GoogleCloudPlatform / practical-ml-vision-book

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 82 units with 889 lines of code in units (14.5% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
1 medium complex units (86 lines of code)
3 simple units (101 lines of code)
78 very simple units (702 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 run_main() in 06_preprocessing/jpeg_to_tfrecord_tft.py	86	13	1
def create_strategy() in 07_training/serverlessml/flowers/utils/util.py	22	6	1
def create_model() in 07_training/serverlessml/flowers/classifier/model.py	39	6	4
def n_largest_rois_in_cell() in 04_detect_segment/utils_box.py	40	6	7
def grid_cell_to_tile_coords() in 04_detect_segment/utils_box.py	19	5	3
def compute_mistakes() in 04_detect_segment/utils_box.py	16	5	9
def create_preproc_dataset() in 07_training/serverlessml/flowers/ingest/tfrecords.py	28	4	4
def train_and_evaluate() in 07_training/serverlessml/flowers/classifier/train.py	54	4	2
def filter_by_bool_and_pad() in 04_detect_segment/utils_box.py	8	4	3
def rois_in_tiles_relative_and_pad() in 04_detect_segment/utils_box.py	10	4	4
def lrfn() in 03_image_models/flowers.py	8	3	1
def load_dataset() in 03_image_models/flowers.py	8	3	2
def data_augment() in 03_image_models/flowers.py	3	3	2
def get_training_dataset() in 03_image_models/flowers.py	8	3	0
def cleanup_dir() in 07_training/serverlessml/flowers/utils/util.py	10	3	1
def training_plot() in 07_training/serverlessml/flowers/utils/plots.py	17	3	3
def assign_record_to_split() in 05_create_dataset/jpeg_to_tfrecord.py	7	3	1
def one_d_intersect() in 04_detect_segment/utils_box.py	14	3	4
def center_in_grid_cell() in 04_detect_segment/utils_box.py	10	3	5
def scale_rois() in 04_detect_segment/utils_box.py	3	3	2
def compute_safe_IOU() in 04_detect_segment/utils_box.py	14	3	4
def standardize() in 04_detect_segment/utils_box.py	7	3	1
def assign_record_to_split() in 06_preprocessing/jpeg_to_tfrecord_tft.py	7	3	1
def create_input_record() in 06_preprocessing/jpeg_to_tfrecord_tft.py	11	3	2
def decode_image() in 03_image_models/flowers.py	4	2	1
def get_validation_dataset() in 03_image_models/flowers.py	6	2	1
def count_data_items() in 03_image_models/flowers.py	3	2	1
def call() in 07_training/serverlessml/flowers/utils/augment.py	11	2	3
def yield_records_for_split() in 05_create_dataset/jpeg_to_tfrecord.py	4	2	2
def write_records() in 05_create_dataset/jpeg_to_tfrecord.py	9	2	3
def boxintersect() in 04_detect_segment/utils_box.py	12	2	3
def make_rois_tile_cell_relative() in 04_detect_segment/utils_box.py	15	2	3
def n_experimental_roi_selection_strategy() in 04_detect_segment/utils_box.py	48	2	6
def zero_where() in 04_detect_segment/utils_box.py	8	2	2
def rois_in_tiles_relative() in 04_detect_segment/utils_box.py	7	2	2
def batch_intersection_over_union() in 04_detect_segment/utils_box.py	19	2	4
def rotate() in 04_detect_segment/utils_box.py	10	2	3
def rot90() in 04_detect_segment/utils_box.py	7	2	3
def random_orientation() in 04_detect_segment/utils_box.py	20	2	3
def yield_records_for_split() in 06_preprocessing/jpeg_to_tfrecord_tft.py	4	2	2
def write_records() in 06_preprocessing/jpeg_to_tfrecord_tft.py	9	2	3
def read_tfrecord() in 03_image_models/flowers.py	11	1	1
def __init__() in 07_training/serverlessml/flowers/ingest/tfrecords.py	4	1	4
def read_from_tfr() in 07_training/serverlessml/flowers/ingest/tfrecords.py	14	1	2
def read_from_jpegfile() in 07_training/serverlessml/flowers/ingest/tfrecords.py	5	1	2
def preprocess() in 07_training/serverlessml/flowers/ingest/tfrecords.py	2	1	2
def create_preproc_image() in 07_training/serverlessml/flowers/ingest/tfrecords.py	4	1	4
def __init__() in 07_training/serverlessml/flowers/utils/augment.py	5	1	6
def export_model() in 07_training/serverlessml/flowers/classifier/model.py	25	1	5
def _string_feature() in 05_create_dataset/jpeg_to_tfrecord.py	2	1	1