Source code for MDAnalysis.analysis.encore.clustering.ClusteringMethod
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"""
clustering frontend --- :mod:`MDAnalysis.analysis.encore.clustering.ClusteringMethod`
=====================================================================================
The module defines classes for interfacing to various clustering 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 numpy as np
import warnings
import logging
# Import native affinity propagation implementation
from . import affinityprop
# Attempt to import scikit-learn clustering algorithms
try:
import sklearn.cluster
except ImportError:
sklearn = None
msg = "sklearn.cluster could not be imported: some functionality will " \
"not be available in encore.fit_clusters()"
warnings.warn(msg, category=ImportWarning)
logging.warning(msg)
del msg
[docs]def encode_centroid_info(clusters, cluster_centers_indices):
"""
Adjust cluster indices to include centroid information
as described in documentation for ClusterCollection
"""
values, indices = np.unique(clusters, return_inverse=True)
for c_center in cluster_centers_indices:
if clusters[c_center] != c_center:
values[indices[c_center]] = c_center
return values[indices]
[docs]class ClusteringMethod (object):
"""
Base class for any Clustering 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
Raises
------
NotImplementedError
Method or behavior needs to be defined by a subclass
"""
raise NotImplementedError("Class {0} doesn't implement __call__()"
.format(self.__class__.__name__))
[docs]class AffinityPropagationNative(ClusteringMethod):
"""
Interface to the natively implemented Affinity propagation procedure.
"""
def __init__(self,
damping=0.9, preference=-1.0,
max_iter=500, convergence_iter=50,
add_noise=True):
"""
Parameters
----------
damping : float, optional
Damping factor (default is 0.9). Parameter for the Affinity
Propagation for clustering.
preference : float, optional
Preference parameter used in the Affinity Propagation algorithm for
clustering (default -1.0). A high preference value results in
many clusters, a low preference will result in fewer numbers of
clusters.
max_iter : int, optional
Maximum number of iterations for affinity propagation (default is
500).
convergence_iter : int, optional
Minimum number of unchanging iterations to achieve convergence
(default is 50). Parameter in the Affinity Propagation for
clustering.
add_noise : bool, optional
Apply noise to similarity matrix before running clustering
(default is True)
"""
self.damping = damping
self.preference = preference
self.max_iter = max_iter
self.convergence_iter = convergence_iter
self.add_noise = add_noise
def __call__(self, distance_matrix):
"""
Parameters
----------
distance_matrix : encore.utils.TriangularMatrix
conformational distance matrix
Returns
-------
numpy.array : array, shape(n_elements)
centroid frames of the clusters for all of the elements
.. versionchanged:: 1.0.0
This method no longer returns ``details``
"""
clusters = affinityprop.AffinityPropagation(
s=distance_matrix * -1., # invert sign
preference=self.preference,
lam=self.damping,
max_iterations = self.max_iter,
convergence = self.convergence_iter,
noise=int(self.add_noise))
return clusters
if sklearn:
[docs] class AffinityPropagation(ClusteringMethod):
"""
Interface to the Affinity propagation clustering procedure implemented
in sklearn.
"""
def __init__(self,
damping=0.9, preference=-1.0,
max_iter=500, convergence_iter=50,
**kwargs):
"""
Parameters
----------
damping : float, optional
Damping factor (default is 0.9). Parameter for the Affinity
Propagation for clustering.
preference : float, optional
Preference parameter used in the Affinity Propagation algorithm
for clustering (default -1.0). A high preference value results
in many clusters, a low preference will result in fewer numbers
of clusters.
max_iter : int, optional
Maximum number of iterations for affinity propagation (default
is 500).
convergence_iter : int, optional
Minimum number of unchanging iterations to achieve convergence
(default is 50). Parameter in the Affinity Propagation for
clustering.
**kwargs : optional
Other keyword arguments are passed to :class:`sklearn.cluster.AffinityPropagation`.
"""
self.ap = \
sklearn.cluster.AffinityPropagation(
damping=damping,
preference=preference,
max_iter=max_iter,
convergence_iter=convergence_iter,
affinity="precomputed",
**kwargs)
def __call__(self, distance_matrix):
"""
Parameters
----------
distance_matrix : encore.utils.TriangularMatrix
conformational distance matrix
Returns
-------
numpy.array : array, shape(n_elements)
centroid frames of the clusters for all of the elements
.. versionchanged:: 1.0.0
This method no longer returns ``details``
"""
logging.info("Starting Affinity Propagation: {0}".format
(self.ap.get_params()))
# Convert from distance matrix to similarity matrix
similarity_matrix = distance_matrix.as_array() * -1
clusters = self.ap.fit_predict(similarity_matrix)
clusters = encode_centroid_info(clusters,
self.ap.cluster_centers_indices_)
return clusters
[docs] class DBSCAN(ClusteringMethod):
"""
Interface to the DBSCAN clustering procedure implemented in sklearn.
