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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 typing import (Union, Iterable, Tuple, Dict) 

 

from pandas import DataFrame 

 

from .chartify_grapher import ChartifyGrapher 

from ..abstract_base_classes.confidence_abc import ConfidenceABC 

from ..abstract_base_classes.confidence_computer_abc import \ 

ConfidenceComputerABC 

from ..abstract_base_classes.confidence_grapher_abc import ConfidenceGrapherABC 

from ..confidence_utils import (validate_categorical_columns, listify, 

get_all_group_columns, validate_data) 

from ..constants import BONFERRONI, NIM_TYPE 

from ...chartgrid import ChartGrid 

from ..frequentist.sample_ratio_test import sample_ratio_test 

 

 

class GenericTest(ConfidenceABC): 

 

def __init__(self, 

data_frame: DataFrame, 

numerator_column: str, 

numerator_sum_squares_column: Union[str, None], 

denominator_column: str, 

categorical_group_columns: Union[str, Iterable], 

ordinal_group_column: Union[str, None] = None, 

interval_size: float = 0.95, 

correction_method: str = BONFERRONI, 

confidence_computer: ConfidenceComputerABC = None, 

confidence_grapher: ConfidenceGrapherABC = None): 

 

validate_categorical_columns(categorical_group_columns) 

self._df = data_frame 

self._numerator = numerator_column 

self._numerator_sumsq = numerator_sum_squares_column 

self._denominator = denominator_column 

self._categorical_group_columns = listify(categorical_group_columns) 

self._ordinal_group_column = ordinal_group_column 

 

self._all_group_columns = get_all_group_columns( 

self._categorical_group_columns, 

self._ordinal_group_column) 

validate_data(self._df, 

self._numerator, 

self._numerator_sumsq, 

self._denominator, 

self._all_group_columns, 

self._ordinal_group_column) 

 

if confidence_computer is not None: 

self._confidence_computer = confidence_computer 

else: 

raise ValueError("Pass in a ConfidenceComputer or use one of the " 

"subclasses e.g. ZTest.") 

 

self._confidence_grapher = confidence_grapher if confidence_grapher \ 

is not None \ 

else ChartifyGrapher( 

data_frame=self._df, 

numerator_column=self._numerator, 

denominator_column=self._denominator, 

categorical_group_columns=self._categorical_group_columns, 

ordinal_group_column=self._ordinal_group_column) 

 

def summary(self) -> DataFrame: 

return self._confidence_computer.compute_summary() 

 

def difference(self, 

level_1: Union[str, Tuple], 

level_2: Union[str, Tuple], 

absolute: bool = True, 

groupby: Union[str, Iterable] = None, 

non_inferiority_margins: NIM_TYPE = None, 

final_expected_sample_size: float = None 

) -> DataFrame: 

self._validate_sequential(final_expected_sample_size, groupby) 

return self._confidence_computer.compute_difference( 

level_1, 

level_2, 

absolute, 

groupby, 

non_inferiority_margins, 

final_expected_sample_size) 

 

def multiple_difference(self, level: Union[str, Tuple], 

absolute: bool = True, 

groupby: Union[str, Iterable] = None, 

level_as_reference: bool = None, 

non_inferiority_margins: NIM_TYPE = None, 

final_expected_sample_size: float = None 

) -> DataFrame: 

self._validate_sequential(final_expected_sample_size, groupby) 

return self._confidence_computer.compute_multiple_difference( 

level, 

absolute, 

groupby, 

level_as_reference, 

non_inferiority_margins, 

final_expected_sample_size) 

 

def summary_plot(self, 

groupby: Union[str, Iterable] = None) -> ChartGrid: 

summary_df = self.summary() 

graph = self._confidence_grapher.plot_summary(summary_df, groupby) 

return graph 

 

def difference_plot(self, 

level_1: Union[str, Tuple], 

level_2: Union[str, Tuple], 

absolute: bool = True, 

groupby: Union[str, Iterable] = None, 

non_inferiority_margins: NIM_TYPE = None, 

use_adjusted_intervals: bool = False, 

final_expected_sample_size: float = None 

) -> ChartGrid: 

difference_df = self.difference(level_1, 

level_2, 

absolute, 

groupby, 

non_inferiority_margins, 

final_expected_sample_size) 

chartgrid = self._confidence_grapher.plot_difference( 

difference_df, 

absolute, 

groupby, 

non_inferiority_margins, 

use_adjusted_intervals) 

return chartgrid 

 

def multiple_difference_plot(self, 

level: Union[str, Tuple], 

absolute: bool = True, 

groupby: Union[str, Iterable] = None, 

level_as_reference: bool = False, 

non_inferiority_margins: NIM_TYPE = None, 

use_adjusted_intervals: bool = False, 

final_expected_sample_size: float = None 

) -> ChartGrid: 

difference_df = self.multiple_difference(level, 

absolute, 

groupby, 

level_as_reference, 

non_inferiority_margins, 

final_expected_sample_size) 

chartgrid = self._confidence_grapher.plot_multiple_difference( 

difference_df, 

absolute, 

groupby, 

level_as_reference, 

non_inferiority_margins, 

use_adjusted_intervals) 

return chartgrid 

 

def sample_ratio_test(self, expected_proportions: Dict 

) -> Tuple[float, DataFrame]: 

return sample_ratio_test(self._df, 

all_group_columns=self._all_group_columns, 

denominator=self._denominator, 

expected_proportions=expected_proportions) 

 

def achieved_power(self, level_1, level_2, 

mde, alpha, groupby=None) -> DataFrame: 

"""Calculated the achieved power of test of differences between 

level 1 and level 2 given a targeted MDE. 

 

Args: 

level_1 (str, tuple of str): Name of first level. 

level_2 (str, tuple of str): Name of second level. 

mde (float): Absolute minimal detectable effect size. 

alpha (float): Type I error rate, cutoff value for determining 

statistical significance. 

groupby (str): Name of column. 

If specified, will return the difference for each level 

of the grouped dimension. 

 

Returns: 

Pandas DataFrame with the following columns: 

- level_1: Name of level 1. 

- level_2: Name of level 2. 

- power: 1 - B, where B is the likelihood of a Type II (false 

negative) error. 

 

""" 

return self._confidence_computer.achieved_power(level_1, level_2, 

mde, alpha, groupby) 

 

def _validate_sequential(self, 

final_expected_sample_size: float, 

groupby: Union[str, Iterable]): 

if final_expected_sample_size is not None: 

if self._ordinal_group_column not in listify(groupby): 

raise ValueError(f"{self._ordinal_group_column} must be in groupby argument to use " 

f"sequential testing with final_expected_sample_size")