Quickstart

This tutorial runs the smallest packaged ground-truth benchmark and opens the path to the generated report. It is intended as a first check that the library, CLI, estimators, result store, and reporter all work together.

Install

From a checkout of the repository:

pip install -e ".[reports]"

For a released package, use:

pip install "lrdbench[reports]"

Run the first benchmark

From the repository root:

python examples/quickstart_pure.py

The script runs configs/suites/smoke_ground_truth.yaml and prints:

• the run_id;
• the result store directory;
• the HTML report path;
• a command for validating the public output contract.

The same run can be launched through the CLI:

lrdbench run configs/suites/smoke_ground_truth.yaml

Before fitting anything, preview the grid with --dry-run:

lrdbench run configs/suites/smoke_ground_truth.yaml --dry-run

The dry run materialises records and prints mode, n_records, n_estimators, n_fit_jobs, and global_seed without writing reports. For stress-test manifests it also prints the clean versus contaminated record counts. Use this check before launching larger public or custom suites.

Packaged suite names can also be inspected with:

lrdbench list-suites

Validate the output

Use the result_store path printed by the command:

lrdbench validate-output reports/<run_id>

The command returns exit code 0 when the expected CSV, HTML, manifest, environment, and artefact files are present with the required columns.

What was benchmarked

The smoke suite generates one fractional Gaussian noise series with known target value and runs the bundled RS estimator. Because this is ground-truth mode, truth-based metrics such as bias, MAE, RMSE, empirical coverage, and interval width are meaningful.

For a fuller explanation of those metrics, continue with Ground-truth benchmark.