amzn / differential-privacy-bayesian-optimization
This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"
Summary
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| Main Code: 72,176 LOC (432 files) = PY (72%) + PYX (14%) + CPP (7%) + PXI (2%) + C (1%) + PXD (<1%) + H (<1%) + YML (<1%) + PS1 (<1%) + CMD (<1%) + CFG (<1%) + IN (<1%) Secondary code: Test: 53,542 LOC (213); Generated: 0 LOC (0); Build & Deploy: 649 LOC (13); Other: 46,628 LOC (367); |
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| Duplication: 10% | |||
| File Size: 15% long (>1000 LOC), 28% short (<= 200 LOC) | |||
| Unit Size: 10% long (>100 LOC), 38% short (<= 10 LOC) | |||
| Conditional Complexity: 11% complex (McCabe index > 50), 44% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (13 components) | ||
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1 year, 10 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
44 files |
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Latest commit date: 2021-05-24
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