ai-content-maker/.venv/Lib/site-packages/sklearn/cluster/_affinity_propagation.py

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"""Affinity Propagation clustering algorithm."""
# Author: Alexandre Gramfort alexandre.gramfort@inria.fr
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
import warnings
from numbers import Integral, Real
import numpy as np
from .._config import config_context
from ..base import BaseEstimator, ClusterMixin, _fit_context
from ..exceptions import ConvergenceWarning
from ..metrics import euclidean_distances, pairwise_distances_argmin
from ..utils import check_random_state
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.validation import check_is_fitted
def _equal_similarities_and_preferences(S, preference):
def all_equal_preferences():
return np.all(preference == preference.flat[0])
def all_equal_similarities():
# Create mask to ignore diagonal of S
mask = np.ones(S.shape, dtype=bool)
np.fill_diagonal(mask, 0)
return np.all(S[mask].flat == S[mask].flat[0])
return all_equal_preferences() and all_equal_similarities()
def _affinity_propagation(
S,
*,
preference,
convergence_iter,
max_iter,
damping,
verbose,
return_n_iter,
random_state,
):
"""Main affinity propagation algorithm."""
n_samples = S.shape[0]
if n_samples == 1 or _equal_similarities_and_preferences(S, preference):
# It makes no sense to run the algorithm in this case, so return 1 or
# n_samples clusters, depending on preferences
warnings.warn(
"All samples have mutually equal similarities. "
"Returning arbitrary cluster center(s)."
)
if preference.flat[0] > S.flat[n_samples - 1]:
return (
(np.arange(n_samples), np.arange(n_samples), 0)
if return_n_iter
else (np.arange(n_samples), np.arange(n_samples))
)
else:
return (
(np.array([0]), np.array([0] * n_samples), 0)
if return_n_iter
else (np.array([0]), np.array([0] * n_samples))
)
# Place preference on the diagonal of S
S.flat[:: (n_samples + 1)] = preference
A = np.zeros((n_samples, n_samples))
R = np.zeros((n_samples, n_samples)) # Initialize messages
# Intermediate results
tmp = np.zeros((n_samples, n_samples))
# Remove degeneracies
S += (
np.finfo(S.dtype).eps * S + np.finfo(S.dtype).tiny * 100
) * random_state.standard_normal(size=(n_samples, n_samples))
# Execute parallel affinity propagation updates
e = np.zeros((n_samples, convergence_iter))
ind = np.arange(n_samples)
for it in range(max_iter):
# tmp = A + S; compute responsibilities
np.add(A, S, tmp)
I = np.argmax(tmp, axis=1)
Y = tmp[ind, I] # np.max(A + S, axis=1)
tmp[ind, I] = -np.inf
Y2 = np.max(tmp, axis=1)
# tmp = Rnew
np.subtract(S, Y[:, None], tmp)
tmp[ind, I] = S[ind, I] - Y2
# Damping
tmp *= 1 - damping
R *= damping
R += tmp
# tmp = Rp; compute availabilities
np.maximum(R, 0, tmp)
tmp.flat[:: n_samples + 1] = R.flat[:: n_samples + 1]
# tmp = -Anew
tmp -= np.sum(tmp, axis=0)
dA = np.diag(tmp).copy()
tmp.clip(0, np.inf, tmp)
tmp.flat[:: n_samples + 1] = dA
# Damping
tmp *= 1 - damping
A *= damping
A -= tmp
# Check for convergence
E = (np.diag(A) + np.diag(R)) > 0
e[:, it % convergence_iter] = E
K = np.sum(E, axis=0)
if it >= convergence_iter:
se = np.sum(e, axis=1)
unconverged = np.sum((se == convergence_iter) + (se == 0)) != n_samples
if (not unconverged and (K > 0)) or (it == max_iter):
never_converged = False
if verbose:
print("Converged after %d iterations." % it)
break
else:
never_converged = True
if verbose:
print("Did not converge")
I = np.flatnonzero(E)
K = I.size # Identify exemplars
if K > 0:
if never_converged:
warnings.warn(
(
"Affinity propagation did not converge, this model "
"may return degenerate cluster centers and labels."
),
ConvergenceWarning,
)
c = np.argmax(S[:, I], axis=1)
c[I] = np.arange(K) # Identify clusters
# Refine the final set of exemplars and clusters and return results
for k in range(K):
ii = np.where(c == k)[0]
j = np.argmax(np.sum(S[ii[:, np.newaxis], ii], axis=0))
I[k] = ii[j]
c = np.argmax(S[:, I], axis=1)
c[I] = np.arange(K)
labels = I[c]
# Reduce labels to a sorted, gapless, list
cluster_centers_indices = np.unique(labels)
labels = np.searchsorted(cluster_centers_indices, labels)
else:
warnings.warn(
(
"Affinity propagation did not converge and this model "
"will not have any cluster centers."
