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Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/sklearn/cluster/_kmeans.py

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Machine learning
2023-09-20 19:46:58 +02:00
"""K-means clustering."""
# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Thomas Rueckstiess <ruecksti@in.tum.de>
# James Bergstra <james.bergstra@umontreal.ca>
# Jan Schlueter <scikit-learn@jan-schlueter.de>
# Nelle Varoquaux
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# License: BSD 3 clause
from abc import ABC, abstractmethod
from numbers import Integral, Real
import warnings
import numpy as np
import scipy.sparse as sp
from ..base import (
BaseEstimator,
ClusterMixin,
TransformerMixin,
ClassNamePrefixFeaturesOutMixin,
)
from ..metrics.pairwise import euclidean_distances
from ..metrics.pairwise import _euclidean_distances
from ..utils.extmath import row_norms, stable_cumsum
from ..utils.fixes import threadpool_limits
from ..utils.fixes import threadpool_info
from ..utils.sparsefuncs_fast import assign_rows_csr
from ..utils.sparsefuncs import mean_variance_axis
from ..utils import check_array
from ..utils import check_random_state
from ..utils.validation import check_is_fitted, _check_sample_weight
from ..utils.validation import _is_arraylike_not_scalar
from ..utils._param_validation import Hidden
from ..utils._param_validation import Interval
from ..utils._param_validation import StrOptions
from ..utils._param_validation import validate_params
from ..utils._openmp_helpers import _openmp_effective_n_threads
from ..utils._readonly_array_wrapper import ReadonlyArrayWrapper
from ..exceptions import ConvergenceWarning
from ._k_means_common import CHUNK_SIZE
from ._k_means_common import _inertia_dense
from ._k_means_common import _inertia_sparse
from ._k_means_common import _is_same_clustering
from ._k_means_minibatch import _minibatch_update_dense
from ._k_means_minibatch import _minibatch_update_sparse
from ._k_means_lloyd import lloyd_iter_chunked_dense
from ._k_means_lloyd import lloyd_iter_chunked_sparse
from ._k_means_elkan import init_bounds_dense
from ._k_means_elkan import init_bounds_sparse
from ._k_means_elkan import elkan_iter_chunked_dense
from ._k_means_elkan import elkan_iter_chunked_sparse
###############################################################################
# Initialization heuristic
@validate_params(
{
"X": ["array-like", "sparse matrix"],
"n_clusters": [Interval(Integral, 1, None, closed="left")],
"x_squared_norms": ["array-like", None],
"random_state": ["random_state"],
"n_local_trials": [Interval(Integral, 1, None, closed="left"), None],
}
)
def kmeans_plusplus(
X, n_clusters, *, x_squared_norms=None, random_state=None, n_local_trials=None
):
"""Init n_clusters seeds according to k-means++.
.. versionadded:: 0.24
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data to pick seeds from.
n_clusters : int
The number of centroids to initialize.
x_squared_norms : array-like of shape (n_samples,), default=None
Squared Euclidean norm of each data point.
random_state : int or RandomState instance, default=None
Determines random number generation for centroid initialization. Pass
an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
n_local_trials : int, default=None
The number of seeding trials for each center (except the first),
of which the one reducing inertia the most is greedily chosen.
Set to None to make the number of trials depend logarithmically
on the number of seeds (2+log(k)) which is the recommended setting.
Setting to 1 disables the greedy cluster selection and recovers the
vanilla k-means++ algorithm which was empirically shown to work less
well than its greedy variant.
Returns
-------
centers : ndarray of shape (n_clusters, n_features)
The initial centers for k-means.
indices : ndarray of shape (n_clusters,)
The index location of the chosen centers in the data array X. For a
given index and center, X[index] = center.
Notes
-----
Selects initial cluster centers for k-mean clustering in a smart way
to speed up convergence. see: Arthur, D. and Vassilvitskii, S.
"k-means++: the advantages of careful seeding". ACM-SIAM symposium
on Discrete algorithms. 2007
Examples
--------
>>> from sklearn.cluster import kmeans_plusplus
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> centers, indices = kmeans_plusplus(X, n_clusters=2, random_state=0)
>>> centers
array([[10, 4],
[ 1, 0]])
>>> indices
array([4, 2])
"""
# Check data
check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32])
if X.shape[0] < n_clusters:
raise ValueError(
f"n_samples={X.shape[0]} should be >= n_clusters={n_clusters}."
)
# Check parameters
if x_squared_norms is None:
x_squared_norms = row_norms(X, squared=True)
else:
x_squared_norms = check_array(x_squared_norms, dtype=X.dtype, ensure_2d=False)
if x_squared_norms.shape[0] != X.shape[0]:
raise ValueError(
f"The length of x_squared_norms {x_squared_norms.shape[0]} should "
f"be equal to the length of n_samples {X.shape[0]}."
)
random_state = check_random_state(random_state)
# Call private k-means++
centers, indices = _kmeans_plusplus(
X, n_clusters, x_squared_norms, random_state, n_local_trials
)
return centers, indices
def _kmeans_plusplus(X, n_clusters, x_squared_norms, random_state, n_local_trials=None):
"""Computational component for initialization of n_clusters by
k-means++. Prior validation of data is assumed.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The data to pick seeds for.
n_clusters : int
The number of seeds to choose.
x_squared_norms : ndarray of shape (n_samples,)
Squared Euclidean norm of each data point.
random_state : RandomState instance
The generator used to initialize the centers.
See :term:`Glossary <random_state>`.
n_local_trials : int, default=None
The number of seeding trials for each center (except the first),
of which the one reducing inertia the most is greedily chosen.
Set to None to make the number of trials depend logarithmically
on the number of seeds (2+log(k)); this is the default.
Returns
-------
centers : ndarray of shape (n_clusters, n_features)
The initial centers for k-means.
indices : ndarray of shape (n_clusters,)
The index location of the chosen centers in the data array X. For a
given index and center, X[index] = center.
"""
n_samples, n_features = X.shape
centers = np.empty((n_clusters, n_features), dtype=X.dtype)
# Set the number of local seeding trials if none is given
if n_local_trials is None:
# This is what Arthur/Vassilvitskii tried, but did not report
# specific results for other than mentioning in the conclusion
# that it helped.
n_local_trials = 2 + int(np.log(n_clusters))
# Pick first center randomly and track index of point
center_id = random_state.randint(n_samples)
indices = np.full(n_clusters, -1, dtype=int)
if sp.issparse(X):
centers[0] = X[center_id].toarray()
else:
centers[0] = X[center_id]
indices[0] = center_id
# Initialize list of closest distances and calculate current potential
closest_dist_sq = _euclidean_distances(
centers[0, np.newaxis], X, Y_norm_squared=x_squared_norms, squared=True
)
current_pot = closest_dist_sq.sum()
# Pick the remaining n_clusters-1 points
for c in range(1, n_clusters):
# Choose center candidates by sampling with probability proportional
# to the squared distance to the closest existing center
rand_vals = random_state.uniform(size=n_local_trials) * current_pot
candidate_ids = np.searchsorted(stable_cumsum(closest_dist_sq), rand_vals)
# XXX: numerical imprecision can result in a candidate_id out of range
np.clip(candidate_ids, None, closest_dist_sq.size - 1, out=candidate_ids)
# Compute distances to center candidates
distance_to_candidates = _euclidean_distances(
X[candidate_ids], X, Y_norm_squared=x_squared_norms, squared=True
)
# update closest distances squared and potential for each candidate
np.minimum(closest_dist_sq, distance_to_candidates, out=distance_to_candidates)
candidates_pot = distance_to_candidates.sum(axis=1)
# Decide which candidate is the best
best_candidate = np.argmin(candidates_pot)
current_pot = candidates_pot[best_candidate]
closest_dist_sq = distance_to_candidates[best_candidate]
best_candidate = candidate_ids[best_candidate]
# Permanently add best center candidate found in local tries
if sp.issparse(X):
centers[c] = X[best_candidate].toarray()
else:
centers[c] = X[best_candidate]
indices[c] = best_candidate
return centers, indices
###############################################################################
# K-means batch estimation by EM (expectation maximization)
def _tolerance(X, tol):
"""Return a tolerance which is dependent on the dataset."""
