neo-ai / neo-loader
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

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 139 units with 1,048 lines of code in units (78.9% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 3 medium complex units (86 lines of code)
    • 11 simple units (174 lines of code)
    • 125 very simple units (788 lines of code)
0% | 0% | 8% | 16% | 75%
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
py0% | 0% | 8% | 16% | 75%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src/neo_loader0% | 0% | 7% | 18% | 73%
src/neo_loader/helpers0% | 0% | 10% | 8% | 80%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def load_model()
in src/neo_loader/sklearn_model_loader.py
 36 17 1
def __extract_input_and_output_tensors_from_frozen_graph()
in src/neo_loader/helpers/tf_model_helper.py
 24 13 1
def __update_input_data_from_data_shape()
in src/neo_loader/abstract_model_loader.py
 26 13 1
def load_model()
in src/neo_loader/mxnet_model_loader.py
 28 9 1
def __update_output_data_from_relay()
in src/neo_loader/abstract_model_loader.py
 12 8 1
def validate_input_shape()
in src/neo_loader/__init__.py
 11 7 2
def __validata_data_shape_with_input_layer()
in src/neo_loader/keras_model_loader.py
 14 7 2
def __get_model_dir_from_model_artifacts()
in src/neo_loader/tensorflow_model_loader.py
 15 7 1
def __extract_input_and_output_tensors_from_saved_model_v2()
in src/neo_loader/helpers/tf_model_helper.py
 19 6 1
def find_class()
in src/neo_loader/xgboost_model_loader.py
 8 6 3
def __extract_tf_graph()
in src/neo_loader/tensorflow_model_loader.py
 19 6 1
def update_missing_metadata()
in src/neo_loader/sklearn_model_loader.py
 10 6 1
def __get_param_file_from_model_artifact()
in src/neo_loader/mxnet_model_loader.py
 24 6 1
def __get_arg_and_aux_params_from_model_artifact()
in src/neo_loader/mxnet_model_loader.py
 14 6 1
def find_archive()
in src/neo_loader/__init__.py
 8 5 2
def model_type()
in src/neo_loader/helpers/tf_model_helper.py
 7 5 1
def get_metadata()
in src/neo_loader/helpers/tf_model_helper.py
 11 5 1
def load_model()
in src/neo_loader/pytorch_model_loader.py
 27 5 1
def __extract_metadata_and_output_tensor_names_from_model()
in src/neo_loader/tensorflow_model_loader.py
 17 5 1
def __get_outputs_from_relay()
in src/neo_loader/abstract_model_loader.py
 15 5 1