"""
def __init__(self,
eps=0.5,
min_samples=5,
algorithm="auto",
leaf_size=30,
**kwargs):
"""
Parameters
----------
eps : float, optional (default = 0.5)
The maximum distance between two samples for them to be
considered as in the same neighborhood.
min_samples : int, optional (default = 5)
The number of samples (or total weight) in a neighborhood for
a point to be considered as a core point. This includes the
point itself.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
The algorithm to be used by the NearestNeighbors module
to compute pointwise distances and find nearest neighbors.
See NearestNeighbors module documentation for details.
leaf_size : int, optional (default = 30)
Leaf size passed to BallTree or cKDTree. This can affect the
speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends
on the nature of the problem.
sample_weight : array, shape (n_samples,), optional
Weight of each sample, such that a sample with a weight of at
least ``min_samples`` is by itself a core sample; a sample with
negative weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.
"""
self.dbscan = sklearn.cluster.DBSCAN(eps=eps,
min_samples = min_samples,
algorithm=algorithm,
leaf_size = leaf_size,
metric="precomputed",
**kwargs)
def __call__(self, distance_matrix):
"""
Parameters
----------
distance_matrix : encore.utils.TriangularMatrix
conformational distance matrix
Returns
-------
numpy.array : array, shape(n_elements)
centroid frames of the clusters for all of the elements
.. versionchanged:: 1.0.0
This method no longer returns ``details``
"""
logging.info("Starting DBSCAN: {0}".format(
self.dbscan.get_params()))
clusters = self.dbscan.fit_predict(distance_matrix.as_array())
if np.min(clusters == -1):
clusters += 1
# No centroid information is provided by DBSCAN, so we just
# pick random members
cluster_representatives = np.unique(clusters, return_index=True)[1]
clusters = encode_centroid_info(clusters,
cluster_representatives)
return clusters
[docs] class KMeans(ClusteringMethod):
# Whether the method accepts a distance matrix
accepts_distance_matrix = False
"""
Interface to the KMeans clustering procedure implemented in sklearn.
"""
def __init__(self,
n_clusters,
max_iter=300,
n_init=10,
init='k-means++',
algorithm="auto",
tol=1e-4,
verbose=False,
random_state=None,
copy_x=True,
**kwargs):
"""
Parameters
----------
n_clusters : int
The number of clusters to form as well as the number of
centroids to generate.
max_iter : int, optional (default 300)
Maximum number of iterations of the k-means algorithm to run.
n_init : int, optional (default 10)
Number of time the k-means algorithm will be run with different
centroid seeds. The final results will be the best output of
n_init consecutive runs in terms of inertia.
init : {'k-means++', 'random', or ndarray, or a callable}, optional
Method for initialization, default to 'k-means++':
'k-means++' : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.
'random': generate k centroids from a Gaussian with mean and
variance estimated from the data.
If an ndarray is passed, it should be of shape
(n_clusters, n_features) and gives the initial centers.
If a callable is passed, it should take arguments X, k and
and a random state and return an initialization.
tol : float, optional (default 1e-4)
The relative increment in the results before declaring
convergence.
verbose : boolean, optional (default False)
Verbosity mode.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
copy_x : boolean, optional
When pre-computing distances it is more numerically accurate to
center the data first. If copy_x is True, then the original
data is not modified. If False, the original data is modified,
and put back before the function returns, but small numerical
differences may be introduced by subtracting and then adding
the data mean.
"""
self.kmeans = sklearn.cluster.KMeans(n_clusters=n_clusters,
max_iter=max_iter,
n_init=n_init,
init=init,
tol=tol,
verbose=verbose,
random_state=random_state,
copy_x=copy_x,
**kwargs)
def __call__(self, coordinates):
"""
Parameters
----------
coordinates : np.array
trajectory atom coordinates
Returns
-------
numpy.array : array, shape(n_elements)
centroid frames of the clusters for all of the elements
.. versionchanged:: 1.0.0
This method no longer returns ``details``
"""
logging.info("Starting Kmeans: {0}".format(
(self.kmeans.get_params())))
clusters = self.kmeans.fit_predict(coordinates)
distances = self.kmeans.transform(coordinates)
cluster_center_indices = np.argmin(distances, axis=0)
clusters = encode_centroid_info(clusters,
cluster_center_indices)
return clusters