twitter / diffusion-rlConditional 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).

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Conditional Complexity Overall

There are 226 units with 2,350 lines of code in units (85.3% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
2 medium complex units (221 lines of code)
8 simple units (257 lines of code)
216 very simple units (1,872 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 train_agent() in run_bc.py	111	13	7
def train_agent() in run_offline.py	110	11	7
def train() in agents/ql_diffusion.py	43	9	4
def forward() in agents/ed_pcq.py	20	9	3
def __init__() in agents/diffusion.py	38	7	10
def guided_sample() in agents/diffusion.py	33	7	6
def p_sample_loop() in agents/diffusion.py	16	6	5
def sample_multiple_actions() in agents/bc_gan.py	16	6	4
def train() in agents/ql_cvae.py	41	6	4
def train() in agents/ed_pcq.py	50	6	4
def train() in agents/bc_gan2.py	26	4	4
def __init__() in agents/bc_diffusion.py	27	4	11
def __init__() in agents/adw_bc_diffusion.py	36	4	13
def mmd_loss_laplacian() in agents/bc_mmd.py	9	4	4
def __init__() in agents/qgdp.py	33	4	11
def eval_policy() in run_offline.py	19	4	4
def eval_policy() in run_bc.py	19	4	4
def p_mean_variance() in agents/diffusion.py	11	3	5
def sample_t_last() in agents/diffusion.py	15	3	2
def sample_last_few() in agents/diffusion.py	15	3	2
def train() in agents/adw_bc_diffusion.py	27	3	4
def __init__() in agents/bc_mle.py	23	3	8
def sample() in agents/bc_mle.py	16	3	4
def train() in agents/bc_gan.py	22	3	4
def __init__() in agents/bc_w.py	24	3	8
def sample() in agents/bc_w.py	17	3	4
def train() in agents/bc_w.py	10	3	4
def train() in agents/qgdp.py	26	3	4
def fanin_init() in utils/pytorch_util.py	10	3	2
def fanin_init_weights_like() in utils/pytorch_util.py	12	3	1
def sample_action() in agents/bc_gan2.py	6	2	2
def forward() in agents/bc_kl.py	9	2	3
def decode() in agents/bc_kl.py	6	2	3
def log_pis() in agents/bc_kl.py	15	2	4
def train() in agents/bc_kl.py	18	2	4
def train() in agents/bc_diffusion.py	8	2	4
def forward() in agents/bc_cvae.py	9	2	3
def decode() in agents/bc_cvae.py	6	2	3
def train() in agents/bc_cvae.py	11	2	4
def predict_start_from_noise() in agents/diffusion.py	12	2	4
def q_sample() in agents/diffusion.py	8	2	4
def p_losses() in agents/diffusion.py	10	2	5
def step_frozen() in agents/diffusion.py	3	2	1
def sample_t_middle() in agents/diffusion.py	12	2	2
def train() in agents/bc_mle.py	9	2	4
def __init__() in agents/ql_diffusion.py	45	2	18
def step_ema() in agents/ql_diffusion.py	4	2	1
def forward() in agents/bc_mmd.py	9	2	3
def decode() in agents/bc_mmd.py	6	2	3
def decode_multiple() in agents/bc_mmd.py	7	2	4