msmbuilder.feature_selection.FeatureSelector¶

class msmbuilder.feature_selection.FeatureSelector(features, which_feat=None)¶

Concatenates results of multiple feature extraction objects.

This estimator applies a list of feature_extraction objects then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer.

Note: Users should consider using msmbuilder.preprocessing.StandardScaler to normalize their data after combining feature sets.

Parameters:	features : list of (str, msmbuilder.feature_extraction) tuples List of feature_extraction objects to be applied to the data. The first half of each tuple is the name of the feature_extraction. which_feat : list or str Either a string or a list of strings of features to include in the transformer.

Attributes

which_feat

Methods

describe_features(traj)	Return a list of dictionaries describing the 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.
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__(features, which_feat=None)¶

Methods

__init__(features[, which_feat])
describe_features(traj)	Return a list of dictionaries describing the 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.
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.

Attributes

which_feat

describe_features(traj)¶

Return a list of dictionaries describing the features. Follows the ordering of featurizers in self.which_feat.

Parameters:

traj : mdtraj.Trajectory The trajectory to describe

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: atom indicies involved in the feature resseqs: unique residue sequence ids (not necessarily 0-indexed) resids: unique residue ids (0-indexed) featurizer: featurizer dependent featuregroup: other info for the featurizer

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.

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.

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)