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

`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
`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 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.

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)