if tol == 0:
return 0
if sp.issparse(X):
variances = mean_variance_axis(X, axis=0)[1]
else:
variances = np.var(X, axis=0)
return np.mean(variances) * tol
@validate_params(
{
"X": ["array-like", "sparse matrix"],
"n_clusters": [Interval(Integral, 1, None, closed="left")],
"sample_weight": ["array-like", None],
"init": [StrOptions({"k-means++", "random"}), callable, "array-like"],
"n_init": [
StrOptions({"auto"}),
Hidden(StrOptions({"warn"})),
Interval(Integral, 1, None, closed="left"),
],
"max_iter": [Interval(Integral, 1, None, closed="left")],
"verbose": [Interval(Integral, 0, None, closed="left"), bool],
"tol": [Interval(Real, 0, None, closed="left")],
"random_state": ["random_state"],
"copy_x": [bool],
"algorithm": [
StrOptions({"lloyd", "elkan", "auto", "full"}, deprecated={"auto", "full"})
],
"return_n_iter": [bool],
}
)
def k_means(
X,
n_clusters,
*,
sample_weight=None,
init="k-means++",
n_init="warn",
max_iter=300,
verbose=False,
tol=1e-4,
random_state=None,
copy_x=True,
algorithm="lloyd",
return_n_iter=False,
):
"""Perform K-means clustering algorithm.
Read more in the :ref:`User Guide <k_means>`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The observations to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.
n_clusters : int
The number of clusters to form as well as the number of
centroids to generate.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in `X`. If `None`, all observations
are assigned equal weight.
init : {'k-means++', 'random'}, callable or array-like of shape \
(n_clusters, n_features), default='k-means++'
Method for initialization:
- `'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'`: choose `n_clusters` observations (rows) at random from data
for the initial centroids.
- If an array 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`, `n_clusters` and a
random state and return an initialization.
n_init : 'auto' or int, 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.
When `n_init='auto'`, the number of runs depends on the value of init:
10 if using `init='random'`, 1 if using `init='k-means++'`.
.. versionadded:: 1.2
Added 'auto' option for `n_init`.
.. versionchanged:: 1.4
Default value for `n_init` will change from 10 to `'auto'` in version 1.4.
max_iter : int, default=300
Maximum number of iterations of the k-means algorithm to run.
verbose : bool, default=False
Verbosity mode.
tol : float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.
random_state : int, RandomState instance or None, default=None
Determines random number generation for centroid initialization. Use
an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.
copy_x : bool, default=True
When pre-computing distances it is more numerically accurate to center
the data first. If `copy_x` is True (default), 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. Note that if
the original data is not C-contiguous, a copy will be made even if
`copy_x` is False. If the original data is sparse, but not in CSR format,
a copy will be made even if `copy_x` is False.
algorithm : {"lloyd", "elkan", "auto", "full"}, default="lloyd"
K-means algorithm to use. The classical EM-style algorithm is `"lloyd"`.
The `"elkan"` variation can be more efficient on some datasets with
well-defined clusters, by using the triangle inequality. However it's
more memory intensive due to the allocation of an extra array of shape
`(n_samples, n_clusters)`.
`"auto"` and `"full"` are deprecated and they will be removed in
Scikit-Learn 1.3. They are both aliases for `"lloyd"`.
.. versionchanged:: 0.18
Added Elkan algorithm
.. versionchanged:: 1.1
Renamed "full" to "lloyd", and deprecated "auto" and "full".
Changed "auto" to use "lloyd" instead of "elkan".
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Returns
-------
centroid : ndarray of shape (n_clusters, n_features)
Centroids found at the last iteration of k-means.
label : ndarray of shape (n_samples,)
The `label[i]` is the code or index of the centroid the
i'th observation is closest to.
inertia : float
The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).
best_n_iter : int
Number of iterations corresponding to the best results.
Returned only if `return_n_iter` is set to True.
"""
est = KMeans(
n_clusters=n_clusters,
init=init,
n_init=n_init,
max_iter=max_iter,
verbose=verbose,
tol=tol,
random_state=random_state,
copy_x=copy_x,
algorithm=algorithm,
).fit(X, sample_weight=sample_weight)
if return_n_iter:
return est.cluster_centers_, est.labels_, est.inertia_, est.n_iter_
else:
return est.cluster_centers_, est.labels_, est.inertia_
def _kmeans_single_elkan(
X,
sample_weight,
centers_init,
max_iter=300,
verbose=False,
tol=1e-4,
n_threads=1,
):
"""A single run of k-means elkan, assumes preparation completed prior.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The observations to cluster. If sparse matrix, must be in CSR format.
sample_weight : array-like of shape (n_samples,)
The weights for each observation in X.
centers_init : ndarray of shape (n_clusters, n_features)
The initial centers.
max_iter : int, default=300
Maximum number of iterations of the k-means algorithm to run.
verbose : bool, default=False
Verbosity mode.
tol : float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.
It's not advised to set `tol=0` since convergence might never be
declared due to rounding errors. Use a very small number instead.
n_threads : int, default=1
The number of OpenMP threads to use for the computation. Parallelism is
sample-wise on the main cython loop which assigns each sample to its
closest center.
Returns
-------
centroid : ndarray of shape (n_clusters, n_features)
Centroids found at the last iteration of k-means.
label : ndarray of shape (n_samples,)
label[i] is the code or index of the centroid the
i'th observation is closest to.
inertia : float
The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).
n_iter : int
Number of iterations run.
