first commit

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/__init__.py

@@ -0,0 +1,54 @@
+"""
+The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
+algorithms.
+"""
+
+from ._spectral import spectral_clustering, SpectralClustering
+from ._mean_shift import mean_shift, MeanShift, estimate_bandwidth, get_bin_seeds
+from ._affinity_propagation import affinity_propagation, AffinityPropagation
+from ._agglomerative import (
+    ward_tree,
+    AgglomerativeClustering,
+    linkage_tree,
+    FeatureAgglomeration,
+)
+from ._kmeans import k_means, KMeans, MiniBatchKMeans, kmeans_plusplus
+from ._bisect_k_means import BisectingKMeans
+from ._dbscan import dbscan, DBSCAN
+from ._optics import (
+    OPTICS,
+    cluster_optics_dbscan,
+    compute_optics_graph,
+    cluster_optics_xi,
+)
+from ._bicluster import SpectralBiclustering, SpectralCoclustering
+from ._birch import Birch
+
+__all__ = [
+    "AffinityPropagation",
+    "AgglomerativeClustering",
+    "Birch",
+    "DBSCAN",
+    "OPTICS",
+    "cluster_optics_dbscan",
+    "cluster_optics_xi",
+    "compute_optics_graph",
+    "KMeans",
+    "BisectingKMeans",
+    "FeatureAgglomeration",
+    "MeanShift",
+    "MiniBatchKMeans",
+    "SpectralClustering",
+    "affinity_propagation",
+    "dbscan",
+    "estimate_bandwidth",
+    "get_bin_seeds",
+    "k_means",
+    "kmeans_plusplus",
+    "linkage_tree",
+    "mean_shift",
+    "spectral_clustering",
+    "ward_tree",
+    "SpectralBiclustering",
+    "SpectralCoclustering",
+]

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_affinity_propagation.py

@@ -0,0 +1,556 @@
+"""Affinity Propagation clustering algorithm."""
+
+# Author: Alexandre Gramfort alexandre.gramfort@inria.fr
+#        Gael Varoquaux gael.varoquaux@normalesup.org
+
+# License: BSD 3 clause
+
+import numbers
+import warnings
+
+import numpy as np
+
+from ..exceptions import ConvergenceWarning
+from ..base import BaseEstimator, ClusterMixin
+from ..utils import as_float_array, check_random_state
+from ..utils import check_scalar
+from ..utils.validation import check_is_fitted
+from ..metrics import euclidean_distances
+from ..metrics import pairwise_distances_argmin
+from .._config import config_context
+
+
+def _equal_similarities_and_preferences(S, preference):
+    def all_equal_preferences():
+        return np.all(preference == preference.flat[0])
+
+    def all_equal_similarities():
+        # Create mask to ignore diagonal of S
+        mask = np.ones(S.shape, dtype=bool)
+        np.fill_diagonal(mask, 0)
+
+        return np.all(S[mask].flat == S[mask].flat[0])
+
+    return all_equal_preferences() and all_equal_similarities()
+
+
+def affinity_propagation(
+    S,
+    *,
+    preference=None,
+    convergence_iter=15,
+    max_iter=200,
+    damping=0.5,
+    copy=True,
+    verbose=False,
+    return_n_iter=False,
+    random_state=None,
+):
+    """Perform Affinity Propagation Clustering of data.
+
+    Read more in the :ref:`User Guide <affinity_propagation>`.
+
+    Parameters
+    ----------
+
+    S : array-like of shape (n_samples, n_samples)
+        Matrix of similarities between points.
+
+    preference : array-like of shape (n_samples,) or float, default=None
+        Preferences for each point - points with larger values of
+        preferences are more likely to be chosen as exemplars. The number of
+        exemplars, i.e. of clusters, is influenced by the input preferences
+        value. If the preferences are not passed as arguments, they will be
+        set to the median of the input similarities (resulting in a moderate
+        number of clusters). For a smaller amount of clusters, this can be set
+        to the minimum value of the similarities.
+
+    convergence_iter : int, default=15
+        Number of iterations with no change in the number
+        of estimated clusters that stops the convergence.
+
+    max_iter : int, default=200
+        Maximum number of iterations.
+
+    damping : float, default=0.5
+        Damping factor between 0.5 and 1.
+
+    copy : bool, default=True
+        If copy is False, the affinity matrix is modified inplace by the
+        algorithm, for memory efficiency.
+
+    verbose : bool, default=False
+        The verbosity level.
+
+    return_n_iter : bool, default=False
+        Whether or not to return the number of iterations.
+
+    random_state : int, RandomState instance or None, default=None
+        Pseudo-random number generator to control the starting state.
+        Use an int for reproducible results across function calls.
+        See the :term:`Glossary <random_state>`.
+
+        .. versionadded:: 0.23
+            this parameter was previously hardcoded as 0.
+
+    Returns
+    -------
+
+    cluster_centers_indices : ndarray of shape (n_clusters,)
+        Index of clusters centers.
+
+    labels : ndarray of shape (n_samples,)
+        Cluster labels for each point.
+
+    n_iter : int
+        Number of iterations run. Returned only if `return_n_iter` is
+        set to True.
+
+    Notes
+    -----
+    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
+    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
+
+    When the algorithm does not converge, it will still return a arrays of
+    ``cluster_center_indices`` and labels if there are any exemplars/clusters,
+    however they may be degenerate and should be used with caution.
+
+    When all training samples have equal similarities and equal preferences,
+    the assignment of cluster centers and labels depends on the preference.
+    If the preference is smaller than the similarities, a single cluster center
+    and label ``0`` for every sample will be returned. Otherwise, every
+    training sample becomes its own cluster center and is assigned a unique
+    label.
+
+    References
+    ----------
+    Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
+    Between Data Points", Science Feb. 2007
+    """
+    S = as_float_array(S, copy=copy)
+    n_samples = S.shape[0]
+
+    if S.shape[0] != S.shape[1]:
+        raise ValueError("S must be a square array (shape=%s)" % repr(S.shape))
+
+    if preference is None:
+        preference = np.median(S)
+
+    preference = np.array(preference)
+
+    if n_samples == 1 or _equal_similarities_and_preferences(S, preference):
+        # It makes no sense to run the algorithm in this case, so return 1 or
+        # n_samples clusters, depending on preferences
+        warnings.warn(
+            "All samples have mutually equal similarities. "
+            "Returning arbitrary cluster center(s)."
+        )
+        if preference.flat[0] >= S.flat[n_samples - 1]:
+            return (
+                (np.arange(n_samples), np.arange(n_samples), 0)
+                if return_n_iter
+                else (np.arange(n_samples), np.arange(n_samples))
+            )
+        else:
+            return (
+                (np.array([0]), np.array([0] * n_samples), 0)
+                if return_n_iter
+                else (np.array([0]), np.array([0] * n_samples))
+            )
+
+    random_state = check_random_state(random_state)
+
+    # Place preference on the diagonal of S
+    S.flat[:: (n_samples + 1)] = preference
+
+    A = np.zeros((n_samples, n_samples))
+    R = np.zeros((n_samples, n_samples))  # Initialize messages
+    # Intermediate results
+    tmp = np.zeros((n_samples, n_samples))
+
+    # Remove degeneracies
+    S += (
+        np.finfo(S.dtype).eps * S + np.finfo(S.dtype).tiny * 100
+    ) * random_state.standard_normal(size=(n_samples, n_samples))
+
+    # Execute parallel affinity propagation updates
+    e = np.zeros((n_samples, convergence_iter))
+
+    ind = np.arange(n_samples)
+
+    for it in range(max_iter):
+        # tmp = A + S; compute responsibilities
+        np.add(A, S, tmp)
+        I = np.argmax(tmp, axis=1)
+        Y = tmp[ind, I]  # np.max(A + S, axis=1)
+        tmp[ind, I] = -np.inf
+        Y2 = np.max(tmp, axis=1)
+
+        # tmp = Rnew
+        np.subtract(S, Y[:, None], tmp)
+        tmp[ind, I] = S[ind, I] - Y2
+
+        # Damping
+        tmp *= 1 - damping
+        R *= damping
+        R += tmp
+
+        # tmp = Rp; compute availabilities
+        np.maximum(R, 0, tmp)
+        tmp.flat[:: n_samples + 1] = R.flat[:: n_samples + 1]
+
+        # tmp = -Anew
+        tmp -= np.sum(tmp, axis=0)
+        dA = np.diag(tmp).copy()
+        tmp.clip(0, np.inf, tmp)
+        tmp.flat[:: n_samples + 1] = dA
+
+        # Damping
+        tmp *= 1 - damping
+        A *= damping
+        A -= tmp
+
+        # Check for convergence
+        E = (np.diag(A) + np.diag(R)) > 0
+        e[:, it % convergence_iter] = E
+        K = np.sum(E, axis=0)
+
+        if it >= convergence_iter:
+            se = np.sum(e, axis=1)
+            unconverged = np.sum((se == convergence_iter) + (se == 0)) != n_samples
+            if (not unconverged and (K > 0)) or (it == max_iter):
+                never_converged = False
+                if verbose:
+                    print("Converged after %d iterations." % it)
+                break
+    else:
+        never_converged = True
+        if verbose:
+            print("Did not converge")
+
+    I = np.flatnonzero(E)
+    K = I.size  # Identify exemplars
+
+    if K > 0:
+        if never_converged:
+            warnings.warn(
+                "Affinity propagation did not converge, this model "
+                "may return degenerate cluster centers and labels.",
+                ConvergenceWarning,
+            )
+        c = np.argmax(S[:, I], axis=1)
+        c[I] = np.arange(K)  # Identify clusters
+        # Refine the final set of exemplars and clusters and return results
+        for k in range(K):
+            ii = np.where(c == k)[0]
+            j = np.argmax(np.sum(S[ii[:, np.newaxis], ii], axis=0))
+            I[k] = ii[j]
+
+        c = np.argmax(S[:, I], axis=1)
+        c[I] = np.arange(K)
+        labels = I[c]
+        # Reduce labels to a sorted, gapless, list
+        cluster_centers_indices = np.unique(labels)
+        labels = np.searchsorted(cluster_centers_indices, labels)
+    else:
+        warnings.warn(
+            "Affinity propagation did not converge and this model "
+            "will not have any cluster centers.",
+            ConvergenceWarning,
+        )
+        labels = np.array([-1] * n_samples)
+        cluster_centers_indices = []
+
+    if return_n_iter:
+        return cluster_centers_indices, labels, it + 1
+    else:
+        return cluster_centers_indices, labels
+
+
+###############################################################################
+
+
+class AffinityPropagation(ClusterMixin, BaseEstimator):
+    """Perform Affinity Propagation Clustering of data.
+
+    Read more in the :ref:`User Guide <affinity_propagation>`.
+
+    Parameters
+    ----------
+    damping : float, default=0.5
+        Damping factor in the range `[0.5, 1.0)` is the extent to
+        which the current value is maintained relative to
+        incoming values (weighted 1 - damping). This in order
+        to avoid numerical oscillations when updating these
+        values (messages).
+
+    max_iter : int, default=200
+        Maximum number of iterations.
+
+    convergence_iter : int, default=15
+        Number of iterations with no change in the number
+        of estimated clusters that stops the convergence.
+
+    copy : bool, default=True
+        Make a copy of input data.
+
+    preference : array-like of shape (n_samples,) or float, default=None
+        Preferences for each point - points with larger values of
+        preferences are more likely to be chosen as exemplars. The number
+        of exemplars, ie of clusters, is influenced by the input
+        preferences value. If the preferences are not passed as arguments,
+        they will be set to the median of the input similarities.
+
+    affinity : {'euclidean', 'precomputed'}, default='euclidean'
+        Which affinity to use. At the moment 'precomputed' and
+        ``euclidean`` are supported. 'euclidean' uses the
+        negative squared euclidean distance between points.
+
+    verbose : bool, default=False
+        Whether to be verbose.
+
+    random_state : int, RandomState instance or None, default=None
+        Pseudo-random number generator to control the starting state.
+        Use an int for reproducible results across function calls.
+        See the :term:`Glossary <random_state>`.
+
+        .. versionadded:: 0.23
+            this parameter was previously hardcoded as 0.
+
+    Attributes
+    ----------
+    cluster_centers_indices_ : ndarray of shape (n_clusters,)
+        Indices of cluster centers.
+
+    cluster_centers_ : ndarray of shape (n_clusters, n_features)
+        Cluster centers (if affinity != ``precomputed``).
+
+    labels_ : ndarray of shape (n_samples,)
+        Labels of each point.
+
+    affinity_matrix_ : ndarray of shape (n_samples, n_samples)
+        Stores the affinity matrix used in ``fit``.
+
+    n_iter_ : int
+        Number of iterations taken to converge.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    AgglomerativeClustering : Recursively merges the pair of
+        clusters that minimally increases a given linkage distance.
+    FeatureAgglomeration : Similar to AgglomerativeClustering,
+        but recursively merges features instead of samples.
+    KMeans : K-Means clustering.
+    MiniBatchKMeans : Mini-Batch K-Means clustering.
+    MeanShift : Mean shift clustering using a flat kernel.
+    SpectralClustering : Apply clustering to a projection
+        of the normalized Laplacian.
+
+    Notes
+    -----
+    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
+    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.
+
+    The algorithmic complexity of affinity propagation is quadratic
+    in the number of points.
+
+    When the algorithm does not converge, it will still return a arrays of
+    ``cluster_center_indices`` and labels if there are any exemplars/clusters,
+    however they may be degenerate and should be used with caution.
+
+    When ``fit`` does not converge, ``cluster_centers_`` is still populated
+    however it may be degenerate. In such a case, proceed with caution.
+    If ``fit`` does not converge and fails to produce any ``cluster_centers_``
+    then ``predict`` will label every sample as ``-1``.
+
+    When all training samples have equal similarities and equal preferences,
+    the assignment of cluster centers and labels depends on the preference.
+    If the preference is smaller than the similarities, ``fit`` will result in
+    a single cluster center and label ``0`` for every sample. Otherwise, every
+    training sample becomes its own cluster center and is assigned a unique
+    label.
+
+    References
+    ----------
+
+    Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
+    Between Data Points", Science Feb. 2007
+
+    Examples
+    --------
+    >>> from sklearn.cluster import AffinityPropagation
+    >>> import numpy as np
+    >>> X = np.array([[1, 2], [1, 4], [1, 0],
+    ...               [4, 2], [4, 4], [4, 0]])
+    >>> clustering = AffinityPropagation(random_state=5).fit(X)
+    >>> clustering
+    AffinityPropagation(random_state=5)
+    >>> clustering.labels_
+    array([0, 0, 0, 1, 1, 1])
+    >>> clustering.predict([[0, 0], [4, 4]])
+    array([0, 1])
+    >>> clustering.cluster_centers_
+    array([[1, 2],
+           [4, 2]])
+    """
+
+    def __init__(
+        self,
+        *,
+        damping=0.5,
+        max_iter=200,
+        convergence_iter=15,
+        copy=True,
+        preference=None,
+        affinity="euclidean",
+        verbose=False,
+        random_state=None,
+    ):
+
+        self.damping = damping
+        self.max_iter = max_iter
+        self.convergence_iter = convergence_iter
+        self.copy = copy
+        self.verbose = verbose
+        self.preference = preference
+        self.affinity = affinity
+        self.random_state = random_state
+
+    def _more_tags(self):
+        return {"pairwise": self.affinity == "precomputed"}
+
+    def fit(self, X, y=None):
+        """Fit the clustering from features, or affinity matrix.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
+                array-like of shape (n_samples, n_samples)
+            Training instances to cluster, or similarities / affinities between
+            instances if ``affinity='precomputed'``. If a sparse feature matrix
+            is provided, it will be converted into a sparse ``csr_matrix``.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self
+            Returns the instance itself.
+        """
+        if self.affinity == "precomputed":
+            accept_sparse = False
+        else:
+            accept_sparse = "csr"
+        X = self._validate_data(X, accept_sparse=accept_sparse)
+        if self.affinity == "precomputed":
+            self.affinity_matrix_ = X
+        elif self.affinity == "euclidean":
+            self.affinity_matrix_ = -euclidean_distances(X, squared=True)
+        else:
+            raise ValueError(
+                "Affinity must be 'precomputed' or 'euclidean'. Got %s instead"
+                % str(self.affinity)
+            )
+
+        check_scalar(
+            self.damping,
+            "damping",
+            target_type=numbers.Real,
+            min_val=0.5,
+            max_val=1,
+            include_boundaries="left",
+        )
+        check_scalar(self.max_iter, "max_iter", target_type=numbers.Integral, min_val=1)
+        check_scalar(
+            self.convergence_iter,
+            "convergence_iter",
+            target_type=numbers.Integral,
+            min_val=1,
+        )
+
+        (
+            self.cluster_centers_indices_,
+            self.labels_,
+            self.n_iter_,
+        ) = affinity_propagation(
+            self.affinity_matrix_,
+            preference=self.preference,
+            max_iter=self.max_iter,
+            convergence_iter=self.convergence_iter,
+            damping=self.damping,
+            copy=self.copy,
+            verbose=self.verbose,
+            return_n_iter=True,
+            random_state=self.random_state,
+        )
+
+        if self.affinity != "precomputed":
+            self.cluster_centers_ = X[self.cluster_centers_indices_].copy()
+
+        return self
+
+    def predict(self, X):
+        """Predict the closest cluster each sample in X belongs to.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            New data to predict. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            Cluster labels.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, reset=False, accept_sparse="csr")
+        if not hasattr(self, "cluster_centers_"):
+            raise ValueError(
+                "Predict method is not supported when affinity='precomputed'."
+            )
+
+        if self.cluster_centers_.shape[0] > 0:
+            with config_context(assume_finite=True):
+                return pairwise_distances_argmin(X, self.cluster_centers_)
+        else:
+            warnings.warn(
+                "This model does not have any cluster centers "
+                "because affinity propagation did not converge. "
+                "Labeling every sample as '-1'.",
+                ConvergenceWarning,
+            )
+            return np.array([-1] * X.shape[0])
+
+    def fit_predict(self, X, y=None):
+        """Fit clustering from features/affinity matrix; return cluster labels.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
+                array-like of shape (n_samples, n_samples)
+            Training instances to cluster, or similarities / affinities between
+            instances if ``affinity='precomputed'``. If a sparse feature matrix
+            is provided, it will be converted into a sparse ``csr_matrix``.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            Cluster labels.
+        """
+        return super().fit_predict(X, y)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_bicluster.py

