kdiagram.plot.evaluation.plot_polar_pr_curve

kdiagram.plot.evaluation.plot_polar_pr_curve(y_true, *y_preds, names=None, title='Polar Precision-Recall Curve', figsize=(8, 8), cmap='viridis', show_grid=True, grid_props=None, savefig=None, dpi=300)

Plots a Polar Precision-Recall (PR) Curve.

This function visualizes the performance of binary classification models by mapping the standard PR curve onto a polar plot. It is particularly useful for evaluating models on imbalanced datasets where ROC curves can be misleading.

Parameters:

y_truenp.ndarray

1D array of true binary labels (0 or 1).

*y_predsnp.ndarray

One or more 1D arrays of predicted probabilities or scores for the positive class.

nameslist of str, optional

Display names for each of the models. If not provided, generic names like 'Model 1' will be generated.

titlestr, default=”Polar Precision-Recall Curve”

The title for the plot.

figsizetuple of (float, float), default=(8, 8)

The figure size in inches.

cmapstr, default=’viridis’

The colormap used to assign a unique color to each model’s curve.

show_gridbool, default=True

Toggle the visibility of the polar grid lines.

grid_propsdict, optional

Custom keyword arguments passed to the grid for styling.

savefigstr, optional

The file path to save the plot. If None, the plot is displayed interactively.

dpiint, default=300

The resolution (dots per inch) for the saved figure.

Returns:

axmatplotlib.axes.Axes

The Matplotlib Axes object containing the plot.

Parameters:

• y_true (ndarray)

• y_preds (ndarray)

• names (list[str] | None)

• title (str)

• figsize (tuple[float, float])

• cmap (str)

• show_grid (bool)

• grid_props (dict[str, Any] | None)

• savefig (str | None)

• dpi (int)

See also

plot_polar_roc

A companion plot for ROC analysis.

sklearn.metrics.precision_recall_curve

The underlying scikit-learn function.

Notes

A Precision-Recall (PR) curve is a standard tool for evaluating binary classifiers, especially on imbalanced data [1]. It plots Precision against Recall at various threshold settings.

(1)\[\text{Precision} = \frac{TP}{TP + FP} \quad , \quad \text{Recall} = \frac{TP}{TP + FN}\]

This function adapts the concept to a polar plot:

• The angle (θ) is mapped to Recall, spanning from 0 at 0° to 1 at 90°.

• The radius (r) is mapped to Precision, spanning from 0 at the center to 1 at the edge.

A “no-skill” classifier, which predicts randomly based on the class distribution, is represented by a horizontal line (a circle in polar coordinates) at a radius equal to the proportion of positive samples. A good model will have a curve that bows outwards towards the top-right corner of the plot, maximizing the area under the curve (Average Precision).

References

[1]

David M. W. Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. International Journal of Machine Learning Technology, 2(1):37–63, 2011. URL: https://arxiv.org/abs/2010.16061, doi:10.48550/arXiv.2010.16061.

Examples

>>> import numpy as np
>>> from sklearn.datasets import make_classification
>>> from kdiagram.plot.evaluation import plot_polar_pr_curve
>>>
>>> # Generate imbalanced binary classification data
>>> X, y_true = make_classification(
...     n_samples=1000,
...     n_classes=2,
...     weights=[0.9, 0.1], # 10% positive class
...     flip_y=0.1,
...     random_state=42
... )
>>>
>>> # Simulate predictions from two models
>>> y_pred_good = y_true * 0.6 + np.random.rand(1000) * 0.4
>>> y_pred_bad = np.random.rand(1000)
>>>
>>> # Generate the plot
>>> ax = plot_polar_pr_curve(
...     y_true,
...     y_pred_good,
...     y_pred_bad,
...     names=["Good Model", "Random Model"]
... )