A toolkit for visualizations in materials informatics
Project description
pymatviz
A toolkit for visualizations in materials informatics.
Note: This project is not associated with or endorsed by pymatgen, but aims to complement it by adding additional plotting functionality.
Installation
pip install pymatviz
Elements
See pymatviz/elements.py.
ptable_heatmap(compositions, log=True) |
ptable_heatmap_ratio(comps_a, comps_b) |
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Sunburst
See pymatviz/sunburst.py.
spacegroup_sunburst([65, 134, 225, ...]) |
spacegroup_sunburst([65, 134, 225, ...], show_values="percent") |
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Structure
plot_structure_2d(pmg_struct) |
plot_structure_2d(pmg_struct, show_unit_cell=False, site_labels=False) |
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Histograms
spacegroup_hist([65, 134, 225, ...]) |
spacegroup_hist(["C2/m", "C2/m", "Fm-3m", ...]) |
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residual_hist(y_true, y_pred) |
hist_elemental_prevalence(compositions, log=True, bar_values='count') |
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Parity Plots
See pymatviz/parity.py.
Uncertainty Calibration
See pymatviz/quantile.py.
qq_gaussian(y_true, y_pred, y_std) |
qq_gaussian(y_true, y_pred, y_std: dict) |
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Ranking
See pymatviz/ranking.py.
err_decay(y_true, y_pred, y_std) |
err_decay(y_true, y_pred, y_std: dict) |
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Cumulative Error and Residual
cum_err(preds, targets) |
cum_res(preds, targets) |
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Classification Metrics
roc_curve(targets, proba_pos) |
precision_recall_curve(targets, proba_pos) |
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Correlation
marchenko_pastur(corr_mat, gamma=ncols/nrows) |
marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows) |
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Migrating from ml-matrics to pymatviz
This library was renamed from ml-matrics to pymatviz between versions 0.3.0 and 0.4.0. To update existing Python files that import ml-matrics in place, run the following commands. On Linux:
find . -iname '*.py' -o -iname '*.ipynb' | xargs sed -i 's/from ml_matrics import/from pymatviz import/g'
find . -iname '*.py' -o -iname '*.ipynb' | xargs sed -i 's/from ml_matrics./from pymatviz./g'
find . -iname '*.py' -o -iname '*.ipynb' | xargs sed -i 's/import ml_matrics/import pymatviz/g'
On Mac, replace sed -i with sed -i "".
Glossary
- Residual
y_res = y_true - y_pred: The difference between ground truth target and model prediction. - Error
y_err = abs(y_true - y_pred): Absolute error between target and model prediction. - Uncertainty
y_std: The model's estimate for its error, i.e. how much the model thinks its prediction can be trusted. (stdfor standard deviation.)
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