facebookresearch / RandomizedValueFunctions
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 153 units with 1,680 lines of code in units (54.6% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 0 medium complex units (0 lines of code)
    • 17 simple units (372 lines of code)
    • 136 very simple units (1,308 lines of code)
0% | 0% | 0% | 22% | 77%
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% | 0% | 22% | 77%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
qlearn/atari0% | 0% | 0% | 22% | 77%
qlearn/toys0% | 0% | 0% | 33% | 66%
qlearn/commun0% | 0% | 0% | 12% | 87%
qlearn/envs0% | 0% | 0% | 56% | 44%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def test()
in qlearn/toys/test.py
 23 9 3
def __init__()
in qlearn/atari/prior_bootstrapped_agent.py
 27 9 4
def __init__()
in qlearn/toys/noisy_agent.py
 21 8 3
def __init__()
in qlearn/toys/bootstrapped_agent.py
 22 8 3
def __init__()
in qlearn/toys/mnf_agent.py
 22 8 3
def __init__()
in qlearn/toys/bayes_backprop_agent.py
 21 8 3
def __init__()
in qlearn/toys/agent.py
 21 8 3
def __init__()
in qlearn/atari/noisy_agent.py
 22 8 4
def __init__()
in qlearn/atari/local_mnf_agent.py
 23 8 4
def __init__()
in qlearn/atari/bootstrapped_agent.py
 23 8 4
def __init__()
in qlearn/atari/mnf_agent.py
 23 8 4
def __init__()
in qlearn/atari/dqn_agent.py
 22 8 4
def step()
in qlearn/envs/nchain.py
 14 8 2
def sample_z()
in qlearn/commun/local_mnf_layer.py
 30 7 4
def initialize_weights()
in qlearn/commun/utils.py
 14 7 1
def __init__()
in qlearn/atari/bayes_backprop_agent.py
 20 6 4
def forward()
in qlearn/commun/local_mnf_layer.py
 24 6 3
def learn()
in qlearn/toys/bootstrapped_agent.py
 26 5 6
def forward()
in qlearn/commun/norm_flows.py
 14 5 3
def learn()
in qlearn/toys/agent.py
 22 4 6