Python: extending

The response head and the metric are registrations, never a fork of the fitting code.

register_learner()

register_learner(name: str, constructor: Callable, overwrite: bool = False)

Make a learner available by name. The learners that ship are registered the same way.

constructor is called with no arguments and returns a Learner, so a learner asked for by name is built with its own defaults.

register_metric()

register_metric(name: str, fn: Callable, overwrite: bool = False)

Register a metric: a function of (y, p) on one held-out cell.

y and p are the observed values of the units in one fold and a model’s predictions for them, both the same length. It returns one number, or a value that is not finite where the cell defines none. Registering one makes it available to grain_ladder by name, with no change to the fitting code.

register_response()

register_response(name: str, spec: dict, overwrite: bool = False)

Register a response head: what the values being predicted are and where a score is defined.

spec is a mapping with prepare(y), returning the response a learner is fitted on; activation, the name of the output transform ("sigmoid" or "identity"); loss, the name of the training objective ("binary_cross_entropy" or "squared_error"); metric, the default metric name; and cells(y, folds), returning the mask of scorable cells. Presence-absence with a joint multi-label head is what ships; an abundance or phenology response is a registration rather than a second fitting path. Every learner that ships reads loss and activation from here: the encoders train under the loss and predict through the activation, and the learners fitting one model per response take the family the loss names, logistic or Gaussian. The combiner minimises the same loss. An optional weights(y) returns a [unit, variable] array of case weights every learner fits under; the shipped head’s is timesift.response.positive_weights, and a head without one fits unweighted.

learners()

learners()

The learners registered under this session.

metrics()

metrics()

The metrics registered under this session.

responses()

responses()

The response heads registered under this session.

get_learner()

get_learner(learner)

A Learner, whether it arrived as one or as the name of a registered one.

resolve_metric()

resolve_metric(metric, default: str | None = None)

The function that scores and the name a report prints, from either way a metric is given.

A metric reaches a run as a registered name or as a function of (y, p), and everything that rescores afterwards – the ensemble row of a report, an occlusion profile – needs the function rather than a name to look up again. Both travel with the fit. A function has no name to print, and reads as <function> on both sides rather than as whatever the language calls an anonymous one.