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

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

Methods

describe_features(traj) Generic method for describing features.
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.
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.
featurize  
fit  
__init__(atom_indices=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([atom_indices]) Initialize self.
describe_features(traj) Generic method for describing features.
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.
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.
describe_features(traj)

Generic method for describing features.

Parameters:
traj : mdtraj.Trajectory

Trajectory to use

Returns:
feature_descs : list of dict

Dictionary describing each feature with the following information about the atoms participating in each feature

  • resnames: unique names of residues
  • atominds: the four atom indicies
  • resseqs: unique residue sequence ids (not necessarily 0-indexed)
  • resids: unique residue ids (0-indexed)
  • featurizer: Featurizer name
  • featuregroup: Other information

Notes

Method resorts to returning N/A for everything if describe_features in not implemented in the sub_class

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.

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
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 latter 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)