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# Copyright 2017-2020 Spotify AB 

# 

# Licensed 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 abc import ABC, abstractmethod 

from typing import (Union, Iterable) 

 

from pandas import DataFrame 

 

from ..constants import NIM_TYPE 

 

 

class ConfidenceComputerABC(ABC): 

 

@abstractmethod 

def __init__(self, 

data_frame: DataFrame, 

numerator_column: str, 

numerator_sum_squares_column: str, 

denominator_column: str, 

categorical_group_columns: str, 

ordinal_group_column: str, 

interval_size: float, 

correction_method: str): 

pass 

 

@abstractmethod 

def compute_summary(self) -> DataFrame: 

"""Return Pandas DataFrame with summary statistics. 

""" 

pass 

 

@abstractmethod 

def compute_difference(self, 

level_1: Union[str, Iterable], 

level_2: Union[str, Iterable], 

absolute: bool, 

groupby: Union[str, Iterable], 

nims: NIM_TYPE, 

final_expected_sample_size: float 

) -> DataFrame: 

"""Return dataframe containing the difference in means between 

group 1 and 2, p-value and confidence interval 

""" 

pass 

 

@abstractmethod 

def compute_multiple_difference(self, 

level: Union[str, Iterable], 

absolute: bool, 

groupby: Union[str, Iterable], 

level_as_reference: bool, 

nims: NIM_TYPE, 

final_expected_sample_size: float 

) -> DataFrame: 

"""The pairwise probability that the specific group 

is greater than all other groups. 

""" 

pass 

 

def achieved_power(self, 

level_1: Union[str, Iterable], 

level_2: Union[str, Iterable], 

mde: float, 

alpha: float, 

groupby: Union[str, Iterable]) -> DataFrame: 

pass