"""Algorithms for spectral clustering"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import warnings
from numbers import Integral, Real

import numpy as np
from scipy.linalg import LinAlgError, qr, svd
from scipy.sparse import csc_matrix

from sklearn.base import BaseEstimator, ClusterMixin, _fit_context
from sklearn.cluster._kmeans import k_means
from sklearn.manifold._spectral_embedding import _spectral_embedding
from sklearn.metrics.pairwise import KERNEL_PARAMS, pairwise_kernels
from sklearn.neighbors import NearestNeighbors, kneighbors_graph
from sklearn.utils import as_float_array, check_random_state
from sklearn.utils._param_validation import Interval, StrOptions, validate_params
from sklearn.utils.validation import validate_data


def cluster_qr(vectors):
    """Find the discrete partition closest to the eigenvector embedding.

        This implementation was proposed in [1]_.

    .. versionadded:: 1.1

        Parameters
        ----------
        vectors : array-like, shape: (n_samples, n_clusters)
            The embedding space of the samples.

        Returns
        -------
        labels : array of integers, shape: n_samples
            The cluster labels of vectors.

        References
        ----------
        .. [1] :doi:`Simple, direct, and efficient multi-way spectral clustering, 2019
            Anil Damle, Victor Minden, Lexing Ying
            <10.1093/imaiai/iay008>`

    """

    k = vectors.shape[1]
    _, _, piv = qr(vectors.T, pivoting=True)
    ut, _, v = svd(vectors[piv[:k], :].T)
    vectors = abs(np.dot(vectors, np.dot(ut, v.conj())))
    return vectors.argmax(axis=1)


def discretize(
    vectors, *, copy=True, max_svd_restarts=30, n_iter_max=20, random_state=None
):
    """Search for a partition matrix which is closest to the eigenvector embedding.

    This implementation was proposed in [1]_.

    Parameters
    ----------
    vectors : array-like of shape (n_samples, n_clusters)
        The embedding space of the samples.

    copy : bool, default=True
        Whether to copy vectors, or perform in-place normalization.

    max_svd_restarts : int, default=30
        Maximum number of attempts to restart SVD if convergence fails

    n_iter_max : int, default=30
        Maximum number of iterations to attempt in rotation and partition
        matrix search if machine precision convergence is not reached

    random_state : int, RandomState instance, default=None
        Determines random number generation for rotation matrix initialization.
        Use an int to make the randomness deterministic.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    labels : array of integers, shape: n_samples
        The labels of the clusters.

    References
    ----------

    .. [1] `Multiclass spectral clustering, 2003
           Stella X. Yu, Jianbo Shi
           <https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/yu-shi.pdf>`_

    Notes
    -----

    The eigenvector embedding is used to iteratively search for the
    closest discrete partition.  First, the eigenvector embedding is
    normalized to the space of partition matrices. An optimal discrete
    partition matrix closest to this normalized embedding multiplied by
    an initial rotation is calculated.  Fixing this discrete partition
    matrix, an optimal rotation matrix is calculated.  These two
    calculations are performed until convergence.  The discrete partition
    matrix is returned as the clustering solution.  Used in spectral
    clustering, this method tends to be faster and more robust to random
    initialization than k-means.

    """

    random_state = check_random_state(random_state)

    vectors = as_float_array(vectors, copy=copy)

    eps = np.finfo(float).eps
    n_samples, n_components = vectors.shape

    # Normalize the eigenvectors to an equal length of a vector of ones.
    # Reorient the eigenvectors to point in the negative direction with respect
    # to the first element.  This may have to do with constraining the
    # eigenvectors to lie in a specific quadrant to make the discretization
    # search easier.
    norm_ones = np.sqrt(n_samples)
    for i in range(vectors.shape[1]):
        vectors[:, i] = (vectors[:, i] / np.linalg.norm(vectors[:, i])) * norm_ones
        if vectors[0, i] != 0:
            vectors[:, i] = -1 * vectors[:, i] * np.sign(vectors[0, i])

    # Normalize the rows of the eigenvectors.  Samples should lie on the unit
    # hypersphere centered at the origin.  This transforms the samples in the
    # embedding space to the space of partition matrices.
    vectors = vectors / np.sqrt((vectors**2).sum(axis=1))[:, np.newaxis]

    svd_restarts = 0
    has_converged = False

    # If there is an exception we try to randomize and rerun SVD again
    # do this max_svd_restarts times.
    while (svd_restarts < max_svd_restarts) and not has_converged:
        # Initialize first column of rotation matrix with a row of the
        # eigenvectors
        rotation = np.zeros((n_components, n_components))
        rotation[:, 0] = vectors[random_state.randint(n_samples), :].T

