openai / ppo-ewma - 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 250 units with 1,663 lines of code in units (72.7% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
2 medium complex units (46 lines of code)
18 simple units (407 lines of code)
230 very simple units (1,210 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 parse_dtype() in ppo_ewma/torch_util.py	26	18	1
def singles_to_multi() in ppo_ewma/roller.py	20	11	1
def format_model() in ppo_ewma/torch_util.py	26	9	2
def minibatch_gen() in ppo_ewma/minibatch_optimize.py	22	9	5
def writekvs() in ppo_ewma/logger.py	27	9	2
def read_tb() in ppo_ewma/logger.py	30	9	1
def train_fn() in ppo_ewma/train.py	82	8	35
def dumpkvs() in ppo_ewma/logger.py	20	7	1
def rcm() in ppo_ewma/log_save_helper.py	7	6	4
def log() in ppo_ewma/log_save_helper.py	26	6	1
def save() in ppo_ewma/log_save_helper.py	17	6	1
def make_minibatches() in ppo_ewma/ppg.py	19	6	2
def _fmt_item() in ppo_ewma/minibatch_optimize.py	13	6	2
def mpi_weighted_mean() in ppo_ewma/logger.py	16	6	2
def writekvs() in ppo_ewma/logger.py	24	6	2
def make_output_format() in ppo_ewma/logger.py	14	6	3
def residual() in ppo_ewma/impala_cnn.py	10	6	2
def tree_multimap() in ppo_ewma/tree_util.py	14	6	3
def process() in ppo_ewma/vec_monitor2.py	18	6	1
def train_with_losses_and_opt() in ppo_ewma/ppo.py	22	6	3
def torch_setup() in ppo_ewma/torch_util.py	17	5	2
def gather_roller_stats() in ppo_ewma/log_save_helper.py	19	5	2
def aux_train() in ppo_ewma/ppg.py	21	5	6
def multi_step() in ppo_ewma/roller.py	22	5	3
def batch_len() in ppo_ewma/torch_util.py	9	4	1
def NormedLinear() in ppo_ewma/torch_util.py	12	4	4
def flatten_tensors() in ppo_ewma/torch_util.py	8	4	3
def sync_params() in ppo_ewma/torch_util.py	12	4	5
def forward() in ppo_ewma/ppg.py	17	4	4
def __eq__() in ppo_ewma/tree_util.py	9	4	2
def single_step() in ppo_ewma/roller.py	19	4	2
def have_cuda() in ppo_ewma/torch_util.py	4	3	0
def _get_local_rank_size() in ppo_ewma/torch_util.py	11	3	1
def explained_variance() in ppo_ewma/torch_util.py	13	3	3
def warn_no_gradient() in ppo_ewma/torch_util.py	4	3	2
def __call__() in ppo_ewma/log_save_helper.py	10	3	1
def _flatten() in ppo_ewma/log_save_helper.py	2	3	1
def _nanmoments_local() in ppo_ewma/log_save_helper.py	8	3	2
def get_procgen_venv() in ppo_ewma/envs.py	8	3	5
def update() in ppo_ewma/ppg.py	8	3	2
def _fmt_row() in ppo_ewma/minibatch_optimize.py	5	3	3
def dict_mean() in ppo_ewma/minibatch_optimize.py	2	3	1
def writeseq() in ppo_ewma/logger.py	8	3	2
def writekvs() in ppo_ewma/logger.py	6	3	2
def profile_kv() in ppo_ewma/logger.py	11	3	2
def profile() in ppo_ewma/logger.py	16	3	1
def dump_kwargs() in ppo_ewma/logger.py	23	3	1
def _do_log() in ppo_ewma/logger.py	4	3	2
def tensor_distr_builder() in ppo_ewma/distr_builder.py	12	3	1
def forward() in ppo_ewma/impala_cnn.py	7	3	2