tensorflow / model-remediation

Model Remediation is a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.

Summary

		Main Code: 1,230 LOC (29 files) = PY (100%) Secondary code: Test: 2,840 LOC (14); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 145 LOC (4);
		Duplication: 1%
		File Size: 0% long (>1000 LOC), 79% short (<= 200 LOC)
		Unit Size: 0% long (>100 LOC), 46% short (<= 10 LOC)
		Conditional Complexity: 0% complex (McCabe index > 50), 49% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (12 components)
		1 year, 3 months old 80% of code older than 365 days 0% of code not updated in the past 365 days
		0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
	Features of interest: TODOs 2 files

Commits Trend

Latest commit date: 2022-01-27

3 commits (30 days) 3 contributors (30 days)

Commits 3 49 95 Contributors 3 6 11 2022 2021 2020

show commits trend per language

Commits	2022 2021 2020 2 43 59 py
Contributors	2022 2021 2020 2 6 6 py