        # To initialize the rest of the rotation matrix, find the rows
        # of the eigenvectors that are as orthogonal to each other as
        # possible
        c = np.zeros(n_samples)
        for j in range(1, n_components):
            # Accumulate c to ensure row is as orthogonal as possible to
            # previous picks as well as current one
            c += np.abs(np.dot(vectors, rotation[:, j - 1]))
            rotation[:, j] = vectors[c.argmin(), :].T

        last_objective_value = 0.0
        n_iter = 0

        while not has_converged:
            n_iter += 1

            t_discrete = np.dot(vectors, rotation)

            labels = t_discrete.argmax(axis=1)
            vectors_discrete = csc_matrix(
                (np.ones(len(labels)), (np.arange(0, n_samples), labels)),
                shape=(n_samples, n_components),
            )

            t_svd = vectors_discrete.T @ vectors

            try:
                U, S, Vh = np.linalg.svd(t_svd)
            except LinAlgError:
                svd_restarts += 1
                print("SVD did not converge, randomizing and trying again")
                break

            ncut_value = 2.0 * (n_samples - S.sum())
            if (abs(ncut_value - last_objective_value) < eps) or (n_iter > n_iter_max):
                has_converged = True
            else:
                # otherwise calculate rotation and continue
                last_objective_value = ncut_value
                rotation = np.dot(Vh.T, U.T)

    if not has_converged:
        raise LinAlgError("SVD did not converge")
    return labels


@validate_params(
    {"affinity": ["array-like", "sparse matrix"]},
    prefer_skip_nested_validation=False,
)
def spectral_clustering(
    affinity,
    *,
    n_clusters=8,
    n_components=None,
    eigen_solver=None,
    random_state=None,
    n_init=10,
    eigen_tol="auto",
    assign_labels="kmeans",
    verbose=False,
):
    """Apply clustering to a projection of the normalized Laplacian.

    In practice Spectral Clustering is very useful when the structure of
    the individual clusters is highly non-convex or more generally when
    a measure of the center and spread of the cluster is not a suitable
    description of the complete cluster. For instance, when clusters are
    nested circles on the 2D plane.

    If affinity is the adjacency matrix of a graph, this method can be
    used to find normalized graph cuts [1]_, [2]_.

    Read more in the :ref:`User Guide <spectral_clustering>`.

    Parameters
    ----------
    affinity : {array-like, sparse matrix} of shape (n_samples, n_samples)
        The affinity matrix describing the relationship of the samples to
        embed. **Must be symmetric**.

        Possible examples:
          - adjacency matrix of a graph,
          - heat kernel of the pairwise distance matrix of the samples,
          - symmetric k-nearest neighbours connectivity matrix of the samples.

    n_clusters : int, default=None
        Number of clusters to extract.

    n_components : int, default=n_clusters
        Number of eigenvectors to use for the spectral embedding.

    eigen_solver : {None, 'arpack', 'lobpcg', or 'amg'}
        The eigenvalue decomposition method. If None then ``'arpack'`` is used.
        See [4]_ for more details regarding ``'lobpcg'``.
        Eigensolver ``'amg'`` runs ``'lobpcg'`` with optional
        Algebraic MultiGrid preconditioning and requires pyamg to be installed.
        It can be faster on very large sparse problems [6]_ and [7]_.

    random_state : int, RandomState instance, default=None
        A pseudo random number generator used for the initialization
        of the lobpcg eigenvectors decomposition when `eigen_solver ==
        'amg'`, and for the K-Means initialization. Use an int to make
        the results deterministic across calls (See
        :term:`Glossary <random_state>`).

        .. note::
            When using `eigen_solver == 'amg'`,
            it is necessary to also fix the global numpy seed with
            `np.random.seed(int)` to get deterministic results. See
            https://github.com/pyamg/pyamg/issues/139 for further
            information.

    n_init : 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. Only used if
        ``assign_labels='kmeans'``.

    eigen_tol : float, default="auto"
        Stopping criterion for eigendecomposition of the Laplacian matrix.
        If `eigen_tol="auto"` then the passed tolerance will depend on the
        `eigen_solver`:

        - If `eigen_solver="arpack"`, then `eigen_tol=0.0`;
        - If `eigen_solver="lobpcg"` or `eigen_solver="amg"`, then
          `eigen_tol=None` which configures the underlying `lobpcg` solver to
          automatically resolve the value according to their heuristics. See,
          :func:`scipy.sparse.linalg.lobpcg` for details.

