aws-samples / amazon-sagemaker-credit-risk-prediction-explainability-bias-detection
Unit Size

The distribution of size of units (measured in lines of code).

Intro
  • Unit size measurements show the distribution of size of units of code (methods, functions...).
  • Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
  • You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
Unit Size Overall
  • There are 7 units with 89 lines of code in units (7.2% of code).
    • 0 very long units (0 lines of code)
    • 0 long units (0 lines of code)
    • 1 medium size units (36 lines of code)
    • 3 small units (41 lines of code)
    • 3 very small units (12 lines of code)
0% | 0% | 40% | 46% | 13%
Legend:
101+
51-100
21-50
11-20
1-10
Unit Size per Extension
101+
51-100
21-50
11-20
1-10
py0% | 0% | 40% | 46% | 13%
Unit Size per Logical Component
primary logical decomposition
101+
51-100
21-50
11-20
1-10
training0% | 0% | 69% | 30% | 0%
inference/sklearn0% | 0% | 0% | 63% | 36%
inference/xgboost0% | 0% | 0% | 73% | 26%
Alternative Visuals
Longest Units
Top 7 longest units
Unit# linesMcCabe index# params
def main()
in training/train_xgboost.py
 36 1 0
def parse_args()
in training/train_xgboost.py
 16 1 0
def input_fn()
in inference/sklearn/inference.py
 14 4 2
def input_fn()
in inference/xgboost/inference.py
 11 2 2
def predict_fn()
in inference/sklearn/inference.py
 5 1 2
def model_fn()
in inference/xgboost/inference.py
 4 1 1
def model_fn()
in inference/sklearn/inference.py
 3 1 1