msmbuilder.featurizer.AtomPairsFeaturizer¶

class msmbuilder.featurizer.AtomPairsFeaturizer(pair_indices, periodic=False, exponent=1.0)¶

Featurizer based on distances between specified pairs of atoms.

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 of the distances between the specified pairs of atoms.

Parameters:

pair_indices : np.ndarray, shape=(n_pairs, 2), dtype=int

Each row gives the indices of two atoms involved in the interaction.

periodic : bool, default=False

If periodic is True and the trajectory contains unitcell information, we will compute distances under the minimum image convention.

exponent : float

Modify the distances by raising them to this exponent.

Methods

`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 via pairwise
`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.

__init__(pair_indices, periodic=False, exponent=1.0)¶

Methods

`__init__`(pair_indices[, periodic, exponent])
`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 via pairwise
`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 via pairwise atom-atom distances

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.