logml.report.plotters.baseline_modeling

Functions

build_annotated_heapmap(data, **kwargs)	Widget for plotting annotated heatmaps (like confusion matrices).
build_scatter_plot(data, **kwargs)	Widget for plotting scatter plots on one figure.
plot_classification_curves(data, curve_cfg, ...)	Plots a given classification curves for a gien model.
plot_confusion_matrix(data, labels, model)	Plots a given confusion matrix for a given model as annotated heatmap.
plot_hpo_losses(hpo_losses)	For each model plots HPO trace - (loss, iteration) in form of scatter.
plot_regression_predictions(predictions, ...)	Plots true vs predicted for all CV folds for a given model model.
plot_regression_predictions_folded(...)	Plots true vs predicted for all CV folds for a given model model.

logml.report.plotters.baseline_modeling.build_scatter_plot(data: List[Dict], **kwargs)

Widget for plotting scatter plots on one figure.

logml.report.plotters.baseline_modeling.build_annotated_heapmap(data: Dict, **kwargs)

Widget for plotting annotated heatmaps (like confusion matrices).

logml.report.plotters.baseline_modeling.plot_hpo_losses(hpo_losses: Dict[str, List[float]])

For each model plots HPO trace - (loss, iteration) in form of scatter.

logml.report.plotters.baseline_modeling.plot_classification_curves(data: List[Dict], curve_cfg: Dict, model: str)

Plots a given classification curves for a gien model.

logml.report.plotters.baseline_modeling.plot_confusion_matrix(data: numpy.array, labels: List, model: str)

Plots a given confusion matrix for a given model as annotated heatmap.

logml.report.plotters.baseline_modeling.plot_regression_predictions_folded(predictions_per_fold: List, dataset: ModelingDataset, model: str)

Plots true vs predicted for all CV folds for a given model model.

logml.report.plotters.baseline_modeling.plot_regression_predictions(predictions: numpy.ndarray, dataset: ModelingDataset, model: str)

Plots true vs predicted for all CV folds for a given model model.