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tree.pyi
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tree.pyi
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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http:https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import List, Sequence
from pyspark.ml._typing import P, T
from pyspark.ml.linalg import Vector
from pyspark import since as since # noqa: F401
from pyspark.ml.common import inherit_doc as inherit_doc # noqa: F401
from pyspark.ml.param import Param, Params as Params
from pyspark.ml.param.shared import ( # noqa: F401
HasCheckpointInterval as HasCheckpointInterval,
HasMaxIter as HasMaxIter,
HasSeed as HasSeed,
HasStepSize as HasStepSize,
HasValidationIndicatorCol as HasValidationIndicatorCol,
HasWeightCol as HasWeightCol,
Param as Param,
TypeConverters as TypeConverters,
)
from pyspark.ml.wrapper import JavaPredictionModel as JavaPredictionModel
class _DecisionTreeModel(JavaPredictionModel[T]):
@property
def numNodes(self) -> int: ...
@property
def depth(self) -> int: ...
@property
def toDebugString(self) -> str: ...
def predictLeaf(self, value: Vector) -> float: ...
class _DecisionTreeParams(HasCheckpointInterval, HasSeed, HasWeightCol):
leafCol: Param[str]
maxDepth: Param[int]
maxBins: Param[int]
minInstancesPerNode: Param[int]
minWeightFractionPerNode: Param[float]
minInfoGain: Param[float]
maxMemoryInMB: Param[int]
cacheNodeIds: Param[bool]
def __init__(self) -> None: ...
def setLeafCol(self: P, value: str) -> P: ...
def getLeafCol(self) -> str: ...
def getMaxDepth(self) -> int: ...
def getMaxBins(self) -> int: ...
def getMinInstancesPerNode(self) -> int: ...
def getMinInfoGain(self) -> float: ...
def getMaxMemoryInMB(self) -> int: ...
def getCacheNodeIds(self) -> bool: ...
class _TreeEnsembleModel(JavaPredictionModel[T]):
@property
def trees(self) -> Sequence[_DecisionTreeModel]: ...
@property
def getNumTrees(self) -> int: ...
@property
def treeWeights(self) -> List[float]: ...
@property
def totalNumNodes(self) -> int: ...
@property
def toDebugString(self) -> str: ...
class _TreeEnsembleParams(_DecisionTreeParams):
subsamplingRate: Param[float]
supportedFeatureSubsetStrategies: List[str]
featureSubsetStrategy: Param[str]
def __init__(self) -> None: ...
def getSubsamplingRate(self) -> float: ...
def getFeatureSubsetStrategy(self) -> str: ...
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]
validationTol: Param[float]
def getValidationTol(self) -> float: ...
class _HasVarianceImpurity(Params):
supportedImpurities: List[str]
impurity: Param[str]
def __init__(self) -> None: ...
def getImpurity(self) -> str: ...
class _TreeClassifierParams(Params):
supportedImpurities: List[str]
impurity: Param[str]
def __init__(self) -> None: ...
def getImpurity(self) -> str: ...
class _TreeRegressorParams(_HasVarianceImpurity): ...