Bases: object

Base class for algorithms for learning a function ‘f: X → Y’.

function#

The function to learn. A subclass of BaseLearner might modify the user’s supplied function.

Type

callable: X → Y

data#

function evaluated at certain points.

Type

dict: X → Y

pending_points#

Points that have been requested but have not been evaluated yet.

Type

set

npoints#

The number of evaluated points that have been added to the learner.

Type

int

Notes

Subclasses may define a plot method that takes no parameters and returns a holoviews plot.

Choose the next ‘n’ points to evaluate.

Parameters
• n (int) – The number of points to choose.

• tell_pending (bool, default: True) – If True, add the chosen points to this learner’s pending_points. Set this to False if you do not want to modify the state of the learner.

copy_from(other)[source]#

Copy over the data from another learner.

Parameters

other (BaseLearner object) – The learner from which the data is copied.

data: dict#

Load the data of a learner from a pickle file.

Parameters
• fname (str) – The filename from which to load the learner’s data.

• compress (bool, default True) – If the data is compressed when saved, one must load it with compression too.

abstract loss(real=True)[source]#

Return the loss for the current state of the learner.

Parameters

real (bool, default: True) – If False, return the “expected” loss, i.e. the loss including the as-yet unevaluated points (possibly by interpolation).

abstract new()[source]#

Return a new learner with the same function and parameters.

npoints: int#
pending_points: set#
abstract remove_unfinished()[source]#

Remove uncomputed data from the learner.

save(fname, compress=True)[source]#

Save the data of the learner into a pickle file.

Parameters
• fname (str) – The filename into which to save the learner’s data.

• compress (bool, default True) – Compress the data upon saving using ‘gzip’. When saving using compression, one must load it with compression too.

tell(x, y)[source]#

Tell the learner about a single value.

Parameters
• x (A value from the function domain) –

• y (A value from the function image) –

tell_many(xs, ys)[source]#

Tell the learner about some values.

Parameters
• xs (Iterable of values from the function domain) –

• ys (Iterable of values from the function image) –

abstract tell_pending(x)[source]#

Tell the learner that ‘x’ has been requested such that it’s not suggested again.