aws-samples / sagemaker-model-monitor-bring-your-own-container
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 40 units with 359 lines of code in units (85.5% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 0 medium complex units (0 lines of code)
    • 0 simple units (0 lines of code)
    • 40 very simple units (359 lines of code)
0% | 0% | 0% | 0% | 100%
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% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src0% | 0% | 0% | 0% | 100%
ROOT0% | 0% | 0% | 0% | 100%
script0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def compute_drift()
in src/drift_detector.py
 25 5 2
def compute_drift_multiple_inst()
in src/drift_detector.py
 10 5 2
def auto_impute_df()
in src/utils.py
 8 5 1
14 5 3
12 5 2
def get_column_types()
in src/utils.py
 8 4 1
def push()
in docker_utils.py
 14 4 3
def _ecr_login_if_needed()
in docker_utils.py
 8 4 1
def _execute()
in docker_utils.py
 11 4 2
def _check_output()
in docker_utils.py
 14 4 3
def __plot_top_k_drifts()
in src/drift_visualizer.py
 12 3 4
14 3 1
def cum_sum_prob()
in src/utils.py
 9 3 1
16 3 3
4 3 2
15 3 1
def _stream_output()
in docker_utils.py
 8 3 1
def compute_accuracy_with_drift()
in src/drift_detector.py
 12 2 3
def main()
in src/drift_detector.py
 20 2 3
8 2 2