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_mds.py
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"""
Multi-dimensional Scaling (MDS).
"""
# author: Nelle Varoquaux <nelle.varoquaux@gmail.com>
# License: BSD
import numpy as np
from joblib import Parallel, effective_n_jobs
import warnings
from ..base import BaseEstimator
from ..metrics import euclidean_distances
from ..utils import check_random_state, check_array, check_symmetric
from ..isotonic import IsotonicRegression
from ..utils.fixes import delayed
def _smacof_single(
dissimilarities,
metric=True,
n_components=2,
init=None,
max_iter=300,
verbose=0,
eps=1e-3,
random_state=None,
):
"""Computes multidimensional scaling using SMACOF algorithm.
Parameters
----------
dissimilarities : ndarray of shape (n_samples, n_samples)
Pairwise dissimilarities between the points. Must be symmetric.
metric : bool, default=True
Compute metric or nonmetric SMACOF algorithm.
n_components : int, default=2
Number of dimensions in which to immerse the dissimilarities. If an
``init`` array is provided, this option is overridden and the shape of
``init`` is used to determine the dimensionality of the embedding
space.
init : ndarray of shape (n_samples, n_components), default=None
Starting configuration of the embedding to initialize the algorithm. By
default, the algorithm is initialized with a randomly chosen array.
max_iter : int, default=300
Maximum number of iterations of the SMACOF algorithm for a single run.
verbose : int, default=0
Level of verbosity.
eps : float, default=1e-3
Relative tolerance with respect to stress at which to declare
convergence.
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
X : ndarray of shape (n_samples, n_components)
Coordinates of the points in a ``n_components``-space.
stress : float
The final value of the stress (sum of squared distance of the
disparities and the distances for all constrained points).
n_iter : int
The number of iterations corresponding to the best stress.
"""
dissimilarities = check_symmetric(dissimilarities, raise_exception=True)
n_samples = dissimilarities.shape[0]
random_state = check_random_state(random_state)
sim_flat = ((1 - np.tri(n_samples)) * dissimilarities).ravel()
sim_flat_w = sim_flat[sim_flat != 0]
if init is None:
# Randomly choose initial configuration
X = random_state.uniform(size=n_samples * n_components)
X = X.reshape((n_samples, n_components))
else:
# overrides the parameter p
n_components = init.shape[1]
if n_samples != init.shape[0]:
raise ValueError(
"init matrix should be of shape (%d, %d)" % (n_samples, n_components)
)
X = init
old_stress = None
ir = IsotonicRegression()
for it in range(max_iter):
# Compute distance and monotonic regression
dis = euclidean_distances(X)
if metric:
disparities = dissimilarities
else:
dis_flat = dis.ravel()
# dissimilarities with 0 are considered as missing values
dis_flat_w = dis_flat[sim_flat != 0]
# Compute the disparities using a monotonic regression
disparities_flat = ir.fit_transform(sim_flat_w, dis_flat_w)
disparities = dis_flat.copy()
disparities[sim_flat != 0] = disparities_flat
disparities = disparities.reshape((n_samples, n_samples))
disparities *= np.sqrt(
(n_samples * (n_samples - 1) / 2) / (disparities**2).sum()
)
# Compute stress
stress = ((dis.ravel() - disparities.ravel()) ** 2).sum() / 2
# Update X using the Guttman transform
dis[dis == 0] = 1e-5
ratio = disparities / dis
B = -ratio
B[np.arange(len(B)), np.arange(len(B))] += ratio.sum(axis=1)
X = 1.0 / n_samples * np.dot(B, X)
dis = np.sqrt((X**2).sum(axis=1)).sum()
if verbose >= 2:
print("it: %d, stress %s" % (it, stress))
if old_stress is not None:
if (old_stress - stress / dis) < eps:
if verbose:
print("breaking at iteration %d with stress %s" % (it, stress))
break
old_stress = stress / dis
return X, stress, it + 1
def smacof(
dissimilarities,
*,
metric=True,
n_components=2,
init=None,
n_init=8,
n_jobs=None,
max_iter=300,
verbose=0,
eps=1e-3,
random_state=None,
return_n_iter=False,
):
"""Compute multidimensional scaling using the SMACOF algorithm.
The SMACOF (Scaling by MAjorizing a COmplicated Function) algorithm is a
multidimensional scaling algorithm which minimizes an objective function
(the *stress*) using a majorization technique. Stress majorization, also
known as the Guttman Transform, guarantees a monotone convergence of
stress, and is more powerful than traditional techniques such as gradient
descent.
