2020-09-24 01:15:36 -04:00
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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 Optional, Union
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from pyspark.context import SparkContext
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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.mllib.linalg import Vector
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from pyspark.mllib.regression import LabeledPoint, LinearModel, StreamingLinearAlgorithm
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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 float64, ndarray # type: ignore[import]
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class LinearClassificationModel(LinearModel):
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def __init__(self, weights: Vector, intercept: float) -> None: ...
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def setThreshold(self, value: float) -> None: ...
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@property
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def threshold(self) -> Optional[float]: ...
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def clearThreshold(self) -> None: ...
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@overload
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def predict(self, test: VectorLike) -> Union[int, float, float64]: ...
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@overload
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def predict(self, test: RDD[VectorLike]) -> RDD[Union[int, float]]: ...
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class LogisticRegressionModel(LinearClassificationModel):
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def __init__(
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self, weights: Vector, intercept: float, numFeatures: int, numClasses: int
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) -> None: ...
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@property
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def numFeatures(self) -> int: ...
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@property
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def numClasses(self) -> int: ...
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@overload
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def predict(self, x: VectorLike) -> Union[int, float]: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[Union[int, float]]: ...
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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) -> LogisticRegressionModel: ...
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class LogisticRegressionWithSGD:
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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: str = ...,
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intercept: bool = ...,
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validateData: bool = ...,
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convergenceTol: float = ...,
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) -> LogisticRegressionModel: ...
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class LogisticRegressionWithLBFGS:
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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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initialWeights: Optional[VectorLike] = ...,
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regParam: float = ...,
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regType: str = ...,
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intercept: bool = ...,
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corrections: int = ...,
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tolerance: float = ...,
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validateData: bool = ...,
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numClasses: int = ...,
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) -> LogisticRegressionModel: ...
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class SVMModel(LinearClassificationModel):
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def __init__(self, weights: Vector, intercept: float) -> None: ...
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@overload
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def predict(self, x: VectorLike) -> float64: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[float64]: ...
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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) -> SVMModel: ...
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class SVMWithSGD:
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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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regType: str = ...,
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intercept: bool = ...,
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validateData: bool = ...,
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convergenceTol: float = ...,
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) -> SVMModel: ...
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class NaiveBayesModel(Saveable, Loader[NaiveBayesModel]):
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labels: ndarray
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pi: ndarray
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theta: ndarray
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2020-11-24 19:27:04 -05:00
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def __init__(self, labels: ndarray, pi: ndarray, theta: ndarray) -> None: ...
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2020-09-24 01:15:36 -04:00
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@overload
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def predict(self, x: VectorLike) -> float64: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[float64]: ...
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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) -> NaiveBayesModel: ...
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class NaiveBayes:
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@classmethod
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def train(cls, data: RDD[VectorLike], lambda_: float = ...) -> NaiveBayesModel: ...
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class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
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stepSize: float
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numIterations: int
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regParam: float
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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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regParam: 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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) -> StreamingLogisticRegressionWithSGD: ...
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def trainOn(self, dstream: DStream[LabeledPoint]) -> None: ...
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