"""
n_samples = X.shape[0]
n_clusters = centers_init.shape[0]
# Buffers to avoid new allocations at each iteration.
centers = centers_init
centers_new = np.zeros_like(centers)
weight_in_clusters = np.zeros(n_clusters, dtype=X.dtype)
labels = np.full(n_samples, -1, dtype=np.int32)
labels_old = labels.copy()
center_half_distances = euclidean_distances(centers) / 2
distance_next_center = np.partition(
np.asarray(center_half_distances), kth=1, axis=0
)[1]
upper_bounds = np.zeros(n_samples, dtype=X.dtype)
lower_bounds = np.zeros((n_samples, n_clusters), dtype=X.dtype)
center_shift = np.zeros(n_clusters, dtype=X.dtype)
if sp.issparse(X):
init_bounds = init_bounds_sparse
elkan_iter = elkan_iter_chunked_sparse
_inertia = _inertia_sparse
else:
init_bounds = init_bounds_dense
elkan_iter = elkan_iter_chunked_dense
_inertia = _inertia_dense
init_bounds(
X,
centers,
center_half_distances,
labels,
upper_bounds,
lower_bounds,
n_threads=n_threads,
)
strict_convergence = False
for i in range(max_iter):
elkan_iter(
X,
sample_weight,
centers,
centers_new,
weight_in_clusters,
center_half_distances,
distance_next_center,
upper_bounds,
lower_bounds,
labels,
center_shift,
n_threads,
)
# compute new pairwise distances between centers and closest other
# center of each center for next iterations
center_half_distances = euclidean_distances(centers_new) / 2
distance_next_center = np.partition(
np.asarray(center_half_distances), kth=1, axis=0
)[1]
if verbose:
inertia = _inertia(X, sample_weight, centers, labels, n_threads)
print(f"Iteration {i}, inertia {inertia}")
centers, centers_new = centers_new, centers
if np.array_equal(labels, labels_old):
# First check the labels for strict convergence.
if verbose:
print(f"Converged at iteration {i}: strict convergence.")
strict_convergence = True
break
else:
# No strict convergence, check for tol based convergence.
center_shift_tot = (center_shift**2).sum()
if center_shift_tot <= tol:
if verbose:
print(
f"Converged at iteration {i}: center shift "
f"{center_shift_tot} within tolerance {tol}."
)
break
labels_old[:] = labels
if not strict_convergence:
# rerun E-step so that predicted labels match cluster centers
elkan_iter(
X,
sample_weight,
centers,
centers,
weight_in_clusters,
center_half_distances,
distance_next_center,
upper_bounds,
lower_bounds,
labels,
center_shift,
n_threads,
update_centers=False,
)
inertia = _inertia(X, sample_weight, centers, labels, n_threads)
return labels, inertia, centers, i + 1
def _kmeans_single_lloyd(
X,
sample_weight,
centers_init,
max_iter=300,
verbose=False,
tol=1e-4,
n_threads=1,
):
"""A single run of k-means lloyd, assumes preparation completed prior.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The observations to cluster. If sparse matrix, must be in CSR format.
sample_weight : ndarray of shape (n_samples,)
The weights for each observation in X.
centers_init : ndarray of shape (n_clusters, n_features)
The initial centers.
max_iter : int, default=300
Maximum number of iterations of the k-means algorithm to run.
verbose : bool, default=False
Verbosity mode
tol : float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.
It's not advised to set `tol=0` since convergence might never be
declared due to rounding errors. Use a very small number instead.
n_threads : int, default=1
The number of OpenMP threads to use for the computation. Parallelism is
sample-wise on the main cython loop which assigns each sample to its
closest center.
Returns
-------
centroid : ndarray of shape (n_clusters, n_features)
Centroids found at the last iteration of k-means.
label : ndarray of shape (n_samples,)
label[i] is the code or index of the centroid the
i'th observation is closest to.
inertia : float
The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).
n_iter : int
Number of iterations run.
"""
n_clusters = centers_init.shape[0]
# Buffers to avoid new allocations at each iteration.
centers = centers_init
centers_new = np.zeros_like(centers)
labels = np.full(X.shape[0], -1, dtype=np.int32)
labels_old = labels.copy()
weight_in_clusters = np.zeros(n_clusters, dtype=X.dtype)
center_shift = np.zeros(n_clusters, dtype=X.dtype)
if sp.issparse(X):
lloyd_iter = lloyd_iter_chunked_sparse
_inertia = _inertia_sparse
else:
lloyd_iter = lloyd_iter_chunked_dense
_inertia = _inertia_dense
strict_convergence = False
# Threadpoolctl context to limit the number of threads in second level of
# nested parallelism (i.e. BLAS) to avoid oversubscription.
with threadpool_limits(limits=1, user_api="blas"):
for i in range(max_iter):
lloyd_iter(
X,
sample_weight,
centers,
centers_new,
weight_in_clusters,
labels,
center_shift,
n_threads,
)
if verbose:
inertia = _inertia(X, sample_weight, centers, labels, n_threads)
print(f"Iteration {i}, inertia {inertia}.")
centers, centers_new = centers_new, centers
if np.array_equal(labels, labels_old):
# First check the labels for strict convergence.
if verbose:
print(f"Converged at iteration {i}: strict convergence.")
strict_convergence = True
break
else:
# No strict convergence, check for tol based convergence.
center_shift_tot = (center_shift**2).sum()
if center_shift_tot <= tol:
if verbose:
print(
f"Converged at iteration {i}: center shift "
f"{center_shift_tot} within tolerance {tol}."
)
break
labels_old[:] = labels
if not strict_convergence:
# rerun E-step so that predicted labels match cluster centers
lloyd_iter(
X,
sample_weight,
centers,
centers,
weight_in_clusters,
labels,
center_shift,
n_threads,
update_centers=False,
)
inertia = _inertia(X, sample_weight, centers, labels, n_threads)
return labels, inertia, centers, i + 1
def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True):
"""E step of the K-means EM algorithm.
Compute the labels and the inertia of the given samples and centers.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input samples to assign to the labels. If sparse matrix, must
be in CSR format.
sample_weight : ndarray of shape (n_samples,)
The weights for each observation in X.
x_squared_norms : ndarray of shape (n_samples,)
Precomputed squared euclidean norm of each data point, to speed up
computations.
centers : ndarray of shape (n_clusters, n_features)
The cluster centers.
n_threads : int, default=1
The number of OpenMP threads to use for the computation. Parallelism is
sample-wise on the main cython loop which assigns each sample to its
closest center.
return_inertia : bool, default=True
Whether to compute and return the inertia.
Returns
-------
labels : ndarray of shape (n_samples,)
The resulting assignment.
inertia : float
Sum of squared distances of samples to their closest cluster center.
Inertia is only returned if return_inertia is True.
