GoogleCloudPlatform / clearbox - Conditional Complexity

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 124 units with 531 lines of code in units (47.9% 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)
124 very simple units (531 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def __init__() in clearbox/metrics/ndcg.py	10	5	3
def _draw_boxplots() in clearbox/visualization.py	18	4	3
def weights() in clearbox/models/regression.py	5	3	1
def encode() in clearbox/features.py	3	3	1
def __mul__() in clearbox/features.py	4	3	2
def evaluate() in clearbox/features.py	7	3	2
def __init__() in clearbox/formula.py	10	3	3
def name() in clearbox/metrics/ndcg.py	2	2	1
def _norm_cdf() in clearbox/models/probabilistic.py	2	2	2
def _norm_pdf() in clearbox/models/probabilistic.py	2	2	2
def _train_model() in clearbox/training.py	34	2	1
def weights() in clearbox/training.py	6	2	1
def __add__() in clearbox/features.py	4	2	2
def __sub__() in clearbox/features.py	4	2	2
def _serialize_to_str() in clearbox/features.py	5	2	1
def serialize_to_ranking_expression() in clearbox/features.py	7	2	1
def evaluate() in clearbox/features.py	4	2	2
def __init__() in clearbox/features.py	6	2	4
def evaluate() in clearbox/features.py	13	2	2
def _serialize_to_str() in clearbox/features.py	5	2	1
def __init__() in clearbox/features.py	4	2	2
def evaluate() in clearbox/features.py	5	2	2
def _serialize_to_str() in clearbox/features.py	2	2	1
def serialize_to_ranking_expression() in clearbox/features.py	4	2	1
def __init__() in clearbox/features.py	4	2	2
def evaluate() in clearbox/features.py	5	2	2
def _serialize_arg_to_str() in clearbox/features.py	5	2	2
def _serialize_arg_to_ranking_expression() in clearbox/features.py	5	2	2
def _serialize_to_str() in clearbox/features.py	2	2	1
def serialize_to_ranking_expression() in clearbox/features.py	4	2	1
def feature_names() in clearbox/formula.py	2	2	1
def name() in clearbox/metrics/base.py	2	1	1
def compute() in clearbox/metrics/base.py	5	1	0
def _validate_compute_args() in clearbox/metrics/base.py	5	1	0
def __init__() in clearbox/metrics/average.py	4	1	0
def name() in clearbox/metrics/average.py	2	1	1
def compute() in clearbox/metrics/average.py	5	1	0
def name() in clearbox/metrics/auroc.py	2	1	1
def compute() in clearbox/metrics/auroc.py	5	1	0
def compute() in clearbox/metrics/ndcg.py	5	1	0
def __init__() in clearbox/metrics/recall.py	5	1	0
def name() in clearbox/metrics/recall.py	2	1	1
def _binarize_target() in clearbox/metrics/recall.py	2	1	0
def compute() in clearbox/metrics/recall.py	5	1	0
def fit() in clearbox/models/base.py	5	1	0
def weights() in clearbox/models/base.py	2	1	1
def predict() in clearbox/models/base.py	2	1	2
def new() in clearbox/models/base.py	2	1	1
def fit() in clearbox/models/probabilistic.py	2	1	3
def predict() in clearbox/models/probabilistic.py	2	1	0