Added confusion matrix for fairness evaluator

[fabrizio-credo]

Describe your changes

• Leveraged confusion matrix function in Performance to create a disaggregated version in Fairness.

Issue ticket number and link

Known outstanding issues that are not fully accounted for

Checklist before requesting a review

• [ ] I have performed a self-review of my code
• [ ] I have built basic tests for new functionality (particularly new evaluators)
• [ ] If new libraries have been added, I have checked that readthedocs API documentation is constructed correctly
• [ ] Will this be part of a major product update? If yes, please write one phrase about this update.

Extra-mile Checklist

• [ ] I have thought expansively about edge cases and written tests for them

[fabrizio-credo]

@IanAtCredo if there are no changes to be made, I will push the amend to the version_check file and merge.

[github-actions[bot]]

Coverage Report

File	Stmts	Miss	Cover	Missing
credoai
__init__.py	3	0	100%
_version.py	1	0	100%
credoai/artifacts
__init__.py	7	0	100%
credoai/artifacts/data
__init__.py	0	0	100%
base_data.py	106	13	88%	53, 153, 156, 171, 178, 185, 189, 194, 197, 200, 212, 215, 222
comparison_data.py	63	13	79%	53, 60, 71, 76, 81, 90, 96, 100, 105, 114, 147, 153, 156
tabular_data.py	42	6	86%	52, 76, 80, 99, 101, 108
credoai/artifacts/model
__init__.py	0	0	100%
base_model.py	42	2	95%	57, 103
classification_model.py	50	18	64%	69–72, 88–120
comparison_model.py	11	0	100%
constants_model.py	5	0	100%
regression_model.py	11	4	64%	41–43, 46
credoai/evaluators
__init__.py	15	0	100%
data_fairness.py	160	13	92%	85–92, 100, 225, 252, 282–294, 411, 446–447
data_profiler.py	61	4	93%	49, 73–74, 93
deepchecks.py	40	3	92%	113–122
equity.py	113	6	95%	73, 153–155, 226–227
evaluator.py	73	6	92%	67, 70, 89, 115, 184, 191
fairness.py	126	2	98%	115, 264
feature_drift.py	59	1	98%	66
identity_verification.py	112	2	98%	144–145
model_profiler.py	103	32	69%	95–101, 117–130, 158–161, 174–179, 182–214, 256–257, 266–267, 305
performance.py	87	7	92%	108, 129–135
privacy.py	118	4	97%	410, 447–449
ranking_fairness.py	112	14	88%	144–145, 165, 184, 190–191, 387–409, 414–444
security.py	97	1	99%	309
shap.py	87	14	84%	117, 125–126, 136–142, 168–169, 251–252, 282–290
survival_fairness.py	67	50	25%	27–31, 34–46, 51–62, 65–76, 79–97, 100, 103, 106
credoai/evaluators/utils
__init__.py	3	0	100%
fairlearn.py	18	1	94%	93
utils.py	8	1	88%	9
validation.py	87	25	71%	23, 43–44, 46–48, 55, 65, 67, 71–76, 89, 92, 95, 98–99, 116–123, 129–135, 138
credoai/governance
__init__.py	1	0	100%
credoai/lens
__init__.py	2	0	100%
lens.py	206	13	94%	59, 201–202, 238–243, 300, 342, 366, 448, 463, 467, 479
lens_validation.py	70	32	54%	41, 45, 49–51, 67, 70, 75–79, 88, 93–96, 123, 126–144, 172–174
pipeline_creator.py	60	12	80%	20–21, 37, 79–91
utils.py	39	28	28%	20–27, 49–52, 71–82, 99, 106–109, 128–135
credoai/modules
__init__.py	3	0	100%
constants_deepchecks.py	2	0	100%
constants_metrics.py	19	0	100%
constants_threshold_metrics.py	3	0	100%
metric_utils.py	24	18	25%	15–30, 34–55
metrics.py	88	13	85%	63, 67, 70–71, 74, 84, 123, 135–140, 178, 185, 187
metrics_credoai.py	167	49	71%	68–69, 73, 93–102, 107–109, 132–160, 176–179, 206, 230–231, 294–296, 372–378, 414–415, 485–486, 534, 638
stats.py	97	50	48%	15–18, 21–26, 29–31, 34–39, 42–56, 59–64, 106, 132–159, 191, 202–217
stats_utils.py	5	3	40%	5–8
credoai/prism
__init__.py	3	0	100%
compare.py	35	2	94%	71, 87
prism.py	36	4	89%	46, 48, 59, 86
task.py	17	2	88%	30, 37
credoai/prism/comparators
__init_.py	0	0	100%
comparator.py	17	3	82%	34, 42, 47
metric_comparator.py	44	2	95%	125, 131
credoai/utils
__init__.py	5	0	100%
common.py	104	33	68%	55, 72–73, 79, 88–95, 106–107, 124–130, 135, 140–145, 156–163, 190
constants.py	2	0	100%
dataset_utils.py	61	35	43%	23, 26–31, 50, 54–55, 88–119
logging.py	55	13	76%	10–11, 14, 19–20, 23, 27, 44, 58–62
model_utils.py	73	46	37%	17–22, 32–33, 36–37, 42–47, 63–108, 114–121
version_check.py	11	0	100%
TOTAL	3136	600	81%

[fabrizio-credo]

@IanAtCredo Fairness depends on sensitive feature. If we have multiple sensitive features (e.g., race and gender) these will generate different confusion matrixes. Maybe I misunderstood