`bencher.results.bench_result_base`

Module Contents

Classes

`ReduceType`	Generic enumeration.
`EmptyContainer`	A wrapper for list like containers that only appends if the item is not None
`BenchResultBase`

class bencher.results.bench_result_base.ReduceType

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO

SQUEEZE

REDUCE

NONE

class bencher.results.bench_result_base.EmptyContainer(pane)

A wrapper for list like containers that only appends if the item is not None

append(child)

get()

class bencher.results.bench_result_base.BenchResultBase(bench_cfg: bencher.bench_cfg.BenchCfg)

Bases: bencher.results.optuna_result.OptunaResult

result_samples() → int: The number of samples in the results dataframe

to_hv_dataset(reduce: ReduceType = ReduceType.AUTO, result_var: bencher.variables.results.ResultVar = None) → holoviews.Dataset

Generate a holoviews dataset from the xarray dataset.

Parameters:: reduce (ReduceType, optional) – Optionally perform reduce options on the dataset. By default the returned dataset will calculate the mean and standard devation over the “repeat” dimension so that the dataset plays nicely with most of the holoviews plot types. Reduce.Sqeeze is used if there is only 1 repeat and you want the “reduce” variable removed from the dataset. ReduceType.None returns an unaltered dataset. Defaults to ReduceType.AUTO.
Returns:: results in the form of a holoviews dataset
Return type:: hv.Dataset

to_dataset(reduce: ReduceType = ReduceType.AUTO, result_var: bencher.variables.results.ResultVar = None) → xarray.Dataset

Generate a summarised xarray dataset.

Parameters:: reduce (ReduceType, optional) – Optionally perform reduce options on the dataset. By default the returned dataset will calculate the mean and standard devation over the “repeat” dimension so that the dataset plays nicely with most of the holoviews plot types. Reduce.Sqeeze is used if there is only 1 repeat and you want the “reduce” variable removed from the dataset. ReduceType.None returns an unaltered dataset. Defaults to ReduceType.AUTO.
Returns:: results in the form of an xarray dataset
Return type:: xr.Dataset

get_optimal_vec(result_var: bencher.variables.parametrised_sweep.ParametrizedSweep, input_vars: List[bencher.variables.parametrised_sweep.ParametrizedSweep]) → List[Any]

Get the optimal values from the sweep as a vector.

Parameters:

result_var (bch.ParametrizedSweep) – Optimal values of this result variable
input_vars (List[bch.ParametrizedSweep]) – Define which input vars values are returned in the vector

Returns:

A vector of optimal values for the desired input vector

Return type:

List[Any]

get_optimal_value_indices(result_var: bencher.variables.parametrised_sweep.ParametrizedSweep) → xarray.DataArray

Get an xarray mask of the values with the best values found during a parameter sweep

Parameters:: result_var (bch.ParametrizedSweep) – Optimal value of this result variable
Returns:: xarray mask of optimal values
Return type:: xr.DataArray

get_optimal_inputs(result_var: bencher.variables.parametrised_sweep.ParametrizedSweep, keep_existing_consts: bool = True, as_dict: bool = False) → Tuple[bencher.variables.parametrised_sweep.ParametrizedSweep, Any] | dict[bencher.variables.parametrised_sweep.ParametrizedSweep, Any]

Get a list of tuples of optimal variable names and value pairs, that can be fed in as constant values to subsequent parameter sweeps

Parameters:

result_var (bch.ParametrizedSweep) – Optimal values of this result variable
keep_existing_consts (bool) – Include any const values that were defined as part of the parameter sweep
as_dict (bool) – return value as a dictionary

Returns:

Tuples of variable name and optimal values

Return type:

Tuple[bch.ParametrizedSweep, Any]|[ParametrizedSweep, Any]

describe_sweep()

get_best_holomap(name: str = None)

get_hmap(name: str = None)

to_plot_title() → str

title_from_ds(dataset: xarray.Dataset, result_var: param.Parameter, **kwargs)

get_results_var_list(result_var: bencher.variables.parametrised_sweep.ParametrizedSweep = None) → List[bencher.variables.results.ResultVar]

map_plots(plot_callback: callable, result_var: bencher.variables.parametrised_sweep.ParametrizedSweep = None, row: EmptyContainer = None) → panel.Row | None

map_plot_panes(plot_callback: callable, hv_dataset: holoviews.Dataset = None, target_dimension: int = 2, result_var: bencher.variables.results.ResultVar = None, result_types=None, **kwargs) → panel.Row | None

filter(plot_callback: callable, plot_filter=None, float_range: bencher.plotting.plot_filter.VarRange = VarRange(0, None), cat_range: bencher.plotting.plot_filter.VarRange = VarRange(0, None), vector_len: bencher.plotting.plot_filter.VarRange = VarRange(1, 1), result_vars: bencher.plotting.plot_filter.VarRange = VarRange(1, 1), panel_range: bencher.plotting.plot_filter.VarRange = VarRange(0, 0), repeats_range: bencher.plotting.plot_filter.VarRange = VarRange(1, None), input_range: bencher.plotting.plot_filter.VarRange = VarRange(1, None), reduce: ReduceType = ReduceType.AUTO, target_dimension: int = 2, result_var: bencher.variables.results.ResultVar = None, result_types=None, **kwargs)

to_panes_multi_panel(hv_dataset: holoviews.Dataset, result_var: bencher.variables.results.ResultVar, plot_callback: callable = None, target_dimension: int = 1, **kwargs)

_to_panes_da(dataset: xarray.Dataset, plot_callback: callable = None, target_dimension=1, horizontal=False, result_var=None, **kwargs) → panel.panel

to_sweep_summary(**kwargs)

to_title(panel_name: str = None) → panel.pane.Markdown

to_description(width: int = 800) → panel.pane.Markdown

bencher.results.bench_result_base

Module Contents

Classes

`bencher.results.bench_result_base`