),
ConvergenceWarning,
)
labels = np.array([-1] * n_samples)
cluster_centers_indices = []
if return_n_iter:
return cluster_centers_indices, labels, it + 1
else:
return cluster_centers_indices, labels
###############################################################################
# Public API
@validate_params(
{
"S": ["array-like"],
"return_n_iter": ["boolean"],
},
prefer_skip_nested_validation=False,
)
def affinity_propagation(
S,
*,
preference=None,
convergence_iter=15,
max_iter=200,
damping=0.5,
copy=True,
verbose=False,
return_n_iter=False,
random_state=None,
):
"""Perform Affinity Propagation Clustering of data.
Read more in the :ref:`User Guide <affinity_propagation>`.
Parameters
----------
S : array-like of shape (n_samples, n_samples)
Matrix of similarities between points.
preference : array-like of shape (n_samples,) or float, default=None
Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number of
exemplars, i.e. of clusters, is influenced by the input preferences
value. If the preferences are not passed as arguments, they will be
set to the median of the input similarities (resulting in a moderate
number of clusters). For a smaller amount of clusters, this can be set
to the minimum value of the similarities.
convergence_iter : int, default=15
Number of iterations with no change in the number
of estimated clusters that stops the convergence.
max_iter : int, default=200
Maximum number of iterations.
damping : float, default=0.5
Damping factor between 0.5 and 1.
copy : bool, default=True
If copy is False, the affinity matrix is modified inplace by the
algorithm, for memory efficiency.
verbose : bool, default=False
The verbosity level.
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
random_state : int, RandomState instance or None, default=None
Pseudo-random number generator to control the starting state.
Use an int for reproducible results across function calls.
See the :term:`Glossary <random_state>`.
.. versionadded:: 0.23
this parameter was previously hardcoded as 0.
Returns
-------
cluster_centers_indices : ndarray of shape (n_clusters,)
Index of clusters centers.
labels : ndarray of shape (n_samples,)
Cluster labels for each point.
n_iter : int
Number of iterations run. Returned only if `return_n_iter` is
set to True.
Notes
-----
For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
<sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
When the algorithm does not converge, it will still return a arrays of
``cluster_center_indices`` and labels if there are any exemplars/clusters,
however they may be degenerate and should be used with caution.
When all training samples have equal similarities and equal preferences,
the assignment of cluster centers and labels depends on the preference.
If the preference is smaller than the similarities, a single cluster center
and label ``0`` for every sample will be returned. Otherwise, every
training sample becomes its own cluster center and is assigned a unique
label.
References
----------
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
Examples
--------
>>> import numpy as np
>>> from sklearn.cluster import affinity_propagation
>>> from sklearn.metrics.pairwise import euclidean_distances
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [4, 2], [4, 4], [4, 0]])
>>> S = -euclidean_distances(X, squared=True)
>>> cluster_centers_indices, labels = affinity_propagation(S, random_state=0)
>>> cluster_centers_indices
array([0, 3])
>>> labels
array([0, 0, 0, 1, 1, 1])
"""
estimator = AffinityPropagation(
damping=damping,
max_iter=max_iter,
convergence_iter=convergence_iter,
copy=copy,
preference=preference,
affinity="precomputed",
verbose=verbose,
random_state=random_state,
).fit(S)
if return_n_iter:
return estimator.cluster_centers_indices_, estimator.labels_, estimator.n_iter_
return estimator.cluster_centers_indices_, estimator.labels_
class AffinityPropagation(ClusterMixin, BaseEstimator):
"""Perform Affinity Propagation Clustering of data.
Read more in the :ref:`User Guide <affinity_propagation>`.
Parameters
----------
damping : float, default=0.5
Damping factor in the range `[0.5, 1.0)` is the extent to
which the current value is maintained relative to
incoming values (weighted 1 - damping). This in order
to avoid numerical oscillations when updating these
values (messages).
max_iter : int, default=200
Maximum number of iterations.
convergence_iter : int, default=15
Number of iterations with no change in the number
of estimated clusters that stops the convergence.
copy : bool, default=True
Make a copy of input data.
preference : array-like of shape (n_samples,) or float, default=None
Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number
of exemplars, ie of clusters, is influenced by the input
preferences value. If the preferences are not passed as arguments,
they will be set to the median of the input similarities.
affinity : {'euclidean', 'precomputed'}, default='euclidean'
Which affinity to use. At the moment 'precomputed' and
``euclidean`` are supported. 'euclidean' uses the
negative squared euclidean distance between points.
verbose : bool, default=False
Whether to be verbose.
random_state : int, RandomState instance or None, default=None
Pseudo-random number generator to control the starting state.
Use an int for reproducible results across function calls.
See the :term:`Glossary <random_state>`.
.. versionadded:: 0.23
this parameter was previously hardcoded as 0.
Attributes
----------
cluster_centers_indices_ : ndarray of shape (n_clusters,)
Indices of cluster centers.
cluster_centers_ : ndarray of shape (n_clusters, n_features)
Cluster centers (if affinity != ``precomputed``).
labels_ : ndarray of shape (n_samples,)
Labels of each point.
affinity_matrix_ : ndarray of shape (n_samples, n_samples)
Stores the affinity matrix used in ``fit``.
n_iter_ : int
Number of iterations taken to converge.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
See Also
--------
AgglomerativeClustering : Recursively merges the pair of
clusters that minimally increases a given linkage distance.
