msmbuilder.decomposition.
SparseTICA
(n_components=None, lag_time=1, rho=0.01, kinetic_mapping=False, epsilon=1e-06, shrinkage=None, tolerance=1e-06, maxiter=10000, verbose=False)¶Sparse time-structure Independent Component Analysis (tICA).
Linear dimensionality reduction which finds sparse linear combinations of the input features which decorrelate most slowly. These can be used for feature selection and/or dimensionality reduction.
Warning
This model is currently experimental, and may undergo significant changes or bug fixes in upcoming releases.
Note
Unlike (dense) tICA, the sparse solver isn’t guaranteed to find the best global solution. The eigenvalues (timescales) aren’t necessarily going to be found from slowest to fastest. Although this class sorts by eigenvalue after running the solver, by increasing n_components, you could theoretically find more slow processes.
Parameters: |
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components_
¶array-like, shape (n_components, n_features) – Components with maximum autocorrelation.
offset_correlation_
¶array-like, shape (n_features, n_features) – Symmetric time-lagged correlation matrix, C=E[(x_t)^T x_{t+lag}].
eigenvalues_
¶array-like, shape (n_features,) – Psuedo-eigenvalues of the tICA generalized eigenproblem, in decreasing order.
eigenvectors_
¶array-like, shape (n_components, n_features) – Sparse psuedo-eigenvectors of the tICA generalized eigenproblem. The vectors give a set of “directions” through configuration space along which the system relaxes towards equilibrium.
means_
¶array, shape (n_features,) – The mean of the data along each feature
n_observations_
¶int – Total number of data points fit by the model. Note that the model is “reset” by calling fit() with new sequences, whereas partial_fit() updates the fit with new data, and is suitable for online learning.
n_sequences_
¶int – Total number of sequences fit by the model. Note that the model is “reset” by calling fit() with new sequences, whereas partial_fit() updates the fit with new data, and is suitable for
online learning.
timescales_
¶array-like, shape (n_components,) – The implied timescales of the tICA model, given by -offset / log(eigenvalues)
See also
References
[1] | McGibbon, R. T. and V. S. Pande “Identification of sparse, slow reaction coordinates from molular dynamics simulations” In preparation. |
[2] | Sriperumbudur, B. K., D. A. Torres, and G. R. Lanckriet. “A majorization-minimization approach to the sparse generalized eigenvalue problem.” Machine learning 85.1-2 (2011): 3-39. |
[3] | Mackey, L. “Deflation Methods for Sparse PCA.” NIPS. Vol. 21. 2008. |
[4] | Noe, F. and Clementi, C. arXiv arXiv:1506.06259 [physics.comp-ph] (2015) |
__init__
(n_components=None, lag_time=1, rho=0.01, kinetic_mapping=False, epsilon=1e-06, shrinkage=None, tolerance=1e-06, maxiter=10000, verbose=False)¶Methods
__init__ ([n_components, lag_time, rho, ...]) |
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fit (sequences[, y]) |
Fit the model with a collection of sequences. |
fit_transform (sequences[, y]) |
Fit the model with X and apply the dimensionality reduction on X. |
get_params ([deep]) |
Get parameters for this estimator. |
partial_fit (X) |
Fit the model with X. |
partial_transform (features) |
Apply the dimensionality reduction on X. |
score (sequences[, y]) |
Score the model on new data using the generalized matrix Rayleigh quotient |
set_params (\*\*params) |
Set the parameters of this estimator. |
summarize () |
Some summary information. |
transform (sequences) |
Apply the dimensionality reduction on X. |
Attributes
components_ |
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covariance_ |
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eigenvalues_ |
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eigenvectors_ |
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means_ |
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offset_correlation_ |
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score_ |
Training score of the model, computed as the generalized matrix, |
timescales_ |
fit
(sequences, y=None)¶Fit the model with a collection of sequences.
This method is not online. Any state accumulated from previous calls to fit() or partial_fit() will be cleared. For online learning, use partial_fit.
Parameters: |
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Returns: | self – Returns the instance itself. |
Return type: | object |
fit_transform
(sequences, y=None)¶Fit the model with X and apply the dimensionality reduction on X.
This method is not online. Any state accumulated from previous calls to fit() or partial_fit() will be cleared. For online learning, use partial_fit.
Parameters: |
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Returns: | sequence_new |
Return type: | list of array-like, each of shape (n_samples_i, n_components) |
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_fit
(X)¶Fit the model with X.
This method is suitable for online learning. The state of the model will be updated with the new data X.
Parameters: | X (array-like, shape (n_samples, n_features)) – Training data, where n_samples in the number of samples and n_features is the number of features. |
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Returns: | self – Returns the instance itself. |
Return type: | object |
partial_transform
(features)¶Apply the dimensionality reduction on X.
Parameters: | features (array-like, shape (n_samples, n_features)) – Training data, where n_samples in the number of samples and n_features is the number of features. This function acts on a single featurized trajectory. |
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Returns: | sequence_new – TICA-projected features |
Return type: | array-like, shape (n_samples, n_components) |
Notes
This function acts on a single featurized trajectory.
score
(sequences, y=None)¶Score the model on new data using the generalized matrix Rayleigh quotient
Parameters: | sequences (list of array, each of shape (n_samples_i, n_features)) – Test data. A list of sequences in afeature space, each of which is a 2D array of possibily different lengths, but the same number of features. |
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Returns: | gmrq – Generalized matrix Rayleigh quotient. This number indicates how
well the top n_timescales+1 eigenvectors of this tICA model perform
as slowly decorrelating collective variables for the new data in
sequences . |
Return type: | float |
References
[1] | McGibbon, R. T. and V. S. Pande, “Variational cross-validation of slow dynamical modes in molecular kinetics” J. Chem. Phys. 142, 124105 (2015) |
score_
¶Training score of the model, computed as the generalized matrix, Rayleigh quotient, the sum of the first n_components eigenvalues
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
()¶Some summary information.
transform
(sequences)¶Apply the dimensionality reduction on X.
Parameters: | sequences (list of array-like, each of shape (n_samples_i, n_features)) – Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features. |
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Returns: | sequence_new |
Return type: | list of array-like, each of shape (n_samples_i, n_components) |