@@ -0,0 +1,627 @@
+"""Spectral biclustering algorithms."""
+# Authors : Kemal Eren
+# License: BSD 3 clause
+
+from abc import ABCMeta, abstractmethod
+
+import numpy as np
+import numbers
+
+from scipy.linalg import norm
+from scipy.sparse import dia_matrix, issparse
+from scipy.sparse.linalg import eigsh, svds
+
+from . import KMeans, MiniBatchKMeans
+from ..base import BaseEstimator, BiclusterMixin
+from ..utils import check_random_state
+from ..utils import check_scalar
+
+from ..utils.extmath import make_nonnegative, randomized_svd, safe_sparse_dot
+
+from ..utils.validation import assert_all_finite
+
+
+__all__ = ["SpectralCoclustering", "SpectralBiclustering"]
+
+
+def _scale_normalize(X):
+    """Normalize ``X`` by scaling rows and columns independently.
+
+    Returns the normalized matrix and the row and column scaling
+    factors.
+    """
+    X = make_nonnegative(X)
+    row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1))).squeeze()
+    col_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=0))).squeeze()
+    row_diag = np.where(np.isnan(row_diag), 0, row_diag)
+    col_diag = np.where(np.isnan(col_diag), 0, col_diag)
+    if issparse(X):
+        n_rows, n_cols = X.shape
+        r = dia_matrix((row_diag, [0]), shape=(n_rows, n_rows))
+        c = dia_matrix((col_diag, [0]), shape=(n_cols, n_cols))
+        an = r * X * c
+    else:
+        an = row_diag[:, np.newaxis] * X * col_diag
+    return an, row_diag, col_diag
+
+
+def _bistochastic_normalize(X, max_iter=1000, tol=1e-5):
+    """Normalize rows and columns of ``X`` simultaneously so that all
+    rows sum to one constant and all columns sum to a different
+    constant.
+    """
+    # According to paper, this can also be done more efficiently with
+    # deviation reduction and balancing algorithms.
+    X = make_nonnegative(X)
+    X_scaled = X
+    for _ in range(max_iter):
+        X_new, _, _ = _scale_normalize(X_scaled)
+        if issparse(X):
+            dist = norm(X_scaled.data - X.data)
+        else:
+            dist = norm(X_scaled - X_new)
+        X_scaled = X_new
+        if dist is not None and dist < tol:
+            break
+    return X_scaled
+
+
+def _log_normalize(X):
+    """Normalize ``X`` according to Kluger's log-interactions scheme."""
+    X = make_nonnegative(X, min_value=1)
+    if issparse(X):
+        raise ValueError(
+            "Cannot compute log of a sparse matrix,"
+            " because log(x) diverges to -infinity as x"
+            " goes to 0."
+        )
+    L = np.log(X)
+    row_avg = L.mean(axis=1)[:, np.newaxis]
+    col_avg = L.mean(axis=0)
+    avg = L.mean()
+    return L - row_avg - col_avg + avg
+
+
+class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):
+    """Base class for spectral biclustering."""
+
+    @abstractmethod
+    def __init__(
+        self,
+        n_clusters=3,
+        svd_method="randomized",
+        n_svd_vecs=None,
+        mini_batch=False,
+        init="k-means++",
+        n_init=10,
+        random_state=None,
+    ):
+        self.n_clusters = n_clusters
+        self.svd_method = svd_method
+        self.n_svd_vecs = n_svd_vecs
+        self.mini_batch = mini_batch
+        self.init = init
+        self.n_init = n_init
+        self.random_state = random_state
+
+    def _check_parameters(self, n_samples):
+        legal_svd_methods = ("randomized", "arpack")
+        if self.svd_method not in legal_svd_methods:
+            raise ValueError(
+                "Unknown SVD method: '{0}'. svd_method must be one of {1}.".format(
+                    self.svd_method, legal_svd_methods
+                )
+            )
+        check_scalar(self.n_init, "n_init", target_type=numbers.Integral, min_val=1)
+
+    def fit(self, X, y=None):
+        """Create a biclustering for X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            SpectralBiclustering instance.
+        """
+        X = self._validate_data(X, accept_sparse="csr", dtype=np.float64)
+        self._check_parameters(X.shape[0])
+        self._fit(X)
+        return self
+
+    def _svd(self, array, n_components, n_discard):
+        """Returns first `n_components` left and right singular
+        vectors u and v, discarding the first `n_discard`.
+        """
+        if self.svd_method == "randomized":
+            kwargs = {}
+            if self.n_svd_vecs is not None:
+                kwargs["n_oversamples"] = self.n_svd_vecs
+            u, _, vt = randomized_svd(
+                array, n_components, random_state=self.random_state, **kwargs
+            )
+
+        elif self.svd_method == "arpack":
+            u, _, vt = svds(array, k=n_components, ncv=self.n_svd_vecs)
+            if np.any(np.isnan(vt)):
+                # some eigenvalues of A * A.T are negative, causing
+                # sqrt() to be np.nan. This causes some vectors in vt
+                # to be np.nan.
+                A = safe_sparse_dot(array.T, array)
+                random_state = check_random_state(self.random_state)
+                # initialize with [-1,1] as in ARPACK
+                v0 = random_state.uniform(-1, 1, A.shape[0])
+                _, v = eigsh(A, ncv=self.n_svd_vecs, v0=v0)
+                vt = v.T
+            if np.any(np.isnan(u)):
+                A = safe_sparse_dot(array, array.T)
+                random_state = check_random_state(self.random_state)
+                # initialize with [-1,1] as in ARPACK
+                v0 = random_state.uniform(-1, 1, A.shape[0])
+                _, u = eigsh(A, ncv=self.n_svd_vecs, v0=v0)
+
+        assert_all_finite(u)
+        assert_all_finite(vt)
+        u = u[:, n_discard:]
+        vt = vt[n_discard:]
+        return u, vt.T
+
+    def _k_means(self, data, n_clusters):
+        if self.mini_batch:
+            model = MiniBatchKMeans(
+                n_clusters,
+                init=self.init,
+                n_init=self.n_init,
+                random_state=self.random_state,
+            )
+        else:
+            model = KMeans(
+                n_clusters,
+                init=self.init,
+                n_init=self.n_init,
+                random_state=self.random_state,
+            )
+        model.fit(data)
+        centroid = model.cluster_centers_
+        labels = model.labels_
+        return centroid, labels
+
+    def _more_tags(self):
+        return {
+            "_xfail_checks": {
+                "check_estimators_dtypes": "raises nan error",
+                "check_fit2d_1sample": "_scale_normalize fails",
+                "check_fit2d_1feature": "raises apply_along_axis error",
+                "check_estimator_sparse_data": "does not fail gracefully",
+                "check_methods_subset_invariance": "empty array passed inside",
+                "check_dont_overwrite_parameters": "empty array passed inside",
+                "check_fit2d_predict1d": "empty array passed inside",
+            }
+        }
+
+
+class SpectralCoclustering(BaseSpectral):
+    """Spectral Co-Clustering algorithm (Dhillon, 2001).
+
+    Clusters rows and columns of an array `X` to solve the relaxed
+    normalized cut of the bipartite graph created from `X` as follows:
+    the edge between row vertex `i` and column vertex `j` has weight
+    `X[i, j]`.
+
+    The resulting bicluster structure is block-diagonal, since each
+    row and each column belongs to exactly one bicluster.
+
+    Supports sparse matrices, as long as they are nonnegative.
+
+    Read more in the :ref:`User Guide <spectral_coclustering>`.
+
+    Parameters
+    ----------
+    n_clusters : int, default=3
+        The number of biclusters to find.
+
+    svd_method : {'randomized', 'arpack'}, default='randomized'
+        Selects the algorithm for finding singular vectors. May be
+        'randomized' or 'arpack'. If 'randomized', use
+        :func:`sklearn.utils.extmath.randomized_svd`, which may be faster
+        for large matrices. If 'arpack', use
+        :func:`scipy.sparse.linalg.svds`, which is more accurate, but
+        possibly slower in some cases.
+
+    n_svd_vecs : int, default=None
+        Number of vectors to use in calculating the SVD. Corresponds
+        to `ncv` when `svd_method=arpack` and `n_oversamples` when
+        `svd_method` is 'randomized`.
+
+    mini_batch : bool, default=False
+        Whether to use mini-batch k-means, which is faster but may get
+        different results.
+
+    init : {'k-means++', 'random', or ndarray of shape \
+            (n_clusters, n_features), default='k-means++'
+        Method for initialization of k-means algorithm; defaults to
+        'k-means++'.
+
+    n_init : int, default=10
+        Number of random initializations that are tried with the
+        k-means algorithm.
+
+        If mini-batch k-means is used, the best initialization is
+        chosen and the algorithm runs once. Otherwise, the algorithm
+        is run for each initialization and the best solution chosen.
+
+    random_state : int, RandomState instance, default=None
+        Used for randomizing the singular value decomposition and the k-means
+        initialization. Use an int to make the randomness deterministic.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    rows_ : array-like of shape (n_row_clusters, n_rows)
+        Results of the clustering. `rows[i, r]` is True if
+        cluster `i` contains row `r`. Available only after calling ``fit``.
+
+    columns_ : array-like of shape (n_column_clusters, n_columns)
+        Results of the clustering, like `rows`.
+
+    row_labels_ : array-like of shape (n_rows,)
+        The bicluster label of each row.
+
+    column_labels_ : array-like of shape (n_cols,)
+        The bicluster label of each column.
+
+    biclusters_ : tuple of two ndarrays
+        The tuple contains the `rows_` and `columns_` arrays.
+
+    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
+    --------
+    SpectralBiclustering : Partitions rows and columns under the assumption
+        that the data has an underlying checkerboard structure.
+
+    References
+    ----------
+    * :doi:`Dhillon, Inderjit S, 2001. Co-clustering documents and words using
+      bipartite spectral graph partitioning.
+      <10.1145/502512.502550>`
+
+    Examples
+    --------
+    >>> from sklearn.cluster import SpectralCoclustering
+    >>> import numpy as np
+    >>> X = np.array([[1, 1], [2, 1], [1, 0],
+    ...               [4, 7], [3, 5], [3, 6]])
+    >>> clustering = SpectralCoclustering(n_clusters=2, random_state=0).fit(X)
+    >>> clustering.row_labels_ #doctest: +SKIP
+    array([0, 1, 1, 0, 0, 0], dtype=int32)
+    >>> clustering.column_labels_ #doctest: +SKIP
+    array([0, 0], dtype=int32)
+    >>> clustering
+    SpectralCoclustering(n_clusters=2, random_state=0)
+    """
+
+    def __init__(
+        self,
+        n_clusters=3,
+        *,
+        svd_method="randomized",
+        n_svd_vecs=None,
+        mini_batch=False,
+        init="k-means++",
+        n_init=10,
+        random_state=None,
+    ):
+        super().__init__(
+            n_clusters, svd_method, n_svd_vecs, mini_batch, init, n_init, random_state
+        )
+
+    def _check_parameters(self, n_samples):
+        super()._check_parameters(n_samples)
+        check_scalar(
+            self.n_clusters,
+            "n_clusters",
+            target_type=numbers.Integral,
+            min_val=1,
+            max_val=n_samples,
+        )
+
+    def _fit(self, X):
+        normalized_data, row_diag, col_diag = _scale_normalize(X)
+        n_sv = 1 + int(np.ceil(np.log2(self.n_clusters)))
+        u, v = self._svd(normalized_data, n_sv, n_discard=1)
+        z = np.vstack((row_diag[:, np.newaxis] * u, col_diag[:, np.newaxis] * v))
+
+        _, labels = self._k_means(z, self.n_clusters)
+
+        n_rows = X.shape[0]
+        self.row_labels_ = labels[:n_rows]
+        self.column_labels_ = labels[n_rows:]
+
+        self.rows_ = np.vstack([self.row_labels_ == c for c in range(self.n_clusters)])
+        self.columns_ = np.vstack(
+            [self.column_labels_ == c for c in range(self.n_clusters)]
+        )
+
+
+class SpectralBiclustering(BaseSpectral):
+    """Spectral biclustering (Kluger, 2003).
+
+    Partitions rows and columns under the assumption that the data has
+    an underlying checkerboard structure. For instance, if there are
+    two row partitions and three column partitions, each row will
+    belong to three biclusters, and each column will belong to two
+    biclusters. The outer product of the corresponding row and column
+    label vectors gives this checkerboard structure.
+
+    Read more in the :ref:`User Guide <spectral_biclustering>`.
+
+    Parameters
+    ----------
+    n_clusters : int or tuple (n_row_clusters, n_column_clusters), default=3
+        The number of row and column clusters in the checkerboard
+        structure.
+
+    method : {'bistochastic', 'scale', 'log'}, default='bistochastic'
+        Method of normalizing and converting singular vectors into
+        biclusters. May be one of 'scale', 'bistochastic', or 'log'.
+        The authors recommend using 'log'. If the data is sparse,
+        however, log normalization will not work, which is why the
+        default is 'bistochastic'.
+
+        .. warning::
+           if `method='log'`, the data must be sparse.
+
+    n_components : int, default=6
+        Number of singular vectors to check.
+
+    n_best : int, default=3
+        Number of best singular vectors to which to project the data
+        for clustering.
+
+    svd_method : {'randomized', 'arpack'}, default='randomized'
+        Selects the algorithm for finding singular vectors. May be
+        'randomized' or 'arpack'. If 'randomized', uses
+        :func:`~sklearn.utils.extmath.randomized_svd`, which may be faster
+        for large matrices. If 'arpack', uses
+        `scipy.sparse.linalg.svds`, which is more accurate, but
+        possibly slower in some cases.
+
+    n_svd_vecs : int, default=None
+        Number of vectors to use in calculating the SVD. Corresponds
+        to `ncv` when `svd_method=arpack` and `n_oversamples` when
+        `svd_method` is 'randomized`.
+
+    mini_batch : bool, default=False
+        Whether to use mini-batch k-means, which is faster but may get
+        different results.
+
+    init : {'k-means++', 'random'} or ndarray of (n_clusters, n_features), \
+            default='k-means++'
+        Method for initialization of k-means algorithm; defaults to
+        'k-means++'.
+
+    n_init : int, default=10
+        Number of random initializations that are tried with the
+        k-means algorithm.
+
+        If mini-batch k-means is used, the best initialization is
+        chosen and the algorithm runs once. Otherwise, the algorithm
+        is run for each initialization and the best solution chosen.
+
+    random_state : int, RandomState instance, default=None
+        Used for randomizing the singular value decomposition and the k-means
+        initialization. Use an int to make the randomness deterministic.
+        See :term:`Glossary <random_state>`.
+
+    Attributes
+    ----------
+    rows_ : array-like of shape (n_row_clusters, n_rows)
+        Results of the clustering. `rows[i, r]` is True if
+        cluster `i` contains row `r`. Available only after calling ``fit``.
+
+    columns_ : array-like of shape (n_column_clusters, n_columns)
+        Results of the clustering, like `rows`.
+
+    row_labels_ : array-like of shape (n_rows,)
+        Row partition labels.
+
+    column_labels_ : array-like of shape (n_cols,)
+        Column partition labels.
+
+    biclusters_ : tuple of two ndarrays
+        The tuple contains the `rows_` and `columns_` arrays.
+
+    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
+    --------
+    SpectralCoclustering : Spectral Co-Clustering algorithm (Dhillon, 2001).
+
+    References
+    ----------
+
+    * :doi:`Kluger, Yuval, et. al., 2003. Spectral biclustering of microarray
+      data: coclustering genes and conditions.
+      <10.1101/gr.648603>`
+
+    Examples
+    --------
+    >>> from sklearn.cluster import SpectralBiclustering
+    >>> import numpy as np
+    >>> X = np.array([[1, 1], [2, 1], [1, 0],
+    ...               [4, 7], [3, 5], [3, 6]])
+    >>> clustering = SpectralBiclustering(n_clusters=2, random_state=0).fit(X)
+    >>> clustering.row_labels_
+    array([1, 1, 1, 0, 0, 0], dtype=int32)
+    >>> clustering.column_labels_
+    array([0, 1], dtype=int32)
+    >>> clustering
+    SpectralBiclustering(n_clusters=2, random_state=0)
+    """
+
+    def __init__(
+        self,
+        n_clusters=3,
+        *,
+        method="bistochastic",
+        n_components=6,
+        n_best=3,
+        svd_method="randomized",
+        n_svd_vecs=None,
+        mini_batch=False,
+        init="k-means++",
+        n_init=10,
+        random_state=None,
+    ):
+        super().__init__(
+            n_clusters, svd_method, n_svd_vecs, mini_batch, init, n_init, random_state
+        )
+        self.method = method
+        self.n_components = n_components
+        self.n_best = n_best
+
+    def _check_parameters(self, n_samples):
+        super()._check_parameters(n_samples)
+        legal_methods = ("bistochastic", "scale", "log")
+        if self.method not in legal_methods:
+            raise ValueError(
+                "Unknown method: '{0}'. method must be one of {1}.".format(
+                    self.method, legal_methods
+                )
+            )
+        try:
+            check_scalar(
+                self.n_clusters,
+                "n_clusters",
+                target_type=numbers.Integral,
+                min_val=1,
+                max_val=n_samples,
+            )
+        except (ValueError, TypeError):
+            try:
+                n_row_clusters, n_column_clusters = self.n_clusters
+                check_scalar(
+                    n_row_clusters,
+                    "n_row_clusters",
+                    target_type=numbers.Integral,
+                    min_val=1,
+                    max_val=n_samples,
+                )
+                check_scalar(
+                    n_column_clusters,
+                    "n_column_clusters",
+                    target_type=numbers.Integral,
+                    min_val=1,
+                    max_val=n_samples,
+                )
+            except (ValueError, TypeError) as e:
+                raise ValueError(
+                    "Incorrect parameter n_clusters has value:"
+                    f" {self.n_clusters}. It should either be a single integer"
+                    " or an iterable with two integers:"
+                    " (n_row_clusters, n_column_clusters)"
+                    " And the values are should be in the"
+                    " range: (1, n_samples)"
+                ) from e
+        check_scalar(
+            self.n_components, "n_components", target_type=numbers.Integral, min_val=1
+        )
+        check_scalar(
+            self.n_best,
+            "n_best",
+            target_type=numbers.Integral,
+            min_val=1,
+            max_val=self.n_components,
+        )
+
+    def _fit(self, X):
+        n_sv = self.n_components
+        if self.method == "bistochastic":
+            normalized_data = _bistochastic_normalize(X)
+            n_sv += 1
+        elif self.method == "scale":
+            normalized_data, _, _ = _scale_normalize(X)
+            n_sv += 1
+        elif self.method == "log":
+            normalized_data = _log_normalize(X)
+        n_discard = 0 if self.method == "log" else 1
+        u, v = self._svd(normalized_data, n_sv, n_discard)
+        ut = u.T
+        vt = v.T
+
+        try:
+            n_row_clusters, n_col_clusters = self.n_clusters
+        except TypeError:
+            n_row_clusters = n_col_clusters = self.n_clusters
+
+        best_ut = self._fit_best_piecewise(ut, self.n_best, n_row_clusters)
+
+        best_vt = self._fit_best_piecewise(vt, self.n_best, n_col_clusters)
+
+        self.row_labels_ = self._project_and_cluster(X, best_vt.T, n_row_clusters)
+
+        self.column_labels_ = self._project_and_cluster(X.T, best_ut.T, n_col_clusters)
+
+        self.rows_ = np.vstack(
+            [
+                self.row_labels_ == label
+                for label in range(n_row_clusters)
+                for _ in range(n_col_clusters)
+            ]
+        )
+        self.columns_ = np.vstack(
+            [
+                self.column_labels_ == label
+                for _ in range(n_row_clusters)
+                for label in range(n_col_clusters)
+            ]
+        )
+
+    def _fit_best_piecewise(self, vectors, n_best, n_clusters):
+        """Find the ``n_best`` vectors that are best approximated by piecewise
+        constant vectors.
+
+        The piecewise vectors are found by k-means; the best is chosen
+        according to Euclidean distance.
+
+        """
+
+        def make_piecewise(v):
+            centroid, labels = self._k_means(v.reshape(-1, 1), n_clusters)
+            return centroid[labels].ravel()
+
+        piecewise_vectors = np.apply_along_axis(make_piecewise, axis=1, arr=vectors)
+        dists = np.apply_along_axis(norm, axis=1, arr=(vectors - piecewise_vectors))
+        result = vectors[np.argsort(dists)[:n_best]]
+        return result
+
+    def _project_and_cluster(self, data, vectors, n_clusters):
+        """Project ``data`` to ``vectors`` and cluster the result."""
+        projected = safe_sparse_dot(data, vectors)
+        _, labels = self._k_means(projected, n_clusters)
+        return labels

