BAR

The BAR estimator is a light wrapper around the implementation of the Bennett Acceptance Ratio (BAR) method [Bennett1976] from pymbar (pymbar.other_estimators.bar()). It uses information from neighboring sampled states to generate an estimate for the free energy difference between these state.

See also

alchemlyb.estimators.MBAR

API Reference

class alchemlyb.estimators.BAR(maximum_iterations: int = 10000, relative_tolerance: float = 1e-07, method: str = 'false-position', verbose: bool = False)

Bennett acceptance ratio (BAR).

Parameters:

• maximum_iterations (int, optional) – Set to limit the maximum number of iterations performed.

• relative_tolerance (float, optional) – Set to determine the relative tolerance convergence criteria.

• method (str, optional, default='false-position') – choice of method to solve BAR nonlinear equations, one of ‘self-consistent-iteration’ or ‘false-position’ (default: ‘false-position’)

• verbose (bool, optional) – Set to True if verbose debug output is desired.

delta_f_

The estimated dimensionless free energy difference between each state.

Type:

DataFrame

d_delta_f_

The estimated statistical uncertainty (one standard deviation) in dimensionless free energy differences.

Type:

DataFrame

states_

Lambda states for which free energy differences were obtained.

Type:

list

Notes

See [Bennett1976] for details of the derivation and cite the paper (together with [Shirts2008] for the Python implementation in pymbar) when using BAR in published work.

When possible, use MBAR instead of BAR as it makes better use of the available data.

See also

MBAR, pymbar.other_estimators.bar

Changed in version 1.0.0: delta_f_, d_delta_f_, states_ are view of the original object.

Changed in version 2.4.0: Added assessment of lambda states represented in the indices of u_nk to provide meaningful errors to ensure proper use.

__init__(maximum_iterations: int = 10000, relative_tolerance: float = 1e-07, method: str = 'false-position', verbose: bool = False) → None

fit(u_nk: DataFrame) → BAR

Compute overlap matrix of reduced potentials using Bennett acceptance ratio.

Parameters:

u_nk (DataFrame) – u_nk[n,k] is the reduced potential energy of uncorrelated configuration n evaluated at state k.

classmethod __init_subclass__(**kwargs)

Set the set_{method}_request methods.

This uses PEP-487 [1] to set the set_{method}_request methods. It looks for the information available in the set default values which are set using __metadata_request__* class attributes, or inferred from method signatures.

The __metadata_request__* class attributes are used when a method does not explicitly accept a metadata through its arguments or if the developer would like to specify a request value for those metadata which are different from the default None.

References

[1]

https://www.python.org/dev/peps/pep-0487

property d_delta_f_: DataFrame | None

property d_delta_h_: DataFrame | None

property d_delta_sT_: DataFrame | None

property delta_f_: DataFrame | None

property delta_h_: DataFrame | None

property delta_sT_: DataFrame | None

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_fit_request(*, u_nk: bool | None | str = '$UNCHANGED$') → BAR

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

u_nk (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for u_nk parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

property states_: list[Any] | None