alibaba / graphlearn-for-pytorch - 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 521 units with 4,977 lines of code in units (40.6% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
11 medium complex units (758 lines of code)
37 simple units (993 lines of code)
473 very simple units (3,226 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 init_rpc() in graphlearn_torch/python/distributed/rpc.py	92	22	5
def _sampling_worker_loop() in graphlearn_torch/python/distributed/dist_sampling_producer.py	94	18	11
def load() in graphlearn_torch/python/data/table_dataset.py	98	17	14
def __init__() in graphlearn_torch/python/distributed/dist_graph.py	49	16	10
def merge_hetero_sampler_output() in graphlearn_torch/python/utils/common.py	32	16	5
def __init__() in graphlearn_torch/python/distributed/dist_neighbor_sampler.py	88	14	14
def partition() in graphlearn_torch/python/partition/base.py	44	14	3
def build_partition_feature() in graphlearn_torch/python/partition/base.py	94	14	11
std::tuple ToCSR() in graphlearn_torch/v6d/vineyard_utils.cc	70	14	6
def __init__() in graphlearn_torch/python/data/graph.py	47	13	9
def __init__() in graphlearn_torch/python/distributed/dist_feature.py	50	12	13
def _collate_fn() in graphlearn_torch/python/loader/link_loader.py	30	10	3
def _collate_fn() in graphlearn_torch/python/loader/node_loader.py	29	10	3
def convert_to_tensor() in graphlearn_torch/python/utils/tensor.py	22	10	2
def __init__() in graphlearn_torch/python/distributed/dist_options.py	40	9	9
def __init__() in graphlearn_torch/python/distributed/dist_options.py	44	9	20
def __init__() in graphlearn_torch/python/distributed/dist_link_neighbor_loader.py	46	9	17
def _build_features() in graphlearn_torch/python/data/dataset.py	31	9	7
def lazy_init_sampler() in graphlearn_torch/python/sampler/neighbor_sampler.py	22	9	1
def partition() in graphlearn_torch/python/distributed/dist_random_partitioner.py	55	8	1
def __init__() in graphlearn_torch/python/distributed/dist_neighbor_loader.py	41	8	15
def lazy_init() in graphlearn_torch/python/data/graph.py	30	8	1
def __init__() in graphlearn_torch/python/data/feature.py	34	8	9
def index_select() in graphlearn_torch/python/utils/common.py	19	8	2
std::tuple CPURandomNegativeSampler::Sample() in graphlearn_torch/csrc/cpu/random_negative_sampler.cc	48	8	3
std::shared_ptr GetGraphFromVineyard() in graphlearn_torch/v6d/vineyard_utils.cc	24	8	2
def all_gather() in graphlearn_torch/python/distributed/rpc.py	48	7	2
def shutdown() in graphlearn_torch/python/distributed/dist_sampling_producer.py	16	7	1
def assign_server_by_order() in graphlearn_torch/python/distributed/dist_context.py	21	7	0
def _split_and_init() in graphlearn_torch/python/data/feature.py	26	7	1
def apply_to_all_tensor() in graphlearn_torch/python/utils/tensor.py	17	7	4
def cast() in graphlearn_torch/python/utils/mixin.py	12	7	3
def load_and_concatenate_tensors() in graphlearn_torch/python/utils/common.py	17	7	2
void CPUSubGraphOp::Induce() in graphlearn_torch/csrc/cpu/subgraph_op.cc	28	7	5
torch::Tensor LoadVertexFeatures() in graphlearn_torch/v6d/vineyard_utils.cc	36	7	4
torch::Tensor LoadEdgeFeatures() in graphlearn_torch/v6d/vineyard_utils.cc	37	7	4
def partition() in graphlearn_torch/python/distributed/dist_table_dataset.py	34	6	1
def fetch_one_sampled_message() in graphlearn_torch/python/distributed/dist_server.py	16	6	2
def shutdown() in graphlearn_torch/python/distributed/dist_loader.py	15	6	1
def shutdown_client() in graphlearn_torch/python/distributed/dist_client.py	17	6	0
def share_ipc() in graphlearn_torch/python/data/feature.py	20	6	1
def parse_size() in graphlearn_torch/python/utils/units.py	10	6	1
def format_hetero_sampler_output() in graphlearn_torch/python/utils/common.py	10	6	3
def __init__() in graphlearn_torch/python/sampler/neighbor_sampler.py	42	6	6
def lazy_init_neg_sampler() in graphlearn_torch/python/sampler/neighbor_sampler.py	20	6	1
def _set_num_neighbors_and_num_hops() in graphlearn_torch/python/sampler/neighbor_sampler.py	10	6	2
def __init__() in graphlearn_torch/python/partition/partition_book.py	9	6	4
def recv() in graphlearn_torch/python/channel/remote_channel.py	17	6	2
def rpc_sync_data_partitions() in graphlearn_torch/python/distributed/rpc.py	19	5	2
def rpc_register() in graphlearn_torch/python/distributed/rpc.py	20	5	1