aws-samples / amazon-sagemaker-tsp-deep-rl
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 25 units with 701 lines of code in units (85.4% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 1 medium complex units (19 lines of code)
    • 6 simple units (402 lines of code)
    • 18 very simple units (280 lines of code)
0% | 0% | 2% | 57% | 39%
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% | 2% | 57% | 39%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src0% | 0% | 2% | 57% | 39%
notebooks0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def train_batch()
in src/train.py
 19 11 9
def train_epoch()
in src/train.py
 51 10 9
def train_batch_sl()
in src/train.py
 22 9 8
def train_epoch_sl()
in src/train.py
 42 8 9
def get_options()
in src/options.py
 130 7 1
def main()
in src/streamlit_demo.py
 26 6 0
def _run_rl()
in src/run.py
 131 6 1
def clip_grad_norms()
in src/train.py
 11 5 2
def _run_sl()
in src/run.py
 98 4 1
def predict_fn()
in src/inference.py
 19 4 2
def get_inner_model()
in src/train.py
 2 3 1
def generate_one_tsp_problem()
in src/streamlit_demo.py
 29 3 2
def plot_route_on_normspace()
in src/streamlit_demo.py
 15 3 5
def inference_endpoint()
in src/streamlit_demo.py
 18 3 4
def input_fn()
in src/inference.py
 14 3 2
def output_fn()
in src/inference.py
 7 3 2
def set_decode_type()
in src/train.py
 4 2 2
def rollout()
in src/train.py
 14 2 3
6 2 3
def run()
in src/run.py
 5 2 1