msmbuilder.featurizer.GaussianSolventFeaturizer¶
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class
msmbuilder.featurizer.
GaussianSolventFeaturizer
(solute_indices, solvent_indices, sigma, periodic=False)¶ Featurizer on weighted pairwise distance between solute and solvent.
We apply a Gaussian kernel to each solute-solvent pairwise distance and sum the kernels for each solute atom, resulting in a vector of len(solute_indices).
The values can be physically interpreted as the degree of solvation of each solute atom.
Parameters: solute_indices : np.ndarray, shape=(n_solute,)
Indices of solute atoms
solvent_indices : np.ndarray, shape=(n_solvent,)
Indices of solvent atoms
sigma : float
Sets the length scale for the gaussian kernel
periodic : bool
Whether to consider a periodic system in distance calculations
References
..[1] Gu, Chen, et al. BMC Bioinformatics 14, no. Suppl 2 (January 21, 2013): S8. doi:10.1186/1471-2105-14-S2-S8.
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 calculation 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__
(solute_indices, solvent_indices, sigma, periodic=False)¶
Methods
__init__
(solute_indices, solvent_indices, sigma)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 calculation 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. -
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.
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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.
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partial_transform
(traj)¶ Featurize an MD trajectory into a vector space via calculation of solvent fingerprints
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
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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
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summarize
()¶ Return some diagnostic summary statistics about this Markov model
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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)
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