aws-samples / amazon-sagemaker-analyze-model-predictions
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

Intro
  • Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
  • Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
  • Conditional complexity is measured at the unit level (methods, functions...).
  • Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
Learn more...
Conditional Complexity Overall
  • There are 14 units with 221 lines of code in units (47.6% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 2 medium complex units (56 lines of code)
    • 1 simple units (13 lines of code)
    • 11 very simple units (152 lines of code)
0% | 0% | 25% | 5% | 68%
Legend:
51+
26-50
11-25
6-10
1-5
Alternative Visuals
Conditional Complexity per Extension
51+
26-50
11-25
6-10
1-5
py0% | 0% | 25% | 5% | 68%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
ROOT0% | 0% | 56% | 13% | 31%
code0% | 0% | 0% | 0% | 100%
docker0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 14 most complex units
Unit# linesMcCabe index# params
24 11 2
32 11 5
13 6 4
def load_model()
in utils.py
 11 5 0
def forward_hook()
in code/custom_hook.py
 9 3 4
def model_fn()
in code/pretrained_model_with_debugger_hook.py
 27 2 1
def transform_fn()
in code/pretrained_model_with_debugger_hook.py
 22 2 4
def get_environment()
in docker/evaluation.py
 34 2 0
def __init__()
in code/pretrained_model_with_debugger_hook.py
 4 1 4
def image_gradients()
in code/custom_hook.py
 2 1 2
def model_fn()
in code/pretrained_model.py
 9 1 1
def transform_fn()
in code/pretrained_model.py
 14 1 4
9 1 0
11 1 9