[SPARK-30543] Add bootstrap Param to RandomForest

third_party/3/pyspark/ml/classification.pyi

@@ -186,8 +186,8 @@ class DecisionTreeClassificationModel(_DecisionTreeModel, JavaProbabilisticClass
 class _RandomForestClassifierParams(_RandomForestParams, _TreeClassifierParams): ...
 
 class RandomForestClassifier(JavaProbabilisticClassifier[RandomForestClassificationModel], _RandomForestClassifierParams, JavaMLWritable, JavaMLReadable[RandomForestClassifier]):
-    def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., seed: Optional[int] = ..., subsamplingRate: float = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> None: ...
-    def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., seed: Optional[int] = ..., impurity: str = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., subsamplingRate: float = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> RandomForestClassifier: ...
+    def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., seed: Optional[int] = ..., subsamplingRate: float = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> None: ...
+    def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., seed: Optional[int] = ..., impurity: str = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., subsamplingRate: float = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> RandomForestClassifier: ...
     def setMaxDepth(self, value: int) -> RandomForestClassifier: ...
     def setMaxBins(self, value: int) -> RandomForestClassifier: ...
     def setMinInstancesPerNode(self, value: int) -> RandomForestClassifier: ...
@@ -196,6 +196,7 @@ class RandomForestClassifier(JavaProbabilisticClassifier[RandomForestClassificat
     def setCacheNodeIds(self, value: bool) -> RandomForestClassifier: ...
     def setImpurity(self, value: str) -> RandomForestClassifier: ...
     def setNumTrees(self, value: int) -> RandomForestClassifier: ...
+    def setBootstrap(self, value: bool) -> RandomForestClassifier: ...
     def setSubsamplingRate(self, value: float) -> RandomForestClassifier: ...
     def setFeatureSubsetStrategy(self, value: str) -> RandomForestClassifier: ...
     def setSeed(self, value: int) -> RandomForestClassifier: ...

third_party/3/pyspark/ml/regression.pyi

@@ -140,8 +140,8 @@ class DecisionTreeRegressionModel(_DecisionTreeModel[T], JavaMLWritable, JavaMLR
 class _RandomForestRegressorParams(_RandomForestParams, _TreeRegressorParams): ...
 
 class RandomForestRegressor(JavaPredictor[RandomForestRegressionModel], _RandomForestRegressorParams, JavaMLWritable, JavaMLReadable[RandomForestRegressor]):
-    def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> None: ...
-    def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> RandomForestRegressor: ...
+    def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> None: ...
+    def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> RandomForestRegressor: ...
     def setMaxDepth(self, value: int) -> RandomForestRegressor: ...
     def setMaxBins(self, value: int) -> RandomForestRegressor: ...
     def setMinInstancesPerNode(self, value: int) -> RandomForestRegressor: ...
@@ -150,6 +150,7 @@ class RandomForestRegressor(JavaPredictor[RandomForestRegressionModel], _RandomF
     def setCacheNodeIds(self, value: bool) -> RandomForestRegressor: ...
     def setImpurity(self, value: str) -> RandomForestRegressor: ...
     def setNumTrees(self, value: int) -> RandomForestRegressor: ...
+    def setBootstrap(self, value: bool) -> RandomForestRegressor: ...
     def setSubsamplingRate(self, value: float) -> RandomForestRegressor: ...
     def setFeatureSubsetStrategy(self, value: str) -> RandomForestRegressor: ...
     def setCheckpointInterval(self, value: int) -> RandomForestRegressor: ...

third_party/3/pyspark/ml/tree.pyi

@@ -56,8 +56,10 @@ class _TreeEnsembleParams(_DecisionTreeParams):
 
 class _RandomForestParams(_TreeEnsembleParams):
     numTrees: Param[int]
+    bootstrap: Param[bool]
     def __init__(self) -> None: ...
     def getNumTrees(self) -> int: ...
+    def getBootstrap(self) -> bool: ...
 
 class _GBTParams(_TreeEnsembleParams, HasMaxIter, HasStepSize, HasValidationIndicatorCol):
     stepSize: Param[float]
@@ -70,7 +72,7 @@ class _HasVarianceImpurity(Params):
     def __init__(self) -> None: ...
     def getImpurity(self) -> str: ...
 
-class _TreeClassifierParams:
+class _TreeClassifierParams(Params):
     supportedImpurities: List[str]
     impurity: Param[str]
     def __init__(self) -> None: ...