adaptive.BaseLearner

class adaptive.learner.BaseLearner

Bases: ABC

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.

abstract ask(n, tell_pending=True)

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)

Copy over the data from another learner.

Parameters:

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

data: dict[Any, Any]

function: Callable[[...], Any]

load(fname, compress=True)

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 load_dataframe(df: pandas.DataFrame, with_default_function_args: bool = True, function_prefix: str = 'function.', **kwargs: Any) → None

Load data from a pandas.DataFrame.

If with_default_function_args is True, then learner.function’s default arguments are set (using functools.partial) from the values in the pandas.DataFrame.

abstract loss(real=True)

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()

Return a new learner with the same function and parameters.

npoints: int

pending_points: set

abstract remove_unfinished()

Remove uncomputed data from the learner.

save(fname, compress=True)

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)

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)

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)

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

abstract to_dataframe(with_default_function_args: bool = True, function_prefix: str = 'function.', **kwargs: Any) → pandas.DataFrame

Return the data as a pandas.DataFrame.

Parameters:

• with_default_function_args (bool, optional) – Include the learner.function’s default arguments as a column, by default True

• function_prefix (str, optional) – Prefix to the learner.function’s default arguments’ names, by default “function.”

• x_name (str, optional) – Name of the input value, by default “x”

• y_name (str, optional) – Name of the output value, by default “y”

Return type:

pandas.DataFrame