Estimate the importance of each feature.
`featureSubsetStrategy`
`featuresCol`
`featuresCols`
`getNumTrees`	Number of trees in ensemble.
`hasSummary`	Indicates whether a training summary exists for this model instance.
`impurity`
`labelCol`
`maxBins`
`maxDepth`
`minInstancesPerNode`
`numClasses`	Number of classes (values which the label can take).
`numFeatures`	Returns the number of features the model was trained on.
`numTrees`
`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`
`seed`
`supportedFeatureSubsetStrategies`
`supportedImpurities`
`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.

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() → RandomForestClassificationModel#: Return the PySpark ML RandomForestClassificationModel

evaluate(dataset: DataFrame) → Union[BinaryRandomForestClassificationSummary, RandomForestClassificationSummary]#

Evaluates the model on a test dataset.

Parameters:

datasetpyspark.sql.DataFrame: Test dataset to evaluate model on.

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 or featuresCols

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 1.6.0.

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.

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.

getSeed() → int#: Gets the value of seed 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: Vector) → float#: Predict label for the given features.

predictLeaf(value: Vector) → float#: Predict the indices of the leaves corresponding to the feature vector.

predictProbability(value: Vector) → Vector#: Predict the probability of each class given the features.

predictRaw(value: Vector) → Vector#: Raw prediction for each possible label.

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.

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

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

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

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.

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

impurity: Param[str] = Param(parent='undefined', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: entropy, gini')#

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.')#

numClasses#: Number of classes (values which the label can take).

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 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.')#

seed: Param[int] = Param(parent='undefined', name='seed', doc='random seed.')#

supportedFeatureSubsetStrategies: List[str] = ['auto', 'all', 'onethird', 'sqrt', 'log2']#

supportedImpurities: List[str] = ['entropy', 'gini']#

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.