msmbuilder.featurizer.GaussianSolventFeaturizer¶

class msmbuilder.featurizer.GaussianSolventFeaturizer(solute_indices, solvent_indices, sigma, periodic=False)¶

Featurizer on weighted pairwise distance between solute and solvent.

We apply a Gaussian kernel to each solute-solvent pairwise distance and sum the kernels for each solute atom, resulting in a vector of len(solute_indices).

The values can be physically interpreted as the degree of solvation of each solute atom.

Parameters:	solute_indices : np.ndarray, shape=(n_solute,) Indices of solute atoms solvent_indices : np.ndarray, shape=(n_solvent,) Indices of solvent atoms sigma : float Sets the length scale for the gaussian kernel periodic : bool Whether to consider a periodic system in distance calculations

References

..[1] Gu, Chen, et al. BMC Bioinformatics 14, no. Suppl 2 (January 21, 2013): S8. doi:10.1186/1471-2105-14-S2-S8.

Methods

featurize(traj)
fit(traj_list[, y])
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
partial_transform(traj)	Featurize an MD trajectory into a vector space via calculation
save(filename)
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(traj_list[, y])	Featurize a several trajectories.

partial_transform(traj)¶

Featurize an MD trajectory into a vector space via calculation of solvent fingerprints

Parameters:	traj : mdtraj.Trajectory A molecular dynamics trajectory to featurize.
Returns:	features : np.ndarray, dtype=float, shape=(n_samples, n_features) A featurized trajectory is a 2D array of shape (length_of_trajectory x n_features) where each features[i] vector is computed by applying the featurization function to the `i`th snapshot of the input trajectory.

See also

transform

simultaneously featurize a collection of MD trajectories

fit_transform(X, y=None, **fit_params)¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

set_params(**params)¶

Set the parameters of this estimator.

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

Returns:	self

summarize()¶

Return some diagnostic summary statistics about this Markov model

transform(traj_list, y=None)¶

Featurize a several trajectories.

Parameters:	traj_list : list(mdtraj.Trajectory) Trajectories to be featurized.
Returns:	features : list(np.ndarray), length = len(traj_list) The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features)