adaptive.AverageLearner

class adaptive.AverageLearner(function: Callable[[int], Union[float, float64, int, int64]], atol: Optional[float] = None, rtol: Optional[float] = None, min_npoints: int = 2)

Bases: 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[Union[float, numpy.float64]]]

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.

load_dataframe(df: DataFrame, with_default_function_args: bool = True, function_prefix: str = 'function.', seed_name: str = 'seed', y_name: str = 'y')

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, float64]

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, float64]

The average of all values in data.

property n_requested: int

new() → AverageLearner

Create a copy of AverageLearner without the data.

plot()

Returns a histogram of the evaluated data.

Returns:

A histogram of the evaluated data.

Return type:

holoviews.element.Histogram

remove_unfinished()

Remove uncomputed data from the learner.

property std: Union[float, float64]

The corrected sample standard deviation of the values in data.

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

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

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') → 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.”

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

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

Return type:

pandas.DataFrame

Raises:

ImportError – If pandas is not installed.

to_numpy()

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