API Reference#

Package Sections#

Data

Handles data splitting and preprocessing for machine learning models.

Configuration

Interface for defining what models should be trained and how they should be trained.

Evaluation

Classes providing methods to evaluate models and plot results.

Training

Brings together the data and configuration to train models.

Reporting

Generates an HTML report from training results.

Object	Description
`AlgorithmWrapper`	Wraps a machine learning algorithm and provides an interface using the algorithm.
`Configuration`	Provide an interface for creating experiment groups.
`ConfigurationManager`	Process the ExperimentGroups and prepare the required DataManagers.
`DataManager`	Handles the grouping, splitting, and scaling of data. Arguments are used to define the splitting strategy.
`DataSplitInfo`	Stores and analyzes training and testing datasets, providing methods for calculating descriptive statistics and visualizing feature distributions.
`EvaluationManager`	Provides methods for evaluating models and generating plots.
`Experiment`	Stores all the data needed for one experiment run.
`ExperimentFactory`	Create a que of Experiments from an ExperimentGroup.
`ExperimentGroup`	Groups experiments that will be run with the same settings.
`FileFormatter`	Formats log messages with a visual separator between log entries.
`MetricManager`	Stores MetricWrapper instances that define evaluation metrics.
`MetricWrapper`	Wraps a metric function and provides a convenient interface using the metric.
`ReportManager`	Generates HTML report from training results.
`TqdmLoggingHandler`	Logs messages to stdout or stderr using tqdm.
`TrainingManager`	Coordinates the training process, loading the data and running the experiments.
`Workflow`	Defines the steps to take when training a model.
`find_project_root`	Finds the project root directory containing .briskconfig.