"""
n_samples = X.shape[0]
n_clusters = centers.shape[0]
labels = np.full(n_samples, -1, dtype=np.int32)
center_shift = np.zeros(n_clusters, dtype=centers.dtype)
if sp.issparse(X):
_labels = lloyd_iter_chunked_sparse
_inertia = _inertia_sparse
else:
_labels = lloyd_iter_chunked_dense
_inertia = _inertia_dense
X = ReadonlyArrayWrapper(X)
centers = ReadonlyArrayWrapper(centers)
_labels(
X,
sample_weight,
centers,
centers_new=None,
weight_in_clusters=None,
labels=labels,
center_shift=center_shift,
n_threads=n_threads,
update_centers=False,
)
if return_inertia:
inertia = _inertia(X, sample_weight, centers, labels, n_threads)
return labels, inertia
return labels
def _labels_inertia_threadpool_limit(
X, sample_weight, centers, n_threads=1, return_inertia=True
):
"""Same as _labels_inertia but in a threadpool_limits context."""
with threadpool_limits(limits=1, user_api="blas"):
result = _labels_inertia(X, sample_weight, centers, n_threads, return_inertia)
return result
class _BaseKMeans(
ClassNamePrefixFeaturesOutMixin, TransformerMixin, ClusterMixin, BaseEstimator, ABC
):
"""Base class for KMeans and MiniBatchKMeans"""
_parameter_constraints: dict = {
"n_clusters": [Interval(Integral, 1, None, closed="left")],
"init": [StrOptions({"k-means++", "random"}), callable, "array-like"],
"n_init": [
StrOptions({"auto"}),
Hidden(StrOptions({"warn"})),
Interval(Integral, 1, None, closed="left"),
],
"max_iter": [Interval(Integral, 1, None, closed="left")],
"tol": [Interval(Real, 0, None, closed="left")],
"verbose": ["verbose"],
"random_state": ["random_state"],
}
def __init__(
self,
n_clusters,
*,
init,
n_init,
max_iter,
tol,
verbose,
random_state,
):
self.n_clusters = n_clusters
self.init = init
self.max_iter = max_iter
self.tol = tol
self.n_init = n_init
self.verbose = verbose
self.random_state = random_state
def _check_params_vs_input(self, X, default_n_init=None):
# n_clusters
if X.shape[0] < self.n_clusters:
raise ValueError(
f"n_samples={X.shape[0]} should be >= n_clusters={self.n_clusters}."
)
# tol
self._tol = _tolerance(X, self.tol)
# n-init
# TODO(1.4): Remove
self._n_init = self.n_init
if self._n_init == "warn":
warnings.warn(
"The default value of `n_init` will change from "
f"{default_n_init} to 'auto' in 1.4. Set the value of `n_init`"
" explicitly to suppress the warning",
FutureWarning,
)
self._n_init = default_n_init
if self._n_init == "auto":
if self.init == "k-means++":
self._n_init = 1
else:
self._n_init = default_n_init
if _is_arraylike_not_scalar(self.init) and self._n_init != 1:
warnings.warn(
"Explicit initial center position passed: performing only"
f" one init in {self.__class__.__name__} instead of "
f"n_init={self._n_init}.",
RuntimeWarning,
stacklevel=2,
)
self._n_init = 1
@abstractmethod
def _warn_mkl_vcomp(self, n_active_threads):
"""Issue an estimator specific warning when vcomp and mkl are both present
This method is called by `_check_mkl_vcomp`.
"""
def _check_mkl_vcomp(self, X, n_samples):
"""Check when vcomp and mkl are both present"""
# The BLAS call inside a prange in lloyd_iter_chunked_dense is known to
# cause a small memory leak when there are less chunks than the number
# of available threads. It only happens when the OpenMP library is
# vcomp (microsoft OpenMP) and the BLAS library is MKL. see #18653
if sp.issparse(X):
return
n_active_threads = int(np.ceil(n_samples / CHUNK_SIZE))
if n_active_threads < self._n_threads:
modules = threadpool_info()
has_vcomp = "vcomp" in [module["prefix"] for module in modules]
has_mkl = ("mkl", "intel") in [
(module["internal_api"], module.get("threading_layer", None))
for module in modules
]
if has_vcomp and has_mkl:
self._warn_mkl_vcomp(n_active_threads)
def _validate_center_shape(self, X, centers):
"""Check if centers is compatible with X and n_clusters."""
if centers.shape[0] != self.n_clusters:
raise ValueError(
f"The shape of the initial centers {centers.shape} does not "
f"match the number of clusters {self.n_clusters}."
)
if centers.shape[1] != X.shape[1]:
raise ValueError(
f"The shape of the initial centers {centers.shape} does not "
f"match the number of features of the data {X.shape[1]}."
)
def _check_test_data(self, X):
X = self._validate_data(
X,
accept_sparse="csr",
reset=False,
dtype=[np.float64, np.float32],
order="C",
accept_large_sparse=False,
)
return X
def _init_centroids(
self, X, x_squared_norms, init, random_state, init_size=None, n_centroids=None
):
"""Compute the initial centroids.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input samples.
x_squared_norms : ndarray of shape (n_samples,)
Squared euclidean norm of each data point. Pass it if you have it
at hands already to avoid it being recomputed here.
init : {'k-means++', 'random'}, callable or ndarray of shape \
(n_clusters, n_features)
Method for initialization.
random_state : RandomState instance
Determines random number generation for centroid initialization.
See :term:`Glossary <random_state>`.
init_size : int, default=None
Number of samples to randomly sample for speeding up the
initialization (sometimes at the expense of accuracy).
n_centroids : int, default=None
Number of centroids to initialize.
If left to 'None' the number of centroids will be equal to
number of clusters to form (self.n_clusters)
Returns
-------
centers : ndarray of shape (n_clusters, n_features)
"""
n_samples = X.shape[0]
n_clusters = self.n_clusters if n_centroids is None else n_centroids
if init_size is not None and init_size < n_samples:
init_indices = random_state.randint(0, n_samples, init_size)
X = X[init_indices]
x_squared_norms = x_squared_norms[init_indices]
n_samples = X.shape[0]
if isinstance(init, str) and init == "k-means++":
centers, _ = _kmeans_plusplus(
X,
n_clusters,
random_state=random_state,
x_squared_norms=x_squared_norms,
)
elif isinstance(init, str) and init == "random":
seeds = random_state.permutation(n_samples)[:n_clusters]
centers = X[seeds]
elif _is_arraylike_not_scalar(self.init):
centers = init
elif callable(init):
centers = init(X, n_clusters, random_state=random_state)
centers = check_array(centers, dtype=X.dtype, copy=False, order="C")
self._validate_center_shape(X, centers)
if sp.issparse(centers):
centers = centers.toarray()
return centers
def fit_predict(self, X, y=None, sample_weight=None):
"""Compute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by
predict(X).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
Returns
-------
labels : ndarray of shape (n_samples,)
Index of the cluster each sample belongs to.
"""
return self.fit(X, sample_weight=sample_weight).labels_
def predict(self, X, sample_weight=None):
"""Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, `cluster_centers_` is called
the code book and each value returned by `predict` is the index of
the closest code in the code book.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
Returns
-------
labels : ndarray of shape (n_samples,)
Index of the cluster each sample belongs to.
"""
check_is_fitted(self)
X = self._check_test_data(X)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
labels = _labels_inertia_threadpool_limit(
X,
sample_weight,
self.cluster_centers_,
n_threads=self._n_threads,
return_inertia=False,
)
return labels
def fit_transform(self, X, y=None, sample_weight=None):
"""Compute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
Returns
-------
X_new : ndarray of shape (n_samples, n_clusters)
X transformed in the new space.
"""
return self.fit(X, sample_weight=sample_weight)._transform(X)
def transform(self, X):
"""Transform X to a cluster-distance space.
In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
`transform` will typically be dense.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
Returns
-------
X_new : ndarray of shape (n_samples, n_clusters)
X transformed in the new space.
"""
check_is_fitted(self)
X = self._check_test_data(X)
return self._transform(X)
def _transform(self, X):
"""Guts of transform method; no input validation."""
return euclidean_distances(X, self.cluster_centers_)
def score(self, X, y=None, sample_weight=None):
"""Opposite of the value of X on the K-means objective.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
Returns
-------
score : float
Opposite of the value of X on the K-means objective.