        Note that when using `eigen_solver="lobpcg"` or `eigen_solver="amg"`
        values of `tol<1e-5` may lead to convergence issues and should be
        avoided.

        .. versionadded:: 1.2
           Added 'auto' option.

    assign_labels : {'kmeans', 'discretize', 'cluster_qr'}, default='kmeans'
        The strategy to use to assign labels in the embedding
        space.  There are three ways to assign labels after the Laplacian
        embedding.  k-means can be applied and is a popular choice. But it can
        also be sensitive to initialization. Discretization is another
        approach which is less sensitive to random initialization [3]_.
        The cluster_qr method [5]_ directly extracts clusters from eigenvectors
        in spectral clustering. In contrast to k-means and discretization, cluster_qr
        has no tuning parameters and is not an iterative method, yet may outperform
        k-means and discretization in terms of both quality and speed. For a detailed
        comparison of clustering strategies, refer to the following example:
        :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`.

        .. versionchanged:: 1.1
           Added new labeling method 'cluster_qr'.

    verbose : bool, default=False
        Verbosity mode.

        .. versionadded:: 0.24

    Returns
    -------
    labels : array of integers, shape: n_samples
        The labels of the clusters.

    Notes
    -----
    The graph should contain only one connected component, elsewhere
    the results make little sense.

    This algorithm solves the normalized cut for `k=2`: it is a
    normalized spectral clustering.

    References
    ----------

    .. [1] :doi:`Normalized cuts and image segmentation, 2000
           Jianbo Shi, Jitendra Malik
           <10.1109/34.868688>`

    .. [2] :doi:`A Tutorial on Spectral Clustering, 2007
           Ulrike von Luxburg
           <10.1007/s11222-007-9033-z>`

    .. [3] `Multiclass spectral clustering, 2003
           Stella X. Yu, Jianbo Shi
           <https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/yu-shi.pdf>`_

    .. [4] :doi:`Toward the Optimal Preconditioned Eigensolver:
           Locally Optimal Block Preconditioned Conjugate Gradient Method, 2001
           A. V. Knyazev
           SIAM Journal on Scientific Computing 23, no. 2, pp. 517-541.
           <10.1137/S1064827500366124>`

    .. [5] :doi:`Simple, direct, and efficient multi-way spectral clustering, 2019
           Anil Damle, Victor Minden, Lexing Ying
           <10.1093/imaiai/iay008>`

    .. [6] :doi:`Multiscale Spectral Image Segmentation Multiscale preconditioning
           for computing eigenvalues of graph Laplacians in image segmentation, 2006
           Andrew Knyazev
           <10.13140/RG.2.2.35280.02565>`

    .. [7] :doi:`Preconditioned spectral clustering for stochastic block partition
           streaming graph challenge (Preliminary version at arXiv.)
           David Zhuzhunashvili, Andrew Knyazev
           <10.1109/HPEC.2017.8091045>`

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics.pairwise import pairwise_kernels
    >>> from sklearn.cluster import spectral_clustering
    >>> X = np.array([[1, 1], [2, 1], [1, 0],
    ...               [4, 7], [3, 5], [3, 6]])
    >>> affinity = pairwise_kernels(X, metric='rbf')
    >>> spectral_clustering(
    ...     affinity=affinity, n_clusters=2, assign_labels="discretize", random_state=0
    ... )
    array([1, 1, 1, 0, 0, 0])
    """

    clusterer = SpectralClustering(
        n_clusters=n_clusters,
        n_components=n_components,
        eigen_solver=eigen_solver,
        random_state=random_state,
        n_init=n_init,
        affinity="precomputed",
        eigen_tol=eigen_tol,
        assign_labels=assign_labels,
        verbose=verbose,
    ).fit(affinity)

    return clusterer.labels_


class SpectralClustering(ClusterMixin, BaseEstimator):
    """Apply clustering to a projection of the normalized Laplacian.

    In practice Spectral Clustering is very useful when the structure of
    the individual clusters is highly non-convex, or more generally when
    a measure of the center and spread of the cluster is not a suitable
    description of the complete cluster, such as when clusters are
    nested circles on the 2D plane.

    If the affinity matrix is the adjacency matrix of a graph, this method
    can be used to find normalized graph cuts [1]_, [2]_.