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_birch.py

@@ -0,0 +1,760 @@
+# Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
+#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
+#          Joel Nothman <joel.nothman@gmail.com>
+# License: BSD 3 clause
+
+import warnings
+import numbers
+import numpy as np
+from scipy import sparse
+from math import sqrt
+
+from ..metrics import pairwise_distances_argmin
+from ..metrics.pairwise import euclidean_distances
+from ..base import (
+    TransformerMixin,
+    ClusterMixin,
+    BaseEstimator,
+    _ClassNamePrefixFeaturesOutMixin,
+)
+from ..utils.extmath import row_norms
+from ..utils import check_scalar, deprecated
+from ..utils.validation import check_is_fitted
+from ..exceptions import ConvergenceWarning
+from . import AgglomerativeClustering
+from .._config import config_context
+
+
+def _iterate_sparse_X(X):
+    """This little hack returns a densified row when iterating over a sparse
+    matrix, instead of constructing a sparse matrix for every row that is
+    expensive.
+    """
+    n_samples = X.shape[0]
+    X_indices = X.indices
+    X_data = X.data
+    X_indptr = X.indptr
+
+    for i in range(n_samples):
+        row = np.zeros(X.shape[1])
+        startptr, endptr = X_indptr[i], X_indptr[i + 1]
+        nonzero_indices = X_indices[startptr:endptr]
+        row[nonzero_indices] = X_data[startptr:endptr]
+        yield row
+
+
+def _split_node(node, threshold, branching_factor):
+    """The node has to be split if there is no place for a new subcluster
+    in the node.
+    1. Two empty nodes and two empty subclusters are initialized.
+    2. The pair of distant subclusters are found.
+    3. The properties of the empty subclusters and nodes are updated
+       according to the nearest distance between the subclusters to the
+       pair of distant subclusters.
+    4. The two nodes are set as children to the two subclusters.
+    """
+    new_subcluster1 = _CFSubcluster()
+    new_subcluster2 = _CFSubcluster()
+    new_node1 = _CFNode(
+        threshold=threshold,
+        branching_factor=branching_factor,
+        is_leaf=node.is_leaf,
+        n_features=node.n_features,
+    )
+    new_node2 = _CFNode(
+        threshold=threshold,
+        branching_factor=branching_factor,
+        is_leaf=node.is_leaf,
+        n_features=node.n_features,
+    )
+    new_subcluster1.child_ = new_node1
+    new_subcluster2.child_ = new_node2
+
+    if node.is_leaf:
+        if node.prev_leaf_ is not None:
+            node.prev_leaf_.next_leaf_ = new_node1
+        new_node1.prev_leaf_ = node.prev_leaf_
+        new_node1.next_leaf_ = new_node2
+        new_node2.prev_leaf_ = new_node1
+        new_node2.next_leaf_ = node.next_leaf_
+        if node.next_leaf_ is not None:
+            node.next_leaf_.prev_leaf_ = new_node2
+
+    dist = euclidean_distances(
+        node.centroids_, Y_norm_squared=node.squared_norm_, squared=True
+    )
+    n_clusters = dist.shape[0]
+
+    farthest_idx = np.unravel_index(dist.argmax(), (n_clusters, n_clusters))
+    node1_dist, node2_dist = dist[(farthest_idx,)]
+
+    node1_closer = node1_dist < node2_dist
+    for idx, subcluster in enumerate(node.subclusters_):
+        if node1_closer[idx]:
+            new_node1.append_subcluster(subcluster)
+            new_subcluster1.update(subcluster)
+        else:
+            new_node2.append_subcluster(subcluster)
+            new_subcluster2.update(subcluster)
+    return new_subcluster1, new_subcluster2
+
+
+class _CFNode:
+    """Each node in a CFTree is called a CFNode.
+
+    The CFNode can have a maximum of branching_factor
+    number of CFSubclusters.
+
+    Parameters
+    ----------
+    threshold : float
+        Threshold needed for a new subcluster to enter a CFSubcluster.
+
+    branching_factor : int
+        Maximum number of CF subclusters in each node.
+
+    is_leaf : bool
+        We need to know if the CFNode is a leaf or not, in order to
+        retrieve the final subclusters.
+
+    n_features : int
+        The number of features.
+
+    Attributes
+    ----------
+    subclusters_ : list
+        List of subclusters for a particular CFNode.
+
+    prev_leaf_ : _CFNode
+        Useful only if is_leaf is True.
+
+    next_leaf_ : _CFNode
+        next_leaf. Useful only if is_leaf is True.
+        the final subclusters.
+
+    init_centroids_ : ndarray of shape (branching_factor + 1, n_features)
+        Manipulate ``init_centroids_`` throughout rather than centroids_ since
+        the centroids are just a view of the ``init_centroids_`` .
+
+    init_sq_norm_ : ndarray of shape (branching_factor + 1,)
+        manipulate init_sq_norm_ throughout. similar to ``init_centroids_``.
+
+    centroids_ : ndarray of shape (branching_factor + 1, n_features)
+        View of ``init_centroids_``.
+
+    squared_norm_ : ndarray of shape (branching_factor + 1,)
+        View of ``init_sq_norm_``.
+
+    """
+
+    def __init__(self, *, threshold, branching_factor, is_leaf, n_features):
+        self.threshold = threshold
+        self.branching_factor = branching_factor
+        self.is_leaf = is_leaf
+        self.n_features = n_features
+
+        # The list of subclusters, centroids and squared norms
+        # to manipulate throughout.
+        self.subclusters_ = []
+        self.init_centroids_ = np.zeros((branching_factor + 1, n_features))
+        self.init_sq_norm_ = np.zeros((branching_factor + 1))
+        self.squared_norm_ = []
+        self.prev_leaf_ = None
+        self.next_leaf_ = None
+
+    def append_subcluster(self, subcluster):
+        n_samples = len(self.subclusters_)
+        self.subclusters_.append(subcluster)
+        self.init_centroids_[n_samples] = subcluster.centroid_
+        self.init_sq_norm_[n_samples] = subcluster.sq_norm_
+
+        # Keep centroids and squared norm as views. In this way
+        # if we change init_centroids and init_sq_norm_, it is
+        # sufficient,
+        self.centroids_ = self.init_centroids_[: n_samples + 1, :]
+        self.squared_norm_ = self.init_sq_norm_[: n_samples + 1]
+
+    def update_split_subclusters(self, subcluster, new_subcluster1, new_subcluster2):
+        """Remove a subcluster from a node and update it with the
+        split subclusters.
+        """
+        ind = self.subclusters_.index(subcluster)
+        self.subclusters_[ind] = new_subcluster1
+        self.init_centroids_[ind] = new_subcluster1.centroid_
+        self.init_sq_norm_[ind] = new_subcluster1.sq_norm_
+        self.append_subcluster(new_subcluster2)
+
+    def insert_cf_subcluster(self, subcluster):
+        """Insert a new subcluster into the node."""
+        if not self.subclusters_:
+            self.append_subcluster(subcluster)
+            return False
+
+        threshold = self.threshold
+        branching_factor = self.branching_factor
+        # We need to find the closest subcluster among all the
+        # subclusters so that we can insert our new subcluster.
+        dist_matrix = np.dot(self.centroids_, subcluster.centroid_)
+        dist_matrix *= -2.0
+        dist_matrix += self.squared_norm_
+        closest_index = np.argmin(dist_matrix)
+        closest_subcluster = self.subclusters_[closest_index]
+
+        # If the subcluster has a child, we need a recursive strategy.
+        if closest_subcluster.child_ is not None:
+            split_child = closest_subcluster.child_.insert_cf_subcluster(subcluster)
+
+            if not split_child:
+                # If it is determined that the child need not be split, we
+                # can just update the closest_subcluster
+                closest_subcluster.update(subcluster)
+                self.init_centroids_[closest_index] = self.subclusters_[
+                    closest_index
+                ].centroid_
+                self.init_sq_norm_[closest_index] = self.subclusters_[
+                    closest_index
+                ].sq_norm_
+                return False
+
+            # things not too good. we need to redistribute the subclusters in
+            # our child node, and add a new subcluster in the parent
+            # subcluster to accommodate the new child.
+            else:
+                new_subcluster1, new_subcluster2 = _split_node(
+                    closest_subcluster.child_, threshold, branching_factor
+                )
+                self.update_split_subclusters(
+                    closest_subcluster, new_subcluster1, new_subcluster2
+                )
+
+                if len(self.subclusters_) > self.branching_factor:
+                    return True
+                return False
+
+        # good to go!
+        else:
+            merged = closest_subcluster.merge_subcluster(subcluster, self.threshold)
+            if merged:
+                self.init_centroids_[closest_index] = closest_subcluster.centroid_
+                self.init_sq_norm_[closest_index] = closest_subcluster.sq_norm_
+                return False
+
+            # not close to any other subclusters, and we still
+            # have space, so add.
+            elif len(self.subclusters_) < self.branching_factor:
+                self.append_subcluster(subcluster)
+                return False
+
+            # We do not have enough space nor is it closer to an
+            # other subcluster. We need to split.
+            else:
+                self.append_subcluster(subcluster)
+                return True
+
+
+class _CFSubcluster:
+    """Each subcluster in a CFNode is called a CFSubcluster.
+
+    A CFSubcluster can have a CFNode has its child.
+
+    Parameters
+    ----------
+    linear_sum : ndarray of shape (n_features,), default=None
+        Sample. This is kept optional to allow initialization of empty
+        subclusters.
+
+    Attributes
+    ----------
+    n_samples_ : int
+        Number of samples that belong to each subcluster.
+
+    linear_sum_ : ndarray
+        Linear sum of all the samples in a subcluster. Prevents holding
+        all sample data in memory.
+
+    squared_sum_ : float
+        Sum of the squared l2 norms of all samples belonging to a subcluster.
+
+    centroid_ : ndarray of shape (branching_factor + 1, n_features)
+        Centroid of the subcluster. Prevent recomputing of centroids when
+        ``CFNode.centroids_`` is called.
+
+    child_ : _CFNode
+        Child Node of the subcluster. Once a given _CFNode is set as the child
+        of the _CFNode, it is set to ``self.child_``.
+
+    sq_norm_ : ndarray of shape (branching_factor + 1,)
+        Squared norm of the subcluster. Used to prevent recomputing when
+        pairwise minimum distances are computed.
+    """
+
+    def __init__(self, *, linear_sum=None):
+        if linear_sum is None:
+            self.n_samples_ = 0
+            self.squared_sum_ = 0.0
+            self.centroid_ = self.linear_sum_ = 0
+        else:
+            self.n_samples_ = 1
+            self.centroid_ = self.linear_sum_ = linear_sum
+            self.squared_sum_ = self.sq_norm_ = np.dot(
+                self.linear_sum_, self.linear_sum_
+            )
+        self.child_ = None
+
+    def update(self, subcluster):
+        self.n_samples_ += subcluster.n_samples_
+        self.linear_sum_ += subcluster.linear_sum_
+        self.squared_sum_ += subcluster.squared_sum_
+        self.centroid_ = self.linear_sum_ / self.n_samples_
+        self.sq_norm_ = np.dot(self.centroid_, self.centroid_)
+
+    def merge_subcluster(self, nominee_cluster, threshold):
+        """Check if a cluster is worthy enough to be merged. If
+        yes then merge.
+        """
+        new_ss = self.squared_sum_ + nominee_cluster.squared_sum_
+        new_ls = self.linear_sum_ + nominee_cluster.linear_sum_
+        new_n = self.n_samples_ + nominee_cluster.n_samples_
+        new_centroid = (1 / new_n) * new_ls
+        new_sq_norm = np.dot(new_centroid, new_centroid)
+
+        # The squared radius of the cluster is defined:
+        #   r^2  = sum_i ||x_i - c||^2 / n
+        # with x_i the n points assigned to the cluster and c its centroid:
+        #   c = sum_i x_i / n
+        # This can be expanded to:
+        #   r^2 = sum_i ||x_i||^2 / n - 2 < sum_i x_i / n, c> + n ||c||^2 / n
+        # and therefore simplifies to:
+        #   r^2 = sum_i ||x_i||^2 / n - ||c||^2
+        sq_radius = new_ss / new_n - new_sq_norm
+
+        if sq_radius <= threshold**2:
+            (
+                self.n_samples_,
+                self.linear_sum_,
+                self.squared_sum_,
+                self.centroid_,
+                self.sq_norm_,
+            ) = (new_n, new_ls, new_ss, new_centroid, new_sq_norm)
+            return True
+        return False
+
+    @property
+    def radius(self):
+        """Return radius of the subcluster"""
+        # Because of numerical issues, this could become negative
+        sq_radius = self.squared_sum_ / self.n_samples_ - self.sq_norm_
+        return sqrt(max(0, sq_radius))
+
+
+class Birch(
+    _ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, BaseEstimator
+):
+    """Implements the BIRCH clustering algorithm.
+
+    It is a memory-efficient, online-learning algorithm provided as an
+    alternative to :class:`MiniBatchKMeans`. It constructs a tree
+    data structure with the cluster centroids being read off the leaf.
+    These can be either the final cluster centroids or can be provided as input
+    to another clustering algorithm such as :class:`AgglomerativeClustering`.
+
+    Read more in the :ref:`User Guide <birch>`.
+
+    .. versionadded:: 0.16
+
+    Parameters
+    ----------
+    threshold : float, default=0.5
+        The radius of the subcluster obtained by merging a new sample and the
+        closest subcluster should be lesser than the threshold. Otherwise a new
+        subcluster is started. Setting this value to be very low promotes
+        splitting and vice-versa.
+
+    branching_factor : int, default=50
+        Maximum number of CF subclusters in each node. If a new samples enters
+        such that the number of subclusters exceed the branching_factor then
+        that node is split into two nodes with the subclusters redistributed
+        in each. The parent subcluster of that node is removed and two new
+        subclusters are added as parents of the 2 split nodes.
+
+    n_clusters : int, instance of sklearn.cluster model, default=3
+        Number of clusters after the final clustering step, which treats the
+        subclusters from the leaves as new samples.
+
+        - `None` : the final clustering step is not performed and the
+          subclusters are returned as they are.
+
+        - :mod:`sklearn.cluster` Estimator : If a model is provided, the model
+          is fit treating the subclusters as new samples and the initial data
+          is mapped to the label of the closest subcluster.
+
+        - `int` : the model fit is :class:`AgglomerativeClustering` with
+          `n_clusters` set to be equal to the int.
+
+    compute_labels : bool, default=True
+        Whether or not to compute labels for each fit.
+
+    copy : bool, default=True
+        Whether or not to make a copy of the given data. If set to False,
+        the initial data will be overwritten.
+
+    Attributes
+    ----------
+    root_ : _CFNode
+        Root of the CFTree.
+
+    dummy_leaf_ : _CFNode
+        Start pointer to all the leaves.
+
+    subcluster_centers_ : ndarray
+        Centroids of all subclusters read directly from the leaves.
+
+    subcluster_labels_ : ndarray
+        Labels assigned to the centroids of the subclusters after
+        they are clustered globally.
+
+    labels_ : ndarray of shape (n_samples,)
+        Array of labels assigned to the input data.
+        if partial_fit is used instead of fit, they are assigned to the
+        last batch of data.
+
+    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 implementation that does incremental updates
+        of the centers' positions using mini-batches.
+
+    Notes
+    -----
+    The tree data structure consists of nodes with each node consisting of
+    a number of subclusters. The maximum number of subclusters in a node
+    is determined by the branching factor. Each subcluster maintains a
+    linear sum, squared sum and the number of samples in that subcluster.
+    In addition, each subcluster can also have a node as its child, if the
+    subcluster is not a member of a leaf node.
+
+    For a new point entering the root, it is merged with the subcluster closest
+    to it and the linear sum, squared sum and the number of samples of that
+    subcluster are updated. This is done recursively till the properties of
+    the leaf node are updated.
+
+    References
+    ----------
+    * Tian Zhang, Raghu Ramakrishnan, Maron Livny
+      BIRCH: An efficient data clustering method for large databases.
+      https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
+
+    * Roberto Perdisci
+      JBirch - Java implementation of BIRCH clustering algorithm
+      https://code.google.com/archive/p/jbirch
+
+    Examples
+    --------
+    >>> from sklearn.cluster import Birch
+    >>> X = [[0, 1], [0.3, 1], [-0.3, 1], [0, -1], [0.3, -1], [-0.3, -1]]
+    >>> brc = Birch(n_clusters=None)
+    >>> brc.fit(X)
+    Birch(n_clusters=None)
+    >>> brc.predict(X)
+    array([0, 0, 0, 1, 1, 1])
+    """
+
+    def __init__(
+        self,
+        *,
+        threshold=0.5,
+        branching_factor=50,
+        n_clusters=3,
+        compute_labels=True,
+        copy=True,
+    ):
+        self.threshold = threshold
+        self.branching_factor = branching_factor
+        self.n_clusters = n_clusters
+        self.compute_labels = compute_labels
+        self.copy = copy
+
+    # TODO: Remove in 1.2
+    # mypy error: Decorated property not supported
+    @deprecated(  # type: ignore
+        "`fit_` is deprecated in 1.0 and will be removed in 1.2."
+    )
+    @property
+    def fit_(self):
+        return self._deprecated_fit
+
+    # TODO: Remove in 1.2
+    # mypy error: Decorated property not supported
+    @deprecated(  # type: ignore
+        "`partial_fit_` is deprecated in 1.0 and will be removed in 1.2."
+    )
+    @property
+    def partial_fit_(self):
+        return self._deprecated_partial_fit
+
+    def fit(self, X, y=None):
+        """
+        Build a CF Tree for the input data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self
+            Fitted estimator.
+        """
+
+        # Validating the scalar parameters.
+        check_scalar(
+            self.threshold,
+            "threshold",
+            target_type=numbers.Real,
+            min_val=0.0,
+            include_boundaries="neither",
+        )
+        check_scalar(
+            self.branching_factor,
+            "branching_factor",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="neither",
+        )
+        if isinstance(self.n_clusters, numbers.Number):
+            check_scalar(
+                self.n_clusters,
+                "n_clusters",
+                target_type=numbers.Integral,
+                min_val=1,
+            )
+
+        # TODO: Remove deprecated flags in 1.2
+        self._deprecated_fit, self._deprecated_partial_fit = True, False
+        return self._fit(X, partial=False)
+
+    def _fit(self, X, partial):
+        has_root = getattr(self, "root_", None)
+        first_call = not (partial and has_root)
+
+        X = self._validate_data(
+            X, accept_sparse="csr", copy=self.copy, reset=first_call
+        )
+        threshold = self.threshold
+        branching_factor = self.branching_factor
+
+        n_samples, n_features = X.shape
+
+        # If partial_fit is called for the first time or fit is called, we
+        # start a new tree.
+        if first_call:
+            # The first root is the leaf. Manipulate this object throughout.
+            self.root_ = _CFNode(
+                threshold=threshold,
+                branching_factor=branching_factor,
+                is_leaf=True,
+                n_features=n_features,
+            )
+
+            # To enable getting back subclusters.
+            self.dummy_leaf_ = _CFNode(
+                threshold=threshold,
+                branching_factor=branching_factor,
+                is_leaf=True,
+                n_features=n_features,
+            )
+            self.dummy_leaf_.next_leaf_ = self.root_
+            self.root_.prev_leaf_ = self.dummy_leaf_
+
+        # Cannot vectorize. Enough to convince to use cython.
+        if not sparse.issparse(X):
+            iter_func = iter
+        else:
+            iter_func = _iterate_sparse_X
+
+        for sample in iter_func(X):
+            subcluster = _CFSubcluster(linear_sum=sample)
+            split = self.root_.insert_cf_subcluster(subcluster)
+
+            if split:
+                new_subcluster1, new_subcluster2 = _split_node(
+                    self.root_, threshold, branching_factor
+                )
+                del self.root_
+                self.root_ = _CFNode(
+                    threshold=threshold,
+                    branching_factor=branching_factor,
+                    is_leaf=False,
+                    n_features=n_features,
+                )
+                self.root_.append_subcluster(new_subcluster1)
+                self.root_.append_subcluster(new_subcluster2)
+
+        centroids = np.concatenate([leaf.centroids_ for leaf in self._get_leaves()])
+        self.subcluster_centers_ = centroids
+        self._n_features_out = self.subcluster_centers_.shape[0]
+
+        self._global_clustering(X)
+        return self
+
+    def _get_leaves(self):
+        """
+        Retrieve the leaves of the CF Node.
+
+        Returns
+        -------
+        leaves : list of shape (n_leaves,)
+            List of the leaf nodes.
+        """
+        leaf_ptr = self.dummy_leaf_.next_leaf_
+        leaves = []
+        while leaf_ptr is not None:
+            leaves.append(leaf_ptr)
+            leaf_ptr = leaf_ptr.next_leaf_
+        return leaves
+
+    def partial_fit(self, X=None, y=None):
+        """
+        Online learning. Prevents rebuilding of CFTree from scratch.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), \
+            default=None
+            Input data. If X is not provided, only the global clustering
+            step is done.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self
+            Fitted estimator.
+        """
+        # TODO: Remove deprecated flags in 1.2
+        self._deprecated_partial_fit, self._deprecated_fit = True, False
+        if X is None:
+            # Perform just the final global clustering step.
+            self._global_clustering()
+            return self
+        else:
+            return self._fit(X, partial=True)
+
+    def _check_fit(self, X):
+        check_is_fitted(self)
+
+        if (
+            hasattr(self, "subcluster_centers_")
+            and X.shape[1] != self.subcluster_centers_.shape[1]
+        ):
+            raise ValueError(
+                "Training data and predicted data do not have same number of features."
+            )
+
+    def predict(self, X):
+        """
+        Predict data using the ``centroids_`` of subclusters.
+
+        Avoid computation of the row norms of X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        labels : ndarray of shape(n_samples,)
+            Labelled data.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, accept_sparse="csr", reset=False)
+        return self._predict(X)
+
+    def _predict(self, X):
+        """Predict data using the ``centroids_`` of subclusters."""
+        kwargs = {"Y_norm_squared": self._subcluster_norms}
+
+        with config_context(assume_finite=True):
+            argmin = pairwise_distances_argmin(
+                X, self.subcluster_centers_, metric_kwargs=kwargs
+            )
+        return self.subcluster_labels_[argmin]
+
+    def transform(self, X):
+        """
+        Transform X into subcluster centroids dimension.
+
+        Each dimension represents the distance from the sample point to each
+        cluster centroid.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        X_trans : {array-like, sparse matrix} of shape (n_samples, n_clusters)
+            Transformed data.
+        """
+        check_is_fitted(self)
+        self._validate_data(X, accept_sparse="csr", reset=False)
+        with config_context(assume_finite=True):
+            return euclidean_distances(X, self.subcluster_centers_)
+
+    def _global_clustering(self, X=None):
+        """
+        Global clustering for the subclusters obtained after fitting
+        """
+        clusterer = self.n_clusters
+        centroids = self.subcluster_centers_
+        compute_labels = (X is not None) and self.compute_labels
+
+        # Preprocessing for the global clustering.
+        not_enough_centroids = False
+        if isinstance(clusterer, numbers.Integral):
+            clusterer = AgglomerativeClustering(n_clusters=self.n_clusters)
+            # There is no need to perform the global clustering step.
+            if len(centroids) < self.n_clusters:
+                not_enough_centroids = True
+        elif clusterer is not None and not hasattr(clusterer, "fit_predict"):
+            raise TypeError(
+                "n_clusters should be an instance of ClusterMixin or an int"
+            )
+
+        # To use in predict to avoid recalculation.
+        self._subcluster_norms = row_norms(self.subcluster_centers_, squared=True)
+
+        if clusterer is None or not_enough_centroids:
+            self.subcluster_labels_ = np.arange(len(centroids))
+            if not_enough_centroids:
+                warnings.warn(
+                    "Number of subclusters found (%d) by BIRCH is less "
+                    "than (%d). Decrease the threshold."
+                    % (len(centroids), self.n_clusters),
+                    ConvergenceWarning,
+                )
+        else:
+            # The global clustering step that clusters the subclusters of
+            # the leaves. It assumes the centroids of the subclusters as
+            # samples and finds the final centroids.
+            self.subcluster_labels_ = clusterer.fit_predict(self.subcluster_centers_)
+
+        if compute_labels:
+            self.labels_ = self._predict(X)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_bisect_k_means.py

@@ -0,0 +1,538 @@
+"""Bisecting K-means clustering."""
+# Author: Michal Krawczyk <mkrwczyk.1@gmail.com>
+
+import warnings
+
+import numpy as np
+import scipy.sparse as sp
+
+from ._kmeans import _BaseKMeans
+from ._kmeans import _kmeans_single_elkan
+from ._kmeans import _kmeans_single_lloyd
+from ._kmeans import _labels_inertia_threadpool_limit
+from ._k_means_common import _inertia_dense
+from ._k_means_common import _inertia_sparse
+from ..utils.extmath import row_norms
+from ..utils._openmp_helpers import _openmp_effective_n_threads
+from ..utils.validation import check_is_fitted
+from ..utils.validation import _check_sample_weight
+from ..utils.validation import check_random_state
+from ..utils.validation import _is_arraylike_not_scalar
+
+
+class _BisectingTree:
+    """Tree structure representing the hierarchical clusters of BisectingKMeans."""
+
+    def __init__(self, center, indices, score):
+        """Create a new cluster node in the tree.
+
+        The node holds the center of this cluster and the indices of the data points
+        that belong to it.
+        """
+        self.center = center
+        self.indices = indices
+        self.score = score
+
+        self.left = None
+        self.right = None
+
+    def split(self, labels, centers, scores):
+        """Split the cluster node into two subclusters."""
+        self.left = _BisectingTree(
+            indices=self.indices[labels == 0], center=centers[0], score=scores[0]
+        )
+        self.right = _BisectingTree(
+            indices=self.indices[labels == 1], center=centers[1], score=scores[1]
+        )
+
+        # reset the indices attribute to save memory
+        self.indices = None
+
+    def get_cluster_to_bisect(self):
+        """Return the cluster node to bisect next.
+
+        It's based on the score of the cluster, which can be either the number of
+        data points assigned to that cluster or the inertia of that cluster
+        (see `bisecting_strategy` for details).
+        """
+        max_score = None
+
+        for cluster_leaf in self.iter_leaves():
+            if max_score is None or cluster_leaf.score > max_score:
+                max_score = cluster_leaf.score
+                best_cluster_leaf = cluster_leaf
+
+        return best_cluster_leaf
+
+    def iter_leaves(self):
+        """Iterate over all the cluster leaves in the tree."""
+        if self.left is None:
+            yield self
+        else:
+            yield from self.left.iter_leaves()
+            yield from self.right.iter_leaves()
+
+
+class BisectingKMeans(_BaseKMeans):
+    """Bisecting K-Means clustering.
+
+    Read more in the :ref:`User Guide <bisect_k_means>`.
+
+    .. versionadded:: 1.1
+
+    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'} or callable, default='random'
+        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 a callable is passed, it should take arguments X, n_clusters and a
+        random state and return an initialization.
+
+    n_init : int, default=1
+        Number of time the inner k-means algorithm will be run with different
+        centroid seeds in each bisection.
+        That will result producing for each bisection best output of n_init
+        consecutive runs in terms of inertia.
+
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for centroid initialization
+        in inner K-Means. Use an int to make the randomness deterministic.
+        See :term:`Glossary <random_state>`.
+
+    max_iter : int, default=300
+        Maximum number of iterations of the inner k-means algorithm at each
+        bisection.
+
+    verbose : int, default=0
+        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. Used in inner k-means algorithm at each bisection to pick
+        best possible clusters.
+
+    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"}, default="lloyd"
+        Inner K-means algorithm used in bisection.
+        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)`.
+
+    bisecting_strategy : {"biggest_inertia", "largest_cluster"},\
+            default="biggest_inertia"
+        Defines how bisection should be performed:
+
+         - "biggest_inertia" means that BisectingKMeans will always check
+            all calculated cluster for cluster with biggest SSE
+            (Sum of squared errors) and bisect it. This approach concentrates on
+            precision, but may be costly in terms of execution time (especially for
+            larger amount of data points).
+
+         - "largest_cluster" - BisectingKMeans will always split cluster with
+            largest amount of points assigned to it from all clusters
+            previously calculated. That should work faster than picking by SSE
+            ('biggest_inertia') and may produce similar results in most cases.
+
+    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_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    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.
+
+    See Also
+    --------
+    KMeans : Original implementation of K-Means algorithm.
+
+    Notes
+    -----
+    It might be inefficient when n_cluster is less than 3, due to unnecassary
+    calculations for that case.
+
+    Examples
+    --------
+    >>> from sklearn.cluster import BisectingKMeans
+    >>> import numpy as np
+    >>> X = np.array([[1, 2], [1, 4], [1, 0],
+    ...               [10, 2], [10, 4], [10, 0],
+    ...               [10, 6], [10, 8], [10, 10]])
+    >>> bisect_means = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
+    >>> bisect_means.labels_
+    array([2, 2, 2, 0, 0, 0, 1, 1, 1], dtype=int32)
+    >>> bisect_means.predict([[0, 0], [12, 3]])
+    array([2, 0], dtype=int32)
+    >>> bisect_means.cluster_centers_
+    array([[10.,  2.],
+           [10.,  8.],
+           [ 1., 2.]])
+    """
+
+    def __init__(
+        self,
+        n_clusters=8,
+        *,
+        init="random",
+        n_init=1,
+        random_state=None,
+        max_iter=300,
+        verbose=0,
+        tol=1e-4,
+        copy_x=True,
+        algorithm="lloyd",
+        bisecting_strategy="biggest_inertia",
+    ):
+
+        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.copy_x = copy_x
+        self.algorithm = algorithm
+        self.bisecting_strategy = bisecting_strategy
+
+    def _check_params(self, X):
+        super()._check_params(X)
+
+        # algorithm
+        if self.algorithm not in ("lloyd", "elkan"):
+            raise ValueError(
+                "Algorithm must be either 'lloyd' or 'elkan', "
+                f"got {self.algorithm} instead."
+            )
+
+        # bisecting_strategy
+        if self.bisecting_strategy not in ["biggest_inertia", "largest_cluster"]:
+            raise ValueError(
+                "Bisect Strategy must be 'biggest_inertia' or 'largest_cluster'. "
+                f"Got {self.bisecting_strategy} instead."
+            )
+
+        # init
+        if _is_arraylike_not_scalar(self.init):
+            raise ValueError("BisectingKMeans does not support init as array.")
+
+    def _warn_mkl_vcomp(self, n_active_threads):
+        """Warn when vcomp and mkl are both present"""
+        warnings.warn(
+            "BisectingKMeans 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 _inertia_per_cluster(self, X, centers, labels, sample_weight):
+        """Calculate the sum of squared errors (inertia) per cluster.
+
+        Parameters
+        ----------
+        X : {ndarray, csr_matrix} of shape (n_samples, n_features)
+            The input samples.
+
+        centers : ndarray of shape (n_clusters, n_features)
+            The cluster centers.
+
+        labels : ndarray of shape (n_samples,)
+            Index of the cluster each sample belongs to.
+
+        sample_weight : ndarray of shape (n_samples,)
+            The weights for each observation in X.
+
+        Returns
+        -------
+        inertia_per_cluster : ndarray of shape (n_clusters,)
+            Sum of squared errors (inertia) for each cluster.
+        """
+        _inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense
+
+        inertia_per_cluster = np.empty(centers.shape[1])
+        for label in range(centers.shape[0]):
+            inertia_per_cluster[label] = _inertia(
+                X, sample_weight, centers, labels, self._n_threads, single_label=label
+            )
+
+        return inertia_per_cluster
+
+    def _bisect(self, X, x_squared_norms, sample_weight, cluster_to_bisect):
+        """Split a cluster into 2 subsclusters.
+
+        Parameters
+        ----------
+        X : {ndarray, csr_matrix} of shape (n_samples, n_features)
+            Training instances to cluster.
+
+        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.
+
+        cluster_to_bisect : _BisectingTree node object
+            The cluster node to split.
+        """
+        X = X[cluster_to_bisect.indices]
+        x_squared_norms = x_squared_norms[cluster_to_bisect.indices]
+        sample_weight = sample_weight[cluster_to_bisect.indices]
+
+        best_inertia = None
+
+        # Split samples in X into 2 clusters.
+        # Repeating `n_init` times to obtain best clusters
+        for _ in range(self.n_init):
+            centers_init = self._init_centroids(
+                X, x_squared_norms, self.init, self._random_state, n_centroids=2
+            )
+
+            labels, inertia, centers, _ = self._kmeans_single(
+                X,
+                sample_weight,
+                centers_init,
+                max_iter=self.max_iter,
+                verbose=self.verbose,
+                tol=self.tol,
+                x_squared_norms=x_squared_norms,
+                n_threads=self._n_threads,
+            )
+
+            # allow small tolerance on the inertia to accommodate for
+            # non-deterministic rounding errors due to parallel computation
+            if best_inertia is None or inertia < best_inertia * (1 - 1e-6):
+                best_labels = labels
+                best_centers = centers
+                best_inertia = inertia
+
+        if self.verbose:
+            print(f"New centroids from bisection: {best_centers}")
+
+        if self.bisecting_strategy == "biggest_inertia":
+            scores = self._inertia_per_cluster(
+                X, best_centers, best_labels, sample_weight
+            )
+        else:  # bisecting_strategy == "largest_cluster"
+            scores = np.bincount(best_labels)
+
+        cluster_to_bisect.split(best_labels, best_centers, scores)
+
+    def fit(self, X, y=None, sample_weight=None):
+        """Compute bisecting k-means clustering.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+
+            Training instances to cluster.
+
+            .. note:: The data will be converted to C ordering,
+                which will cause a memory copy
+                if the given data is not C-contiguous.
+
+        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
+            Fitted estimator.
+        """
+        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(X)
+        self._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()
+
+        if self.algorithm == "lloyd" or self.n_clusters == 1:
+            self._kmeans_single = _kmeans_single_lloyd
+            self._check_mkl_vcomp(X, X.shape[0])
+        else:
+            self._kmeans_single = _kmeans_single_elkan
+
+        # Subtract of mean of X for more accurate distance computations
+        if not sp.issparse(X):
+            self._X_mean = X.mean(axis=0)
+            X -= self._X_mean
+
+        # Initialize the hierarchical clusters tree
+        self._bisecting_tree = _BisectingTree(
+            indices=np.arange(X.shape[0]),
+            center=X.mean(axis=0),
+            score=0,
+        )
+
+        x_squared_norms = row_norms(X, squared=True)
+
+        for _ in range(self.n_clusters - 1):
+            # Chose cluster to bisect
+            cluster_to_bisect = self._bisecting_tree.get_cluster_to_bisect()
+
+            # Split this cluster into 2 subclusters
+            self._bisect(X, x_squared_norms, sample_weight, cluster_to_bisect)
+
+        # Aggregate final labels and centers from the bisecting tree
+        self.labels_ = np.full(X.shape[0], -1, dtype=np.int32)
+        self.cluster_centers_ = np.empty((self.n_clusters, X.shape[1]), dtype=X.dtype)
+
+        for i, cluster_node in enumerate(self._bisecting_tree.iter_leaves()):
+            self.labels_[cluster_node.indices] = i
+            self.cluster_centers_[i] = cluster_node.center
+            cluster_node.label = i  # label final clusters for future prediction
+            cluster_node.indices = None  # release memory
+
+        # Restore original data
+        if not sp.issparse(X):
+            X += self._X_mean
+            self.cluster_centers_ += self._X_mean
+
+        _inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense
+        self.inertia_ = _inertia(
+            X, sample_weight, self.cluster_centers_, self.labels_, self._n_threads
+        )
+
+        self._n_features_out = self.cluster_centers_.shape[0]
+
+        return self
+
+    def predict(self, X):
+        """Predict which cluster each sample in X belongs to.
+
+        Prediction is made by going down the hierarchical tree
+        in searching of closest leaf cluster.
+
+        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.
+
+        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)
+        x_squared_norms = row_norms(X, squared=True)
+
+        # sample weights are unused but necessary in cython helpers
+        sample_weight = np.ones_like(x_squared_norms)
+
+        labels = self._predict_recursive(
+            X, x_squared_norms, sample_weight, self._bisecting_tree
+        )
+
+        return labels
+
+    def _predict_recursive(self, X, x_squared_norms, sample_weight, cluster_node):
+        """Predict recursively by going down the hierarchical tree.
+
+        Parameters
+        ----------
+        X : {ndarray, csr_matrix} of shape (n_samples, n_features)
+            The data points, currently assigned to `cluster_node`, to predict between
+            the subclusters of this node.
+
+        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.
+
+        cluster_node : _BisectingTree node object
+            The cluster node of the hierarchical tree.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            Index of the cluster each sample belongs to.
+        """
+        if cluster_node.left is None:
+            # This cluster has no subcluster. Labels are just the label of the cluster.
+            return np.full(X.shape[0], cluster_node.label, dtype=np.int32)
+
+        # Determine if data points belong to the left or right subcluster
+        centers = np.vstack((cluster_node.left.center, cluster_node.right.center))
+        if hasattr(self, "_X_mean"):
+            centers += self._X_mean
+
+        cluster_labels = _labels_inertia_threadpool_limit(
+            X,
+            sample_weight,
+            x_squared_norms,
+            centers,
+            self._n_threads,
+            return_inertia=False,
+        )
+        mask = cluster_labels == 0
+
+        # Compute the labels for each subset of the data points.
+        labels = np.full(X.shape[0], -1, dtype=np.int32)
+
+        labels[mask] = self._predict_recursive(
+            X[mask], x_squared_norms[mask], sample_weight[mask], cluster_node.left
+        )
+
+        labels[~mask] = self._predict_recursive(
+            X[~mask], x_squared_norms[~mask], sample_weight[~mask], cluster_node.right
+        )
+
+        return labels
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float64, np.float32]}

