Brisk CLI#

How to use the Brisk CLI#

Brisk provides a command-line interface (CLI) to help you create and run machine learning projects. The CLI is installed automatically when you install Brisk into a virtual environment, making it available through the brisk command in your terminal.

To see all available commands and options, run:

brisk --help

Common Commands#

create#

The create command initializes a new Brisk project with all the necessary files and directory structure:

brisk create -n <project_name>

Arguments:

  • -n, --project_name (required): Name of the project directory to create

Example:

brisk create -n my_regression_project

This creates a new directory structure with the following files:

  • .briskconfig: Project configuration file

  • settings.py: Configuration settings for experiments

  • algorithms.py: Algorithm definitions

  • metrics.py: Metric definitions

  • data.py: Data management setup

  • training.py: Training manager setup

  • workflows/: Directory for workflow files

  • datasets/: Directory for data storage

run#

The run command runs the experiments defined in the settings.py file using the specified workflow. It is best to call this command from the root of your project directory to ensure all the files are found.

brisk run -w <workflow> -n <results_name> --disable_report

Arguments:

  • -w, --workflow (required): Name of the workflow file (without .py extension) in the workflows/ directory

  • -n, --results_name (optional): Custom name for the results directory

  • --disable_report (optional): Flag to disable HTML report generation

Example:

brisk run -w my_workflow -n experiment_1_results

This runs the workflow defined in workflows/my_workflow.py and saves the results in a directory named “experiment_1_results” within the results directory.

load_data#

The load_data command wraps the load_sklearn_dataset function from scikit-learn and saves the dataset as a CSV file in the project’s datasets directory.

brisk load_data --dataset <dataset_name> --dataset_name <custom_name>

Arguments:

  • --dataset (required): Name of the sklearn dataset to load (options: iris, wine, breast_cancer, diabetes, linnerud)

  • --dataset_name (optional): Custom name to save the dataset as

Example:

brisk load_data --dataset diabetes --dataset_name diabetes_data

This downloads the diabetes dataset from scikit-learn and saves it as “diabetes_data.csv” in your project’s datasets directory.

create_data#

The create_data command generates synthetic datasets for testing:

brisk create_data --data_type <type> [options]

Arguments:

  • --data_type (required): Type of dataset to generate (classification or regression)

  • --n_samples (optional): Number of samples to generate (default: 100)

  • --n_features (optional): Number of features to generate (default: 20)

  • --n_classes (optional): Number of classes for classification (default: 2)

  • --random_state (optional): Random seed for reproducibility (default: 42)

  • --dataset_name (optional): Name for the dataset file (default: synthetic_dataset)

Example:

brisk create_data --data_type regression --n_samples 500 --n_features 10 --dataset_name synthetic_regression

This creates a synthetic regression dataset with 500 samples and 10 features, saving it as “synthetic_regression.csv” in your project’s datasets directory.

Working with the CLI#

The Brisk CLI is designed to be used from the root of your project directory. When running commands, Brisk will look for the .briskconfig file to identify the project root.