    When calling ``fit``, an affinity matrix is constructed using either
    a kernel function such the Gaussian (aka RBF) kernel with Euclidean
    distance ``d(X, X)``::

            np.exp(-gamma * d(X,X) ** 2)

    or a k-nearest neighbors connectivity matrix.

    Alternatively, a user-provided affinity matrix can be specified by
    setting ``affinity='precomputed'``.

    Read more in the :ref:`User Guide <spectral_clustering>`.

    Parameters
    ----------
    n_clusters : int, default=8
        The dimension of the projection subspace.

    eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None
        The eigenvalue decomposition strategy to use. AMG requires pyamg
        to be installed. It can be faster on very large, sparse problems,
        but may also lead to instabilities. If None, then ``'arpack'`` is
        used. See [4]_ for more details regarding `'lobpcg'`.

    n_components : int, default=None
        Number of eigenvectors to use for the spectral embedding. If None,
        defaults to `n_clusters`.

    random_state : int, RandomState instance, default=None
        A pseudo random number generator used for the initialization
        of the lobpcg eigenvectors decomposition when `eigen_solver ==
        'amg'`, and for the K-Means initialization. Use an int to make
        the results deterministic across calls (See
        :term:`Glossary <random_state>`).

        .. note::
            When using `eigen_solver == 'amg'`,
            it is necessary to also fix the global numpy seed with
            `np.random.seed(int)` to get deterministic results. See
            https://github.com/pyamg/pyamg/issues/139 for further
            information.

    n_init : 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. Only used if
        ``assign_labels='kmeans'``.

    gamma : float, default=1.0
        Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels.
        Ignored for ``affinity='nearest_neighbors'``, ``affinity='precomputed'``
        or ``affinity='precomputed_nearest_neighbors'``.

    affinity : str or callable, default='rbf'
        How to construct the affinity matrix.
         - 'nearest_neighbors': construct the affinity matrix by computing a
           graph of nearest neighbors.
         - 'rbf': construct the affinity matrix using a radial basis function
           (RBF) kernel.
         - 'precomputed': interpret ``X`` as a precomputed affinity matrix,
           where larger values indicate greater similarity between instances.
         - 'precomputed_nearest_neighbors': interpret ``X`` as a sparse graph
           of precomputed distances, and construct a binary affinity matrix
           from the ``n_neighbors`` nearest neighbors of each instance.
         - one of the kernels supported by
           :func:`~sklearn.metrics.pairwise.pairwise_kernels`.

        Only kernels that produce similarity scores (non-negative values that
        increase with similarity) should be used. This property is not checked
        by the clustering algorithm.

    n_neighbors : int, default=10
        Number of neighbors to use when constructing the affinity matrix using
        the nearest neighbors method. Ignored for ``affinity='rbf'``.

    eigen_tol : float, default="auto"
        Stopping criterion for eigen decomposition of the Laplacian matrix.
        If `eigen_tol="auto"` then the passed tolerance will depend on the
        `eigen_solver`:

        - If `eigen_solver="arpack"`, then `eigen_tol=0.0`;
        - If `eigen_solver="lobpcg"` or `eigen_solver="amg"`, then
          `eigen_tol=None` which configures the underlying `lobpcg` solver to
          automatically resolve the value according to their heuristics. See,
          :func:`scipy.sparse.linalg.lobpcg` for details.

        Note that when using `eigen_solver="lobpcg"` or `eigen_solver="amg"`
        values of `tol<1e-5` may lead to convergence issues and should be
        avoided.

        .. versionadded:: 1.2
           Added 'auto' option.

    assign_labels : {'kmeans', 'discretize', 'cluster_qr'}, default='kmeans'
        The strategy for assigning labels in the embedding space. There are two
        ways to assign labels after the Laplacian embedding. k-means is a
        popular choice, but it can be sensitive to initialization.
        Discretization is another approach which is less sensitive to random
        initialization [3]_.
        The cluster_qr method [5]_ directly extract clusters from eigenvectors
        in spectral clustering. In contrast to k-means and discretization, cluster_qr
        has no tuning parameters and runs no iterations, yet may outperform
        k-means and discretization in terms of both quality and speed.

        .. versionchanged:: 1.1
           Added new labeling method 'cluster_qr'.

    degree : float, default=3
        Degree of the polynomial kernel. Ignored by other kernels.

    coef0 : float, default=1
        Zero coefficient for polynomial and sigmoid kernels.
        Ignored by other kernels.

    kernel_params : dict of str to any, default=None
        Parameters (keyword arguments) and values for kernel passed as
        callable object. Ignored by other kernels.

    n_jobs : int, default=None
        The number of parallel jobs to run when `affinity='nearest_neighbors'`
        or `affinity='precomputed_nearest_neighbors'`. The neighbors search
        will be done 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.

    verbose : bool, default=False
        Verbosity mode.

