BAR¶

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

API Reference¶

class alchemlyb.estimators.BAR(maximum_iterations=10000, relative_tolerance=1e-07, method='false-position', verbose=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

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

fit(u_nk)¶

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.

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¶

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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