91 lines
2.9 KiB
Python
91 lines
2.9 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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from typing import Generic, List, Optional, TypeVar
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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.regression import LabeledPoint
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from pyspark.rdd import RDD
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from pyspark.sql.dataframe import DataFrame
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T = TypeVar("T")
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class MLUtils:
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@staticmethod
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def loadLibSVMFile(
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sc: SparkContext,
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path: str,
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numFeatures: int = ...,
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minPartitions: Optional[int] = ...,
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) -> RDD[LabeledPoint]: ...
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@staticmethod
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def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: str) -> None: ...
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@staticmethod
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def loadLabeledPoints(
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sc: SparkContext, path: str, minPartitions: Optional[int] = ...
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) -> RDD[LabeledPoint]: ...
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@staticmethod
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def appendBias(data: Vector) -> Vector: ...
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@staticmethod
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def loadVectors(sc: SparkContext, path: str) -> RDD[Vector]: ...
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@staticmethod
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def convertVectorColumnsToML(dataset: DataFrame, *cols: str) -> DataFrame: ...
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@staticmethod
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def convertVectorColumnsFromML(dataset: DataFrame, *cols: str) -> DataFrame: ...
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@staticmethod
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def convertMatrixColumnsToML(dataset: DataFrame, *cols: str) -> DataFrame: ...
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@staticmethod
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def convertMatrixColumnsFromML(dataset: DataFrame, *cols: str) -> DataFrame: ...
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class Saveable:
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def save(self, sc: SparkContext, path: str) -> None: ...
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class JavaSaveable(Saveable):
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def save(self, sc: SparkContext, path: str) -> None: ...
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class Loader(Generic[T]):
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> T: ...
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class JavaLoader(Loader[T]):
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> T: ...
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class LinearDataGenerator:
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@staticmethod
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def generateLinearInput(
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intercept: float,
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weights: VectorLike,
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xMean: VectorLike,
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xVariance: VectorLike,
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nPoints: int,
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seed: int,
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eps: float,
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) -> List[LabeledPoint]: ...
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@staticmethod
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def generateLinearRDD(
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sc: SparkContext,
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nexamples: int,
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nfeatures: int,
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eps: float,
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nParts: int = ...,
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intercept: float = ...,
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) -> RDD[LabeledPoint]: ...
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