Source code for MDAnalysis.analysis.encore.dimensionality_reduction.DimensionalityReductionMethod

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"""
dimensionality reduction frontend --- :mod:`MDAnalysis.analysis.encore.clustering.DimensionalityReductionMethod`
================================================================================================================

The module defines classes for interfacing to various dimensionality reduction
algorithms. One has been implemented natively, and will always be available,
while others are available only if scikit-learn is installed

:Author: Matteo Tiberti, Wouter Boomsma, Tone Bengtsen

.. versionadded:: 0.16.0

"""
import logging
import warnings

# Import native affinity propagation implementation
from . import stochasticproxembed

# Attempt to import scikit-learn clustering algorithms
try:
    import sklearn.decomposition
except ImportError:
    sklearn = None
    import warnings
    warnings.warn("sklearn.decomposition could not be imported: some "
                  "functionality will not be available in "
                  "encore.dimensionality_reduction()", category=ImportWarning)


[docs] class DimensionalityReductionMethod (object): """ Base class for any Dimensionality Reduction Method """ # Whether the method accepts a distance matrix accepts_distance_matrix=True def __call__(self, x): """ Parameters ---------- x either trajectory coordinate data (np.array) or an encore.utils.TriangularMatrix, encoding the conformational distance matrix Returns ------- numpy.array coordinates in reduced space """ raise NotImplementedError("Class {0} doesn't implement __call__()" .format(self.__class__.__name__))
[docs] class StochasticProximityEmbeddingNative(DimensionalityReductionMethod): """ Interface to the natively implemented Affinity propagation procedure. """ def __init__(self, dimension = 2, distance_cutoff = 1.5, min_lam = 0.1, max_lam = 2.0, ncycle = 100, nstep = 10000,): """ Parameters ---------- dimension : int Number of dimensions to which the conformational space will be reduced to (default is 3). min_lam : float, optional Final lambda learning rate (default is 0.1). max_lam : float, optional Starting lambda learning rate parameter (default is 2.0). ncycle : int, optional Number of cycles per run (default is 100). At the end of every cycle, lambda is updated. nstep : int, optional Number of steps per cycle (default is 10000) """ self.dimension = dimension self.distance_cutoff = distance_cutoff self.min_lam = min_lam self.max_lam = max_lam self.ncycle = ncycle self.nstep = nstep self.stressfreq = -1 def __call__(self, distance_matrix): """ Parameters ---------- distance_matrix : encore.utils.TriangularMatrix conformational distance matrix Returns ------- numpy.array coordinates in reduced space """ final_stress, coordinates = \ stochasticproxembed.StochasticProximityEmbedding( s=distance_matrix, rco=self.distance_cutoff, dim=self.dimension, minlam = self.min_lam, maxlam = self.max_lam, ncycle = self.ncycle, nstep = self.nstep, stressfreq = self.stressfreq ) return coordinates, {"final_stress": final_stress}
if sklearn:
[docs] class PrincipalComponentAnalysis(DimensionalityReductionMethod): """ Interface to the PCA dimensionality reduction method implemented in sklearn. """ # Whether the method accepts a distance matrix accepts_distance_matrix = False def __init__(self, dimension = 2, **kwargs): """ Parameters ---------- dimension : int Number of dimensions to which the conformational space will be reduced to (default is 3). """ self.pca = sklearn.decomposition.PCA(n_components=dimension, **kwargs) def __call__(self, coordinates): """ Parameters ---------- coordinates : np.array trajectory atom coordinates Returns ------- numpy.array coordinates in reduced space """ coordinates = self.pca.fit_transform(coordinates) return coordinates.T, {}