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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2020-11-24 20:24:41 -05:00
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from typing import Iterable, Hashable, List, Tuple, Union
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2020-09-24 01:15:36 -04:00
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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.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper
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from pyspark.mllib.linalg import Vector
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.util import JavaLoader, JavaSaveable
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from py4j.java_collections import JavaMap # type: ignore[import]
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class VectorTransformer:
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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class Normalizer(VectorTransformer):
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p: float
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def __init__(self, p: float = ...) -> None: ...
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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class JavaVectorTransformer(JavaModelWrapper, VectorTransformer):
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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class StandardScalerModel(JavaVectorTransformer):
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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def setWithMean(self, withMean: bool) -> StandardScalerModel: ...
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def setWithStd(self, withStd: bool) -> StandardScalerModel: ...
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@property
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def withStd(self) -> bool: ...
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@property
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def withMean(self) -> bool: ...
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@property
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def std(self) -> Vector: ...
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@property
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def mean(self) -> Vector: ...
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class StandardScaler:
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withMean: bool
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withStd: bool
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def __init__(self, withMean: bool = ..., withStd: bool = ...) -> None: ...
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def fit(self, dataset: RDD[VectorLike]) -> StandardScalerModel: ...
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class ChiSqSelectorModel(JavaVectorTransformer):
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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class ChiSqSelector:
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numTopFeatures: int
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selectorType: str
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percentile: float
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fpr: float
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fdr: float
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fwe: float
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def __init__(
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self,
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numTopFeatures: int = ...,
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selectorType: str = ...,
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percentile: float = ...,
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fpr: float = ...,
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fdr: float = ...,
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fwe: float = ...,
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) -> None: ...
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def setNumTopFeatures(self, numTopFeatures: int) -> ChiSqSelector: ...
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def setPercentile(self, percentile: float) -> ChiSqSelector: ...
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def setFpr(self, fpr: float) -> ChiSqSelector: ...
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def setFdr(self, fdr: float) -> ChiSqSelector: ...
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def setFwe(self, fwe: float) -> ChiSqSelector: ...
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def setSelectorType(self, selectorType: str) -> ChiSqSelector: ...
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def fit(self, data: RDD[LabeledPoint]) -> ChiSqSelectorModel: ...
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class PCAModel(JavaVectorTransformer): ...
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class PCA:
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k: int
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def __init__(self, k: int) -> None: ...
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def fit(self, data: RDD[VectorLike]) -> PCAModel: ...
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class HashingTF:
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numFeatures: int
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binary: bool
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def __init__(self, numFeatures: int = ...) -> None: ...
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def setBinary(self, value: bool) -> HashingTF: ...
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def indexOf(self, term: Hashable) -> int: ...
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@overload
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def transform(self, document: Iterable[Hashable]) -> Vector: ...
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@overload
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def transform(self, document: RDD[Iterable[Hashable]]) -> RDD[Vector]: ...
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class IDFModel(JavaVectorTransformer):
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@overload
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def transform(self, x: VectorLike) -> Vector: ...
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@overload
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def transform(self, x: RDD[VectorLike]) -> RDD[Vector]: ...
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def idf(self) -> Vector: ...
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def docFreq(self) -> List[int]: ...
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def numDocs(self) -> int: ...
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class IDF:
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minDocFreq: int
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def __init__(self, minDocFreq: int = ...) -> None: ...
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def fit(self, dataset: RDD[VectorLike]) -> IDFModel: ...
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class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader[Word2VecModel]):
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def transform(self, word: str) -> Vector: ... # type: ignore
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def findSynonyms(self, word: Union[str, VectorLike], num: int) -> Iterable[Tuple[str, float]]: ...
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def getVectors(self) -> JavaMap: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> Word2VecModel: ...
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class Word2Vec:
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vectorSize: int
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learningRate: float
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numPartitions: int
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numIterations: int
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seed: int
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minCount: int
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windowSize: int
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def __init__(self) -> None: ...
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def setVectorSize(self, vectorSize: int) -> Word2Vec: ...
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def setLearningRate(self, learningRate: float) -> Word2Vec: ...
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def setNumPartitions(self, numPartitions: int) -> Word2Vec: ...
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def setNumIterations(self, numIterations: int) -> Word2Vec: ...
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def setSeed(self, seed: int) -> Word2Vec: ...
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def setMinCount(self, minCount: int) -> Word2Vec: ...
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def setWindowSize(self, windowSize: int) -> Word2Vec: ...
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def fit(self, data: RDD[List[str]]) -> Word2VecModel: ...
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class ElementwiseProduct(VectorTransformer):
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scalingVector: Vector
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def __init__(self, scalingVector: Vector) -> None: ...
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@overload
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def transform(self, vector: VectorLike) -> Vector: ...
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@overload
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def transform(self, vector: RDD[VectorLike]) -> RDD[Vector]: ...
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