awslabs / sagemaker-privacy-for-nlp

A solution that helps apply a privacy preserving mechanism to NLP data, using Amazon SageMaker.

Summary
IN
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Main Code: 1,733 LOC (24 files) = PY (55%) + YAML (34%) + MD (6%) + YML (1%) + IN (1%)
Secondary code: Test: 174 LOC (2); Generated: 0 LOC (0); Build & Deploy: 83 LOC (2); Other: 2,021 LOC (6);
Artboard 48 Duplication: 20%
File Size: 0% long (>1000 LOC), 66% short (<= 200 LOC)
Unit Size: 0% long (>100 LOC), 70% short (<= 10 LOC)
Conditional Complexity: 0% complex (McCabe index > 50), 85% simple (McCabe index <= 5)
Logical Component Decomposition: primary (11 components)
files_time

1 year, 1 month old

  • 100% 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)
Straight_Line
Features of interest:
TODOs
1 file
Commits Trend

Latest commit date: 2021-05-18

0
commits
(30 days)
0
contributors
(30 days)
Commits

6

1
Contributors

3

1
2021 2020
show commits trend per language
Reports
Analysis Report
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Source Code Overview
Analysis Report
Components
Analysis Report
Artboard 48
Duplication
Analysis Report
File Size
Analysis Report
files_time
File Age
Analysis Report
File Change Frequency
Analysis Report
Temporal Dependencies
Analysis Report
Commits & Contributors
Analysis Report
Unit Size
Analysis Report
Conditional Complexity
Analysis Report
Straight_Line
Features of Interest
Analysis Report
All Metrics
Analysis Report
Goals & Controls
Analysis Report
Trend
Analysis Report
76_startup_sticky_notes
Notes & Findings
Links

generated by sokrates.dev (configuration) on 2022-01-31