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_dbscan.py

@@ -0,0 +1,462 @@
+"""
+DBSCAN: Density-Based Spatial Clustering of Applications with Noise
+"""
+
+# Author: Robert Layton <robertlayton@gmail.com>
+#         Joel Nothman <joel.nothman@gmail.com>
+#         Lars Buitinck
+#
+# License: BSD 3 clause
+
+import numpy as np
+import numbers
+import warnings
+from scipy import sparse
+
+from ..utils import check_scalar
+from ..base import BaseEstimator, ClusterMixin
+from ..utils.validation import _check_sample_weight
+from ..neighbors import NearestNeighbors
+
+from ._dbscan_inner import dbscan_inner
+
+
+def dbscan(
+    X,
+    eps=0.5,
+    *,
+    min_samples=5,
+    metric="minkowski",
+    metric_params=None,
+    algorithm="auto",
+    leaf_size=30,
+    p=2,
+    sample_weight=None,
+    n_jobs=None,
+):
+    """Perform DBSCAN clustering from vector array or distance matrix.
+
+    Read more in the :ref:`User Guide <dbscan>`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse (CSR) matrix} of shape (n_samples, n_features) or \
+            (n_samples, n_samples)
+        A feature array, or array of distances between samples if
+        ``metric='precomputed'``.
+
+    eps : float, default=0.5
+        The maximum distance between two samples for one to be considered
+        as in the neighborhood of the other. This is not a maximum bound
+        on the distances of points within a cluster. This is the most
+        important DBSCAN parameter to choose appropriately for your data set
+        and distance function.
+
+    min_samples : int, default=5
+        The number of samples (or total weight) in a neighborhood for a point
+        to be considered as a core point. This includes the point itself.
+
+    metric : str or callable, default='minkowski'
+        The metric to use when calculating distance between instances in a
+        feature array. If metric is a string or callable, it must be one of
+        the options allowed by :func:`sklearn.metrics.pairwise_distances` for
+        its metric parameter.
+        If metric is "precomputed", X is assumed to be a distance matrix and
+        must be square during fit.
+        X may be a :term:`sparse graph <sparse graph>`,
+        in which case only "nonzero" elements may be considered neighbors.
+
+    metric_params : dict, default=None
+        Additional keyword arguments for the metric function.
+
+        .. versionadded:: 0.19
+
+    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
+        The algorithm to be used by the NearestNeighbors module
+        to compute pointwise distances and find nearest neighbors.
+        See NearestNeighbors module documentation for details.
+
+    leaf_size : int, default=30
+        Leaf size passed to BallTree or cKDTree. This can affect the speed
+        of the construction and query, as well as the memory required
+        to store the tree. The optimal value depends
+        on the nature of the problem.
+
+    p : float, default=2
+        The power of the Minkowski metric to be used to calculate distance
+        between points.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Weight of each sample, such that a sample with a weight of at least
+        ``min_samples`` is by itself a core sample; a sample with negative
+        weight may inhibit its eps-neighbor from being core.
+        Note that weights are absolute, and default to 1.
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run for neighbors search. ``None`` means
+        1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means
+        using all processors. See :term:`Glossary <n_jobs>` for more details.
+        If precomputed distance are used, parallel execution is not available
+        and thus n_jobs will have no effect.
+
+    Returns
+    -------
+    core_samples : ndarray of shape (n_core_samples,)
+        Indices of core samples.
+
+    labels : ndarray of shape (n_samples,)
+        Cluster labels for each point.  Noisy samples are given the label -1.
+
+    See Also
+    --------
+    DBSCAN : An estimator interface for this clustering algorithm.
+    OPTICS : A similar estimator interface clustering at multiple values of
+        eps. Our implementation is optimized for memory usage.
+
+    Notes
+    -----
+    For an example, see :ref:`examples/cluster/plot_dbscan.py
+    <sphx_glr_auto_examples_cluster_plot_dbscan.py>`.
+
+    This implementation bulk-computes all neighborhood queries, which increases
+    the memory complexity to O(n.d) where d is the average number of neighbors,
+    while original DBSCAN had memory complexity O(n). It may attract a higher
+    memory complexity when querying these nearest neighborhoods, depending
+    on the ``algorithm``.
+
+    One way to avoid the query complexity is to pre-compute sparse
+    neighborhoods in chunks using
+    :func:`NearestNeighbors.radius_neighbors_graph
+    <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>` with
+    ``mode='distance'``, then using ``metric='precomputed'`` here.
+
+    Another way to reduce memory and computation time is to remove
+    (near-)duplicate points and use ``sample_weight`` instead.
+
+    :func:`cluster.optics <sklearn.cluster.optics>` provides a similar
+    clustering with lower memory usage.
+
+    References
+    ----------
+    Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
+    Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
+    In: Proceedings of the 2nd International Conference on Knowledge Discovery
+    and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
+
+    Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
+    DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
+    ACM Transactions on Database Systems (TODS), 42(3), 19.
+    """
+
+    est = DBSCAN(
+        eps=eps,
+        min_samples=min_samples,
+        metric=metric,
+        metric_params=metric_params,
+        algorithm=algorithm,
+        leaf_size=leaf_size,
+        p=p,
+        n_jobs=n_jobs,
+    )
+    est.fit(X, sample_weight=sample_weight)
+    return est.core_sample_indices_, est.labels_
+
+
+class DBSCAN(ClusterMixin, BaseEstimator):
+    """Perform DBSCAN clustering from vector array or distance matrix.
+
+    DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
+    Finds core samples of high density and expands clusters from them.
+    Good for data which contains clusters of similar density.
+
+    Read more in the :ref:`User Guide <dbscan>`.
+
+    Parameters
+    ----------
+    eps : float, default=0.5
+        The maximum distance between two samples for one to be considered
+        as in the neighborhood of the other. This is not a maximum bound
+        on the distances of points within a cluster. This is the most
+        important DBSCAN parameter to choose appropriately for your data set
+        and distance function.
+
+    min_samples : int, default=5
+        The number of samples (or total weight) in a neighborhood for a point
+        to be considered as a core point. This includes the point itself.
+
+    metric : str, or callable, default='euclidean'
+        The metric to use when calculating distance between instances in a
+        feature array. If metric is a string or callable, it must be one of
+        the options allowed by :func:`sklearn.metrics.pairwise_distances` for
+        its metric parameter.
+        If metric is "precomputed", X is assumed to be a distance matrix and
+        must be square. X may be a :term:`sparse graph`, in which
+        case only "nonzero" elements may be considered neighbors for DBSCAN.
+
+        .. versionadded:: 0.17
+           metric *precomputed* to accept precomputed sparse matrix.
+
+    metric_params : dict, default=None
+        Additional keyword arguments for the metric function.
+
+        .. versionadded:: 0.19
+
+    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
+        The algorithm to be used by the NearestNeighbors module
+        to compute pointwise distances and find nearest neighbors.
+        See NearestNeighbors module documentation for details.
+
+    leaf_size : int, default=30
+        Leaf size passed to BallTree or cKDTree. This can affect the speed
+        of the construction and query, as well as the memory required
+        to store the tree. The optimal value depends
+        on the nature of the problem.
+
+    p : float, default=None
+        The power of the Minkowski metric to be used to calculate distance
+        between points. If None, then ``p=2`` (equivalent to the Euclidean
+        distance).
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    Attributes
+    ----------
+    core_sample_indices_ : ndarray of shape (n_core_samples,)
+        Indices of core samples.
+
+    components_ : ndarray of shape (n_core_samples, n_features)
+        Copy of each core sample found by training.
+
+    labels_ : ndarray of shape (n_samples)
+        Cluster labels for each point in the dataset given to fit().
+        Noisy samples are given the label -1.
+
+    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
+    --------
+    OPTICS : A similar clustering at multiple values of eps. Our implementation
+        is optimized for memory usage.
+
+    Notes
+    -----
+    For an example, see :ref:`examples/cluster/plot_dbscan.py
+    <sphx_glr_auto_examples_cluster_plot_dbscan.py>`.
+
+    This implementation bulk-computes all neighborhood queries, which increases
+    the memory complexity to O(n.d) where d is the average number of neighbors,
+    while original DBSCAN had memory complexity O(n). It may attract a higher
+    memory complexity when querying these nearest neighborhoods, depending
+    on the ``algorithm``.
+
+    One way to avoid the query complexity is to pre-compute sparse
+    neighborhoods in chunks using
+    :func:`NearestNeighbors.radius_neighbors_graph
+    <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>` with
+    ``mode='distance'``, then using ``metric='precomputed'`` here.
+
+    Another way to reduce memory and computation time is to remove
+    (near-)duplicate points and use ``sample_weight`` instead.
+
+    :class:`cluster.OPTICS` provides a similar clustering with lower memory
+    usage.
+
+    References
+    ----------
+    Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
+    Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
+    In: Proceedings of the 2nd International Conference on Knowledge Discovery
+    and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
+
+    Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
+    DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
+    ACM Transactions on Database Systems (TODS), 42(3), 19.
+
+    Examples
+    --------
+    >>> from sklearn.cluster import DBSCAN
+    >>> import numpy as np
+    >>> X = np.array([[1, 2], [2, 2], [2, 3],
+    ...               [8, 7], [8, 8], [25, 80]])
+    >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+    >>> clustering.labels_
+    array([ 0,  0,  0,  1,  1, -1])
+    >>> clustering
+    DBSCAN(eps=3, min_samples=2)
+    """
+
+    def __init__(
+        self,
+        eps=0.5,
+        *,
+        min_samples=5,
+        metric="euclidean",
+        metric_params=None,
+        algorithm="auto",
+        leaf_size=30,
+        p=None,
+        n_jobs=None,
+    ):
+        self.eps = eps
+        self.min_samples = min_samples
+        self.metric = metric
+        self.metric_params = metric_params
+        self.algorithm = algorithm
+        self.leaf_size = leaf_size
+        self.p = p
+        self.n_jobs = n_jobs
+
+    def fit(self, X, y=None, sample_weight=None):
+        """Perform DBSCAN clustering from features, or distance matrix.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
+            (n_samples, n_samples)
+            Training instances to cluster, or distances between instances if
+            ``metric='precomputed'``. If a sparse matrix is provided, it will
+            be converted into a sparse ``csr_matrix``.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weight of each sample, such that a sample with a weight of at least
+            ``min_samples`` is by itself a core sample; a sample with a
+            negative weight may inhibit its eps-neighbor from being core.
+            Note that weights are absolute, and default to 1.
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance of self.
+        """
+        X = self._validate_data(X, accept_sparse="csr")
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        # Calculate neighborhood for all samples. This leaves the original
+        # point in, which needs to be considered later (i.e. point i is in the
+        # neighborhood of point i. While True, its useless information)
+        if self.metric == "precomputed" and sparse.issparse(X):
+            # set the diagonal to explicit values, as a point is its own
+            # neighbor
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", sparse.SparseEfficiencyWarning)
+                X.setdiag(X.diagonal())  # XXX: modifies X's internals in-place
+
+        # Validating the scalar parameters.
+        check_scalar(
+            self.eps,
+            "eps",
+            target_type=numbers.Real,
+            min_val=0.0,
+            include_boundaries="neither",
+        )
+        check_scalar(
+            self.min_samples,
+            "min_samples",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+        check_scalar(
+            self.leaf_size,
+            "leaf_size",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+        if self.p is not None:
+            check_scalar(
+                self.p,
+                "p",
+                target_type=numbers.Real,
+                min_val=0.0,
+                include_boundaries="left",
+            )
+        if self.n_jobs is not None:
+            check_scalar(self.n_jobs, "n_jobs", target_type=numbers.Integral)
+
+        neighbors_model = NearestNeighbors(
+            radius=self.eps,
+            algorithm=self.algorithm,
+            leaf_size=self.leaf_size,
+            metric=self.metric,
+            metric_params=self.metric_params,
+            p=self.p,
+            n_jobs=self.n_jobs,
+        )
+        neighbors_model.fit(X)
+        # This has worst case O(n^2) memory complexity
+        neighborhoods = neighbors_model.radius_neighbors(X, return_distance=False)
+
+        if sample_weight is None:
+            n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods])
+        else:
+            n_neighbors = np.array(
+                [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods]
+            )
+
+        # Initially, all samples are noise.
+        labels = np.full(X.shape[0], -1, dtype=np.intp)
+
+        # A list of all core samples found.
+        core_samples = np.asarray(n_neighbors >= self.min_samples, dtype=np.uint8)
+        dbscan_inner(core_samples, neighborhoods, labels)
+
+        self.core_sample_indices_ = np.where(core_samples)[0]
+        self.labels_ = labels
+
+        if len(self.core_sample_indices_):
+            # fix for scipy sparse indexing issue
+            self.components_ = X[self.core_sample_indices_].copy()
+        else:
+            # no core samples
+            self.components_ = np.empty((0, X.shape[1]))
+        return self
+
+    def fit_predict(self, X, y=None, sample_weight=None):
+        """Compute clusters from a data or distance matrix and predict labels.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
+            (n_samples, n_samples)
+            Training instances to cluster, or distances between instances if
+            ``metric='precomputed'``. If a sparse matrix is provided, it will
+            be converted into a sparse ``csr_matrix``.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weight of each sample, such that a sample with a weight of at least
+            ``min_samples`` is by itself a core sample; a sample with a
+            negative weight may inhibit its eps-neighbor from being core.
+            Note that weights are absolute, and default to 1.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            Cluster labels. Noisy samples are given the label -1.
+        """
+        self.fit(X, sample_weight=sample_weight)
+        return self.labels_
+
+    def _more_tags(self):
+        return {"pairwise": self.metric == "precomputed"}

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_feature_agglomeration.py

@@ -0,0 +1,75 @@
+"""
+Feature agglomeration. Base classes and functions for performing feature
+agglomeration.
+"""
+# Author: V. Michel, A. Gramfort
+# License: BSD 3 clause
+
+import numpy as np
+
+from ..base import TransformerMixin
+from ..utils.validation import check_is_fitted
+from scipy.sparse import issparse
+
+###############################################################################
+# Mixin class for feature agglomeration.
+
+
+class AgglomerationTransform(TransformerMixin):
+    """
+    A class for feature agglomeration via the transform interface.
+    """
+
+    def transform(self, X):
+        """
+        Transform a new matrix using the built clustering.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features) or \
+                (n_samples, n_samples)
+            A M by N array of M observations in N dimensions or a length
+            M array of M one-dimensional observations.
+
+        Returns
+        -------
+        Y : ndarray of shape (n_samples, n_clusters) or (n_clusters,)
+            The pooled values for each feature cluster.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(X, reset=False)
+        if self.pooling_func == np.mean and not issparse(X):
+            size = np.bincount(self.labels_)
+            n_samples = X.shape[0]
+            # a fast way to compute the mean of grouped features
+            nX = np.array(
+                [np.bincount(self.labels_, X[i, :]) / size for i in range(n_samples)]
+            )
+        else:
+            nX = [
+                self.pooling_func(X[:, self.labels_ == l], axis=1)
+                for l in np.unique(self.labels_)
+            ]
+            nX = np.array(nX).T
+        return nX
+
+    def inverse_transform(self, Xred):
+        """
+        Inverse the transformation and return a vector of size `n_features`.
+
+        Parameters
+        ----------
+        Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples.
+
+        Returns
+        -------
+        X : ndarray of shape (n_samples, n_features) or (n_features,)
+            A vector of size `n_samples` with the values of `Xred` assigned to
+            each of the cluster of samples.
+        """
+        check_is_fitted(self)
+
+        unil, inverse = np.unique(self.labels_, return_inverse=True)
+        return Xred[..., inverse]

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_k_means_common.pxd

@@ -0,0 +1,20 @@
+from cython cimport floating
+cimport numpy as np
+
+
+cdef floating _euclidean_dense_dense(floating*, floating*, int, bint) nogil
+
+cdef floating _euclidean_sparse_dense(floating[::1], int[::1], floating[::1],
+                                      floating, bint) nogil
+
+cpdef void _relocate_empty_clusters_dense(
+    floating[:, ::1], floating[::1], floating[:, ::1],
+    floating[:, ::1], floating[::1], int[::1])
+
+cpdef void _relocate_empty_clusters_sparse(
+    floating[::1], int[::1], int[::1], floating[::1], floating[:, ::1],
+    floating[:, ::1], floating[::1], int[::1])
+
+cdef void _average_centers(floating[:, ::1], floating[::1])
+
+cdef void _center_shift(floating[:, ::1], floating[:, ::1], floating[::1])

