openai / grok - 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 121 units with 1,514 lines of code in units (53.4% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
6 medium complex units (326 lines of code)
9 simple units (199 lines of code)
106 very simple units (989 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 step() in grok/training.py	77	22	2
def _make_binary_operation_data() in grok/data.py	49	21	3
def validation_epoch_end() in grok/training.py	45	13	2
def load_metric_data() in grok/visualization.py	57	12	3
def training_epoch_end() in grok/training.py	36	11	2
def child() in scripts/create_partial_metrics.py	62	11	1
def check_limits() in grok/visualization.py	9	10	2
def compute_sharpness() in grok/training.py	46	9	2
def _save_activations() in grok/training.py	18	8	3
def train() in grok/training.py	37	7	1
def make_data() in grok/data.py	19	7	5
def get_loss_and_grads() in grok/measure.py	21	7	3
def render() in grok/data.py	13	6	2
def _make_unary_operation_data() in grok/data.py	25	6	2
def calculate_batchsize() in grok/data.py	11	6	2
def _scheduler_lr() in grok/training.py	20	5	2
def first_step() in grok/training.py	12	5	2
def second_step() in grok/training.py	9	5	2
def most_interesting() in grok/visualization.py	24	5	1
def parent() in scripts/create_metrics_for_epochs.py	19	5	1
def training_step() in grok/training.py	25	4	3
def validation_step() in grok/training.py	19	4	3
def test_epoch_end() in grok/training.py	10	4	2
def _grad_norm() in grok/training.py	16	4	1
def encode() in grok/data.py	7	4	3
def forward() in grok/transformer.py	12	4	2
def forward() in grok/transformer.py	14	4	2
def _position_encoding() in grok/transformer.py	14	4	3
def get_sharpness() in grok/measure.py	49	4	5
def get_ax() in scripts/create_metric_graphs.py	11	4	5
def decode() in grok/data.py	10	3	3
def get_dsname() in grok/data.py	8	3	3
def reset_iteration() in grok/data.py	6	3	2
def forward() in grok/transformer.py	14	3	2
def find_inflections() in grok/visualization.py	21	3	2
def filter_archs() in grok/visualization.py	7	3	2
def get_metric_data() in grok/visualization.py	30	3	2
def lp_path_norm() in grok/metrics.py	8	3	6
def get_weights() in grok/measure.py	8	3	1
def _save_inputs() in grok/training.py	7	2	3
def test_step() in grok/training.py	14	2	3
def add_args() in grok/training.py	9	2	1
def __setstate__() in grok/training.py	4	2	2
def __init__() in grok/data.py	4	2	2
def _encode() in grok/data.py	2	2	2
def __init__() in grok/data.py	8	2	6
def _get_operator_and_noise_level() in grok/data.py	6	2	2
def _make_lists() in grok/data.py	8	2	4
def __next__() in grok/data.py	11	2	1
def __init__() in grok/transformer.py	9	2	4