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
- ask(n: int, tell_pending: bool = True) tuple[list[int], list[typing.Union[float, numpy.float64]]] [source]#
Choose the next ‘n’ points to evaluate.
- 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, thenlearner.function
’s default arguments are set (usingfunctools.partial
) from the values in thepandas.DataFrame
.- Parameters
df (pandas.DataFrame) – The data to load.
with_default_function_args (bool, optional) – The
with_default_function_args
used into_dataframe()
, by default Truefunction_prefix (str, optional) – The
function_prefix
used into_dataframe
, by default “function.”seed_name (str, optional) – The
seed_name
used into_dataframe
, by default “seed”y_name (str, optional) – The
y_name
used into_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).
- new() adaptive.learner.average_learner.AverageLearner [source]#
Create a copy of
AverageLearner
without the data.
- plot()[source]#
Returns a histogram of the evaluated data.
- Returns
A histogram of the evaluated data.
- Return type
- 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 Truefunction_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.