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_mean_shift.py

@@ -0,0 +1,514 @@
+"""Mean shift clustering algorithm.
+
+Mean shift clustering aims to discover *blobs* in a smooth density of
+samples. It is a centroid based algorithm, which works by updating candidates
+for centroids to be the mean of the points within a given region. These
+candidates are then filtered in a post-processing stage to eliminate
+near-duplicates to form the final set of centroids.
+
+Seeding is performed using a binning technique for scalability.
+"""
+
+# Authors: Conrad Lee <conradlee@gmail.com>
+#          Alexandre Gramfort <alexandre.gramfort@inria.fr>
+#          Gael Varoquaux <gael.varoquaux@normalesup.org>
+#          Martino Sorbaro <martino.sorbaro@ed.ac.uk>
+
+import numpy as np
+import warnings
+from joblib import Parallel
+
+from collections import defaultdict
+from ..utils.validation import check_is_fitted
+from ..utils.fixes import delayed
+from ..utils import check_random_state, gen_batches, check_array
+from ..base import BaseEstimator, ClusterMixin
+from ..neighbors import NearestNeighbors
+from ..metrics.pairwise import pairwise_distances_argmin
+from .._config import config_context
+
+
+def estimate_bandwidth(X, *, quantile=0.3, n_samples=None, random_state=0, n_jobs=None):
+    """Estimate the bandwidth to use with the mean-shift algorithm.
+
+    That this function takes time at least quadratic in n_samples. For large
+    datasets, it's wise to set that parameter to a small value.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Input points.
+
+    quantile : float, default=0.3
+        Should be between [0, 1]
+        0.5 means that the median of all pairwise distances is used.
+
+    n_samples : int, default=None
+        The number of samples to use. If not given, all samples are used.
+
+    random_state : int, RandomState instance, default=None
+        The generator used to randomly select the samples from input points
+        for bandwidth estimation. Use an int to make the randomness
+        deterministic.
+        See :term:`Glossary <random_state>`.
+
+    n_jobs : int, default=None
+        The number of parallel jobs to run for neighbors search.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    Returns
+    -------
+    bandwidth : float
+        The bandwidth parameter.
+    """
+    X = check_array(X)
+
+    random_state = check_random_state(random_state)
+    if n_samples is not None:
+        idx = random_state.permutation(X.shape[0])[:n_samples]
+        X = X[idx]
+    n_neighbors = int(X.shape[0] * quantile)
+    if n_neighbors < 1:  # cannot fit NearestNeighbors with n_neighbors = 0
+        n_neighbors = 1
+    nbrs = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=n_jobs)
+    nbrs.fit(X)
+
+    bandwidth = 0.0
+    for batch in gen_batches(len(X), 500):
+        d, _ = nbrs.kneighbors(X[batch, :], return_distance=True)
+        bandwidth += np.max(d, axis=1).sum()
+
+    return bandwidth / X.shape[0]
+
+
+# separate function for each seed's iterative loop
+def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
+    # For each seed, climb gradient until convergence or max_iter
+    bandwidth = nbrs.get_params()["radius"]
+    stop_thresh = 1e-3 * bandwidth  # when mean has converged
+    completed_iterations = 0
+    while True:
+        # Find mean of points within bandwidth
+        i_nbrs = nbrs.radius_neighbors([my_mean], bandwidth, return_distance=False)[0]
+        points_within = X[i_nbrs]
+        if len(points_within) == 0:
+            break  # Depending on seeding strategy this condition may occur
+        my_old_mean = my_mean  # save the old mean
+        my_mean = np.mean(points_within, axis=0)
+        # If converged or at max_iter, adds the cluster
+        if (
+            np.linalg.norm(my_mean - my_old_mean) < stop_thresh
+            or completed_iterations == max_iter
+        ):
+            break
+        completed_iterations += 1
+    return tuple(my_mean), len(points_within), completed_iterations
+
+
+def mean_shift(
+    X,
+    *,
+    bandwidth=None,
+    seeds=None,
+    bin_seeding=False,
+    min_bin_freq=1,
+    cluster_all=True,
+    max_iter=300,
+    n_jobs=None,
+):
+    """Perform mean shift clustering of data using a flat kernel.
+
+    Read more in the :ref:`User Guide <mean_shift>`.
+
+    Parameters
+    ----------
+
+    X : array-like of shape (n_samples, n_features)
+        Input data.
+
+    bandwidth : float, default=None
+        Kernel bandwidth.
+
+        If bandwidth is not given, it is determined using a heuristic based on
+        the median of all pairwise distances. This will take quadratic time in
+        the number of samples. The sklearn.cluster.estimate_bandwidth function
+        can be used to do this more efficiently.
+
+    seeds : array-like of shape (n_seeds, n_features) or None
+        Point used as initial kernel locations. If None and bin_seeding=False,
+        each data point is used as a seed. If None and bin_seeding=True,
+        see bin_seeding.
+
+    bin_seeding : bool, default=False
+        If true, initial kernel locations are not locations of all
+        points, but rather the location of the discretized version of
+        points, where points are binned onto a grid whose coarseness
+        corresponds to the bandwidth. Setting this option to True will speed
+        up the algorithm because fewer seeds will be initialized.
+        Ignored if seeds argument is not None.
+
+    min_bin_freq : int, default=1
+       To speed up the algorithm, accept only those bins with at least
+       min_bin_freq points as seeds.
+
+    cluster_all : bool, default=True
+        If true, then all points are clustered, even those orphans that are
+        not within any kernel. Orphans are assigned to the nearest kernel.
+        If false, then orphans are given cluster label -1.
+
+    max_iter : int, default=300
+        Maximum number of iterations, per seed point before the clustering
+        operation terminates (for that seed point), if has not converged yet.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation. This works by computing
+        each of the n_init runs 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.
+
+        .. versionadded:: 0.17
+           Parallel Execution using *n_jobs*.
+
+    Returns
+    -------
+
+    cluster_centers : ndarray of shape (n_clusters, n_features)
+        Coordinates of cluster centers.
+
+    labels : ndarray of shape (n_samples,)
+        Cluster labels for each point.
+
+    Notes
+    -----
+    For an example, see :ref:`examples/cluster/plot_mean_shift.py
+    <sphx_glr_auto_examples_cluster_plot_mean_shift.py>`.
+    """
+    model = MeanShift(
+        bandwidth=bandwidth,
+        seeds=seeds,
+        min_bin_freq=min_bin_freq,
+        bin_seeding=bin_seeding,
+        cluster_all=cluster_all,
+        n_jobs=n_jobs,
+        max_iter=max_iter,
+    ).fit(X)
+    return model.cluster_centers_, model.labels_
+
+
+def get_bin_seeds(X, bin_size, min_bin_freq=1):
+    """Find seeds for mean_shift.
+
+    Finds seeds by first binning data onto a grid whose lines are
+    spaced bin_size apart, and then choosing those bins with at least
+    min_bin_freq points.
+
+    Parameters
+    ----------
+
+    X : array-like of shape (n_samples, n_features)
+        Input points, the same points that will be used in mean_shift.
+
+    bin_size : float
+        Controls the coarseness of the binning. Smaller values lead
+        to more seeding (which is computationally more expensive). If you're
+        not sure how to set this, set it to the value of the bandwidth used
+        in clustering.mean_shift.
+
+    min_bin_freq : int, default=1
+        Only bins with at least min_bin_freq will be selected as seeds.
+        Raising this value decreases the number of seeds found, which
+        makes mean_shift computationally cheaper.
+
+    Returns
+    -------
+    bin_seeds : array-like of shape (n_samples, n_features)
+        Points used as initial kernel positions in clustering.mean_shift.
+    """
+    if bin_size == 0:
+        return X
+
+    # Bin points
+    bin_sizes = defaultdict(int)
+    for point in X:
+        binned_point = np.round(point / bin_size)
+        bin_sizes[tuple(binned_point)] += 1
+
+    # Select only those bins as seeds which have enough members
+    bin_seeds = np.array(
+        [point for point, freq in bin_sizes.items() if freq >= min_bin_freq],
+        dtype=np.float32,
+    )
+    if len(bin_seeds) == len(X):
+        warnings.warn(
+            "Binning data failed with provided bin_size=%f, using data points as seeds."
+            % bin_size
+        )
+        return X
+    bin_seeds = bin_seeds * bin_size
+    return bin_seeds
+
+
+class MeanShift(ClusterMixin, BaseEstimator):
+    """Mean shift clustering using a flat kernel.
+
+    Mean shift clustering aims to discover "blobs" in a smooth density of
+    samples. It is a centroid-based algorithm, which works by updating
+    candidates for centroids to be the mean of the points within a given
+    region. These candidates are then filtered in a post-processing stage to
+    eliminate near-duplicates to form the final set of centroids.
+
+    Seeding is performed using a binning technique for scalability.
+
+    Read more in the :ref:`User Guide <mean_shift>`.
+
+    Parameters
+    ----------
+    bandwidth : float, default=None
+        Bandwidth used in the RBF kernel.
+
+        If not given, the bandwidth is estimated using
+        sklearn.cluster.estimate_bandwidth; see the documentation for that
+        function for hints on scalability (see also the Notes, below).
+
+    seeds : array-like of shape (n_samples, n_features), default=None
+        Seeds used to initialize kernels. If not set,
+        the seeds are calculated by clustering.get_bin_seeds
+        with bandwidth as the grid size and default values for
+        other parameters.
+
+    bin_seeding : bool, default=False
+        If true, initial kernel locations are not locations of all
+        points, but rather the location of the discretized version of
+        points, where points are binned onto a grid whose coarseness
+        corresponds to the bandwidth. Setting this option to True will speed
+        up the algorithm because fewer seeds will be initialized.
+        The default value is False.
+        Ignored if seeds argument is not None.
+
+    min_bin_freq : int, default=1
+       To speed up the algorithm, accept only those bins with at least
+       min_bin_freq points as seeds.
+
+    cluster_all : bool, default=True
+        If true, then all points are clustered, even those orphans that are
+        not within any kernel. Orphans are assigned to the nearest kernel.
+        If false, then orphans are given cluster label -1.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation. This works by computing
+        each of the n_init runs 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, per seed point before the clustering
+        operation terminates (for that seed point), if has not converged yet.
+
+        .. versionadded:: 0.22
+
+    Attributes
+    ----------
+    cluster_centers_ : ndarray of shape (n_clusters, n_features)
+        Coordinates of cluster centers.
+
+    labels_ : ndarray of shape (n_samples,)
+        Labels of each point.
+
+    n_iter_ : int
+        Maximum number of iterations performed on each seed.
+
+        .. versionadded:: 0.22
+
+    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 : K-Means clustering.
+
+    Notes
+    -----
+
+    Scalability:
+
+    Because this implementation uses a flat kernel and
+    a Ball Tree to look up members of each kernel, the complexity will tend
+    towards O(T*n*log(n)) in lower dimensions, with n the number of samples
+    and T the number of points. In higher dimensions the complexity will
+    tend towards O(T*n^2).
+
+    Scalability can be boosted by using fewer seeds, for example by using
+    a higher value of min_bin_freq in the get_bin_seeds function.
+
+    Note that the estimate_bandwidth function is much less scalable than the
+    mean shift algorithm and will be the bottleneck if it is used.
+
+    References
+    ----------
+
+    Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
+    feature space analysis". IEEE Transactions on Pattern Analysis and
+    Machine Intelligence. 2002. pp. 603-619.
+
+    Examples
+    --------
+    >>> from sklearn.cluster import MeanShift
+    >>> import numpy as np
+    >>> X = np.array([[1, 1], [2, 1], [1, 0],
+    ...               [4, 7], [3, 5], [3, 6]])
+    >>> clustering = MeanShift(bandwidth=2).fit(X)
+    >>> clustering.labels_
+    array([1, 1, 1, 0, 0, 0])
+    >>> clustering.predict([[0, 0], [5, 5]])
+    array([1, 0])
+    >>> clustering
+    MeanShift(bandwidth=2)
+    """
+
+    def __init__(
+        self,
+        *,
+        bandwidth=None,
+        seeds=None,
+        bin_seeding=False,
+        min_bin_freq=1,
+        cluster_all=True,
+        n_jobs=None,
+        max_iter=300,
+    ):
+        self.bandwidth = bandwidth
+        self.seeds = seeds
+        self.bin_seeding = bin_seeding
+        self.cluster_all = cluster_all
+        self.min_bin_freq = min_bin_freq
+        self.n_jobs = n_jobs
+        self.max_iter = max_iter
+
+    def fit(self, X, y=None):
+        """Perform clustering.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to cluster.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+               Fitted instance.
+        """
+        X = self._validate_data(X)
+        bandwidth = self.bandwidth
+        if bandwidth is None:
+            bandwidth = estimate_bandwidth(X, n_jobs=self.n_jobs)
+        elif bandwidth <= 0:
+            raise ValueError(
+                "bandwidth needs to be greater than zero or None, got %f" % bandwidth
+            )
+
+        seeds = self.seeds
+        if seeds is None:
+            if self.bin_seeding:
+                seeds = get_bin_seeds(X, bandwidth, self.min_bin_freq)
+            else:
+                seeds = X
+        n_samples, n_features = X.shape
+        center_intensity_dict = {}
+
+        # We use n_jobs=1 because this will be used in nested calls under
+        # parallel calls to _mean_shift_single_seed so there is no need for
+        # for further parallelism.
+        nbrs = NearestNeighbors(radius=bandwidth, n_jobs=1).fit(X)
+
+        # execute iterations on all seeds in parallel
+        all_res = Parallel(n_jobs=self.n_jobs)(
+            delayed(_mean_shift_single_seed)(seed, X, nbrs, self.max_iter)
+            for seed in seeds
+        )
+        # copy results in a dictionary
+        for i in range(len(seeds)):
+            if all_res[i][1]:  # i.e. len(points_within) > 0
+                center_intensity_dict[all_res[i][0]] = all_res[i][1]
+
+        self.n_iter_ = max([x[2] for x in all_res])
+
+        if not center_intensity_dict:
+            # nothing near seeds
+            raise ValueError(
+                "No point was within bandwidth=%f of any seed. Try a different seeding"
+                " strategy                              or increase the bandwidth."
+                % bandwidth
+            )
+
+        # POST PROCESSING: remove near duplicate points
+        # If the distance between two kernels is less than the bandwidth,
+        # then we have to remove one because it is a duplicate. Remove the
+        # one with fewer points.
+
+        sorted_by_intensity = sorted(
+            center_intensity_dict.items(),
+            key=lambda tup: (tup[1], tup[0]),
+            reverse=True,
+        )
+        sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])
+        unique = np.ones(len(sorted_centers), dtype=bool)
+        nbrs = NearestNeighbors(radius=bandwidth, n_jobs=self.n_jobs).fit(
+            sorted_centers
+        )
+        for i, center in enumerate(sorted_centers):
+            if unique[i]:
+                neighbor_idxs = nbrs.radius_neighbors([center], return_distance=False)[
+                    0
+                ]
+                unique[neighbor_idxs] = 0
+                unique[i] = 1  # leave the current point as unique
+        cluster_centers = sorted_centers[unique]
+
+        # ASSIGN LABELS: a point belongs to the cluster that it is closest to
+        nbrs = NearestNeighbors(n_neighbors=1, n_jobs=self.n_jobs).fit(cluster_centers)
+        labels = np.zeros(n_samples, dtype=int)
+        distances, idxs = nbrs.kneighbors(X)
+        if self.cluster_all:
+            labels = idxs.flatten()
+        else:
+            labels.fill(-1)
+            bool_selector = distances.flatten() <= bandwidth
+            labels[bool_selector] = idxs.flatten()[bool_selector]
+
+        self.cluster_centers_, self.labels_ = cluster_centers, labels
+        return self
+
+    def predict(self, X):
+        """Predict the closest cluster each sample in X belongs to.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            New data to predict.
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,)
+            Index of the cluster each sample belongs to.
+        """
+        check_is_fitted(self)
+        X = self._validate_data(X, reset=False)
+        with config_context(assume_finite=True):
+            return pairwise_distances_argmin(X, self.cluster_centers_)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/_spectral.py

@@ -0,0 +1,781 @@
+"""Algorithms for spectral clustering"""
+
+# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
+#         Brian Cheung
+#         Wei LI <kuantkid@gmail.com>
+#         Andrew Knyazev <Andrew.Knyazev@ucdenver.edu>
+# License: BSD 3 clause
+
+import numbers
+import warnings
+
+import numpy as np
+
+from scipy.linalg import LinAlgError, qr, svd
+from scipy.sparse import csc_matrix
+
+from ..base import BaseEstimator, ClusterMixin
+from ..utils import check_random_state, as_float_array, check_scalar
+from ..metrics.pairwise import pairwise_kernels
+from ..neighbors import kneighbors_graph, NearestNeighbors
+from ..manifold import spectral_embedding
+from ._kmeans import k_means
+
+
+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://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.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
+
+
+def spectral_clustering(
+    affinity,
+    *,
+    n_clusters=8,
+    n_components=None,
+    eigen_solver=None,
+    random_state=None,
+    n_init=10,
+    eigen_tol=0.0,
+    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=0.0
+        Stopping criterion for eigendecomposition of the Laplacian matrix
+        when using arpack eigen_solver.
+
+    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.
+
+        .. 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://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.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>`
+    """
+    if assign_labels not in ("kmeans", "discretize", "cluster_qr"):
+        raise ValueError(
+            "The 'assign_labels' parameter should be "
+            "'kmeans' or 'discretize', or 'cluster_qr', "
+            f"but {assign_labels!r} was given"
+        )
+    if isinstance(affinity, np.matrix):
+        raise TypeError(
+            "spectral_clustering does not support passing in affinity as an "
+            "np.matrix. Please convert to a numpy array with np.asarray. For "
+            "more information see: "
+            "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html",  # noqa
+        )
+
+    random_state = check_random_state(random_state)
+    n_components = n_clusters if n_components is None else 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(
+        affinity,
+        n_components=n_components,
+        eigen_solver=eigen_solver,
+        random_state=random_state,
+        eigen_tol=eigen_tol,
+        drop_first=False,
+    )
+    if verbose:
+        print(f"Computing label assignment using {assign_labels}")
+
+    if assign_labels == "kmeans":
+        _, labels, _ = k_means(
+            maps, n_clusters, random_state=random_state, n_init=n_init, verbose=verbose
+        )
+    elif assign_labels == "cluster_qr":
+        labels = cluster_qr(maps)
+    else:
+        labels = discretize(maps, random_state=random_state)
+
+    return 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=n_clusters
+        Number of eigenvectors to use for the spectral embedding.
+
+    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 : 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_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=0.0
+        Stopping criterion for eigendecomposition of the Laplacian matrix
+        when ``eigen_solver='arpack'``.
+
+    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://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf>`_
+
+    .. [4] `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.
+           <https://epubs.siam.org/doi/pdf/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)
+    """
+
+    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=0.0,
+        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
+
+    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 = self._validate_data(
+            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``."
+            )
+
+        check_scalar(
+            self.n_clusters,
+            "n_clusters",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+
+        check_scalar(
+            self.n_init,
+            "n_init",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+
+        check_scalar(
+            self.gamma,
+            "gamma",
+            target_type=numbers.Real,
+            min_val=1.0,
+            include_boundaries="left",
+        )
+
+        check_scalar(
+            self.n_neighbors,
+            "n_neighbors",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+
+        if self.eigen_solver == "arpack":
+            check_scalar(
+                self.eigen_tol,
+                "eigen_tol",
+                target_type=numbers.Real,
+                min_val=0,
+                include_boundaries="left",
+            )
+
+        check_scalar(
+            self.degree,
+            "degree",
+            target_type=numbers.Integral,
+            min_val=1,
+            include_boundaries="left",
+        )
+
+        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)
+        self.labels_ = spectral_clustering(
+            self.affinity_matrix_,
+            n_clusters=self.n_clusters,
+            n_components=self.n_components,
+            eigen_solver=self.eigen_solver,
+            random_state=random_state,
+            n_init=self.n_init,
+            eigen_tol=self.eigen_tol,
+            assign_labels=self.assign_labels,
+            verbose=self.verbose,
+        )
+        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 _more_tags(self):
+        return {
+            "pairwise": self.affinity
+            in ["precomputed", "precomputed_nearest_neighbors"]
+        }

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/setup.py

@@ -0,0 +1,68 @@
+# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
+# License: BSD 3 clause
+import os
+
+import numpy
+
+
+def configuration(parent_package="", top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    libraries = []
+    if os.name == "posix":
+        libraries.append("m")
+
+    config = Configuration("cluster", parent_package, top_path)
+
+    config.add_extension(
+        "_dbscan_inner",
+        sources=["_dbscan_inner.pyx"],
+        include_dirs=[numpy.get_include()],
+        language="c++",
+    )
+
+    config.add_extension(
+        "_hierarchical_fast",
+        sources=["_hierarchical_fast.pyx"],
+        language="c++",
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+    )
+
+    config.add_extension(
+        "_k_means_common",
+        sources=["_k_means_common.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+    )
+
+    config.add_extension(
+        "_k_means_lloyd",
+        sources=["_k_means_lloyd.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+    )
+
+    config.add_extension(
+        "_k_means_elkan",
+        sources=["_k_means_elkan.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+    )
+
+    config.add_extension(
+        "_k_means_minibatch",
+        sources=["_k_means_minibatch.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+    )
+
+    config.add_subpackage("tests")
+
+    return config
+
+
+if __name__ == "__main__":
+    from numpy.distutils.core import setup
+
+    setup(**configuration(top_path="").todict())

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/common.py

@@ -0,0 +1,38 @@
+"""
+Common utilities for testing clustering.
+
+"""
+
+import numpy as np
+
+
+###############################################################################
+# Generate sample data
+
+
+def generate_clustered_data(
+    seed=0, n_clusters=3, n_features=2, n_samples_per_cluster=20, std=0.4
+):
+    prng = np.random.RandomState(seed)
+
+    # the data is voluntary shifted away from zero to check clustering
+    # algorithm robustness with regards to non centered data
+    means = (
+        np.array(
+            [
+                [1, 1, 1, 0],
+                [-1, -1, 0, 1],
+                [1, -1, 1, 1],
+                [-1, 1, 1, 0],
+            ]
+        )
+        + 10
+    )
+
+    X = np.empty((0, n_features))
+    for i in range(n_clusters):
+        X = np.r_[
+            X,
+            means[i][:n_features] + std * prng.randn(n_samples_per_cluster, n_features),
+        ]
+    return X

