Classification Plots#

class PlotConfusionHeatmap(method_name: str, description: str, plot_settings)[source]#

Bases: PlotEvaluator

Plot a heatmap of the confusion matrix for a model.

This evaluator creates a visual heatmap representation of the confusion matrix, showing both the count and percentage of predictions for each class combination. The heatmap uses color intensity to represent the percentage of predictions, making it easy to identify patterns in classification performance.

Parameters:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
plot_settingsPlotSettings: The plot settings containing theme and color configuration

Attributes:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
themeAny: The plot theme for styling plots
primary_colorstr: Primary color for plots
secondary_colorstr: Secondary color for plots
accent_colorstr: Accent color for plots

Examples

Use the confusion matrix heatmap evaluator:

>>> from brisk.evaluation.evaluators import registry
>>> evaluator = registry.get("brisk_plot_confusion_heatmap")
>>> evaluator.plot(model, X, y, "confusion_heatmap")

plot(model: Any, X: ndarray, y: ndarray, filename: str) → None[source]#

Plot a heatmap of the confusion matrix for a model.

Executes the complete plotting workflow for generating a confusion matrix heatmap. This includes generating predictions, calculating the confusion matrix data, creating the plot, and saving the results.

Parameters:

modelAny: The trained classification model with a predict method
Xnp.ndarray: The input features for evaluation
ynp.ndarray: The true target labels
filenamestr: The name of the file to save the plot to (without extension)

Returns:

None

Notes

This method overrides the base plot method to provide classification-specific plotting workflow. It generates predictions using the model and creates a heatmap visualization of the confusion matrix.

The plot is saved with metadata for later analysis and reporting.

class PlotRocCurve(method_name: str, description: str, plot_settings)[source]#

Bases: PlotEvaluator

Plot a receiver operating characteristic curve with area under the curve.

This evaluator creates ROC curve plots for binary classification models, showing the relationship between true positive rate and false positive rate. The plot includes the area under the curve (AUC) score and a reference line for random guessing.

Parameters:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
plot_settingsPlotSettings: The plot settings containing theme and color configuration

Attributes:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
themeAny: The plot theme for styling plots
primary_colorstr: Primary color for plots
secondary_colorstr: Secondary color for plots
accent_colorstr: Accent color for plots

Notes

The ROC curve is a fundamental tool for evaluating binary classification performance. It shows the trade-off between sensitivity (true positive rate) and specificity (1 - false positive rate) across different classification thresholds.

The AUC score provides a single metric for overall performance, with values closer to 1.0 indicating better performance. A score of 0.5 indicates performance equivalent to random guessing.

Examples

Use the ROC curve evaluator:

>>> from brisk.evaluation.evaluators import registry
>>> evaluator = registry.get("brisk_plot_roc_curve")
>>> evaluator.plot(model, X, y, "roc_curve")

plot(model: Any, X: ndarray, y: ndarray, filename: str, pos_label: int | None = 1) → None[source]#

Plot a receiver operating characteristic curve with area under thecurve.

Executes the complete plotting workflow for generating a ROC curve. This includes calculating the ROC curve data, computing the AUC score, creating the plot, and saving the results.

Parameters:

modelAny: The trained binary classification model
Xnp.ndarray: The input features for evaluation
ynp.ndarray: The true binary labels
filenamestr: The name of the file to save the plot to (without extension)
pos_labelint, optional: The label of the positive class, by default 1

Returns:

None

Notes

This method handles different types of binary classification models by automatically detecting whether to use predict_proba, decision_function, or predict methods for obtaining prediction scores.

The plot includes both the ROC curve and a reference line for random guessing, along with the AUC score annotation.

class PlotPrecisionRecallCurve(method_name: str, description: str, plot_settings)[source]#

Bases: PlotEvaluator

Plot a precision-recall curve with area under the curve.

This evaluator creates precision-recall curve plots for binary classification models, showing the relationship between precision and recall across different classification thresholds. The plot includes the average precision (AP) score and a reference line showing the AP score.

Parameters:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
plot_settingsPlotSettings: The plot settings containing theme and color configuration

Attributes:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
themeAny: The plot theme for styling plots
primary_colorstr: Primary color for plots
secondary_colorstr: Secondary color for plots
accent_colorstr: Accent color for plots

Notes

The precision-recall curve is particularly useful for imbalanced datasets where the focus is on the positive class. It shows the trade-off between precision and recall across different classification thresholds.

The average precision (AP) score provides a single metric for overall performance, with values closer to 1.0 indicating better performance. Unlike AUC, AP is more sensitive to the performance on the positive class.

Examples

Use the precision-recall curve evaluator:

>>> from brisk.evaluation.evaluators import registry
>>> evaluator = registry.get("brisk_plot_precision_recall_curve")
>>> evaluator.plot(model, X, y, "precision_recall_curve")

plot(model: BaseEstimator, X: ndarray, y: ndarray, filename: str, pos_label: int | None = 1) → None[source]#

Plot a precision-recall curve with area under the curve.

Executes the complete plotting workflow for generating a precision-recall curve. This includes calculating the curve data, computing the AP score, creating the plot, and saving the results.

Parameters:

modelbase.BaseEstimator: The trained binary classification model
Xnp.ndarray: The input features for evaluation
ynp.ndarray: The true binary labels
filenamestr: The name of the file to save the plot to (without extension)
pos_labelint, optional: The label of the positive class, by default 1

Returns:

None

Notes

This method handles different types of binary classification models by automatically detecting whether to use predict_proba, decision_function, or predict methods for obtaining prediction scores.

The plot includes both the precision-recall curve and a reference line showing the average precision score.

Classification Plots#

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