Python wrapper for CriticalDifferenceDiagrams.jl

The wrapper provides an interface through which the methods of CriticalDifferenceDiagrams.jl are exposed to Python. This short guide assumes you have read the Home section.

Getting started

The wrapper can be installed with pip:

pip install
python -c "import CriticalDifferenceDiagrams_jl"

The second step ensures that all Julia dependencies, including Julia itself, are installed.

The example plot from the Home section is then generated with the following Python code:

import CriticalDifferenceDiagrams_jl as cdd
import pandas as pd
from wget import download

# we generate the above example from the underlying data
df = pd.read_csv("example.csv")

plot = cdd.plot(
    "classifier_name", # the name of the treatment column
    "dataset_name",    # the name of the observation column
    "accuracy",        # the name of the outcome column
    maximize_outcome=True, # compute ranks for minimization (default) or maximization
    title="CriticalDifferenceDiagrams.jl", # give an optional title
    alpha=0.05, # the significance level (default: 0.05)
    adjustment="holm" # "holm" (default) or "bonferroni"

# configure the preamble of PGFPlots.jl (optional)

# export to .svg, .tex, or .pdf"example.svg", plot)

A 2-dimensional plot can be generated in analogy to the example from the Home section:

sequence = [
    ("title 1", cdd.to_pairs(df1, "treatment", "dataset", "outcome")),
    ("title 2", cdd.to_pairs(df2, "treatment", "dataset", "outcome"))
plot = cdd.plot(sequence, maximize_outcome=True, title="2-dimensional")


The first import CriticalDifferenceDiagrams_jl installs all Julia dependencies, which takes some time. Therefore, we recommend to call

python -c "import CriticalDifferenceDiagrams_jl"

as a part of the installation process, as described in the Getting started section.

Calling a method for the first time will be somewhat slow, due to Julia's just-in-time (JIT) compilation mechanism. JIT means that all functions are compiled when they are called for the first time in a session. As a result, the first call is quite slow but subsequent calls are super fast. If your implementation design permits, generate multiple CD diagrams in a single session to benefit from the JIT speedup of subsequent method calls.