openai / ebm_code_release - 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 171 units with 4,007 lines of code in units (90.0% of code).

0 very complex units (0 lines of code)
5 complex units (887 lines of code)
5 medium complex units (342 lines of code)
29 simple units (1,059 lines of code)
132 very simple units (1,719 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 main() in ebm_sandbox.py	109	49	0
def main() in train.py	333	44	0
def train() in train.py	205	38	7
def main() in ais.py	92	26	0
def gentest() in ebm_combine.py	148	26	5
def main() in ebm_combine.py	86	19	0
def conv_block() in utils.py	66	18	21
def __init__() in data.py	52	12	6
def energyevalmix() in ebm_sandbox.py	61	11	4
def genbaseline() in ebm_combine.py	77	11	6
def __init__() in models.py	30	10	8
def conv_block_3d() in utils.py	36	10	14
def _init_inception() in fid.py	36	10	0
def conceptcombine() in ebm_combine.py	69	10	5
def __init__() in data.py	58	10	5
def forward() in models.py	41	9	9
def forward() in models.py	37	9	9
def construct_finetune_label() in ebm_sandbox.py	49	9	6
def construct_label() in ebm_sandbox.py	49	9	6
def _init_inception() in inception.py	37	9	0
def __getitem__() in data.py	18	9	2
def forward() in models.py	28	8	8
def forward() in models.py	32	8	10
def forward() in models.py	37	8	9
def forward() in models.py	41	8	9
def construct_latent() in ebm_sandbox.py	65	8	5
def test() in train.py	78	8	5
def _get_inception_layer() in fid.py	17	8	1
def forward() in models.py	30	7	9
def labelfinetune() in ebm_sandbox.py	32	7	8
def boxcorrupt() in ebm_sandbox.py	34	7	7
def cycleclass() in ebm_sandbox.py	25	7	6
def __getitem__() in data.py	25	7	2
def crossclass() in ebm_sandbox.py	23	6	6
def construct_steps() in ebm_sandbox.py	25	6	5
def get_weight() in utils.py	45	6	3
def optimistic_restore() in utils.py	21	6	3
def remap_restore() in utils.py	19	6	3
def ancestral_sample() in ais.py	22	6	6
def construct_weights() in models.py	23	5	2
def get_inception_score() in inception.py	25	5	2
def get_images() in utils.py	11	5	4
def optimistic_remap_restore() in utils.py	22	5	3
def smart_res_block() in utils.py	62	5	13
def normalize() in utils.py	18	5	4
def spectral_normed_weight() in utils.py	34	5	5
def _create_slots() in custom_adam.py	11	5	2
def bridge_prob_neg_log() in ais.py	12	5	5
def get_activations() in fid.py	19	5	4
def anticorrupt() in ebm_sandbox.py	24	4	6