`culebra.tools.TestOutcome` class¶

class TestOutcome(test: str, data: str, column: str, alpha: float, batches: list, pvalue: numpy.ndarray)¶

Bases: NamedTuple

Create new instance of TestOutcome(test, data, column, alpha, batches, pvalue)

Attributes¶

TestOutcome.alpha: float¶: Significance level

TestOutcome.batches: list¶: Labels of all the analyzed batches

TestOutcome.column: str¶: Column key in the dataframe

TestOutcome.data: str¶: Key for the dataframe containing the data

TestOutcome.pvalue: ndarray¶: p-value(s) returned by the test

TestOutcome.test: str¶: Name of the test

Properties¶

property TestOutcome.success: ndarray¶

Check the success of the test.

Returns:: A boolean array showing where the null hypothesis is met
Return type:: ndarray[bool]

Dunder methods¶

TestOutcome.__repr__() → str¶: Print all the input parameters and outputs returned by a test.

TestOutcome.__str__() → str¶: Pretty print of the success and p-values returned by a test.

This work is supported by projects New Computing Paradigms and Heterogeneous Parallel Architectures for High-Performance and Energy Efficiency of Classification and Optimization Tasks on Biomedical Engineering Applications (HPEE-COBE), with reference PGC2018-098813-B-C31, and Advanced Methods of Biomedical Data Analysis and Brain Modeling Optimized for High-Performance and Energy-Efficient Computing (HPEEC-BIOBRAIN), with reference PID2022-137461NB-C31, both funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, and by the European Regional Development Fund (ERDF).