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stat.pyi
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stat.pyi
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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import Optional
from pyspark.ml.linalg import Matrix, Vector
from pyspark.ml.wrapper import JavaWrapper
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame
from py4j.java_gateway import JavaObject # type: ignore[import]
class ChiSquareTest:
@staticmethod
def test(
dataset: DataFrame, featuresCol: str, labelCol: str, flatten: bool = ...
) -> DataFrame: ...
class Correlation:
@staticmethod
def corr(dataset: DataFrame, column: str, method: str = ...) -> DataFrame: ...
class KolmogorovSmirnovTest:
@staticmethod
def test(
dataset: DataFrame, sampleCol: str, distName: str, *params: float
) -> DataFrame: ...
class Summarizer:
@staticmethod
def mean(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def sum(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def variance(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def std(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def count(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def numNonZeros(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def max(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def min(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def normL1(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def normL2(col: Column, weightCol: Optional[Column] = ...) -> Column: ...
@staticmethod
def metrics(*metrics: str) -> SummaryBuilder: ...
class SummaryBuilder(JavaWrapper):
def __init__(self, jSummaryBuilder: JavaObject) -> None: ...
def summary(
self, featuresCol: Column, weightCol: Optional[Column] = ...
) -> Column: ...
class MultivariateGaussian:
mean: Vector
cov: Matrix
def __init__(self, mean: Vector, cov: Matrix) -> None: ...