baycomp-plotting 1.1.1

Description:

baycompplotting 1.1.1

Baycomp Plotting
The baycomp_plotting is a python package for building good-looking plots of
bayesian posteriors obtained with baycomp.
This package could be useful for scientific purposes, specially in the area of
Machine Learning.
Author

Mario Juez-Gil <[email protected]>
Department of Computer Science
Universidad de Burgos
ADMIRABLE Research Group

Installation
This package can be installed using PIP.
pip install baycomp_plotting

Basic Usage
The package can be imported as follows:
import baycomp_plotting as bplt

Two plotting functions (tern, and dens), and one class with four matplotlib alternative colors (Color) are provided.
Colors
Four alternative colors to default matplotlib colors are provided:

Example:
import baycomp_plotting as bplt

print(bplt.Color.BLUE)

Output:
'#008ece'

Density plots
For plotting the comparison of two classifiers on a single dataset, dens function could be used. It's parameters are the following:

p: baycomp posterior.
label: label of the density function.
ls: line style (use a matplotlib line style) [default: -]
color: density function color [default: Color.BLUE]

Example:
import baycomp_plotting as bplt
import baycomp as bc

posterior = bc.CorrelatedTTest(left_classifier_acc, right_classifier_acc, rope=0.01)
fig = bplt.dens(posterior, label='C1', ls='-', color=bplt.Color.BLUE)

Output:

The output figure will have a new function named add_posterior so you can add more posteriors to the figure. The parameters are the same as for dens.
Example:
import baycomp_plotting as bplt
import baycomp as bc

posterior = bc.CorrelatedTTest(left_classifier_1_acc, right_classifier_acc, rope=0.01)
posterior_1 = bc.CorrelatedTTest(left_classifier_2_acc, right_classifier_acc, rope=0.01)
fig = bplt.dens(posterior, label='C1', ls='-', color=bplt.Color.BLUE)
fig.add_posterior(posterior_1, label='C2', ls=(0,(5,1)), color=bplt.Color.GRAY)
fig.legend() # you can show the legend

Output:

Ternary plots
For plotting the comparison of two classifiers on multiple datasets using a ternary plot, tern function could be used. It's parameters are the following:

p: baycomp posterior.
names: an array containing Left and Right region labels. [default: ["L", "R"]]

Example:
import baycomp_plotting as bplt
import baycomp as bc

posterior = bc.HierarchicalTest(left_classifier_acc, right_classifier_acc, rope=0.01)
fig = bplt.tern(posterior)

Output:

Comparison against baycomp default plots
Density:


Ternary:

Contribute
Feel free to submit any pull requests 😊
Acknowlegments
This work was supported by the pre-doctoral grant (EDU/1100/2017) of the
Consejería de Educación of the Junta de Castilla y León, Spain, and the
European Social Fund.
License
This work is licensed under GNU GPL v3.
Citation policy
Please, cite this work as:
@software{baycomp_plotting,
author = {Mario Juez-Gil},
title = {{mjuez/baycomp_plotting}},
month = nov,
year = 2020,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.4244542},
url = {https://doi.org/10.5281/zenodo.4244542}
}