"""
check_is_fitted(self)
X = self._check_test_data(X)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
_, scores = _labels_inertia_threadpool_limit(
X, sample_weight, self.cluster_centers_, self._n_threads
)
return -scores
def _more_tags(self):
return {
"_xfail_checks": {
"check_sample_weights_invariance": (
"zero sample_weight is not equivalent to removing samples"
),
},
}
class KMeans(_BaseKMeans):
"""K-Means clustering.
Read more in the :ref:`User Guide <k_means>`.
Parameters
----------
n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.
init : {'k-means++', 'random'}, callable or array-like of shape \
(n_clusters, n_features), default='k-means++'
Method for initialization:
'k-means++' : selects initial cluster centroids using sampling based on
an empirical probability distribution of the points' contribution to the
overall inertia. This technique speeds up convergence. The algorithm
implemented is "greedy k-means++". It differs from the vanilla k-means++
by making several trials at each sampling step and choosing the best centroid
among them.
'random': choose `n_clusters` observations (rows) at random from data
for the initial centroids.
If an array 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, n_clusters and a
random state and return an initialization.
n_init : 'auto' or int, default=10
Number of times the k-means algorithm is run with different centroid
seeds. The final results is the best output of `n_init` consecutive runs
in terms of inertia. Several runs are recommended for sparse
high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).
When `n_init='auto'`, the number of runs depends on the value of init:
10 if using `init='random'`, 1 if using `init='k-means++'`.
.. versionadded:: 1.2
Added 'auto' option for `n_init`.
.. versionchanged:: 1.4
Default value for `n_init` will change from 10 to `'auto'` in version 1.4.
max_iter : int, default=300
Maximum number of iterations of the k-means algorithm for a
single run.
tol : float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.
verbose : int, default=0
Verbosity mode.
random_state : int, RandomState instance or None, default=None
Determines random number generation for centroid initialization. Use
an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.
copy_x : bool, default=True
When pre-computing distances it is more numerically accurate to center
the data first. If copy_x is True (default), 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. Note that if
the original data is not C-contiguous, a copy will be made even if
copy_x is False. If the original data is sparse, but not in CSR format,
a copy will be made even if copy_x is False.
algorithm : {"lloyd", "elkan", "auto", "full"}, default="lloyd"
K-means algorithm to use. The classical EM-style algorithm is `"lloyd"`.
The `"elkan"` variation can be more efficient on some datasets with
well-defined clusters, by using the triangle inequality. However it's
more memory intensive due to the allocation of an extra array of shape
`(n_samples, n_clusters)`.
`"auto"` and `"full"` are deprecated and they will be removed in
Scikit-Learn 1.3. They are both aliases for `"lloyd"`.
.. versionchanged:: 0.18
Added Elkan algorithm
.. versionchanged:: 1.1
Renamed "full" to "lloyd", and deprecated "auto" and "full".
Changed "auto" to use "lloyd" instead of "elkan".
Attributes
----------
cluster_centers_ : ndarray of shape (n_clusters, n_features)
Coordinates of cluster centers. If the algorithm stops before fully
converging (see ``tol`` and ``max_iter``), these will not be
consistent with ``labels_``.
labels_ : ndarray of shape (n_samples,)
Labels of each point
inertia_ : float
Sum of squared distances of samples to their closest cluster center,
weighted by the sample weights if provided.
n_iter_ : int
Number of iterations run.
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
--------
MiniBatchKMeans : Alternative online implementation that does incremental
updates of the centers positions using mini-batches.
For large scale learning (say n_samples > 10k) MiniBatchKMeans is
probably much faster than the default batch implementation.
Notes
-----
The k-means problem is solved using either Lloyd's or Elkan's algorithm.
The average complexity is given by O(k n T), where n is the number of
samples and T is the number of iteration.
The worst case complexity is given by O(n^(k+2/p)) with
n = n_samples, p = n_features.
Refer to :doi:`"How slow is the k-means method?" D. Arthur and S. Vassilvitskii -
SoCG2006.<10.1145/1137856.1137880>` for more details.
In practice, the k-means algorithm is very fast (one of the fastest
clustering algorithms available), but it falls in local minima. That's why
it can be useful to restart it several times.
If the algorithm stops before fully converging (because of ``tol`` or
``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent,
i.e. the ``cluster_centers_`` will not be the means of the points in each
cluster. Also, the estimator will reassign ``labels_`` after the last
iteration to make ``labels_`` consistent with ``predict`` on the training
set.
Examples
--------
>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[10., 2.],
[ 1., 2.]])
"""
_parameter_constraints: dict = {
**_BaseKMeans._parameter_constraints,
"copy_x": ["boolean"],
"algorithm": [
StrOptions({"lloyd", "elkan", "auto", "full"}, deprecated={"auto", "full"})
],
}
def __init__(
self,
n_clusters=8,
*,
init="k-means++",
n_init="warn",
max_iter=300,
tol=1e-4,
verbose=0,
random_state=None,
copy_x=True,
algorithm="lloyd",
):
super().__init__(
n_clusters=n_clusters,
init=init,
n_init=n_init,
max_iter=max_iter,
tol=tol,
verbose=verbose,
random_state=random_state,
)
self.copy_x = copy_x
self.algorithm = algorithm
def _check_params_vs_input(self, X):
super()._check_params_vs_input(X, default_n_init=10)
self._algorithm = self.algorithm
if self._algorithm in ("auto", "full"):
warnings.warn(
f"algorithm='{self._algorithm}' is deprecated, it will be "
"removed in 1.3. Using 'lloyd' instead.",
FutureWarning,
)
self._algorithm = "lloyd"
if self._algorithm == "elkan" and self.n_clusters == 1:
warnings.warn(
"algorithm='elkan' doesn't make sense for a single "
"cluster. Using 'lloyd' instead.",
RuntimeWarning,
)
self._algorithm = "lloyd"
def _warn_mkl_vcomp(self, n_active_threads):
"""Warn when vcomp and mkl are both present"""
warnings.warn(
"KMeans is known to have a memory leak on Windows "
"with MKL, when there are less chunks than available "
"threads. You can avoid it by setting the environment"
f" variable OMP_NUM_THREADS={n_active_threads}."
)
def fit(self, X, y=None, sample_weight=None):
"""Compute k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory
copy if the given data is not C-contiguous.
If a sparse matrix is passed, a copy will be made if it's not in
CSR format.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
.. versionadded:: 0.20
Returns
-------
self : object
Fitted estimator.
