huggingface / feel 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 75 units with 808 lines of code in units (45.6% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
2 medium complex units (78 lines of code)
9 simple units (222 lines of code)
64 very simple units (508 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 wrangle_like_data() in app/app.py	43	18	2
def format_history_as_messages() in app/app.py	35	13	1
def transform_rlhf_dataset() in ml/dataset_transformer.py	22	10	4
def load_languages() in app/app.py	24	9	0
def extract_dialogue() in ml/trl_rlhf_data.py	17	9	1
def process() in data/generate_dpo.py	22	8	2
def get_persistent_storage_path() in app/app.py	14	8	1
def evaluate_data() in ml/eval/reward_eval.py	19	7	4
def process() in data/generate_dpo.py	15	6	2
def prepare_oasst_sft_results() in ml/eval/generate.py	14	6	3
def main() in ml/trainer.py	75	6	0
def add_user_message() in app/app.py	9	5	2
def _process_content() in app/app.py	6	5	1
def save_new_language() in app/app.py	20	5	2
def get_config() in app/app.py	7	5	1
def jdump() in ml/eval/utils.py	13	5	5
def __post_init__() in ml/eval/evaluate_arguments.py	5	5	1
def load_model_and_tokenizer() in ml/trainer.py	16	5	1
def load_latest_adapter() in ml/trainer.py	15	5	3
def common_start() in ml/trl_rlhf_data.py	8	5	2
def load_model_and_tokenizer() in ml/eval/generate.py	12	4	3
def generate_responses() in ml/eval/generate.py	15	4	4
def bradley_terry_comparison() in ml/eval/bt.py	36	4	2
def process_evaluation() in ml/eval/reward_eval.py	9	4	3
def __post_init__() in ml/trainer.py	9	4	1
def format_system_message() in app/app.py	11	3	2
def _process_rating() in app/app.py	7	3	1
def judge_responses() in ml/eval/alpaca.py	15	3	3
def generate_files() in ml/eval/generate.py	14	3	0
def evaluator_master_fn() in ml/eval/evaluation_pipeline.py	11	3	6
def _is_file_safe() in app/app.py	5	2	1
def call_pipeline() in app/app.py	21	2	2
def alpaca_evaluator() in ml/eval/alpaca.py	7	2	2
def format_prompt() in ml/eval/generate.py	2	2	1
def _make_r_io_base() in ml/eval/utils.py	4	2	2
def create_tokenizer() in ml/eval/reward_eval.py	3	2	1
def get_reward_output_fn() in ml/eval/reward_eval.py	12	2	2
def get_adapter_path() in ml/trainer.py	6	2	3
def outputs() in data/generate_dpo.py	2	1	1
def inputs() in data/generate_dpo.py	2	1	1
def outputs() in data/generate_dpo.py	2	1	1
def create_inference_client() in app/app.py	2	1	0
def _convert_path_to_data_uri() in app/app.py	6	1	1
def add_fake_like_data() in app/app.py	6	1	0
def respond() in app/app.py	5	1	0
def update_dataframe() in app/app.py	10	1	2
def wrangle_edit_data() in app/app.py	7	1	0
def wrangle_retry_data() in app/app.py	7	1	0
def submit_conversation() in app/app.py	16	1	4
def open_add_language_modal() in app/app.py	2	1	0