97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import abc
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from typing import overload
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from typing import Any, Callable, Generic, List, Optional
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from pyspark.ml._typing import T
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import pyspark.ml._typing
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import pyspark.ml.util
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from pyspark.ml.linalg import DenseVector, Matrix
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class Param(Generic[T]):
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parent: str
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name: str
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doc: str
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typeConverter: Callable[[Any], T]
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def __init__(
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self,
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parent: pyspark.ml.util.Identifiable,
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name: str,
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doc: str,
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typeConverter: Optional[Callable[[Any], T]] = ...,
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) -> None: ...
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def __hash__(self) -> int: ...
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def __eq__(self, other: Any) -> bool: ...
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class TypeConverters:
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@staticmethod
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def identity(value: T) -> T: ...
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@staticmethod
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def toList(value: Any) -> List: ...
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@staticmethod
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def toListFloat(value: Any) -> List[float]: ...
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@staticmethod
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def toListInt(value: Any) -> List[int]: ...
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@staticmethod
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def toListString(value: Any) -> List[str]: ...
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@staticmethod
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def toVector(value: Any) -> DenseVector: ...
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@staticmethod
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def toMatrix(value: Any) -> Matrix: ...
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@staticmethod
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def toFloat(value: Any) -> float: ...
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@staticmethod
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def toInt(value: Any) -> int: ...
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@staticmethod
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def toString(value: Any) -> str: ...
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@staticmethod
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def toBoolean(value: Any) -> bool: ...
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class Params(pyspark.ml.util.Identifiable, metaclass=abc.ABCMeta):
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def __init__(self) -> None: ...
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@property
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def params(self) -> List[Param]: ...
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def explainParam(self, param: str) -> str: ...
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def explainParams(self) -> str: ...
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def getParam(self, paramName: str) -> Param: ...
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@overload
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def isSet(self, param: str) -> bool: ...
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@overload
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def isSet(self, param: Param[Any]) -> bool: ...
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@overload
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def hasDefault(self, param: str) -> bool: ...
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@overload
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def hasDefault(self, param: Param[Any]) -> bool: ...
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@overload
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def isDefined(self, param: str) -> bool: ...
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@overload
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def isDefined(self, param: Param[Any]) -> bool: ...
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def hasParam(self, paramName: str) -> bool: ...
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@overload
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def getOrDefault(self, param: str) -> Any: ...
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@overload
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def getOrDefault(self, param: Param[T]) -> T: ...
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def extractParamMap(
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self, extra: Optional[pyspark.ml._typing.ParamMap] = ...
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) -> pyspark.ml._typing.ParamMap: ...
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def copy(self, extra: Optional[pyspark.ml._typing.ParamMap] = ...) -> Params: ...
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def set(self, param: Param, value: Any) -> None: ...
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def clear(self, param: Param) -> None: ...
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