awslabs / sagemaker-deep-demand-forecast
Using Deep Learning for Demand Forecasting with Amazon SageMaker
Summary
   |
|||
| Main Code: 1,105 LOC (27 files) = PY (57%) + YAML (41%) + IN (<1%) + TOML (<1%) Secondary code: Test: 75 LOC (3); Generated: 0 LOC (0); Build & Deploy: 86 LOC (3); Other: 1,152 LOC (9); |
|||
| Duplication: 3% | |||
| File Size: 0% long (>1000 LOC), 100% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 75% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 90% simple (McCabe index <= 5) | |||
|
|
Logical Component Decomposition: primary (10 components) | ||
|
|
1 year, 10 months old
|
||
|
|
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
1 file |
|||
Latest commit date: 2021-08-16
|
0
commits
(30 days)
0
contributors
(30 days) |
|