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_affinity_propagation.py

@@ -0,0 +1,284 @@
+"""
+Testing for Clustering methods
+
+"""
+
+import numpy as np
+import pytest
+import warnings
+
+from scipy.sparse import csr_matrix
+
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import assert_array_equal
+
+from sklearn.cluster import AffinityPropagation
+from sklearn.cluster._affinity_propagation import _equal_similarities_and_preferences
+from sklearn.cluster import affinity_propagation
+from sklearn.datasets import make_blobs
+from sklearn.metrics import euclidean_distances
+
+n_clusters = 3
+centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
+X, _ = make_blobs(
+    n_samples=60,
+    n_features=2,
+    centers=centers,
+    cluster_std=0.4,
+    shuffle=True,
+    random_state=0,
+)
+
+
+def test_affinity_propagation():
+    # Affinity Propagation algorithm
+    # Compute similarities
+    S = -euclidean_distances(X, squared=True)
+    preference = np.median(S) * 10
+    # Compute Affinity Propagation
+    cluster_centers_indices, labels = affinity_propagation(
+        S, preference=preference, random_state=39
+    )
+
+    n_clusters_ = len(cluster_centers_indices)
+
+    assert n_clusters == n_clusters_
+
+    af = AffinityPropagation(
+        preference=preference, affinity="precomputed", random_state=28
+    )
+    labels_precomputed = af.fit(S).labels_
+
+    af = AffinityPropagation(preference=preference, verbose=True, random_state=37)
+    labels = af.fit(X).labels_
+
+    assert_array_equal(labels, labels_precomputed)
+
+    cluster_centers_indices = af.cluster_centers_indices_
+
+    n_clusters_ = len(cluster_centers_indices)
+    assert np.unique(labels).size == n_clusters_
+    assert n_clusters == n_clusters_
+
+    # Test also with no copy
+    _, labels_no_copy = affinity_propagation(
+        S, preference=preference, copy=False, random_state=74
+    )
+    assert_array_equal(labels, labels_no_copy)
+
+
+def test_affinity_propagation_affinity_shape():
+    """Check the shape of the affinity matrix when using `affinity_propagation."""
+    S = -euclidean_distances(X, squared=True)
+    err_msg = "S must be a square array"
+    with pytest.raises(ValueError, match=err_msg):
+        affinity_propagation(S[:, :-1])
+
+
+@pytest.mark.parametrize(
+    "input, params, err_type, err_msg",
+    [
+        (X, {"damping": 0}, ValueError, "damping == 0, must be >= 0.5"),
+        (X, {"damping": 2}, ValueError, "damping == 2, must be < 1"),
+        (X, {"max_iter": 0}, ValueError, "max_iter == 0, must be >= 1."),
+        (X, {"convergence_iter": 0}, ValueError, "convergence_iter == 0, must be >= 1"),
+        (X, {"affinity": "unknown"}, ValueError, "Affinity must be"),
+        (
+            csr_matrix((3, 3)),
+            {"affinity": "precomputed"},
+            TypeError,
+            "A sparse matrix was passed, but dense data is required",
+        ),
+    ],
+)
+def test_affinity_propagation_params_validation(input, params, err_type, err_msg):
+    """Check the parameters validation in `AffinityPropagation`."""
+    with pytest.raises(err_type, match=err_msg):
+        AffinityPropagation(**params).fit(input)
+
+
+def test_affinity_propagation_predict():
+    # Test AffinityPropagation.predict
+    af = AffinityPropagation(affinity="euclidean", random_state=63)
+    labels = af.fit_predict(X)
+    labels2 = af.predict(X)
+    assert_array_equal(labels, labels2)
+
+
+def test_affinity_propagation_predict_error():
+    # Test exception in AffinityPropagation.predict
+    # Not fitted.
+    af = AffinityPropagation(affinity="euclidean")
+    with pytest.raises(ValueError):
+        af.predict(X)
+
+    # Predict not supported when affinity="precomputed".
+    S = np.dot(X, X.T)
+    af = AffinityPropagation(affinity="precomputed", random_state=57)
+    af.fit(S)
+    with pytest.raises(ValueError):
+        af.predict(X)
+
+
+def test_affinity_propagation_fit_non_convergence():
+    # In case of non-convergence of affinity_propagation(), the cluster
+    # centers should be an empty array and training samples should be labelled
+    # as noise (-1)
+    X = np.array([[0, 0], [1, 1], [-2, -2]])
+
+    # Force non-convergence by allowing only a single iteration
+    af = AffinityPropagation(preference=-10, max_iter=1, random_state=82)
+
+    with pytest.warns(ConvergenceWarning):
+        af.fit(X)
+    assert_array_equal(np.empty((0, 2)), af.cluster_centers_)
+    assert_array_equal(np.array([-1, -1, -1]), af.labels_)
+
+
+def test_affinity_propagation_equal_mutual_similarities():
+    X = np.array([[-1, 1], [1, -1]])
+    S = -euclidean_distances(X, squared=True)
+
+    # setting preference > similarity
+    with pytest.warns(UserWarning, match="mutually equal"):
+        cluster_center_indices, labels = affinity_propagation(S, preference=0)
+
+    # expect every sample to become an exemplar
+    assert_array_equal([0, 1], cluster_center_indices)
+    assert_array_equal([0, 1], labels)
+
+    # setting preference < similarity
+    with pytest.warns(UserWarning, match="mutually equal"):
+        cluster_center_indices, labels = affinity_propagation(S, preference=-10)
+
+    # expect one cluster, with arbitrary (first) sample as exemplar
+    assert_array_equal([0], cluster_center_indices)
+    assert_array_equal([0, 0], labels)
+
+    # setting different preferences
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", UserWarning)
+        cluster_center_indices, labels = affinity_propagation(
+            S, preference=[-20, -10], random_state=37
+        )
+
+    # expect one cluster, with highest-preference sample as exemplar
+    assert_array_equal([1], cluster_center_indices)
+    assert_array_equal([0, 0], labels)
+
+
+def test_affinity_propagation_predict_non_convergence():
+    # In case of non-convergence of affinity_propagation(), the cluster
+    # centers should be an empty array
+    X = np.array([[0, 0], [1, 1], [-2, -2]])
+
+    # Force non-convergence by allowing only a single iteration
+    with pytest.warns(ConvergenceWarning):
+        af = AffinityPropagation(preference=-10, max_iter=1, random_state=75).fit(X)
+
+    # At prediction time, consider new samples as noise since there are no
+    # clusters
+    to_predict = np.array([[2, 2], [3, 3], [4, 4]])
+    with pytest.warns(ConvergenceWarning):
+        y = af.predict(to_predict)
+    assert_array_equal(np.array([-1, -1, -1]), y)
+
+
+def test_affinity_propagation_non_convergence_regressiontest():
+    X = np.array([[1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1]])
+    af = AffinityPropagation(affinity="euclidean", max_iter=2, random_state=34)
+    msg = (
+        "Affinity propagation did not converge, this model may return degenerate"
+        " cluster centers and labels."
+    )
+    with pytest.warns(ConvergenceWarning, match=msg):
+        af.fit(X)
+
+    assert_array_equal(np.array([0, 0, 0]), af.labels_)
+
+
+def test_equal_similarities_and_preferences():
+    # Unequal distances
+    X = np.array([[0, 0], [1, 1], [-2, -2]])
+    S = -euclidean_distances(X, squared=True)
+
+    assert not _equal_similarities_and_preferences(S, np.array(0))
+    assert not _equal_similarities_and_preferences(S, np.array([0, 0]))
+    assert not _equal_similarities_and_preferences(S, np.array([0, 1]))
+
+    # Equal distances
+    X = np.array([[0, 0], [1, 1]])
+    S = -euclidean_distances(X, squared=True)
+
+    # Different preferences
+    assert not _equal_similarities_and_preferences(S, np.array([0, 1]))
+
+    # Same preferences
+    assert _equal_similarities_and_preferences(S, np.array([0, 0]))
+    assert _equal_similarities_and_preferences(S, np.array(0))
+
+
+def test_affinity_propagation_random_state():
+    # Significance of random_state parameter
+    # Generate sample data
+    centers = [[1, 1], [-1, -1], [1, -1]]
+    X, labels_true = make_blobs(
+        n_samples=300, centers=centers, cluster_std=0.5, random_state=0
+    )
+    # random_state = 0
+    ap = AffinityPropagation(convergence_iter=1, max_iter=2, random_state=0)
+    ap.fit(X)
+    centers0 = ap.cluster_centers_
+
+    # random_state = 76
+    ap = AffinityPropagation(convergence_iter=1, max_iter=2, random_state=76)
+    ap.fit(X)
+    centers76 = ap.cluster_centers_
+
+    assert np.mean((centers0 - centers76) ** 2) > 1
+
+
+@pytest.mark.parametrize("centers", [csr_matrix(np.zeros((1, 10))), np.zeros((1, 10))])
+def test_affinity_propagation_convergence_warning_dense_sparse(centers):
+    """Non-regression, see #13334"""
+    rng = np.random.RandomState(42)
+    X = rng.rand(40, 10)
+    y = (4 * rng.rand(40)).astype(int)
+    ap = AffinityPropagation(random_state=46)
+    ap.fit(X, y)
+    ap.cluster_centers_ = centers
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", ConvergenceWarning)
+        assert_array_equal(ap.predict(X), np.zeros(X.shape[0], dtype=int))
+
+
+def test_affinity_propagation_float32():
+    # Test to fix incorrect clusters due to dtype change
+    # (non-regression test for issue #10832)
+    X = np.array(
+        [[1, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 1]], dtype="float32"
+    )
+    afp = AffinityPropagation(preference=1, affinity="precomputed", random_state=0).fit(
+        X
+    )
+    expected = np.array([0, 1, 1, 2])
+    assert_array_equal(afp.labels_, expected)
+
+
+def test_sparse_input_for_predict():
+    # Test to make sure sparse inputs are accepted for predict
+    # (non-regression test for issue #20049)
+    af = AffinityPropagation(affinity="euclidean", random_state=42)
+    af.fit(X)
+    labels = af.predict(csr_matrix((2, 2)))
+    assert_array_equal(labels, (2, 2))
+
+
+def test_sparse_input_for_fit_predict():
+    # Test to make sure sparse inputs are accepted for fit_predict
+    # (non-regression test for issue #20049)
+    af = AffinityPropagation(affinity="euclidean", random_state=42)
+    rng = np.random.RandomState(42)
+    X = csr_matrix(rng.randint(0, 2, size=(5, 5)))
+    labels = af.fit_predict(X)
+    assert_array_equal(labels, (0, 1, 1, 2, 3))

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_bicluster.py

@@ -0,0 +1,280 @@
+"""Testing for Spectral Biclustering methods"""
+
+import numpy as np
+import pytest
+from scipy.sparse import csr_matrix, issparse
+
+from sklearn.model_selection import ParameterGrid
+
+from sklearn.utils._testing import assert_almost_equal
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import assert_array_almost_equal
+
+from sklearn.base import BaseEstimator, BiclusterMixin
+
+from sklearn.cluster import SpectralCoclustering
+from sklearn.cluster import SpectralBiclustering
+from sklearn.cluster._bicluster import _scale_normalize
+from sklearn.cluster._bicluster import _bistochastic_normalize
+from sklearn.cluster._bicluster import _log_normalize
+
+from sklearn.metrics import consensus_score, v_measure_score
+
+from sklearn.datasets import make_biclusters, make_checkerboard
+
+
+class MockBiclustering(BiclusterMixin, BaseEstimator):
+    # Mock object for testing get_submatrix.
+    def __init__(self):
+        pass
+
+    def get_indices(self, i):
+        # Overridden to reproduce old get_submatrix test.
+        return (
+            np.where([True, True, False, False, True])[0],
+            np.where([False, False, True, True])[0],
+        )
+
+
+def test_get_submatrix():
+    data = np.arange(20).reshape(5, 4)
+    model = MockBiclustering()
+
+    for X in (data, csr_matrix(data), data.tolist()):
+        submatrix = model.get_submatrix(0, X)
+        if issparse(submatrix):
+            submatrix = submatrix.toarray()
+        assert_array_equal(submatrix, [[2, 3], [6, 7], [18, 19]])
+        submatrix[:] = -1
+        if issparse(X):
+            X = X.toarray()
+        assert np.all(X != -1)
+
+
+def _test_shape_indices(model):
+    # Test get_shape and get_indices on fitted model.
+    for i in range(model.n_clusters):
+        m, n = model.get_shape(i)
+        i_ind, j_ind = model.get_indices(i)
+        assert len(i_ind) == m
+        assert len(j_ind) == n
+
+
+def test_spectral_coclustering():
+    # Test Dhillon's Spectral CoClustering on a simple problem.
+    param_grid = {
+        "svd_method": ["randomized", "arpack"],
+        "n_svd_vecs": [None, 20],
+        "mini_batch": [False, True],
+        "init": ["k-means++"],
+        "n_init": [10],
+    }
+    random_state = 0
+    S, rows, cols = make_biclusters((30, 30), 3, noise=0.5, random_state=random_state)
+    S -= S.min()  # needs to be nonnegative before making it sparse
+    S = np.where(S < 1, 0, S)  # threshold some values
+    for mat in (S, csr_matrix(S)):
+        for kwargs in ParameterGrid(param_grid):
+            model = SpectralCoclustering(
+                n_clusters=3, random_state=random_state, **kwargs
+            )
+            model.fit(mat)
+
+            assert model.rows_.shape == (3, 30)
+            assert_array_equal(model.rows_.sum(axis=0), np.ones(30))
+            assert_array_equal(model.columns_.sum(axis=0), np.ones(30))
+            assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+            _test_shape_indices(model)
+
+
+def test_spectral_biclustering():
+    # Test Kluger methods on a checkerboard dataset.
+    S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5, random_state=0)
+
+    non_default_params = {
+        "method": ["scale", "log"],
+        "svd_method": ["arpack"],
+        "n_svd_vecs": [20],
+        "mini_batch": [True],
+    }
+
+    for mat in (S, csr_matrix(S)):
+        for param_name, param_values in non_default_params.items():
+            for param_value in param_values:
+
+                model = SpectralBiclustering(
+                    n_clusters=3,
+                    n_init=3,
+                    init="k-means++",
+                    random_state=0,
+                )
+                model.set_params(**dict([(param_name, param_value)]))
+
+                if issparse(mat) and model.get_params().get("method") == "log":
+                    # cannot take log of sparse matrix
+                    with pytest.raises(ValueError):
+                        model.fit(mat)
+                    continue
+                else:
+                    model.fit(mat)
+
+                assert model.rows_.shape == (9, 30)
+                assert model.columns_.shape == (9, 30)
+                assert_array_equal(model.rows_.sum(axis=0), np.repeat(3, 30))
+                assert_array_equal(model.columns_.sum(axis=0), np.repeat(3, 30))
+                assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+                _test_shape_indices(model)
+
+
+def _do_scale_test(scaled):
+    """Check that rows sum to one constant, and columns to another."""
+    row_sum = scaled.sum(axis=1)
+    col_sum = scaled.sum(axis=0)
+    if issparse(scaled):
+        row_sum = np.asarray(row_sum).squeeze()
+        col_sum = np.asarray(col_sum).squeeze()
+    assert_array_almost_equal(row_sum, np.tile(row_sum.mean(), 100), decimal=1)
+    assert_array_almost_equal(col_sum, np.tile(col_sum.mean(), 100), decimal=1)
+
+
+def _do_bistochastic_test(scaled):
+    """Check that rows and columns sum to the same constant."""
+    _do_scale_test(scaled)
+    assert_almost_equal(scaled.sum(axis=0).mean(), scaled.sum(axis=1).mean(), decimal=1)
+
+
+def test_scale_normalize():
+    generator = np.random.RandomState(0)
+    X = generator.rand(100, 100)
+    for mat in (X, csr_matrix(X)):
+        scaled, _, _ = _scale_normalize(mat)
+        _do_scale_test(scaled)
+        if issparse(mat):
+            assert issparse(scaled)
+
+
+def test_bistochastic_normalize():
+    generator = np.random.RandomState(0)
+    X = generator.rand(100, 100)
+    for mat in (X, csr_matrix(X)):
+        scaled = _bistochastic_normalize(mat)
+        _do_bistochastic_test(scaled)
+        if issparse(mat):
+            assert issparse(scaled)
+
+
+def test_log_normalize():
+    # adding any constant to a log-scaled matrix should make it
+    # bistochastic
+    generator = np.random.RandomState(0)
+    mat = generator.rand(100, 100)
+    scaled = _log_normalize(mat) + 1
+    _do_bistochastic_test(scaled)
+
+
+def test_fit_best_piecewise():
+    model = SpectralBiclustering(random_state=0)
+    vectors = np.array([[0, 0, 0, 1, 1, 1], [2, 2, 2, 3, 3, 3], [0, 1, 2, 3, 4, 5]])
+    best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
+    assert_array_equal(best, vectors[:2])
+
+
+def test_project_and_cluster():
+    model = SpectralBiclustering(random_state=0)
+    data = np.array([[1, 1, 1], [1, 1, 1], [3, 6, 3], [3, 6, 3]])
+    vectors = np.array([[1, 0], [0, 1], [0, 0]])
+    for mat in (data, csr_matrix(data)):
+        labels = model._project_and_cluster(mat, vectors, n_clusters=2)
+        assert_almost_equal(v_measure_score(labels, [0, 0, 1, 1]), 1.0)
+
+
+def test_perfect_checkerboard():
+    # XXX Previously failed on build bot (not reproducible)
+    model = SpectralBiclustering(3, svd_method="arpack", random_state=0)
+
+    S, rows, cols = make_checkerboard((30, 30), 3, noise=0, random_state=0)
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+    S, rows, cols = make_checkerboard((40, 30), 3, noise=0, random_state=0)
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+    S, rows, cols = make_checkerboard((30, 40), 3, noise=0, random_state=0)
+    model.fit(S)
+    assert consensus_score(model.biclusters_, (rows, cols)) == 1
+
+
+@pytest.mark.parametrize(
+    "params, type_err, err_msg",
+    [
+        ({"n_init": 0}, ValueError, "n_init == 0, must be >= 1."),
+        ({"n_init": 1.5}, TypeError, "n_init must be an instance of"),
+        (
+            {"n_clusters": "abc"},
+            TypeError,
+            "n_clusters must be an instance of",
+        ),
+        ({"svd_method": "unknown"}, ValueError, "Unknown SVD method: 'unknown'"),
+    ],
+)
+def test_spectralcoclustering_parameter_validation(params, type_err, err_msg):
+    """Check parameters validation in `SpectralBiClustering`"""
+    data = np.arange(25).reshape((5, 5))
+    model = SpectralCoclustering(**params)
+    with pytest.raises(type_err, match=err_msg):
+        model.fit(data)
+
+
+@pytest.mark.parametrize(
+    "params, type_err, err_msg",
+    [
+        ({"n_init": 0}, ValueError, "n_init == 0, must be >= 1."),
+        ({"n_init": 1.5}, TypeError, "n_init must be an instance of"),
+        (
+            {"n_clusters": (3, 3, 3)},
+            ValueError,
+            r"Incorrect parameter n_clusters has value: \(3, 3, 3\)",
+        ),
+        (
+            {"n_clusters": "abc"},
+            ValueError,
+            "Incorrect parameter n_clusters has value: abc",
+        ),
+        (
+            {"n_clusters": (3, "abc")},
+            ValueError,
+            r"Incorrect parameter n_clusters has value: \(3, 'abc'\)",
+        ),
+        (
+            {"n_clusters": ("abc", 3)},
+            ValueError,
+            r"Incorrect parameter n_clusters has value: \('abc', 3\)",
+        ),
+        ({"method": "unknown"}, ValueError, "Unknown method: 'unknown'"),
+        ({"n_components": 0}, ValueError, "n_components == 0, must be >= 1."),
+        ({"n_components": 1.5}, TypeError, "n_components must be an instance of"),
+        ({"n_components": 3, "n_best": 4}, ValueError, "n_best == 4, must be <= 3."),
+        ({"n_best": 0}, ValueError, "n_best == 0, must be >= 1."),
+        ({"n_best": 1.5}, TypeError, "n_best must be an instance of"),
+        ({"svd_method": "unknown"}, ValueError, "Unknown SVD method: 'unknown'"),
+    ],
+)
+def test_spectralbiclustering_parameter_validation(params, type_err, err_msg):
+    """Check parameters validation in `SpectralBiClustering`"""
+    data = np.arange(25).reshape((5, 5))
+    model = SpectralBiclustering(**params)
+    with pytest.raises(type_err, match=err_msg):
+        model.fit(data)
+
+
+@pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering()))
+def test_n_features_in_(est):
+
+    X, _, _ = make_biclusters((3, 3), 3, random_state=0)
+
+    assert not hasattr(est, "n_features_in_")
+    est.fit(X)
+    assert est.n_features_in_ == 3

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_birch.py

@@ -0,0 +1,230 @@
+"""
+Tests for the birch clustering algorithm.
+"""
+
+from scipy import sparse
+import numpy as np
+import pytest
+
+from sklearn.cluster.tests.common import generate_clustered_data
+from sklearn.cluster import Birch
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.datasets import make_blobs
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.linear_model import ElasticNet
+from sklearn.metrics import pairwise_distances_argmin, v_measure_score
+
+from sklearn.utils._testing import assert_almost_equal
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import assert_array_almost_equal
+
+
+def test_n_samples_leaves_roots():
+    # Sanity check for the number of samples in leaves and roots
+    X, y = make_blobs(n_samples=10)
+    brc = Birch()
+    brc.fit(X)
+    n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
+    n_samples_leaves = sum(
+        [sc.n_samples_ for leaf in brc._get_leaves() for sc in leaf.subclusters_]
+    )
+    assert n_samples_leaves == X.shape[0]
+    assert n_samples_root == X.shape[0]
+
+
+def test_partial_fit():
+    # Test that fit is equivalent to calling partial_fit multiple times
+    X, y = make_blobs(n_samples=100)
+    brc = Birch(n_clusters=3)
+    brc.fit(X)
+    brc_partial = Birch(n_clusters=None)
+    brc_partial.partial_fit(X[:50])
+    brc_partial.partial_fit(X[50:])
+    assert_array_almost_equal(brc_partial.subcluster_centers_, brc.subcluster_centers_)
+
+    # Test that same global labels are obtained after calling partial_fit
+    # with None
+    brc_partial.set_params(n_clusters=3)
+    brc_partial.partial_fit(None)
+    assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
+
+
+def test_birch_predict():
+    # Test the predict method predicts the nearest centroid.
+    rng = np.random.RandomState(0)
+    X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10)
+
+    # n_samples * n_samples_per_cluster
+    shuffle_indices = np.arange(30)
+    rng.shuffle(shuffle_indices)
+    X_shuffle = X[shuffle_indices, :]
+    brc = Birch(n_clusters=4, threshold=1.0)
+    brc.fit(X_shuffle)
+    centroids = brc.subcluster_centers_
+    assert_array_equal(brc.labels_, brc.predict(X_shuffle))
+    nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
+    assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
+
+
+def test_n_clusters():
+    # Test that n_clusters param works properly
+    X, y = make_blobs(n_samples=100, centers=10)
+    brc1 = Birch(n_clusters=10)
+    brc1.fit(X)
+    assert len(brc1.subcluster_centers_) > 10
+    assert len(np.unique(brc1.labels_)) == 10
+
+    # Test that n_clusters = Agglomerative Clustering gives
+    # the same results.
+    gc = AgglomerativeClustering(n_clusters=10)
+    brc2 = Birch(n_clusters=gc)
+    brc2.fit(X)
+    assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
+    assert_array_equal(brc1.labels_, brc2.labels_)
+
+    # Test that the wrong global clustering step raises an Error.
+    clf = ElasticNet()
+    brc3 = Birch(n_clusters=clf)
+    err_msg = "n_clusters should be an instance of ClusterMixin or an int"
+    with pytest.raises(TypeError, match=err_msg):
+        brc3.fit(X)
+
+    # Test that a small number of clusters raises a warning.
+    brc4 = Birch(threshold=10000.0)
+    with pytest.warns(ConvergenceWarning):
+        brc4.fit(X)
+
+
+def test_sparse_X():
+    # Test that sparse and dense data give same results
+    X, y = make_blobs(n_samples=100, centers=10)
+    brc = Birch(n_clusters=10)
+    brc.fit(X)
+
+    csr = sparse.csr_matrix(X)
+    brc_sparse = Birch(n_clusters=10)
+    brc_sparse.fit(csr)
+
+    assert_array_equal(brc.labels_, brc_sparse.labels_)
+    assert_array_almost_equal(brc.subcluster_centers_, brc_sparse.subcluster_centers_)
+
+
+def test_partial_fit_second_call_error_checks():
+    # second partial fit calls will error when n_features is not consistent
+    # with the first call
+    X, y = make_blobs(n_samples=100)
+    brc = Birch(n_clusters=3)
+    brc.partial_fit(X, y)
+
+    msg = "X has 1 features, but Birch is expecting 2 features"
+    with pytest.raises(ValueError, match=msg):
+        brc.partial_fit(X[:, [0]], y)
+
+
+def check_branching_factor(node, branching_factor):
+    subclusters = node.subclusters_
+    assert branching_factor >= len(subclusters)
+    for cluster in subclusters:
+        if cluster.child_:
+            check_branching_factor(cluster.child_, branching_factor)
+
+
+def test_branching_factor():
+    # Test that nodes have at max branching_factor number of subclusters
+    X, y = make_blobs()
+    branching_factor = 9
+
+    # Purposefully set a low threshold to maximize the subclusters.
+    brc = Birch(n_clusters=None, branching_factor=branching_factor, threshold=0.01)
+    brc.fit(X)
+    check_branching_factor(brc.root_, branching_factor)
+    brc = Birch(n_clusters=3, branching_factor=branching_factor, threshold=0.01)
+    brc.fit(X)
+    check_branching_factor(brc.root_, branching_factor)
+
+
+def check_threshold(birch_instance, threshold):
+    """Use the leaf linked list for traversal"""
+    current_leaf = birch_instance.dummy_leaf_.next_leaf_
+    while current_leaf:
+        subclusters = current_leaf.subclusters_
+        for sc in subclusters:
+            assert threshold >= sc.radius
+        current_leaf = current_leaf.next_leaf_
+
+
+def test_threshold():
+    # Test that the leaf subclusters have a threshold lesser than radius
+    X, y = make_blobs(n_samples=80, centers=4)
+    brc = Birch(threshold=0.5, n_clusters=None)
+    brc.fit(X)
+    check_threshold(brc, 0.5)
+
+    brc = Birch(threshold=5.0, n_clusters=None)
+    brc.fit(X)
+    check_threshold(brc, 5.0)
+
+
+def test_birch_n_clusters_long_int():
+    # Check that birch supports n_clusters with np.int64 dtype, for instance
+    # coming from np.arange. #16484
+    X, _ = make_blobs(random_state=0)
+    n_clusters = np.int64(5)
+    Birch(n_clusters=n_clusters).fit(X)
+
+
+# TODO: Remove in 1.2
+@pytest.mark.parametrize("attribute", ["fit_", "partial_fit_"])
+def test_birch_fit_attributes_deprecated(attribute):
+    """Test that fit_ and partial_fit_ attributes are deprecated."""
+    msg = f"`{attribute}` is deprecated in 1.0 and will be removed in 1.2"
+    X, y = make_blobs(n_samples=10)
+    brc = Birch().fit(X, y)
+
+    with pytest.warns(FutureWarning, match=msg):
+        getattr(brc, attribute)
+
+
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"threshold": -1.0}, ValueError, "threshold == -1.0, must be > 0.0."),
+        ({"threshold": 0.0}, ValueError, "threshold == 0.0, must be > 0.0."),
+        ({"branching_factor": 0}, ValueError, "branching_factor == 0, must be > 1."),
+        ({"branching_factor": 1}, ValueError, "branching_factor == 1, must be > 1."),
+        (
+            {"branching_factor": 1.5},
+            TypeError,
+            "branching_factor must be an instance of int, not float.",
+        ),
+        ({"branching_factor": -2}, ValueError, "branching_factor == -2, must be > 1."),
+        ({"n_clusters": 0}, ValueError, "n_clusters == 0, must be >= 1."),
+        (
+            {"n_clusters": 2.5},
+            TypeError,
+            "n_clusters must be an instance of int, not float.",
+        ),
+        (
+            {"n_clusters": "whatever"},
+            TypeError,
+            "n_clusters should be an instance of ClusterMixin or an int",
+        ),
+        ({"n_clusters": -3}, ValueError, "n_clusters == -3, must be >= 1."),
+    ],
+)
+def test_birch_params_validation(params, err_type, err_msg):
+    """Check the parameters validation in `Birch`."""
+    X, _ = make_blobs(n_samples=80, centers=4)
+    with pytest.raises(err_type, match=err_msg):
+        Birch(**params).fit(X)
+
+
+def test_feature_names_out():
+    """Check `get_feature_names_out` for `Birch`."""
+    X, _ = make_blobs(n_samples=80, n_features=4, random_state=0)
+    brc = Birch(n_clusters=4)
+    brc.fit(X)
+    n_clusters = brc.subcluster_centers_.shape[0]
+
+    names_out = brc.get_feature_names_out()
+    assert_array_equal([f"birch{i}" for i in range(n_clusters)], names_out)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_bisect_k_means.py

