qbiocode.visualization package#

Visualization Module for QBioCode#

This module provides visualization tools for analyzing and presenting machine learning results, including correlation analysis and performance comparisons between classical and quantum models.

Available Functions#

  • compute_results_correlation: Compute Spearman correlation between metrics

  • plot_results_correlation: Create correlation plots and visualizations

  • publication_style: The journal-figure rcParams, to opt into deliberately

  • PUBLICATION_STYLE: the same settings as a plain dict

  • CorrelationFigures: what plot_results_correlation returns

Usage#

>>> from qbiocode.visualization import plot_results_correlation
>>> figs = plot_results_correlation(correlations_df, save_file_path='plots/corr.pdf')
>>> figs.scatter.savefig('plots/corr_600dpi.png', dpi=600)

Importing this module does not modify matplotlib.rcParams. The journal styling is applied per figure; adopt it for your own figures with:

>>> import matplotlib.pyplot as plt
>>> from qbiocode.visualization import publication_style
>>> with plt.rc_context(publication_style()):
...     fig, ax = plt.subplots()

Submodules:

Summary#

__all__ Classes:

CorrelationFigures

The three figures plot_results_correlation() produces.

__all__ Functions:

compute_results_correlation

This function takes in as input a Pandas Dataframe containing the results and data evaluations for a given dataset.

plot_results_correlation

Plot publication-quality correlation figures from a correlations_df.

publication_style

Return a copy of PUBLICATION_STYLE for use as a matplotlib style.

__all__ Data:

  • PUBLICATION_STYLE

Reference#