aws-samples / automl-blueprint
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 97 units with 1,056 lines of code in units (59.7% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 2 medium complex units (99 lines of code)
    • 5 simple units (111 lines of code)
    • 90 very simple units (846 lines of code)
0% | 0% | 9% | 10% | 80%
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% | 9% | 10% | 80%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
notebook/utils0% | 0% | 25% | 10% | 64%
code/workflow/implementations/autopilot0% | 0% | 0% | 10% | 89%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def run()
in notebook/utils/wf.py
56 14 3
def _monitor_parallel_process()
in notebook/utils/wf.py
43 11 4
def get_automl_job_baseline()
in notebook/utils/trust.py
27 10 4
def get_config()
in code/workflow/implementations/autopilot/bp_init_stage.py
49 6 2
def create_processing_inputs()
in code/workflow/implementations/autopilot/bp_init_stage.py
18 6 4
def init_drivers()
in code/workflow/implementations/autopilot/bp_automl_stage.py
3 6 2
def _get_merged_df()
in notebook/utils/wf.py
14 6 7
def monitor_job_status()
in code/workflow/implementations/autopilot/bp_bias_analysis_stage.py
14 5 2
def monitor_job_status()
in code/workflow/implementations/autopilot/bp_xai_analysis_stage.py
14 5 2
def monitor_status()
in code/workflow/implementations/autopilot/bp_automl_stage.py
27 5 4
def monitor_job_status()
in code/workflow/implementations/autopilot/bp_error_analysis_stage.py
14 5 2
def create_merged_dataset()
in code/workflow/implementations/autopilot/bp_bias_analysis_stage.py
20 4 3
def update_datawrangler_source()
in code/workflow/implementations/autopilot/bp_init_stage.py
11 4 3
def run_sm_autopilot()
in code/workflow/implementations/autopilot/bp_automl_stage.py
22 4 4
def copy_sample_flow_to_local()
in notebook/utils/prep.py
20 4 2
def get_inspector()
in notebook/utils/trust.py
15 4 2
def display_interactive_cm()
in notebook/utils/trust.py
19 4 5
def __init__()
in notebook/utils/wf.py
9 4 4
def get_prepped_data_df()
in notebook/utils/wf.py
12 4 4
def __init__()
in notebook/utils/wf.py
4 4 4