Coverage for /Users/pers/GitHub/confidence/spotify_confidence/analysis/frequentist/chi_squared.py : 60%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
# 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.
ConfidenceComputerABC
data_frame: DataFrame, numerator_column: str, 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):
computer = ChiSquaredComputer( data_frame=data_frame, numerator_column=numerator_column, numerator_sum_squares_column=numerator_column, denominator_column=denominator_column, categorical_group_columns=listify(categorical_group_columns), ordinal_group_column=ordinal_group_column, interval_size=interval_size, correction_method=correction_method.lower())
super(ChiSquared, self).__init__( data_frame, numerator_column, numerator_column, denominator_column, categorical_group_columns, ordinal_group_column, interval_size, correction_method, computer, confidence_grapher)
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: if non_inferiority_margins is not None: raise ValueError('Non-inferiority margins not supported in ' 'ChiSquared. Use StudentsTTest or ZTest instead.') return super(ChiSquared, self).difference( level_1, level_2, absolute, groupby, None, final_expected_sample_size)
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: if non_inferiority_margins is not None: raise ValueError('Non-inferiority margins not supported in ' 'ChiSquared. Use StudentsTTest or ZTest instead.') return super(ChiSquared, self).multiple_difference( level, absolute, groupby, level_as_reference, None, final_expected_sample_size) |