The SMACOF algorithm for metric MDS can be summarized by the following
steps:
1. Set an initial start configuration, randomly or not.
2. Compute the stress
3. Compute the Guttman Transform
4. Iterate 2 and 3 until convergence.
The nonmetric algorithm adds a monotonic regression step before computing
the stress.
Parameters
----------
dissimilarities : ndarray of shape (n_samples, n_samples)
Pairwise dissimilarities between the points. Must be symmetric.
metric : bool, default=True
Compute metric or nonmetric SMACOF algorithm.
n_components : int, default=2
Number of dimensions in which to immerse the dissimilarities. If an
``init`` array is provided, this option is overridden and the shape of
``init`` is used to determine the dimensionality of the embedding
space.
init : ndarray of shape (n_samples, n_components), default=None
Starting configuration of the embedding to initialize the algorithm. By
default, the algorithm is initialized with a randomly chosen array.
n_init : int, default=8
Number of times the SMACOF algorithm will be run with different
initializations. The final results will be the best output of the runs,
determined by the run with the smallest final stress. If ``init`` is
provided, this option is overridden and a single run is performed.
n_jobs : int, default=None
The number of jobs to use for the computation. If multiple
initializations are used (``n_init``), each run of the algorithm is
computed in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
max_iter : int, default=300
Maximum number of iterations of the SMACOF algorithm for a single run.
verbose : int, default=0
Level of verbosity.
eps : float, default=1e-3
Relative tolerance with respect to stress at which to declare
convergence.
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Returns
-------
X : ndarray of shape (n_samples, n_components)
Coordinates of the points in a ``n_components``-space.
stress : float
The final value of the stress (sum of squared distance of the
disparities and the distances for all constrained points).
n_iter : int
The number of iterations corresponding to the best stress. Returned
only if ``return_n_iter`` is set to ``True``.
Notes
-----
"Modern Multidimensional Scaling - Theory and Applications" Borg, I.;
Groenen P. Springer Series in Statistics (1997)
"Nonmetric multidimensional scaling: a numerical method" Kruskal, J.
Psychometrika, 29 (1964)
"Multidimensional scaling by optimizing goodness of fit to a nonmetric
hypothesis" Kruskal, J. Psychometrika, 29, (1964)
"""
dissimilarities = check_array(dissimilarities)
random_state = check_random_state(random_state)
if hasattr(init, "__array__"):
init = np.asarray(init).copy()
if not n_init == 1:
warnings.warn(
"Explicit initial positions passed: "
"performing only one init of the MDS instead of %d" % n_init
)
n_init = 1
best_pos, best_stress = None, None
if effective_n_jobs(n_jobs) == 1:
for it in range(n_init):
pos, stress, n_iter_ = _smacof_single(
dissimilarities,
metric=metric,
n_components=n_components,
init=init,
max_iter=max_iter,
verbose=verbose,
eps=eps,
random_state=random_state,
)
if best_stress is None or stress < best_stress:
best_stress = stress
best_pos = pos.copy()
best_iter = n_iter_
else:
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
results = Parallel(n_jobs=n_jobs, verbose=max(verbose - 1, 0))(
delayed(_smacof_single)(
dissimilarities,
metric=metric,
n_components=n_components,
init=init,
max_iter=max_iter,
verbose=verbose,
eps=eps,
random_state=seed,
)
for seed in seeds
)
positions, stress, n_iters = zip(*results)
best = np.argmin(stress)
best_stress = stress[best]
best_pos = positions[best]
best_iter = n_iters[best]
if return_n_iter:
return best_pos, best_stress, best_iter
else:
return best_pos, best_stress
class MDS(BaseEstimator):
"""Multidimensional scaling.
Read more in the :ref:`User Guide <multidimensional_scaling>`.
Parameters
----------
n_components : int, default=2
Number of dimensions in which to immerse the dissimilarities.
metric : bool, default=True
If ``True``, perform metric MDS; otherwise, perform nonmetric MDS.
n_init : int, default=4
Number of times the SMACOF algorithm will be run with different
initializations. The final results will be the best output of the runs,
determined by the run with the smallest final stress.
max_iter : int, default=300
Maximum number of iterations of the SMACOF algorithm for a single run.
verbose : int, default=0
Level of verbosity.
eps : float, default=1e-3
Relative tolerance with respect to stress at which to declare
convergence.
n_jobs : int, default=None
The number of jobs to use for the computation. If multiple
initializations are used (``n_init``), each run of the algorithm is
computed in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
dissimilarity : {'euclidean', 'precomputed'}, default='euclidean'
Dissimilarity measure to use:
- 'euclidean':
Pairwise Euclidean distances between points in the dataset.
