aws / sagemaker-scikit-learn-extension
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 207 units with 1,519 lines of code in units (75.9% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 2 medium complex units (48 lines of code)
    • 20 simple units (445 lines of code)
    • 185 very simple units (1,026 lines of code)
0% | 0% | 3% | 29% | 67%
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% | 3% | 29% | 67%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src/sagemaker_sklearn_extension/preprocessing0% | 0% | 11% | 28% | 59%
src/sagemaker_sklearn_extension/feature_extraction0% | 0% | 0% | 32% | 67%
src/sagemaker_sklearn_extension/externals0% | 0% | 0% | 28% | 71%
src/sagemaker_sklearn_extension/contrib/taei0% | 0% | 0% | 24% | 75%
src/sagemaker_sklearn_extension/decomposition0% | 0% | 0% | 69% | 30%
src/sagemaker_sklearn_extension/impute0% | 0% | 0% | 13% | 86%
ROOT0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def _build_combinations()
in src/sagemaker_sklearn_extension/preprocessing/data.py
 23 13 3
def inverse_transform()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 25 12 2
def __init__()
in src/sagemaker_sklearn_extension/contrib/taei/nn_utils.py
 25 10 5
def fit()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 32 10 3
def fit()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 19 10 3
def fit()
in src/sagemaker_sklearn_extension/feature_extraction/date_time.py
 19 10 3
def _extract_tsfresh_features()
in src/sagemaker_sklearn_extension/feature_extraction/sequences.py
 38 10 2
def __init__()
in src/sagemaker_sklearn_extension/externals/header.py
 31 10 3
def fit()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 29 9 3
def _woe()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 13 9 5
def fit()
in src/sagemaker_sklearn_extension/decomposition/robust_pca.py
 25 9 3
def inverse_transform()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 16 8 2
def _fit_vectorizer()
in src/sagemaker_sklearn_extension/feature_extraction/text.py
 38 8 3
def _convert_to_numeric()
in src/sagemaker_sklearn_extension/feature_extraction/sequences.py
 24 8 2
def _validate_input()
in src/sagemaker_sklearn_extension/impute/base.py
 11 8 2
def decode_sample()
in src/sagemaker_sklearn_extension/contrib/taei/models.py
 19 7 2
def resample()
in src/sagemaker_sklearn_extension/contrib/taei/star_oversampler.py
 19 7 4
def transform()
in src/sagemaker_sklearn_extension/preprocessing/encoders.py
 15 7 2
def _get_data()
in src/sagemaker_sklearn_extension/externals/read_data.py
 26 7 1
def _add_batch()
in src/sagemaker_sklearn_extension/externals/read_data.py
 22 7 2