tensorflow / workshops
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 31 units with 370 lines of code in units (90.5% 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)
    • 4 simple units (83 lines of code)
    • 26 very simple units (201 lines of code)
0% | 0% | 23% | 22% | 54%
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% | 23% | 22% | 54%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
notebooks0% | 0% | 23% | 22% | 54%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def plot_artifact_lineage()
in tfx_airflow/notebooks/utils.py
86 24 2
def _add_parents()
in tfx_airflow/notebooks/utils.py
15 10 6
def get_source_artifact_of_type()
in tfx_airflow/notebooks/utils.py
21 7 3
def get_dest_artifact_of_type()
in tfx_airflow/notebooks/utils.py
22 7 3
def display_tensorboard()
in tfx_airflow/notebooks/tfx_utils.py
25 6 3
def get_df_from_artifacts_or_executions()
in tfx_airflow/notebooks/utils.py
14 5 2
def _get_value_str()
in tfx_airflow/notebooks/utils.py
8 4 1
def _get_upstream_execution_ids()
in tfx_airflow/notebooks/utils.py
5 4 2
def _add_node_attribute()
in tfx_airflow/notebooks/utils.py
13 4 5
def get_df_from_single_artifact_or_execution()
in tfx_airflow/notebooks/utils.py
10 4 2
def get_execution_for_output_artifact()
in tfx_airflow/notebooks/utils.py
11 4 3
def compare_tfma_analysis()
in tfx_airflow/notebooks/tfx_utils.py
14 3 3
def compare_stats_for_examples()
in tfx_airflow/notebooks/tfx_utils.py
13 3 5
def compare_examples_stats_for_models()
in tfx_airflow/notebooks/tfx_utils.py
10 3 3
def _get_upstream_artifact_ids()
in tfx_airflow/notebooks/utils.py
3 3 2
def get_artifact_lineage()
in tfx_airflow/notebooks/utils.py
5 3 3
def display_artifact_and_execution_properties()
in tfx_airflow/notebooks/utils.py
18 3 3
22 3 4
def display_tfma_analysis()
in tfx_airflow/notebooks/tfx_utils.py
7 2 3
def display_stats_for_examples()
in tfx_airflow/notebooks/tfx_utils.py
7 2 2