FeatureAgglomeration : Similar to AgglomerativeClustering,
but recursively merges features instead of samples.
KMeans : K-Means clustering.
MiniBatchKMeans : Mini-Batch K-Means clustering.
MeanShift : Mean shift clustering using a flat kernel.
SpectralClustering : Apply clustering to a projection
of the normalized Laplacian.
Notes
-----
For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
<sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
The algorithmic complexity of affinity propagation is quadratic
in the number of points.
When the algorithm does not converge, it will still return a arrays of
``cluster_center_indices`` and labels if there are any exemplars/clusters,
however they may be degenerate and should be used with caution.
When ``fit`` does not converge, ``cluster_centers_`` is still populated
however it may be degenerate. In such a case, proceed with caution.
If ``fit`` does not converge and fails to produce any ``cluster_centers_``
then ``predict`` will label every sample as ``-1``.
When all training samples have equal similarities and equal preferences,
the assignment of cluster centers and labels depends on the preference.
If the preference is smaller than the similarities, ``fit`` will result in
a single cluster center and label ``0`` for every sample. Otherwise, every
training sample becomes its own cluster center and is assigned a unique
label.
References
----------
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
Examples
--------
>>> from sklearn.cluster import AffinityPropagation
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [4, 2], [4, 4], [4, 0]])
>>> clustering = AffinityPropagation(random_state=5).fit(X)
>>> clustering
AffinityPropagation(random_state=5)
>>> clustering.labels_
array([0, 0, 0, 1, 1, 1])
>>> clustering.predict([[0, 0], [4, 4]])
array([0, 1])
>>> clustering.cluster_centers_
array([[1, 2],
[4, 2]])
"""
_parameter_constraints: dict = {
"damping": [Interval(Real, 0.5, 1.0, closed="left")],
"max_iter": [Interval(Integral, 1, None, closed="left")],
"convergence_iter": [Interval(Integral, 1, None, closed="left")],
"copy": ["boolean"],
"preference": [
"array-like",
Interval(Real, None, None, closed="neither"),
None,
],
"affinity": [StrOptions({"euclidean", "precomputed"})],
"verbose": ["verbose"],
"random_state": ["random_state"],
}
def __init__(
self,
*,
damping=0.5,
max_iter=200,
convergence_iter=15,
copy=True,
preference=None,
affinity="euclidean",
verbose=False,
random_state=None,
):
self.damping = damping
self.max_iter = max_iter
self.convergence_iter = convergence_iter
self.copy = copy
self.verbose = verbose
self.preference = preference
self.affinity = affinity
self.random_state = random_state
def _more_tags(self):
return {"pairwise": self.affinity == "precomputed"}
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X, y=None):
"""Fit the clustering from features, or affinity matrix.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between
instances if ``affinity='precomputed'``. If a sparse feature matrix
is provided, it will be converted into a sparse ``csr_matrix``.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self
Returns the instance itself.
"""
if self.affinity == "precomputed":
accept_sparse = False
else:
accept_sparse = "csr"
X = self._validate_data(X, accept_sparse=accept_sparse)
if self.affinity == "precomputed":
self.affinity_matrix_ = X.copy() if self.copy else X
else: # self.affinity == "euclidean"
self.affinity_matrix_ = -euclidean_distances(X, squared=True)
if self.affinity_matrix_.shape[0] != self.affinity_matrix_.shape[1]:
raise ValueError(
"The matrix of similarities must be a square array. "
f"Got {self.affinity_matrix_.shape} instead."
)
if self.preference is None:
preference = np.median(self.affinity_matrix_)
else:
preference = self.preference
preference = np.asarray(preference)
random_state = check_random_state(self.random_state)
(
self.cluster_centers_indices_,
self.labels_,
self.n_iter_,
) = _affinity_propagation(
self.affinity_matrix_,
max_iter=self.max_iter,
convergence_iter=self.convergence_iter,
preference=preference,
damping=self.damping,
verbose=self.verbose,
return_n_iter=True,
random_state=random_state,
)
if self.affinity != "precomputed":
self.cluster_centers_ = X[self.cluster_centers_indices_].copy()
return self
def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
labels : ndarray of shape (n_samples,)
Cluster labels.
"""
check_is_fitted(self)
X = self._validate_data(X, reset=False, accept_sparse="csr")
if not hasattr(self, "cluster_centers_"):
raise ValueError(
"Predict method is not supported when affinity='precomputed'."
)
if self.cluster_centers_.shape[0] > 0:
with config_context(assume_finite=True):
return pairwise_distances_argmin(X, self.cluster_centers_)
else:
warnings.warn(
(
"This model does not have any cluster centers "
"because affinity propagation did not converge. "
"Labeling every sample as '-1'."
),
ConvergenceWarning,
)
return np.array([-1] * X.shape[0])
def fit_predict(self, X, y=None):
"""Fit clustering from features/affinity matrix; return cluster labels.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between
instances if ``affinity='precomputed'``. If a sparse feature matrix
is provided, it will be converted into a sparse ``csr_matrix``.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
labels : ndarray of shape (n_samples,)
Cluster labels.
"""
return super().fit_predict(X, y)