pip install pymatviz
See pymatviz/elements.py
.
See pymatviz/sunburst.py
.
spacegroup_sunburst([65, 134, 225, ...]) |
spacegroup_sunburst([65, 134, 225, ...], show_values="percent") |
---|---|
plot_structure_2d(pmg_struct) |
plot_structure_2d(pmg_struct, show_unit_cell=False, site_labels=False) |
---|---|
spacegroup_hist([65, 134, 225, ...]) |
spacegroup_hist([65, 134, 225, ...], show_counts=False) |
---|---|
residual_hist(y_true, y_pred) |
true_pred_hist(y_true, y_pred, y_std) |
See pymatviz/parity.py
.
See pymatviz/quantile.py
.
qq_gaussian(y_true, y_pred, y_std) |
qq_gaussian(y_true, y_pred, y_std: dict) |
---|---|
See pymatviz/ranking.py
.
err_decay(y_true, y_pred, y_std) |
err_decay(y_true, y_pred, y_std: dict) |
---|---|
cum_err(preds, targets) |
cum_res(preds, targets) |
---|---|
roc_curve(targets, proba_pos) |
precision_recall_curve(targets, proba_pos) |
---|---|
marchenko_pastur(corr_mat, gamma=ncols/nrows) |
marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows) |
---|---|
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 . -name '*.py' | xargs sed -i 's/^from ml_matrics import/from pymatviz import/g'
find . -name '*.py' | xargs sed -i 's/^from ml_matrics./from pymatviz./g'
find . -name '*.py' | xargs sed -i 's/^import ml_matrics/import pymatviz/g'
On Mac, replace sed -i
with sed -i ""
.
- 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. (std
for standard deviation.)