        .. versionadded:: 0.24

    Attributes
    ----------
    affinity_matrix_ : array-like of shape (n_samples, n_samples)
        Affinity matrix used for clustering. Available only after calling
        ``fit``.

    labels_ : ndarray of shape (n_samples,)
        Labels of each point

    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
    --------
    sklearn.cluster.KMeans : K-Means clustering.
    sklearn.cluster.DBSCAN : Density-Based Spatial Clustering of
        Applications with Noise.

    Notes
    -----
    A distance matrix for which 0 indicates identical elements and high values
    indicate very dissimilar elements can be transformed into an affinity /
    similarity matrix that is well-suited for the algorithm by
    applying the Gaussian (aka RBF, heat) kernel::

        np.exp(- dist_matrix ** 2 / (2. * delta ** 2))

    where ``delta`` is a free parameter representing the width of the Gaussian
    kernel.

    An alternative is to take a symmetric version of the k-nearest neighbors
    connectivity matrix of the points.

    If the pyamg package is installed, it is used: this greatly
    speeds up computation.

    References
    ----------
    .. [1] :doi:`Normalized cuts and image segmentation, 2000
           Jianbo Shi, Jitendra Malik
           <10.1109/34.868688>`

    .. [2] :doi:`A Tutorial on Spectral Clustering, 2007
           Ulrike von Luxburg
           <10.1007/s11222-007-9033-z>`

    .. [3] `Multiclass spectral clustering, 2003
           Stella X. Yu, Jianbo Shi
           <https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/yu-shi.pdf>`_

    .. [4] :doi:`Toward the Optimal Preconditioned Eigensolver:
           Locally Optimal Block Preconditioned Conjugate Gradient Method, 2001
           A. V. Knyazev
           SIAM Journal on Scientific Computing 23, no. 2, pp. 517-541.
           <10.1137/S1064827500366124>`

    .. [5] :doi:`Simple, direct, and efficient multi-way spectral clustering, 2019
           Anil Damle, Victor Minden, Lexing Ying
           <10.1093/imaiai/iay008>`

    Examples
    --------
    >>> from sklearn.cluster import SpectralClustering
    >>> import numpy as np
    >>> X = np.array([[1, 1], [2, 1], [1, 0],
    ...               [4, 7], [3, 5], [3, 6]])
    >>> clustering = SpectralClustering(n_clusters=2,
    ...         assign_labels='discretize',
    ...         random_state=0).fit(X)
    >>> clustering.labels_
    array([1, 1, 1, 0, 0, 0])
    >>> clustering
    SpectralClustering(assign_labels='discretize', n_clusters=2,
        random_state=0)

    For a comparison of Spectral clustering with other clustering algorithms, see
    :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
    """

    _parameter_constraints: dict = {
        "n_clusters": [Interval(Integral, 1, None, closed="left")],
        "eigen_solver": [StrOptions({"arpack", "lobpcg", "amg"}), None],
        "n_components": [Interval(Integral, 1, None, closed="left"), None],
        "random_state": ["random_state"],
        "n_init": [Interval(Integral, 1, None, closed="left")],
        "gamma": [Interval(Real, 0, None, closed="left")],
        "affinity": [
            callable,
            StrOptions(
                set(KERNEL_PARAMS)
                | {"nearest_neighbors", "precomputed", "precomputed_nearest_neighbors"}
            ),
        ],
        "n_neighbors": [Interval(Integral, 1, None, closed="left")],
        "eigen_tol": [
            Interval(Real, 0.0, None, closed="left"),
            StrOptions({"auto"}),
        ],
        "assign_labels": [StrOptions({"kmeans", "discretize", "cluster_qr"})],
        "degree": [Interval(Real, 0, None, closed="left")],
        "coef0": [Interval(Real, None, None, closed="neither")],
        "kernel_params": [dict, None],
        "n_jobs": [Integral, None],
        "verbose": ["verbose"],
    }

    def __init__(
        self,
        n_clusters=8,
        *,
        eigen_solver=None,
        n_components=None,
        random_state=None,
        n_init=10,
        gamma=1.0,
        affinity="rbf",
        n_neighbors=10,
        eigen_tol="auto",
        assign_labels="kmeans",
        degree=3,
        coef0=1,
        kernel_params=None,
        n_jobs=None,
        verbose=False,
    ):
        self.n_clusters = n_clusters
        self.eigen_solver = eigen_solver
        self.n_components = n_components
        self.random_state = random_state
        self.n_init = n_init
        self.gamma = gamma
        self.affinity = affinity
        self.n_neighbors = n_neighbors
        self.eigen_tol = eigen_tol
        self.assign_labels = assign_labels
        self.degree = degree
        self.coef0 = coef0
        self.kernel_params = kernel_params
        self.n_jobs = n_jobs
        self.verbose = verbose

