adaptive.AverageLearner#

class adaptive.AverageLearner(*args, **kwargs)[source]#

Bases: adaptive.learner.base_learner.BaseLearner

A naive implementation of adaptive computing of averages.

The learned function must depend on an integer input variable that represents the source of randomness.

Parameters

  • atol (float) – Desired absolute tolerance.

  • rtol (float) – Desired relative tolerance.

  • min_npoints (int) – Minimum number of points to sample.

data#

Sampled points and values.

Type

dict

pending_points#

Points that still have to be evaluated.

Type

set

npoints#

Number of evaluated points.

Type

int

ask(n: int, tell_pending: bool = True) → tuple[list[int], list[typing.Union[float, numpy.float64]]][source]#

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.

data: dict#
load_dataframe(df: pandas.core.frame.DataFrame, with_default_function_args: bool = True, function_prefix: str = 'function.', seed_name: str = 'seed', y_name: str = 'y')[source]#

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.

Parameters

  • df (pandas.DataFrame) – The data to load.

  • with_default_function_args (bool, optional) – The with_default_function_args used in to_dataframe(), by default True

  • function_prefix (str, optional) – The function_prefix used in to_dataframe, by default “function.”

  • seed_name (str, optional) – The seed_name used in to_dataframe, by default “seed”

  • y_name (str, optional) – The y_name used in to_dataframe, by default “y”

loss(real: bool = True, *, n=None) → Union[float, numpy.float64][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).

property mean: Union[float, numpy.float64]#

The average of all values in data.

property n_requested: int#
npoints: int#
pending_points: set#
plot()[source]#

Returns a histogram of the evaluated data.

Returns

A histogram of the evaluated data.

Return type

holoviews.element.Histogram

remove_unfinished()[source]#

Remove uncomputed data from the learner.

property std: Union[float, numpy.float64]#

The corrected sample standard deviation of the values in data.

tell(n: Union[int, numpy.int64], value: Union[float, numpy.float64, int, numpy.int64]) → None[source]#

Tell the learner about a single value.

Parameters

  • x (A value from the function domain) –

  • y (A value from the function image) –

tell_pending(n: int) → None[source]#

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

to_dataframe(with_default_function_args: bool = True, function_prefix: str = 'function.', seed_name: str = 'seed', y_name: str = 'y') → pandas.core.frame.DataFrame[source]#

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.”

  • seed_name (str, optional) – Name of the seed parameter, by default “seed”

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

Returns

Return type

pandas.DataFrame

Raises

ImportError – If pandas is not installed.

to_numpy()[source]#

Data as NumPy array of size (npoints, 2) with seeds and values.