@@ -0,0 +1,160 @@
+import numpy as np
+import pytest
+import scipy.sparse as sp
+
+from sklearn.utils._testing import assert_array_equal, assert_allclose
+from sklearn.cluster import BisectingKMeans
+
+
+@pytest.mark.parametrize("bisecting_strategy", ["biggest_inertia", "largest_cluster"])
+def test_three_clusters(bisecting_strategy):
+    """Tries to perform bisect k-means for three clusters to check
+    if splitting data is performed correctly.
+    """
+
+    # X = np.array([[1, 2], [1, 4], [1, 0],
+    #               [10, 2], [10, 4], [10, 0],
+    #               [10, 6], [10, 8], [10, 10]])
+
+    # X[0][1] swapped with X[1][1] intentionally for checking labeling
+    X = np.array(
+        [[1, 2], [10, 4], [1, 0], [10, 2], [1, 4], [10, 0], [10, 6], [10, 8], [10, 10]]
+    )
+    bisect_means = BisectingKMeans(
+        n_clusters=3, random_state=0, bisecting_strategy=bisecting_strategy
+    )
+    bisect_means.fit(X)
+
+    expected_centers = [[10, 2], [10, 8], [1, 2]]
+    expected_predict = [2, 0]
+    expected_labels = [2, 0, 2, 0, 2, 0, 1, 1, 1]
+
+    assert_allclose(expected_centers, bisect_means.cluster_centers_)
+    assert_array_equal(expected_predict, bisect_means.predict([[0, 0], [12, 3]]))
+    assert_array_equal(expected_labels, bisect_means.labels_)
+
+
+def test_sparse():
+    """Test Bisecting K-Means with sparse data.
+
+    Checks if labels and centers are the same between dense and sparse.
+    """
+
+    rng = np.random.RandomState(0)
+
+    X = rng.rand(20, 2)
+    X[X < 0.8] = 0
+    X_csr = sp.csr_matrix(X)
+
+    bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
+
+    bisect_means.fit(X_csr)
+    sparse_centers = bisect_means.cluster_centers_
+
+    bisect_means.fit(X)
+    normal_centers = bisect_means.cluster_centers_
+
+    # Check if results is the same for dense and sparse data
+    assert_allclose(normal_centers, sparse_centers, atol=1e-8)
+
+
+@pytest.mark.parametrize("n_clusters", [4, 5])
+def test_n_clusters(n_clusters):
+    """Test if resulting labels are in range [0, n_clusters - 1]."""
+
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 2)
+
+    bisect_means = BisectingKMeans(n_clusters=n_clusters, random_state=0)
+    bisect_means.fit(X)
+
+    assert_array_equal(np.unique(bisect_means.labels_), np.arange(n_clusters))
+
+
+def test_one_cluster():
+    """Test single cluster."""
+
+    X = np.array([[1, 2], [10, 2], [10, 8]])
+
+    bisect_means = BisectingKMeans(n_clusters=1, random_state=0).fit(X)
+
+    # All labels from fit or predict should be equal 0
+    assert all(bisect_means.labels_ == 0)
+    assert all(bisect_means.predict(X) == 0)
+
+    assert_allclose(bisect_means.cluster_centers_, X.mean(axis=0).reshape(1, -1))
+
+
+@pytest.mark.parametrize(
+    "param, match",
+    [
+        # Test bisecting_strategy param
+        (
+            {"bisecting_strategy": "None"},
+            "Bisect Strategy must be 'biggest_inertia' or 'largest_cluster'",
+        ),
+        # Test init array
+        (
+            {"init": np.ones((5, 2))},
+            "BisectingKMeans does not support init as array.",
+        ),
+    ],
+)
+def test_wrong_params(param, match):
+    """Test Exceptions at check_params function."""
+    rng = np.random.RandomState(0)
+    X = rng.rand(5, 2)
+
+    with pytest.raises(ValueError, match=match):
+        bisect_means = BisectingKMeans(n_clusters=3, **param)
+        bisect_means.fit(X)
+
+
+@pytest.mark.parametrize("is_sparse", [True, False])
+def test_fit_predict(is_sparse):
+    """Check if labels from fit(X) method are same as from fit(X).predict(X)."""
+    rng = np.random.RandomState(0)
+
+    X = rng.rand(10, 2)
+
+    if is_sparse:
+        X[X < 0.8] = 0
+        X = sp.csr_matrix(X)
+
+    bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
+    bisect_means.fit(X)
+
+    assert_array_equal(bisect_means.labels_, bisect_means.predict(X))
+
+
+@pytest.mark.parametrize("is_sparse", [True, False])
+def test_dtype_preserved(is_sparse, global_dtype):
+    """Check that centers dtype is the same as input data dtype."""
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 2).astype(global_dtype, copy=False)
+
+    if is_sparse:
+        X[X < 0.8] = 0
+        X = sp.csr_matrix(X)
+
+    km = BisectingKMeans(n_clusters=3, random_state=0)
+    km.fit(X)
+
+    assert km.cluster_centers_.dtype == global_dtype
+
+
+@pytest.mark.parametrize("is_sparse", [True, False])
+def test_float32_float64_equivalence(is_sparse):
+    """Check that the results are the same between float32 and float64."""
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 2)
+
+    if is_sparse:
+        X[X < 0.8] = 0
+        X = sp.csr_matrix(X)
+
+    km64 = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
+    km32 = BisectingKMeans(n_clusters=3, random_state=0).fit(X.astype(np.float32))
+
+    assert_allclose(km32.cluster_centers_, km64.cluster_centers_)
+    assert_array_equal(km32.labels_, km64.labels_)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_dbscan.py

@@ -0,0 +1,460 @@
+"""
+Tests for DBSCAN clustering algorithm
+"""
+
+import pickle
+
+import numpy as np
+
+import warnings
+
+from scipy.spatial import distance
+from scipy import sparse
+
+import pytest
+
+from sklearn.utils._testing import assert_array_equal
+from sklearn.neighbors import NearestNeighbors
+from sklearn.cluster import DBSCAN
+from sklearn.cluster import dbscan
+from sklearn.cluster.tests.common import generate_clustered_data
+from sklearn.metrics.pairwise import pairwise_distances
+
+
+n_clusters = 3
+X = generate_clustered_data(n_clusters=n_clusters)
+
+
+def test_dbscan_similarity():
+    # Tests the DBSCAN algorithm with a similarity array.
+    # Parameters chosen specifically for this task.
+    eps = 0.15
+    min_samples = 10
+    # Compute similarities
+    D = distance.squareform(distance.pdist(X))
+    D /= np.max(D)
+    # Compute DBSCAN
+    core_samples, labels = dbscan(
+        D, metric="precomputed", eps=eps, min_samples=min_samples
+    )
+    # number of clusters, ignoring noise if present
+    n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
+
+    assert n_clusters_1 == n_clusters
+
+    db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
+    labels = db.fit(D).labels_
+
+    n_clusters_2 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_2 == n_clusters
+
+
+def test_dbscan_feature():
+    # Tests the DBSCAN algorithm with a feature vector array.
+    # Parameters chosen specifically for this task.
+    # Different eps to other test, because distance is not normalised.
+    eps = 0.8
+    min_samples = 10
+    metric = "euclidean"
+    # Compute DBSCAN
+    # parameters chosen for task
+    core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples)
+
+    # number of clusters, ignoring noise if present
+    n_clusters_1 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_1 == n_clusters
+
+    db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
+    labels = db.fit(X).labels_
+
+    n_clusters_2 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_2 == n_clusters
+
+
+def test_dbscan_sparse():
+    core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=0.8, min_samples=10)
+    core_dense, labels_dense = dbscan(X, eps=0.8, min_samples=10)
+    assert_array_equal(core_dense, core_sparse)
+    assert_array_equal(labels_dense, labels_sparse)
+
+
+@pytest.mark.parametrize("include_self", [False, True])
+def test_dbscan_sparse_precomputed(include_self):
+    D = pairwise_distances(X)
+    nn = NearestNeighbors(radius=0.9).fit(X)
+    X_ = X if include_self else None
+    D_sparse = nn.radius_neighbors_graph(X=X_, mode="distance")
+    # Ensure it is sparse not merely on diagonals:
+    assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
+    core_sparse, labels_sparse = dbscan(
+        D_sparse, eps=0.8, min_samples=10, metric="precomputed"
+    )
+    core_dense, labels_dense = dbscan(D, eps=0.8, min_samples=10, metric="precomputed")
+    assert_array_equal(core_dense, core_sparse)
+    assert_array_equal(labels_dense, labels_sparse)
+
+
+def test_dbscan_sparse_precomputed_different_eps():
+    # test that precomputed neighbors graph is filtered if computed with
+    # a radius larger than DBSCAN's eps.
+    lower_eps = 0.2
+    nn = NearestNeighbors(radius=lower_eps).fit(X)
+    D_sparse = nn.radius_neighbors_graph(X, mode="distance")
+    dbscan_lower = dbscan(D_sparse, eps=lower_eps, metric="precomputed")
+
+    higher_eps = lower_eps + 0.7
+    nn = NearestNeighbors(radius=higher_eps).fit(X)
+    D_sparse = nn.radius_neighbors_graph(X, mode="distance")
+    dbscan_higher = dbscan(D_sparse, eps=lower_eps, metric="precomputed")
+
+    assert_array_equal(dbscan_lower[0], dbscan_higher[0])
+    assert_array_equal(dbscan_lower[1], dbscan_higher[1])
+
+
+@pytest.mark.parametrize("use_sparse", [True, False])
+@pytest.mark.parametrize("metric", ["precomputed", "minkowski"])
+def test_dbscan_input_not_modified(use_sparse, metric):
+    # test that the input is not modified by dbscan
+    X = np.random.RandomState(0).rand(10, 10)
+    X = sparse.csr_matrix(X) if use_sparse else X
+    X_copy = X.copy()
+    dbscan(X, metric=metric)
+
+    if use_sparse:
+        assert_array_equal(X.toarray(), X_copy.toarray())
+    else:
+        assert_array_equal(X, X_copy)
+
+
+def test_dbscan_no_core_samples():
+    rng = np.random.RandomState(0)
+    X = rng.rand(40, 10)
+    X[X < 0.8] = 0
+
+    for X_ in [X, sparse.csr_matrix(X)]:
+        db = DBSCAN(min_samples=6).fit(X_)
+        assert_array_equal(db.components_, np.empty((0, X_.shape[1])))
+        assert_array_equal(db.labels_, -1)
+        assert db.core_sample_indices_.shape == (0,)
+
+
+def test_dbscan_callable():
+    # Tests the DBSCAN algorithm with a callable metric.
+    # Parameters chosen specifically for this task.
+    # Different eps to other test, because distance is not normalised.
+    eps = 0.8
+    min_samples = 10
+    # metric is the function reference, not the string key.
+    metric = distance.euclidean
+    # Compute DBSCAN
+    # parameters chosen for task
+    core_samples, labels = dbscan(
+        X, metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree"
+    )
+
+    # number of clusters, ignoring noise if present
+    n_clusters_1 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_1 == n_clusters
+
+    db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
+    labels = db.fit(X).labels_
+
+    n_clusters_2 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_2 == n_clusters
+
+
+def test_dbscan_metric_params():
+    # Tests that DBSCAN works with the metrics_params argument.
+    eps = 0.8
+    min_samples = 10
+    p = 1
+
+    # Compute DBSCAN with metric_params arg
+
+    with warnings.catch_warnings(record=True) as warns:
+        db = DBSCAN(
+            metric="minkowski",
+            metric_params={"p": p},
+            eps=eps,
+            p=None,
+            min_samples=min_samples,
+            algorithm="ball_tree",
+        ).fit(X)
+    assert not warns, warns[0].message
+    core_sample_1, labels_1 = db.core_sample_indices_, db.labels_
+
+    # Test that sample labels are the same as passing Minkowski 'p' directly
+    db = DBSCAN(
+        metric="minkowski", eps=eps, min_samples=min_samples, algorithm="ball_tree", p=p
+    ).fit(X)
+    core_sample_2, labels_2 = db.core_sample_indices_, db.labels_
+
+    assert_array_equal(core_sample_1, core_sample_2)
+    assert_array_equal(labels_1, labels_2)
+
+    # Minkowski with p=1 should be equivalent to Manhattan distance
+    db = DBSCAN(
+        metric="manhattan", eps=eps, min_samples=min_samples, algorithm="ball_tree"
+    ).fit(X)
+    core_sample_3, labels_3 = db.core_sample_indices_, db.labels_
+
+    assert_array_equal(core_sample_1, core_sample_3)
+    assert_array_equal(labels_1, labels_3)
+
+    with pytest.warns(
+        SyntaxWarning,
+        match=(
+            "Parameter p is found in metric_params. "
+            "The corresponding parameter from __init__ "
+            "is ignored."
+        ),
+    ):
+        # Test that checks p is ignored in favor of metric_params={'p': <val>}
+        db = DBSCAN(
+            metric="minkowski",
+            metric_params={"p": p},
+            eps=eps,
+            p=p + 1,
+            min_samples=min_samples,
+            algorithm="ball_tree",
+        ).fit(X)
+        core_sample_4, labels_4 = db.core_sample_indices_, db.labels_
+
+    assert_array_equal(core_sample_1, core_sample_4)
+    assert_array_equal(labels_1, labels_4)
+
+
+def test_dbscan_balltree():
+    # Tests the DBSCAN algorithm with balltree for neighbor calculation.
+    eps = 0.8
+    min_samples = 10
+
+    D = pairwise_distances(X)
+    core_samples, labels = dbscan(
+        D, metric="precomputed", eps=eps, min_samples=min_samples
+    )
+
+    # number of clusters, ignoring noise if present
+    n_clusters_1 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_1 == n_clusters
+
+    db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
+    labels = db.fit(X).labels_
+
+    n_clusters_2 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_2 == n_clusters
+
+    db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="kd_tree")
+    labels = db.fit(X).labels_
+
+    n_clusters_3 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_3 == n_clusters
+
+    db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
+    labels = db.fit(X).labels_
+
+    n_clusters_4 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_4 == n_clusters
+
+    db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm="ball_tree")
+    labels = db.fit(X).labels_
+
+    n_clusters_5 = len(set(labels)) - int(-1 in labels)
+    assert n_clusters_5 == n_clusters
+
+
+def test_input_validation():
+    # DBSCAN.fit should accept a list of lists.
+    X = [[1.0, 2.0], [3.0, 4.0]]
+    DBSCAN().fit(X)  # must not raise exception
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        {"algorithm": "blah"},
+        {"metric": "blah"},
+    ],
+)
+def test_dbscan_badargs(args):
+    # Test bad argument values: these should all raise ValueErrors
+    with pytest.raises(ValueError):
+        dbscan(X, **args)
+
+
+def test_pickle():
+    obj = DBSCAN()
+    s = pickle.dumps(obj)
+    assert type(pickle.loads(s)) == obj.__class__
+
+
+def test_boundaries():
+    # ensure min_samples is inclusive of core point
+    core, _ = dbscan([[0], [1]], eps=2, min_samples=2)
+    assert 0 in core
+    # ensure eps is inclusive of circumference
+    core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2)
+    assert 0 in core
+    core, _ = dbscan([[0], [1], [1]], eps=0.99, min_samples=2)
+    assert 0 not in core
+
+
+def test_weighted_dbscan():
+    # ensure sample_weight is validated
+    with pytest.raises(ValueError):
+        dbscan([[0], [1]], sample_weight=[2])
+    with pytest.raises(ValueError):
+        dbscan([[0], [1]], sample_weight=[2, 3, 4])
+
+    # ensure sample_weight has an effect
+    assert_array_equal([], dbscan([[0], [1]], sample_weight=None, min_samples=6)[0])
+    assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5], min_samples=6)[0])
+    assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5], min_samples=6)[0])
+    assert_array_equal(
+        [0, 1], dbscan([[0], [1]], sample_weight=[6, 6], min_samples=6)[0]
+    )
+
+    # points within eps of each other:
+    assert_array_equal(
+        [0, 1], dbscan([[0], [1]], eps=1.5, sample_weight=[5, 1], min_samples=6)[0]
+    )
+    # and effect of non-positive and non-integer sample_weight:
+    assert_array_equal(
+        [], dbscan([[0], [1]], sample_weight=[5, 0], eps=1.5, min_samples=6)[0]
+    )
+    assert_array_equal(
+        [0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1], eps=1.5, min_samples=6)[0]
+    )
+    assert_array_equal(
+        [0, 1], dbscan([[0], [1]], sample_weight=[6, 0], eps=1.5, min_samples=6)[0]
+    )
+    assert_array_equal(
+        [], dbscan([[0], [1]], sample_weight=[6, -1], eps=1.5, min_samples=6)[0]
+    )
+
+    # for non-negative sample_weight, cores should be identical to repetition
+    rng = np.random.RandomState(42)
+    sample_weight = rng.randint(0, 5, X.shape[0])
+    core1, label1 = dbscan(X, sample_weight=sample_weight)
+    assert len(label1) == len(X)
+
+    X_repeated = np.repeat(X, sample_weight, axis=0)
+    core_repeated, label_repeated = dbscan(X_repeated)
+    core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool)
+    core_repeated_mask[core_repeated] = True
+    core_mask = np.zeros(X.shape[0], dtype=bool)
+    core_mask[core1] = True
+    assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask)
+
+    # sample_weight should work with precomputed distance matrix
+    D = pairwise_distances(X)
+    core3, label3 = dbscan(D, sample_weight=sample_weight, metric="precomputed")
+    assert_array_equal(core1, core3)
+    assert_array_equal(label1, label3)
+
+    # sample_weight should work with estimator
+    est = DBSCAN().fit(X, sample_weight=sample_weight)
+    core4 = est.core_sample_indices_
+    label4 = est.labels_
+    assert_array_equal(core1, core4)
+    assert_array_equal(label1, label4)
+
+    est = DBSCAN()
+    label5 = est.fit_predict(X, sample_weight=sample_weight)
+    core5 = est.core_sample_indices_
+    assert_array_equal(core1, core5)
+    assert_array_equal(label1, label5)
+    assert_array_equal(label1, est.labels_)
+
+
+@pytest.mark.parametrize("algorithm", ["brute", "kd_tree", "ball_tree"])
+def test_dbscan_core_samples_toy(algorithm):
+    X = [[0], [2], [3], [4], [6], [8], [10]]
+    n_samples = len(X)
+
+    # Degenerate case: every sample is a core sample, either with its own
+    # cluster or including other close core samples.
+    core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1)
+    assert_array_equal(core_samples, np.arange(n_samples))
+    assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4])
+
+    # With eps=1 and min_samples=2 only the 3 samples from the denser area
+    # are core samples. All other points are isolated and considered noise.
+    core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2)
+    assert_array_equal(core_samples, [1, 2, 3])
+    assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
+
+    # Only the sample in the middle of the dense area is core. Its two
+    # neighbors are edge samples. Remaining samples are noise.
+    core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3)
+    assert_array_equal(core_samples, [2])
+    assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
+
+    # It's no longer possible to extract core samples with eps=1:
+    # everything is noise.
+    core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4)
+    assert_array_equal(core_samples, [])
+    assert_array_equal(labels, np.full(n_samples, -1.0))
+
+
+def test_dbscan_precomputed_metric_with_degenerate_input_arrays():
+    # see https://github.com/scikit-learn/scikit-learn/issues/4641 for
+    # more details
+    X = np.eye(10)
+    labels = DBSCAN(eps=0.5, metric="precomputed").fit(X).labels_
+    assert len(set(labels)) == 1
+
+    X = np.zeros((10, 10))
+    labels = DBSCAN(eps=0.5, metric="precomputed").fit(X).labels_
+    assert len(set(labels)) == 1
+
+
+def test_dbscan_precomputed_metric_with_initial_rows_zero():
+    # sample matrix with initial two row all zero
+    ar = np.array(
+        [
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
+            [0.0, 0.0, 0.1, 0.1, 0.0, 0.0, 0.3],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
+            [0.0, 0.0, 0.0, 0.0, 0.3, 0.1, 0.0],
+        ]
+    )
+    matrix = sparse.csr_matrix(ar)
+    labels = DBSCAN(eps=0.2, metric="precomputed", min_samples=2).fit(matrix).labels_
+    assert_array_equal(labels, [-1, -1, 0, 0, 0, 1, 1])
+
+
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        ({"eps": -1.0}, ValueError, "eps == -1.0, must be > 0.0."),
+        ({"eps": 0.0}, ValueError, "eps == 0.0, must be > 0.0."),
+        ({"min_samples": 0}, ValueError, "min_samples == 0, must be >= 1."),
+        (
+            {"min_samples": 1.5},
+            TypeError,
+            "min_samples must be an instance of int, not float.",
+        ),
+        ({"min_samples": -2}, ValueError, "min_samples == -2, must be >= 1."),
+        ({"leaf_size": 0}, ValueError, "leaf_size == 0, must be >= 1."),
+        (
+            {"leaf_size": 2.5},
+            TypeError,
+            "leaf_size must be an instance of int, not float.",
+        ),
+        ({"leaf_size": -3}, ValueError, "leaf_size == -3, must be >= 1."),
+        ({"p": -2}, ValueError, "p == -2, must be >= 0.0."),
+        (
+            {"n_jobs": 2.5},
+            TypeError,
+            "n_jobs must be an instance of int, not float.",
+        ),
+    ],
+)
+def test_dbscan_params_validation(params, err_type, err_msg):
+    """Check the parameters validation in `DBSCAN`."""
+    with pytest.raises(err_type, match=err_msg):
+        DBSCAN(**params).fit(X)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_feature_agglomeration.py