"""
self._validate_params()
X = self._validate_data(
X,
accept_sparse="csr",
dtype=[np.float64, np.float32],
order="C",
copy=self.copy_x,
accept_large_sparse=False,
)
self._check_params_vs_input(X)
random_state = check_random_state(self.random_state)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
self._n_threads = _openmp_effective_n_threads()
# Validate init array
init = self.init
init_is_array_like = _is_arraylike_not_scalar(init)
if init_is_array_like:
init = check_array(init, dtype=X.dtype, copy=True, order="C")
self._validate_center_shape(X, init)
# subtract of mean of x for more accurate distance computations
if not sp.issparse(X):
X_mean = X.mean(axis=0)
# The copy was already done above
X -= X_mean
if init_is_array_like:
init -= X_mean
# precompute squared norms of data points
x_squared_norms = row_norms(X, squared=True)
if self._algorithm == "elkan":
kmeans_single = _kmeans_single_elkan
else:
kmeans_single = _kmeans_single_lloyd
self._check_mkl_vcomp(X, X.shape[0])
best_inertia, best_labels = None, None
for i in range(self._n_init):
# Initialize centers
centers_init = self._init_centroids(
X, x_squared_norms=x_squared_norms, init=init, random_state=random_state
)
if self.verbose:
print("Initialization complete")
# run a k-means once
labels, inertia, centers, n_iter_ = kmeans_single(
X,
sample_weight,
centers_init,
max_iter=self.max_iter,
verbose=self.verbose,
tol=self._tol,
n_threads=self._n_threads,
)
# determine if these results are the best so far
# we chose a new run if it has a better inertia and the clustering is
# different from the best so far (it's possible that the inertia is
# slightly better even if the clustering is the same with potentially
# permuted labels, due to rounding errors)
if best_inertia is None or (
inertia < best_inertia
and not _is_same_clustering(labels, best_labels, self.n_clusters)
):
best_labels = labels
best_centers = centers
best_inertia = inertia
best_n_iter = n_iter_
if not sp.issparse(X):
if not self.copy_x:
X += X_mean
best_centers += X_mean
distinct_clusters = len(set(best_labels))
if distinct_clusters < self.n_clusters:
warnings.warn(
"Number of distinct clusters ({}) found smaller than "
"n_clusters ({}). Possibly due to duplicate points "
"in X.".format(distinct_clusters, self.n_clusters),
ConvergenceWarning,
stacklevel=2,
)
self.cluster_centers_ = best_centers
self._n_features_out = self.cluster_centers_.shape[0]
self.labels_ = best_labels
self.inertia_ = best_inertia
self.n_iter_ = best_n_iter
return self
def _mini_batch_step(
X,
sample_weight,
centers,
centers_new,
weight_sums,
random_state,
random_reassign=False,
reassignment_ratio=0.01,
verbose=False,
n_threads=1,
):
"""Incremental update of the centers for the Minibatch K-Means algorithm.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The original data array. If sparse, must be in CSR format.
x_squared_norms : ndarray of shape (n_samples,)
Squared euclidean norm of each data point.
sample_weight : ndarray of shape (n_samples,)
The weights for each observation in X.
centers : ndarray of shape (n_clusters, n_features)
The cluster centers before the current iteration
centers_new : ndarray of shape (n_clusters, n_features)
The cluster centers after the current iteration. Modified in-place.
weight_sums : ndarray of shape (n_clusters,)
The vector in which we keep track of the numbers of points in a
cluster. This array is modified in place.
random_state : RandomState instance
Determines random number generation for low count centers reassignment.
See :term:`Glossary <random_state>`.
random_reassign : boolean, default=False
If True, centers with very low counts are randomly reassigned
to observations.
reassignment_ratio : float, default=0.01
Control the fraction of the maximum number of counts for a
center to be reassigned. A higher value means that low count
centers are more likely to be reassigned, which means that the
model will take longer to converge, but should converge in a
better clustering.
verbose : bool, default=False
Controls the verbosity.
n_threads : int, default=1
The number of OpenMP threads to use for the computation.
Returns
-------
inertia : float
Sum of squared distances of samples to their closest cluster center.
The inertia is computed after finding the labels and before updating
the centers.
"""
# Perform label assignment to nearest centers
# For better efficiency, it's better to run _mini_batch_step in a
# threadpool_limit context than using _labels_inertia_threadpool_limit here
labels, inertia = _labels_inertia(X, sample_weight, centers, n_threads=n_threads)
# Update centers according to the labels
if sp.issparse(X):
_minibatch_update_sparse(
X, sample_weight, centers, centers_new, weight_sums, labels, n_threads
)
else:
_minibatch_update_dense(
ReadonlyArrayWrapper(X),
sample_weight,
centers,
centers_new,
weight_sums,
labels,
n_threads,
)
# Reassign clusters that have very low weight
if random_reassign and reassignment_ratio > 0:
to_reassign = weight_sums < reassignment_ratio * weight_sums.max()
# pick at most .5 * batch_size samples as new centers
if to_reassign.sum() > 0.5 * X.shape[0]:
indices_dont_reassign = np.argsort(weight_sums)[int(0.5 * X.shape[0]) :]
to_reassign[indices_dont_reassign] = False
n_reassigns = to_reassign.sum()
if n_reassigns:
# Pick new clusters amongst observations with uniform probability
new_centers = random_state.choice(
X.shape[0], replace=False, size=n_reassigns
)
if verbose:
print(f"[MiniBatchKMeans] Reassigning {n_reassigns} cluster centers.")
if sp.issparse(X):
assign_rows_csr(
X,
new_centers.astype(np.intp, copy=False),
np.where(to_reassign)[0].astype(np.intp, copy=False),
centers_new,
)
else:
centers_new[to_reassign] = X[new_centers]
# reset counts of reassigned centers, but don't reset them too small
# to avoid instant reassignment. This is a pretty dirty hack as it
# also modifies the learning rates.
weight_sums[to_reassign] = np.min(weight_sums[~to_reassign])
return inertia
class MiniBatchKMeans(_BaseKMeans):
"""
Mini-Batch K-Means clustering.
Read more in the :ref:`User Guide <mini_batch_kmeans>`.
Parameters
----------
n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.
init : {'k-means++', 'random'}, callable or array-like of shape \
(n_clusters, n_features), default='k-means++'
Method for initialization:
'k-means++' : selects initial cluster centroids using sampling based on
an empirical probability distribution of the points' contribution to the
overall inertia. This technique speeds up convergence. The algorithm
implemented is "greedy k-means++". It differs from the vanilla k-means++
by making several trials at each sampling step and choosing the best centroid
among them.
'random': choose `n_clusters` observations (rows) at random from data
for the initial centroids.
If an array 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, n_clusters and a
random state and return an initialization.
max_iter : int, default=100
Maximum number of iterations over the complete dataset before
stopping independently of any early stopping criterion heuristics.
batch_size : int, default=1024
Size of the mini batches.
For faster computations, you can set the ``batch_size`` greater than
256 * number of cores to enable parallelism on all cores.
.. versionchanged:: 1.0
`batch_size` default changed from 100 to 1024.
verbose : int, default=0
Verbosity mode.
compute_labels : bool, default=True
Compute label assignment and inertia for the complete dataset
once the minibatch optimization has converged in fit.
random_state : int, RandomState instance or None, default=None
Determines random number generation for centroid initialization and
random reassignment. Use an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.
tol : float, default=0.0
Control early stopping based on the relative center changes as
measured by a smoothed, variance-normalized of the mean center
squared position changes. This early stopping heuristics is
closer to the one used for the batch variant of the algorithms
but induces a slight computational and memory overhead over the
inertia heuristic.