    @_fit_context(prefer_skip_nested_validation=True)
    def fit(self, X, y=None):
        """Perform spectral clustering from features, or affinity matrix.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
                (n_samples, n_samples)
            Training instances to cluster, similarities / affinities between
            instances if ``affinity='precomputed'``, or distances between
            instances if ``affinity='precomputed_nearest_neighbors``. If a
            sparse matrix is provided in a format other than ``csr_matrix``,
            ``csc_matrix``, or ``coo_matrix``, it will be converted into a
            sparse ``csr_matrix``.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            A fitted instance of the estimator.
        """
        X = validate_data(
            self,
            X,
            accept_sparse=["csr", "csc", "coo"],
            dtype=np.float64,
            ensure_min_samples=2,
        )
        allow_squared = self.affinity in [
            "precomputed",
            "precomputed_nearest_neighbors",
        ]
        if X.shape[0] == X.shape[1] and not allow_squared:
            warnings.warn(
                "The spectral clustering API has changed. ``fit``"
                "now constructs an affinity matrix from data. To use"
                " a custom affinity matrix, "
                "set ``affinity=precomputed``."
            )

        if self.affinity == "nearest_neighbors":
            connectivity = kneighbors_graph(
                X, n_neighbors=self.n_neighbors, include_self=True, n_jobs=self.n_jobs
            )
            self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)
        elif self.affinity == "precomputed_nearest_neighbors":
            estimator = NearestNeighbors(
                n_neighbors=self.n_neighbors, n_jobs=self.n_jobs, metric="precomputed"
            ).fit(X)
            connectivity = estimator.kneighbors_graph(X=X, mode="connectivity")
            self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)
        elif self.affinity == "precomputed":
            self.affinity_matrix_ = X
        else:
            params = self.kernel_params
            if params is None:
                params = {}
            if not callable(self.affinity):
                params["gamma"] = self.gamma
                params["degree"] = self.degree
                params["coef0"] = self.coef0
            self.affinity_matrix_ = pairwise_kernels(
                X, metric=self.affinity, filter_params=True, **params
            )

        random_state = check_random_state(self.random_state)
        n_components = (
            self.n_clusters if self.n_components is None else self.n_components
        )
        # We now obtain the real valued solution matrix to the
        # relaxed Ncut problem, solving the eigenvalue problem
        # L_sym x = lambda x  and recovering u = D^-1/2 x.
        # The first eigenvector is constant only for fully connected graphs
        # and should be kept for spectral clustering (drop_first = False)
        # See spectral_embedding documentation.
        maps = _spectral_embedding(
            self.affinity_matrix_,
            n_components=n_components,
            eigen_solver=self.eigen_solver,
            random_state=random_state,
            eigen_tol=self.eigen_tol,
            drop_first=False,
        )
        if self.verbose:
            print(f"Computing label assignment using {self.assign_labels}")

        if self.assign_labels == "kmeans":
            _, self.labels_, _ = k_means(
                maps,
                self.n_clusters,
                random_state=random_state,
                n_init=self.n_init,
                verbose=self.verbose,
            )
        elif self.assign_labels == "cluster_qr":
            self.labels_ = cluster_qr(maps)
        else:
            self.labels_ = discretize(maps, random_state=random_state)

        return self

    def fit_predict(self, X, y=None):
        """Perform spectral clustering on `X` and return cluster labels.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
                (n_samples, n_samples)
            Training instances to cluster, similarities / affinities between
            instances if ``affinity='precomputed'``, or distances between
            instances if ``affinity='precomputed_nearest_neighbors``. If a
            sparse matrix is provided in a format other than ``csr_matrix``,
            ``csc_matrix``, or ``coo_matrix``, 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)

    def __sklearn_tags__(self):
        tags = super().__sklearn_tags__()
        tags.input_tags.sparse = True
        tags.input_tags.pairwise = self.affinity in [
            "precomputed",
            "precomputed_nearest_neighbors",
        ]
        return tags
