156 lines
5.1 KiB
Python
156 lines
5.1 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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from typing import overload
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from typing import Iterable, Optional, Tuple, TypeVar
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from pyspark.rdd import RDD
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from pyspark.mllib._typing import VectorLike
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from pyspark.context import SparkContext
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from pyspark.mllib.linalg import Vector
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from pyspark.mllib.util import Saveable, Loader
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from pyspark.streaming.dstream import DStream
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from numpy import ndarray # type: ignore[import]
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K = TypeVar("K")
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class LabeledPoint:
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label: int
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features: Vector
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def __init__(self, label: float, features: Iterable[float]) -> None: ...
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def __reduce__(self) -> Tuple[type, Tuple[bytes]]: ...
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class LinearModel:
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def __init__(self, weights: Vector, intercept: float) -> None: ...
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@property
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def weights(self) -> Vector: ...
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@property
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def intercept(self) -> float: ...
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class LinearRegressionModelBase(LinearModel):
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@overload
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def predict(self, x: Vector) -> float: ...
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@overload
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def predict(self, x: RDD[Vector]) -> RDD[float]: ...
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class LinearRegressionModel(LinearRegressionModelBase):
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def save(self, sc: SparkContext, path: str) -> None: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> LinearRegressionModel: ...
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class LinearRegressionWithSGD:
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@classmethod
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def train(
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cls,
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data: RDD[LabeledPoint],
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iterations: int = ...,
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step: float = ...,
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miniBatchFraction: float = ...,
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initialWeights: Optional[VectorLike] = ...,
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regParam: float = ...,
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regType: Optional[str] = ...,
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intercept: bool = ...,
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validateData: bool = ...,
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convergenceTol: float = ...,
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) -> LinearRegressionModel: ...
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class LassoModel(LinearRegressionModelBase):
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def save(self, sc: SparkContext, path: str) -> None: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> LassoModel: ...
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class LassoWithSGD:
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@classmethod
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def train(
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cls,
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data: RDD[LabeledPoint],
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iterations: int = ...,
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step: float = ...,
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regParam: float = ...,
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miniBatchFraction: float = ...,
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initialWeights: Optional[VectorLike] = ...,
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intercept: bool = ...,
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validateData: bool = ...,
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convergenceTol: float = ...,
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) -> LassoModel: ...
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class RidgeRegressionModel(LinearRegressionModelBase):
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def save(self, sc: SparkContext, path: str) -> None: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> RidgeRegressionModel: ...
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class RidgeRegressionWithSGD:
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@classmethod
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def train(
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cls,
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data: RDD[LabeledPoint],
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iterations: int = ...,
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step: float = ...,
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regParam: float = ...,
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miniBatchFraction: float = ...,
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initialWeights: Optional[VectorLike] = ...,
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intercept: bool = ...,
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validateData: bool = ...,
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convergenceTol: float = ...,
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) -> RidgeRegressionModel: ...
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class IsotonicRegressionModel(Saveable, Loader[IsotonicRegressionModel]):
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boundaries: ndarray
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predictions: ndarray
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isotonic: bool
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def __init__(
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self, boundaries: ndarray, predictions: ndarray, isotonic: bool
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) -> None: ...
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@overload
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def predict(self, x: Vector) -> ndarray: ...
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@overload
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def predict(self, x: RDD[Vector]) -> RDD[ndarray]: ...
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def save(self, sc: SparkContext, path: str) -> None: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> IsotonicRegressionModel: ...
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class IsotonicRegression:
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@classmethod
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def train(
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cls, data: RDD[VectorLike], isotonic: bool = ...
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) -> IsotonicRegressionModel: ...
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class StreamingLinearAlgorithm:
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def __init__(self, model: LinearModel) -> None: ...
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def latestModel(self) -> LinearModel: ...
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def predictOn(self, dstream: DStream[VectorLike]) -> DStream[float]: ...
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def predictOnValues(
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self, dstream: DStream[Tuple[K, VectorLike]]
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) -> DStream[Tuple[K, float]]: ...
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class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm):
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stepSize: float
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numIterations: int
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miniBatchFraction: float
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convergenceTol: float
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def __init__(
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self,
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stepSize: float = ...,
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numIterations: int = ...,
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miniBatchFraction: float = ...,
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convergenceTol: float = ...,
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) -> None: ...
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def setInitialWeights(
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self, initialWeights: VectorLike
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) -> StreamingLinearRegressionWithSGD: ...
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def trainOn(self, dstream: DStream[LabeledPoint]) -> None: ...
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