To disable convergence detection based on normalized center
change, set tol to 0.0 (default).
max_no_improvement : int, default=10
Control early stopping based on the consecutive number of mini
batches that does not yield an improvement on the smoothed inertia.
To disable convergence detection based on inertia, set
max_no_improvement to None.
init_size : int, default=None
Number of samples to randomly sample for speeding up the
initialization (sometimes at the expense of accuracy): the
only algorithm is initialized by running a batch KMeans on a
random subset of the data. This needs to be larger than n_clusters.
If `None`, the heuristic is `init_size = 3 * batch_size` if
`3 * batch_size < n_clusters`, else `init_size = 3 * n_clusters`.
n_init : 'auto' or int, default=3
Number of random initializations that are tried.
In contrast to KMeans, the algorithm is only run once, using the best of
the `n_init` initializations as measured by inertia. Several runs are
recommended for sparse high-dimensional problems (see
:ref:`kmeans_sparse_high_dim`).
When `n_init='auto'`, the number of runs depends on the value of init:
3 if using `init='random'`, 1 if using `init='k-means++'`.
.. versionadded:: 1.2
Added 'auto' option for `n_init`.
.. versionchanged:: 1.4
Default value for `n_init` will change from 3 to `'auto'` in version 1.4.
reassignment_ratio : float, default=0.01
Control the fraction of the maximum number of counts for a center to
be reassigned. A higher value means that low count centers are more
easily reassigned, which means that the model will take longer to
converge, but should converge in a better clustering. However, too high
a value may cause convergence issues, especially with a small batch
size.
Attributes
----------
cluster_centers_ : ndarray of shape (n_clusters, n_features)
Coordinates of cluster centers.
labels_ : ndarray of shape (n_samples,)
Labels of each point (if compute_labels is set to True).
inertia_ : float
The value of the inertia criterion associated with the chosen
partition if compute_labels is set to True. If compute_labels is set to
False, it's an approximation of the inertia based on an exponentially
weighted average of the batch inertiae.
The inertia is defined as the sum of square distances of samples to
their cluster center, weighted by the sample weights if provided.
n_iter_ : int
Number of iterations over the full dataset.
n_steps_ : int
Number of minibatches processed.
.. versionadded:: 1.0
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
--------
KMeans : The classic implementation of the clustering method based on the
Lloyd's algorithm. It consumes the whole set of input data at each
iteration.
Notes
-----
See https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
When there are too few points in the dataset, some centers may be
duplicated, which means that a proper clustering in terms of the number
of requesting clusters and the number of returned clusters will not
always match. One solution is to set `reassignment_ratio=0`, which
prevents reassignments of clusters that are too small.
Examples
--------
>>> from sklearn.cluster import MiniBatchKMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [4, 2], [4, 0], [4, 4],
... [4, 5], [0, 1], [2, 2],
... [3, 2], [5, 5], [1, -1]])
>>> # manually fit on batches
>>> kmeans = MiniBatchKMeans(n_clusters=2,
... random_state=0,
... batch_size=6,
... n_init="auto")
>>> kmeans = kmeans.partial_fit(X[0:6,:])
>>> kmeans = kmeans.partial_fit(X[6:12,:])
>>> kmeans.cluster_centers_
array([[2. , 1. ],
[3.5, 4.5]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
>>> # fit on the whole data
>>> kmeans = MiniBatchKMeans(n_clusters=2,
... random_state=0,
... batch_size=6,
... max_iter=10,
... n_init="auto").fit(X)
>>> kmeans.cluster_centers_
array([[3.97727273, 2.43181818],
[1.125 , 1.6 ]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([1, 0], dtype=int32)
"""
_parameter_constraints: dict = {
**_BaseKMeans._parameter_constraints,
"batch_size": [Interval(Integral, 1, None, closed="left")],
"compute_labels": ["boolean"],
"max_no_improvement": [Interval(Integral, 0, None, closed="left"), None],
"init_size": [Interval(Integral, 1, None, closed="left"), None],
"reassignment_ratio": [Interval(Real, 0, None, closed="left")],
}
def __init__(
self,
n_clusters=8,
*,
init="k-means++",
max_iter=100,
batch_size=1024,
verbose=0,
compute_labels=True,
random_state=None,
tol=0.0,
max_no_improvement=10,
init_size=None,
n_init="warn",
reassignment_ratio=0.01,
):
super().__init__(
n_clusters=n_clusters,
init=init,
max_iter=max_iter,
verbose=verbose,
random_state=random_state,
tol=tol,
n_init=n_init,
)
self.max_no_improvement = max_no_improvement
self.batch_size = batch_size
self.compute_labels = compute_labels
self.init_size = init_size
self.reassignment_ratio = reassignment_ratio
def _check_params_vs_input(self, X):
super()._check_params_vs_input(X, default_n_init=3)
self._batch_size = min(self.batch_size, X.shape[0])
# init_size
self._init_size = self.init_size
if self._init_size is None:
self._init_size = 3 * self._batch_size
if self._init_size < self.n_clusters:
self._init_size = 3 * self.n_clusters
elif self._init_size < self.n_clusters:
warnings.warn(
f"init_size={self._init_size} should be larger than "
f"n_clusters={self.n_clusters}. Setting it to "
"min(3*n_clusters, n_samples)",
RuntimeWarning,
stacklevel=2,
)
self._init_size = 3 * self.n_clusters
self._init_size = min(self._init_size, X.shape[0])
# reassignment_ratio
if self.reassignment_ratio < 0:
raise ValueError(
"reassignment_ratio should be >= 0, got "
f"{self.reassignment_ratio} instead."
)
def _warn_mkl_vcomp(self, n_active_threads):
"""Warn when vcomp and mkl are both present"""
warnings.warn(
"MiniBatchKMeans is known to have a memory leak on "
"Windows with MKL, when there are less chunks than "
"available threads. You can prevent it by setting "
f"batch_size >= {self._n_threads * CHUNK_SIZE} or by "
"setting the environment variable "
f"OMP_NUM_THREADS={n_active_threads}"
)
def _mini_batch_convergence(
self, step, n_steps, n_samples, centers_squared_diff, batch_inertia
):
"""Helper function to encapsulate the early stopping logic"""
# Normalize inertia to be able to compare values when
# batch_size changes
batch_inertia /= self._batch_size
# count steps starting from 1 for user friendly verbose mode.
step = step + 1
# Ignore first iteration because it's inertia from initialization.
if step == 1:
if self.verbose:
print(
f"Minibatch step {step}/{n_steps}: mean batch "
f"inertia: {batch_inertia}"
)
return False
# Compute an Exponentially Weighted Average of the inertia to
# monitor the convergence while discarding minibatch-local stochastic
# variability: https://en.wikipedia.org/wiki/Moving_average
if self._ewa_inertia is None:
self._ewa_inertia = batch_inertia
else:
alpha = self._batch_size * 2.0 / (n_samples + 1)
alpha = min(alpha, 1)
self._ewa_inertia = self._ewa_inertia * (1 - alpha) + batch_inertia * alpha
# Log progress to be able to monitor convergence
if self.verbose:
print(
f"Minibatch step {step}/{n_steps}: mean batch inertia: "
f"{batch_inertia}, ewa inertia: {self._ewa_inertia}"
)
# Early stopping based on absolute tolerance on squared change of
# centers position
if self._tol > 0.0 and centers_squared_diff <= self._tol:
if self.verbose:
print(f"Converged (small centers change) at step {step}/{n_steps}")
return True
# Early stopping heuristic due to lack of improvement on smoothed
# inertia
if self._ewa_inertia_min is None or self._ewa_inertia < self._ewa_inertia_min:
self._no_improvement = 0
self._ewa_inertia_min = self._ewa_inertia
else:
self._no_improvement += 1
if (
self.max_no_improvement is not None
and self._no_improvement >= self.max_no_improvement
):
if self.verbose:
print(
"Converged (lack of improvement in inertia) at step "
f"{step}/{n_steps}"
)
return True
return False
def _random_reassign(self):
"""Check if a random reassignment needs to be done.