- 'precomputed':
Pre-computed dissimilarities are passed directly to ``fit`` and
``fit_transform``.
Attributes
----------
embedding_ : ndarray of shape (n_samples, n_components)
Stores the position of the dataset in the embedding space.
stress_ : float
The final value of the stress (sum of squared distance of the
disparities and the distances for all constrained points).
dissimilarity_matrix_ : ndarray of shape (n_samples, n_samples)
Pairwise dissimilarities between the points. Symmetric matrix that:
- either uses a custom dissimilarity matrix by setting `dissimilarity`
to 'precomputed';
- or constructs a dissimilarity matrix from data using
Euclidean distances.
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
n_iter_ : int
The number of iterations corresponding to the best stress.
See Also
--------
sklearn.decomposition.PCA : Principal component analysis that is a linear
dimensionality reduction method.
sklearn.decomposition.KernelPCA : Non-linear dimensionality reduction using
kernels and PCA.
TSNE : T-distributed Stochastic Neighbor Embedding.
Isomap : Manifold learning based on Isometric Mapping.
LocallyLinearEmbedding : Manifold learning using Locally Linear Embedding.
SpectralEmbedding : Spectral embedding for non-linear dimensionality.
References
----------
"Modern Multidimensional Scaling - Theory and Applications" Borg, I.;
Groenen P. Springer Series in Statistics (1997)
"Nonmetric multidimensional scaling: a numerical method" Kruskal, J.
Psychometrika, 29 (1964)
"Multidimensional scaling by optimizing goodness of fit to a nonmetric
hypothesis" Kruskal, J. Psychometrika, 29, (1964)
Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import MDS
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = MDS(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)
"""
def __init__(
self,
n_components=2,
*,
metric=True,
n_init=4,
max_iter=300,
verbose=0,
eps=1e-3,
n_jobs=None,
random_state=None,
dissimilarity="euclidean",
):
self.n_components = n_components
self.dissimilarity = dissimilarity
self.metric = metric
self.n_init = n_init
self.max_iter = max_iter
self.eps = eps
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
def _more_tags(self):
return {"pairwise": self.dissimilarity == "precomputed"}
def fit(self, X, y=None, init=None):
"""
Compute the position of the points in the embedding space.
Parameters
----------
X : array-like of shape (n_samples, n_features) or \
(n_samples, n_samples)
Input data. If ``dissimilarity=='precomputed'``, the input should
be the dissimilarity matrix.
y : Ignored
Not used, present for API consistency by convention.
init : ndarray of shape (n_samples,), default=None
Starting configuration of the embedding to initialize the SMACOF
algorithm. By default, the algorithm is initialized with a randomly
chosen array.
Returns
-------
self : object
Fitted estimator.
"""
self.fit_transform(X, init=init)
return self
def fit_transform(self, X, y=None, init=None):
"""
Fit the data from `X`, and returns the embedded coordinates.
Parameters
----------
X : array-like of shape (n_samples, n_features) or \
(n_samples, n_samples)
Input data. If ``dissimilarity=='precomputed'``, the input should
be the dissimilarity matrix.
y : Ignored
Not used, present for API consistency by convention.
init : ndarray of shape (n_samples,), default=None
Starting configuration of the embedding to initialize the SMACOF
algorithm. By default, the algorithm is initialized with a randomly
chosen array.
Returns
-------
X_new : ndarray of shape (n_samples, n_components)
X transformed in the new space.
"""
X = self._validate_data(X)
if X.shape[0] == X.shape[1] and self.dissimilarity != "precomputed":
warnings.warn(
"The MDS API has changed. ``fit`` now constructs an"
" dissimilarity matrix from data. To use a custom "
"dissimilarity matrix, set "
"``dissimilarity='precomputed'``."
)
if self.dissimilarity == "precomputed":
self.dissimilarity_matrix_ = X
elif self.dissimilarity == "euclidean":
self.dissimilarity_matrix_ = euclidean_distances(X)
else:
raise ValueError(
"Proximity must be 'precomputed' or 'euclidean'. Got %s instead"
% str(self.dissimilarity)
)
self.embedding_, self.stress_, self.n_iter_ = smacof(
self.dissimilarity_matrix_,
metric=self.metric,
n_components=self.n_components,
init=init,
n_init=self.n_init,
n_jobs=self.n_jobs,
max_iter=self.max_iter,
verbose=self.verbose,
eps=self.eps,
random_state=self.random_state,
return_n_iter=True,
)
return self.embedding_