# Objects and settings at a glance

Every object you can create and every setting you can change, on one
page. Each entry links to the page that explains it in depth; the
full signatures live in the {doc}`API reference <../api/index>`.

## Defining the space

{doc}`ParameterSpace <../api/space>` holds the parameters of your
study. Five parameter kinds are supported, freely mixed in one
space: an explicit list of values (discrete), a `('continuous', low,
high)` range sampled on a fine grid (add `'log'` for
logarithmically spaced values), an `('integer', low, high)` range,
and `('nominal', ...)` or `('ordinal', ...)` categories. The
{doc}`parameter types guide <how-to/parameter_types>` walks
through all of them.

## Shaping the design (before running)

All of these are methods of {doc}`Sampler <../api/sampler>`, called
before `run()`:

`set_design(n_samples, n_validation, extra_sets)`
: How many runs to place, how many validation points to reserve, and
  any additional named point sets. Guidance on choosing the size is
  in {doc}`How large should a design be? <explanation/sample_size>`.

`add_prescribed(points)`
: Runs that already exist and must be kept in the design, for
  example from an earlier campaign.

`load_design(points)`
: Resume from a complete previous design; `run()` then builds only
  what is missing around it.

`add_focus(region, n)`
: A region of the space that deserves extra points.

`add_exclusion(region)` and feasibility constraints
: Combinations that must never be proposed. See
  {doc}`constraints and exclusions <how-to/constraints_exclusions>`.

`add_set(name, points)`
: Extra labelled point sets, such as a hand-picked test set.

`set_optimizer(name, **options)`
: Tune the optimisation budget (iterations, restarts) of the chosen
  algorithm.

`set_dimension_weights(weights)`
: Make some parameters count more than others in the distance
  calculations.

## Running

`Sampler.run(criteria, algorithm, seed, n_repeats, n_jobs)`
: Builds the design. Seven criteria and three algorithms are
  available; the {doc}`criterion guide
  <explanation/choosing_criterion>` and the {doc}`algorithm guide
  <explanation/choosing_algorithm>` explain when to pick which.

`Sampler.compare(criteria, algorithms, n_repeats)`
: When you are unsure, run several combinations under a shared
  baseline and get a ranked table; see
  {doc}`comparing designs <how-to/compare_designs>`.

## Working with the result

`run()` returns a result object with everything in one place:

`summary()` and `quality_report()`
: The design counts, and the statistical quality evidence against a
  Monte Carlo baseline. How to read the report is covered in
  {doc}`quality metrics <explanation/quality_metrics>`.

`plot(kind)`
: Eight plot kinds: `'pairplot'`, `'1d'`, `'2d'`, `'distances'`,
  `'correlation'`, `'quality'`, `'comparison'`,
  `'comparison_matrix'`.

`to_csv()`, `to_excel()`, `to_json()`, `to_markdown()`,
`to_latex()`, `to_html()`
: Exports for running, sharing and publishing; see
  {doc}`exporting results <how-to/export_reports>`.

`compare()` returns a comparison object of its own, with
`summary()`, `plot()` (the percentile heat map), `to_markdown()` and
`best_result`.

## Growing a design over time

The {doc}`sequential module <../api/sequential>` extends finished
designs: `extend` adds new well-placed runs, `fill_around`
concentrates new runs near interesting points, `subsample` picks a
representative pilot subset, `run_order` orders runs so early stops
still leave a balanced design, `k_fold_split` builds
cross-validation folds, and `nested` creates designs within designs.
The workflow is shown in {doc}`sequential designs
<how-to/sequential_designs>`.