huggingface / movie-shot-categorizer 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).

Conditional Complexity Overall

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

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 14 most complex units

Unit	# lines	McCabe index	# params
def train_model() in train.py	174	41	2
def preprocess_batch() in data.py	27	13	1
def collate_fn() in data.py	38	10	3
def convert_config_types() in train.py	15	9	1
def accuracy() in accuracy.py	122	7	1
def compute_metrics() in accuracy.py	15	5	1
def evaluate_model() in train.py	26	5	7
def main() in train.py	33	4	1
def infer() in inference.py	18	2	3
def get_dataset() in data.py	11	2	4
def load() in inference.py	8	1	1
def load_config() in train.py	5	1	1
def create_data_loaders() in train.py	6	1	0
def forward_with_model() in train.py	5	1	4