49 lines
1.9 KiB
Python
49 lines
1.9 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 Any, Optional
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from pyspark.ml._typing import P, T, JM, ParamMap
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from pyspark.ml import Estimator, Predictor, PredictionModel, Transformer, Model
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from pyspark.ml.base import _PredictorParams
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from pyspark.ml.param import Param, Params
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class JavaWrapper:
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def __init__(self, java_obj: Optional[Any] = ...) -> None: ...
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def __del__(self) -> None: ...
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class JavaParams(JavaWrapper, Params, metaclass=abc.ABCMeta):
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def copy(self: P, extra: Optional[ParamMap] = ...) -> P: ...
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def clear(self, param: Param) -> None: ...
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class JavaEstimator(JavaParams, Estimator[JM], metaclass=abc.ABCMeta): ...
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class JavaTransformer(JavaParams, Transformer, metaclass=abc.ABCMeta): ...
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class JavaModel(JavaTransformer, Model, metaclass=abc.ABCMeta):
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def __init__(self, java_model: Optional[Any] = ...) -> None: ...
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class JavaPredictor(
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Predictor[JM], JavaEstimator, _PredictorParams, metaclass=abc.ABCMeta
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): ...
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class JavaPredictionModel(PredictionModel[T], JavaModel, _PredictorParams):
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@property
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def numFeatures(self) -> int: ...
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def predict(self, value: T) -> float: ...
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