Do random reassignments each time 10 * n_clusters samples have been
processed.
If there are empty clusters we always want to reassign.
"""
self._n_since_last_reassign += self._batch_size
if (self._counts == 0).any() or self._n_since_last_reassign >= (
10 * self.n_clusters
):
self._n_since_last_reassign = 0
return True
return False
def fit(self, X, y=None, sample_weight=None):
"""Compute the centroids on X by chunking it into mini-batches.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.
If a sparse matrix is passed, a copy will be made if it's not in
CSR format.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
.. versionadded:: 0.20
Returns
-------
self : object
Fitted estimator.
"""
self._validate_params()
X = self._validate_data(
X,
accept_sparse="csr",
dtype=[np.float64, np.float32],
order="C",
accept_large_sparse=False,
)
self._check_params_vs_input(X)
random_state = check_random_state(self.random_state)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
self._n_threads = _openmp_effective_n_threads()
n_samples, n_features = X.shape
# Validate init array
init = self.init
if _is_arraylike_not_scalar(init):
init = check_array(init, dtype=X.dtype, copy=True, order="C")
self._validate_center_shape(X, init)
self._check_mkl_vcomp(X, self._batch_size)
# precompute squared norms of data points
x_squared_norms = row_norms(X, squared=True)
# Validation set for the init
validation_indices = random_state.randint(0, n_samples, self._init_size)
X_valid = X[validation_indices]
sample_weight_valid = sample_weight[validation_indices]
# perform several inits with random subsets
best_inertia = None
for init_idx in range(self._n_init):
if self.verbose:
print(f"Init {init_idx + 1}/{self._n_init} with method {init}")
# Initialize the centers using only a fraction of the data as we
# expect n_samples to be very large when using MiniBatchKMeans.
cluster_centers = self._init_centroids(
X,
x_squared_norms=x_squared_norms,
init=init,
random_state=random_state,
init_size=self._init_size,
)
# Compute inertia on a validation set.
_, inertia = _labels_inertia_threadpool_limit(
X_valid,
sample_weight_valid,
cluster_centers,
n_threads=self._n_threads,
)
if self.verbose:
print(f"Inertia for init {init_idx + 1}/{self._n_init}: {inertia}")
if best_inertia is None or inertia < best_inertia:
init_centers = cluster_centers
best_inertia = inertia
centers = init_centers
centers_new = np.empty_like(centers)
# Initialize counts
self._counts = np.zeros(self.n_clusters, dtype=X.dtype)
# Attributes to monitor the convergence
self._ewa_inertia = None
self._ewa_inertia_min = None
self._no_improvement = 0
# Initialize number of samples seen since last reassignment
self._n_since_last_reassign = 0
n_steps = (self.max_iter * n_samples) // self._batch_size
with threadpool_limits(limits=1, user_api="blas"):
# Perform the iterative optimization until convergence
for i in range(n_steps):
# Sample a minibatch from the full dataset
minibatch_indices = random_state.randint(0, n_samples, self._batch_size)
# Perform the actual update step on the minibatch data
batch_inertia = _mini_batch_step(
X=X[minibatch_indices],
sample_weight=sample_weight[minibatch_indices],
centers=centers,
centers_new=centers_new,
weight_sums=self._counts,
random_state=random_state,
random_reassign=self._random_reassign(),
reassignment_ratio=self.reassignment_ratio,
verbose=self.verbose,
n_threads=self._n_threads,
)
if self._tol > 0.0:
centers_squared_diff = np.sum((centers_new - centers) ** 2)
else:
centers_squared_diff = 0
centers, centers_new = centers_new, centers
# Monitor convergence and do early stopping if necessary
if self._mini_batch_convergence(
i, n_steps, n_samples, centers_squared_diff, batch_inertia
):
break
self.cluster_centers_ = centers
self._n_features_out = self.cluster_centers_.shape[0]
self.n_steps_ = i + 1
self.n_iter_ = int(np.ceil(((i + 1) * self._batch_size) / n_samples))
if self.compute_labels:
self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(
X,
sample_weight,
self.cluster_centers_,
n_threads=self._n_threads,
)
else:
self.inertia_ = self._ewa_inertia * n_samples
return self
def partial_fit(self, X, y=None, sample_weight=None):
"""Update k means estimate on a single mini-batch X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.
If a sparse matrix is passed, a copy will be made if it's not in
CSR format.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight.
Returns
-------
self : object
Return updated estimator.
"""
has_centers = hasattr(self, "cluster_centers_")
if not has_centers:
self._validate_params()
X = self._validate_data(
X,
accept_sparse="csr",
dtype=[np.float64, np.float32],
order="C",
accept_large_sparse=False,
reset=not has_centers,
)
self._random_state = getattr(
self, "_random_state", check_random_state(self.random_state)
)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
self.n_steps_ = getattr(self, "n_steps_", 0)
# precompute squared norms of data points
x_squared_norms = row_norms(X, squared=True)
if not has_centers:
# this instance has not been fitted yet (fit or partial_fit)
self._check_params_vs_input(X)
self._n_threads = _openmp_effective_n_threads()
# Validate init array
init = self.init
if _is_arraylike_not_scalar(init):
init = check_array(init, dtype=X.dtype, copy=True, order="C")
self._validate_center_shape(X, init)
self._check_mkl_vcomp(X, X.shape[0])
# initialize the cluster centers
self.cluster_centers_ = self._init_centroids(
X,
x_squared_norms=x_squared_norms,
init=init,
random_state=self._random_state,
init_size=self._init_size,
)
# Initialize counts
self._counts = np.zeros(self.n_clusters, dtype=X.dtype)
# Initialize number of samples seen since last reassignment
self._n_since_last_reassign = 0
with threadpool_limits(limits=1, user_api="blas"):
_mini_batch_step(
X,
sample_weight=sample_weight,
centers=self.cluster_centers_,
centers_new=self.cluster_centers_,
weight_sums=self._counts,
random_state=self._random_state,
random_reassign=self._random_reassign(),
reassignment_ratio=self.reassignment_ratio,
verbose=self.verbose,
n_threads=self._n_threads,
)
if self.compute_labels:
self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(
X,
sample_weight,
self.cluster_centers_,
n_threads=self._n_threads,
)
self.n_steps_ += 1
self._n_features_out = self.cluster_centers_.shape[0]
return self
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