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: |
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References
..[1] Gu, Chen, et al. BMC Bioinformatics 14, no. Suppl 2 (January 21, 2013): S8. doi:10.1186/1471-2105-14-S2-S8.
__init__
(solute_indices, solvent_indices, sigma, periodic=False)¶Methods
__init__ (solute_indices, solvent_indices, sigma) |
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describe_features (traj) |
Generic method for describing features. |
featurize (traj) |
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fit (traj_list[, y]) |
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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. |
describe_features
(traj)¶Generic method for describing features.
Parameters: | traj (mdtraj.Trajectory) – Trajectory to use |
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Returns: | feature_descs – Dictionary describing each feature with the following information
about the atoms participating in each feature
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Return type: | list of dict |
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: |
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Returns: | X_new – Transformed array. |
Return type: | numpy array of shape [n_samples, n_features_new] |
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. |
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Returns: | params – Parameter names mapped to their values. |
Return type: | mapping of string to any |
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. |
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Returns: | 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. |
Return type: | np.ndarray, dtype=float, shape=(n_samples, n_features) |
See also
transform()
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: | |
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Return type: | 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. |
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Returns: | features – The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features) |
Return type: | list(np.ndarray), length = len(traj_list) |