78 lines
2.8 KiB
Python
78 lines
2.8 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 Optional
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from pyspark.ml.linalg import Matrix, Vector
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from pyspark.ml.wrapper import JavaWrapper
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from pyspark.sql.column import Column
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from pyspark.sql.dataframe import DataFrame
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from py4j.java_gateway import JavaObject # type: ignore[import]
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class ChiSquareTest:
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@staticmethod
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def test(
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dataset: DataFrame, featuresCol: str, labelCol: str, flatten: bool = ...
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) -> DataFrame: ...
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class Correlation:
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@staticmethod
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def corr(dataset: DataFrame, column: str, method: str = ...) -> DataFrame: ...
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class KolmogorovSmirnovTest:
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@staticmethod
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def test(
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dataset: DataFrame, sampleCol: str, distName: str, *params: float
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) -> DataFrame: ...
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class Summarizer:
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@staticmethod
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def mean(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def sum(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def variance(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def std(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def count(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def numNonZeros(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def max(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def min(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def normL1(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def normL2(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
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@staticmethod
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def metrics(*metrics: str) -> SummaryBuilder: ...
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class SummaryBuilder(JavaWrapper):
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def __init__(self, jSummaryBuilder: JavaObject) -> None: ...
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def summary(
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self, featuresCol: Column, weightCol: Optional[Column] = ...
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) -> Column: ...
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class MultivariateGaussian:
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mean: Vector
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cov: Matrix
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def __init__(self, mean: Vector, cov: Matrix) -> None: ...
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