msmbuilder.featurizer.DRIDFeaturizer¶

class msmbuilder.featurizer.DRIDFeaturizer(atom_indices=None)¶

Featurizer based on distribution of reciprocal interatomic distances (DRID)

This featurizer transforms a dataset containing MD trajectories into a vector dataset by representing each frame in each of the MD trajectories by a vector containing the first three moments of a collection of reciprocal interatomic distances. For details, see [1].

Parameters:	atom_indices : array-like of ints, default=None Which atom indices to use during DRID featurization. If None, all atoms are used

References

[R23]

Zhou, Caflisch; Distribution of Reciprocal of Interatomic Distances: A Fast Structural Metric. JCTC 2012 doi:10.1021/ct3003145

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 using the distribution of reciprocal interatomic distance (DRID) method.
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 using the distribution of reciprocal interatomic distance (DRID) method.

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)