@@ -0,0 +1,55 @@
+"""
+Tests for sklearn.cluster._feature_agglomeration
+"""
+# Authors: Sergul Aydore 2017
+import numpy as np
+
+from numpy.testing import assert_array_equal
+from sklearn.cluster import FeatureAgglomeration
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.datasets import make_blobs
+
+
+def test_feature_agglomeration():
+    n_clusters = 1
+    X = np.array([0, 0, 1]).reshape(1, 3)  # (n_samples, n_features)
+
+    agglo_mean = FeatureAgglomeration(n_clusters=n_clusters, pooling_func=np.mean)
+    agglo_median = FeatureAgglomeration(n_clusters=n_clusters, pooling_func=np.median)
+    agglo_mean.fit(X)
+    agglo_median.fit(X)
+
+    assert np.size(np.unique(agglo_mean.labels_)) == n_clusters
+    assert np.size(np.unique(agglo_median.labels_)) == n_clusters
+    assert np.size(agglo_mean.labels_) == X.shape[1]
+    assert np.size(agglo_median.labels_) == X.shape[1]
+
+    # Test transform
+    Xt_mean = agglo_mean.transform(X)
+    Xt_median = agglo_median.transform(X)
+    assert Xt_mean.shape[1] == n_clusters
+    assert Xt_median.shape[1] == n_clusters
+    assert Xt_mean == np.array([1 / 3.0])
+    assert Xt_median == np.array([0.0])
+
+    # Test inverse transform
+    X_full_mean = agglo_mean.inverse_transform(Xt_mean)
+    X_full_median = agglo_median.inverse_transform(Xt_median)
+    assert np.unique(X_full_mean[0]).size == n_clusters
+    assert np.unique(X_full_median[0]).size == n_clusters
+
+    assert_array_almost_equal(agglo_mean.transform(X_full_mean), Xt_mean)
+    assert_array_almost_equal(agglo_median.transform(X_full_median), Xt_median)
+
+
+def test_feature_agglomeration_feature_names_out():
+    """Check `get_feature_names_out` for `FeatureAgglomeration`."""
+    X, _ = make_blobs(n_features=6, random_state=0)
+    agglo = FeatureAgglomeration(n_clusters=3)
+    agglo.fit(X)
+    n_clusters = agglo.n_clusters_
+
+    names_out = agglo.get_feature_names_out()
+    assert_array_equal(
+        [f"featureagglomeration{i}" for i in range(n_clusters)], names_out
+    )

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_hierarchical.py

@@ -0,0 +1,918 @@
+"""
+Several basic tests for hierarchical clustering procedures
+
+"""
+# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
+#          Matteo Visconti di Oleggio Castello 2014
+# License: BSD 3 clause
+import itertools
+from tempfile import mkdtemp
+import shutil
+import pytest
+from functools import partial
+
+import numpy as np
+from scipy import sparse
+from scipy.cluster import hierarchy
+from scipy.sparse.csgraph import connected_components
+
+from sklearn.metrics.cluster import adjusted_rand_score
+from sklearn.metrics.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS
+from sklearn.utils._testing import assert_almost_equal, create_memmap_backed_data
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import ignore_warnings
+
+from sklearn.cluster import ward_tree
+from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration
+from sklearn.cluster._agglomerative import (
+    _hc_cut,
+    _TREE_BUILDERS,
+    linkage_tree,
+    _fix_connectivity,
+)
+from sklearn.feature_extraction.image import grid_to_graph
+from sklearn.metrics import DistanceMetric
+from sklearn.metrics.pairwise import (
+    PAIRED_DISTANCES,
+    cosine_distances,
+    manhattan_distances,
+    pairwise_distances,
+)
+from sklearn.metrics.cluster import normalized_mutual_info_score
+from sklearn.neighbors import kneighbors_graph
+from sklearn.cluster._hierarchical_fast import (
+    average_merge,
+    max_merge,
+    mst_linkage_core,
+)
+from sklearn.utils._fast_dict import IntFloatDict
+from sklearn.utils._testing import assert_array_equal
+from sklearn.datasets import make_moons, make_circles
+
+
+def test_linkage_misc():
+    # Misc tests on linkage
+    rng = np.random.RandomState(42)
+    X = rng.normal(size=(5, 5))
+    with pytest.raises(ValueError):
+        AgglomerativeClustering(linkage="foo").fit(X)
+
+    with pytest.raises(ValueError):
+        linkage_tree(X, linkage="foo")
+
+    with pytest.raises(ValueError):
+        linkage_tree(X, connectivity=np.ones((4, 4)))
+
+    # Smoke test FeatureAgglomeration
+    FeatureAgglomeration().fit(X)
+
+    # test hierarchical clustering on a precomputed distances matrix
+    dis = cosine_distances(X)
+
+    res = linkage_tree(dis, affinity="precomputed")
+    assert_array_equal(res[0], linkage_tree(X, affinity="cosine")[0])
+
+    # test hierarchical clustering on a precomputed distances matrix
+    res = linkage_tree(X, affinity=manhattan_distances)
+    assert_array_equal(res[0], linkage_tree(X, affinity="manhattan")[0])
+
+
+def test_structured_linkage_tree():
+    # Check that we obtain the correct solution for structured linkage trees.
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    # Avoiding a mask with only 'True' entries
+    mask[4:7, 4:7] = 0
+    X = rng.randn(50, 100)
+    connectivity = grid_to_graph(*mask.shape)
+    for tree_builder in _TREE_BUILDERS.values():
+        children, n_components, n_leaves, parent = tree_builder(
+            X.T, connectivity=connectivity
+        )
+        n_nodes = 2 * X.shape[1] - 1
+        assert len(children) + n_leaves == n_nodes
+        # Check that ward_tree raises a ValueError with a connectivity matrix
+        # of the wrong shape
+        with pytest.raises(ValueError):
+            tree_builder(X.T, connectivity=np.ones((4, 4)))
+        # Check that fitting with no samples raises an error
+        with pytest.raises(ValueError):
+            tree_builder(X.T[:0], connectivity=connectivity)
+
+
+def test_unstructured_linkage_tree():
+    # Check that we obtain the correct solution for unstructured linkage trees.
+    rng = np.random.RandomState(0)
+    X = rng.randn(50, 100)
+    for this_X in (X, X[0]):
+        # With specified a number of clusters just for the sake of
+        # raising a warning and testing the warning code
+        with ignore_warnings():
+            with pytest.warns(UserWarning):
+                children, n_nodes, n_leaves, parent = ward_tree(this_X.T, n_clusters=10)
+        n_nodes = 2 * X.shape[1] - 1
+        assert len(children) + n_leaves == n_nodes
+
+    for tree_builder in _TREE_BUILDERS.values():
+        for this_X in (X, X[0]):
+            with ignore_warnings():
+                with pytest.warns(UserWarning):
+                    children, n_nodes, n_leaves, parent = tree_builder(
+                        this_X.T, n_clusters=10
+                    )
+            n_nodes = 2 * X.shape[1] - 1
+            assert len(children) + n_leaves == n_nodes
+
+
+def test_height_linkage_tree():
+    # Check that the height of the results of linkage tree is sorted.
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    X = rng.randn(50, 100)
+    connectivity = grid_to_graph(*mask.shape)
+    for linkage_func in _TREE_BUILDERS.values():
+        children, n_nodes, n_leaves, parent = linkage_func(
+            X.T, connectivity=connectivity
+        )
+        n_nodes = 2 * X.shape[1] - 1
+        assert len(children) + n_leaves == n_nodes
+
+
+def test_agglomerative_clustering_wrong_arg_memory():
+    # Test either if an error is raised when memory is not
+    # either a str or a joblib.Memory instance
+    rng = np.random.RandomState(0)
+    n_samples = 100
+    X = rng.randn(n_samples, 50)
+    memory = 5
+    clustering = AgglomerativeClustering(memory=memory)
+    with pytest.raises(ValueError):
+        clustering.fit(X)
+
+
+def test_zero_cosine_linkage_tree():
+    # Check that zero vectors in X produce an error when
+    # 'cosine' affinity is used
+    X = np.array([[0, 1], [0, 0]])
+    msg = "Cosine affinity cannot be used when X contains zero vectors"
+    with pytest.raises(ValueError, match=msg):
+        linkage_tree(X, affinity="cosine")
+
+
+@pytest.mark.parametrize("n_clusters, distance_threshold", [(None, 0.5), (10, None)])
+@pytest.mark.parametrize("compute_distances", [True, False])
+@pytest.mark.parametrize("linkage", ["ward", "complete", "average", "single"])
+def test_agglomerative_clustering_distances(
+    n_clusters, compute_distances, distance_threshold, linkage
+):
+    # Check that when `compute_distances` is True or `distance_threshold` is
+    # given, the fitted model has an attribute `distances_`.
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    n_samples = 100
+    X = rng.randn(n_samples, 50)
+    connectivity = grid_to_graph(*mask.shape)
+
+    clustering = AgglomerativeClustering(
+        n_clusters=n_clusters,
+        connectivity=connectivity,
+        linkage=linkage,
+        distance_threshold=distance_threshold,
+        compute_distances=compute_distances,
+    )
+    clustering.fit(X)
+    if compute_distances or (distance_threshold is not None):
+        assert hasattr(clustering, "distances_")
+        n_children = clustering.children_.shape[0]
+        n_nodes = n_children + 1
+        assert clustering.distances_.shape == (n_nodes - 1,)
+    else:
+        assert not hasattr(clustering, "distances_")
+
+
+def test_agglomerative_clustering():
+    # Check that we obtain the correct number of clusters with
+    # agglomerative clustering.
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    n_samples = 100
+    X = rng.randn(n_samples, 50)
+    connectivity = grid_to_graph(*mask.shape)
+    for linkage in ("ward", "complete", "average", "single"):
+        clustering = AgglomerativeClustering(
+            n_clusters=10, connectivity=connectivity, linkage=linkage
+        )
+        clustering.fit(X)
+        # test caching
+        try:
+            tempdir = mkdtemp()
+            clustering = AgglomerativeClustering(
+                n_clusters=10,
+                connectivity=connectivity,
+                memory=tempdir,
+                linkage=linkage,
+            )
+            clustering.fit(X)
+            labels = clustering.labels_
+            assert np.size(np.unique(labels)) == 10
+        finally:
+            shutil.rmtree(tempdir)
+        # Turn caching off now
+        clustering = AgglomerativeClustering(
+            n_clusters=10, connectivity=connectivity, linkage=linkage
+        )
+        # Check that we obtain the same solution with early-stopping of the
+        # tree building
+        clustering.compute_full_tree = False
+        clustering.fit(X)
+        assert_almost_equal(normalized_mutual_info_score(clustering.labels_, labels), 1)
+        clustering.connectivity = None
+        clustering.fit(X)
+        assert np.size(np.unique(clustering.labels_)) == 10
+        # Check that we raise a TypeError on dense matrices
+        clustering = AgglomerativeClustering(
+            n_clusters=10,
+            connectivity=sparse.lil_matrix(connectivity.toarray()[:10, :10]),
+            linkage=linkage,
+        )
+        with pytest.raises(ValueError):
+            clustering.fit(X)
+
+    # Test that using ward with another metric than euclidean raises an
+    # exception
+    clustering = AgglomerativeClustering(
+        n_clusters=10,
+        connectivity=connectivity.toarray(),
+        affinity="manhattan",
+        linkage="ward",
+    )
+    with pytest.raises(ValueError):
+        clustering.fit(X)
+
+    # Test using another metric than euclidean works with linkage complete
+    for affinity in PAIRED_DISTANCES.keys():
+        # Compare our (structured) implementation to scipy
+        clustering = AgglomerativeClustering(
+            n_clusters=10,
+            connectivity=np.ones((n_samples, n_samples)),
+            affinity=affinity,
+            linkage="complete",
+        )
+        clustering.fit(X)
+        clustering2 = AgglomerativeClustering(
+            n_clusters=10, connectivity=None, affinity=affinity, linkage="complete"
+        )
+        clustering2.fit(X)
+        assert_almost_equal(
+            normalized_mutual_info_score(clustering2.labels_, clustering.labels_), 1
+        )
+
+    # Test that using a distance matrix (affinity = 'precomputed') has same
+    # results (with connectivity constraints)
+    clustering = AgglomerativeClustering(
+        n_clusters=10, connectivity=connectivity, linkage="complete"
+    )
+    clustering.fit(X)
+    X_dist = pairwise_distances(X)
+    clustering2 = AgglomerativeClustering(
+        n_clusters=10,
+        connectivity=connectivity,
+        affinity="precomputed",
+        linkage="complete",
+    )
+    clustering2.fit(X_dist)
+    assert_array_equal(clustering.labels_, clustering2.labels_)
+
+
+def test_agglomerative_clustering_memory_mapped():
+    """AgglomerativeClustering must work on mem-mapped dataset.
+
+    Non-regression test for issue #19875.
+    """
+    rng = np.random.RandomState(0)
+    Xmm = create_memmap_backed_data(rng.randn(50, 100))
+    AgglomerativeClustering(affinity="euclidean", linkage="single").fit(Xmm)
+
+
+def test_ward_agglomeration():
+    # Check that we obtain the correct solution in a simplistic case
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    X = rng.randn(50, 100)
+    connectivity = grid_to_graph(*mask.shape)
+    agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity)
+    agglo.fit(X)
+    assert np.size(np.unique(agglo.labels_)) == 5
+
+    X_red = agglo.transform(X)
+    assert X_red.shape[1] == 5
+    X_full = agglo.inverse_transform(X_red)
+    assert np.unique(X_full[0]).size == 5
+    assert_array_almost_equal(agglo.transform(X_full), X_red)
+
+    # Check that fitting with no samples raises a ValueError
+    with pytest.raises(ValueError):
+        agglo.fit(X[:0])
+
+
+def test_single_linkage_clustering():
+    # Check that we get the correct result in two emblematic cases
+    moons, moon_labels = make_moons(noise=0.05, random_state=42)
+    clustering = AgglomerativeClustering(n_clusters=2, linkage="single")
+    clustering.fit(moons)
+    assert_almost_equal(
+        normalized_mutual_info_score(clustering.labels_, moon_labels), 1
+    )
+
+    circles, circle_labels = make_circles(factor=0.5, noise=0.025, random_state=42)
+    clustering = AgglomerativeClustering(n_clusters=2, linkage="single")
+    clustering.fit(circles)
+    assert_almost_equal(
+        normalized_mutual_info_score(clustering.labels_, circle_labels), 1
+    )
+
+
+def assess_same_labelling(cut1, cut2):
+    """Util for comparison with scipy"""
+    co_clust = []
+    for cut in [cut1, cut2]:
+        n = len(cut)
+        k = cut.max() + 1
+        ecut = np.zeros((n, k))
+        ecut[np.arange(n), cut] = 1
+        co_clust.append(np.dot(ecut, ecut.T))
+    assert (co_clust[0] == co_clust[1]).all()
+
+
+def test_sparse_scikit_vs_scipy():
+    # Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
+    n, p, k = 10, 5, 3
+    rng = np.random.RandomState(0)
+
+    # Not using a lil_matrix here, just to check that non sparse
+    # matrices are well handled
+    connectivity = np.ones((n, n))
+    for linkage in _TREE_BUILDERS.keys():
+        for i in range(5):
+            X = 0.1 * rng.normal(size=(n, p))
+            X -= 4.0 * np.arange(n)[:, np.newaxis]
+            X -= X.mean(axis=1)[:, np.newaxis]
+
+            out = hierarchy.linkage(X, method=linkage)
+
+            children_ = out[:, :2].astype(int, copy=False)
+            children, _, n_leaves, _ = _TREE_BUILDERS[linkage](
+                X, connectivity=connectivity
+            )
+
+            # Sort the order of child nodes per row for consistency
+            children.sort(axis=1)
+            assert_array_equal(
+                children,
+                children_,
+                "linkage tree differs from scipy impl for linkage: " + linkage,
+            )
+
+            cut = _hc_cut(k, children, n_leaves)
+            cut_ = _hc_cut(k, children_, n_leaves)
+            assess_same_labelling(cut, cut_)
+
+    # Test error management in _hc_cut
+    with pytest.raises(ValueError):
+        _hc_cut(n_leaves + 1, children, n_leaves)
+
+
+# Make sure our custom mst_linkage_core gives
+# the same results as scipy's builtin
+@pytest.mark.parametrize("seed", range(5))
+def test_vector_scikit_single_vs_scipy_single(seed):
+    n_samples, n_features, n_clusters = 10, 5, 3
+    rng = np.random.RandomState(seed)
+    X = 0.1 * rng.normal(size=(n_samples, n_features))
+    X -= 4.0 * np.arange(n_samples)[:, np.newaxis]
+    X -= X.mean(axis=1)[:, np.newaxis]
+
+    out = hierarchy.linkage(X, method="single")
+    children_scipy = out[:, :2].astype(int)
+
+    children, _, n_leaves, _ = _TREE_BUILDERS["single"](X)
+
+    # Sort the order of child nodes per row for consistency
+    children.sort(axis=1)
+    assert_array_equal(
+        children,
+        children_scipy,
+        "linkage tree differs from scipy impl for single linkage.",
+    )
+
+    cut = _hc_cut(n_clusters, children, n_leaves)
+    cut_scipy = _hc_cut(n_clusters, children_scipy, n_leaves)
+    assess_same_labelling(cut, cut_scipy)
+
+
+# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
+@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
+@pytest.mark.parametrize("metric_param_grid", METRICS_DEFAULT_PARAMS)
+def test_mst_linkage_core_memory_mapped(metric_param_grid):
+    """The MST-LINKAGE-CORE algorithm must work on mem-mapped dataset.
+
+    Non-regression test for issue #19875.
+    """
+    rng = np.random.RandomState(seed=1)
+    X = rng.normal(size=(20, 4))
+    Xmm = create_memmap_backed_data(X)
+    metric, param_grid = metric_param_grid
+    keys = param_grid.keys()
+    for vals in itertools.product(*param_grid.values()):
+        kwargs = dict(zip(keys, vals))
+        distance_metric = DistanceMetric.get_metric(metric, **kwargs)
+        mst = mst_linkage_core(X, distance_metric)
+        mst_mm = mst_linkage_core(Xmm, distance_metric)
+        np.testing.assert_equal(mst, mst_mm)
+
+
+def test_identical_points():
+    # Ensure identical points are handled correctly when using mst with
+    # a sparse connectivity matrix
+    X = np.array([[0, 0, 0], [0, 0, 0], [1, 1, 1], [1, 1, 1], [2, 2, 2], [2, 2, 2]])
+    true_labels = np.array([0, 0, 1, 1, 2, 2])
+    connectivity = kneighbors_graph(X, n_neighbors=3, include_self=False)
+    connectivity = 0.5 * (connectivity + connectivity.T)
+    connectivity, n_components = _fix_connectivity(X, connectivity, "euclidean")
+
+    for linkage in ("single", "average", "average", "ward"):
+        clustering = AgglomerativeClustering(
+            n_clusters=3, linkage=linkage, connectivity=connectivity
+        )
+        clustering.fit(X)
+
+        assert_almost_equal(
+            normalized_mutual_info_score(clustering.labels_, true_labels), 1
+        )
+
+
+def test_connectivity_propagation():
+    # Check that connectivity in the ward tree is propagated correctly during
+    # merging.
+    X = np.array(
+        [
+            (0.014, 0.120),
+            (0.014, 0.099),
+            (0.014, 0.097),
+            (0.017, 0.153),
+            (0.017, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.153),
+            (0.018, 0.152),
+            (0.018, 0.149),
+            (0.018, 0.144),
+        ]
+    )
+    connectivity = kneighbors_graph(X, 10, include_self=False)
+    ward = AgglomerativeClustering(
+        n_clusters=4, connectivity=connectivity, linkage="ward"
+    )
+    # If changes are not propagated correctly, fit crashes with an
+    # IndexError
+    ward.fit(X)
+
+
+def test_ward_tree_children_order():
+    # Check that children are ordered in the same way for both structured and
+    # unstructured versions of ward_tree.
+
+    # test on five random datasets
+    n, p = 10, 5
+    rng = np.random.RandomState(0)
+
+    connectivity = np.ones((n, n))
+    for i in range(5):
+        X = 0.1 * rng.normal(size=(n, p))
+        X -= 4.0 * np.arange(n)[:, np.newaxis]
+        X -= X.mean(axis=1)[:, np.newaxis]
+
+        out_unstructured = ward_tree(X)
+        out_structured = ward_tree(X, connectivity=connectivity)
+
+        assert_array_equal(out_unstructured[0], out_structured[0])
+
+
+def test_ward_linkage_tree_return_distance():
+    # Test return_distance option on linkage and ward trees
+
+    # test that return_distance when set true, gives same
+    # output on both structured and unstructured clustering.
+    n, p = 10, 5
+    rng = np.random.RandomState(0)
+
+    connectivity = np.ones((n, n))
+    for i in range(5):
+        X = 0.1 * rng.normal(size=(n, p))
+        X -= 4.0 * np.arange(n)[:, np.newaxis]
+        X -= X.mean(axis=1)[:, np.newaxis]
+
+        out_unstructured = ward_tree(X, return_distance=True)
+        out_structured = ward_tree(X, connectivity=connectivity, return_distance=True)
+
+        # get children
+        children_unstructured = out_unstructured[0]
+        children_structured = out_structured[0]
+
+        # check if we got the same clusters
+        assert_array_equal(children_unstructured, children_structured)
+
+        # check if the distances are the same
+        dist_unstructured = out_unstructured[-1]
+        dist_structured = out_structured[-1]
+
+        assert_array_almost_equal(dist_unstructured, dist_structured)
+
+        for linkage in ["average", "complete", "single"]:
+            structured_items = linkage_tree(
+                X, connectivity=connectivity, linkage=linkage, return_distance=True
+            )[-1]
+            unstructured_items = linkage_tree(X, linkage=linkage, return_distance=True)[
+                -1
+            ]
+            structured_dist = structured_items[-1]
+            unstructured_dist = unstructured_items[-1]
+            structured_children = structured_items[0]
+            unstructured_children = unstructured_items[0]
+            assert_array_almost_equal(structured_dist, unstructured_dist)
+            assert_array_almost_equal(structured_children, unstructured_children)
+
+    # test on the following dataset where we know the truth
+    # taken from scipy/cluster/tests/hierarchy_test_data.py
+    X = np.array(
+        [
+            [1.43054825, -7.5693489],
+            [6.95887839, 6.82293382],
+            [2.87137846, -9.68248579],
+            [7.87974764, -6.05485803],
+            [8.24018364, -6.09495602],
+            [7.39020262, 8.54004355],
+        ]
+    )
+    # truth
+    linkage_X_ward = np.array(
+        [
+            [3.0, 4.0, 0.36265956, 2.0],
+            [1.0, 5.0, 1.77045373, 2.0],
+            [0.0, 2.0, 2.55760419, 2.0],
+            [6.0, 8.0, 9.10208346, 4.0],
+            [7.0, 9.0, 24.7784379, 6.0],
+        ]
+    )
+
+    linkage_X_complete = np.array(
+        [
+            [3.0, 4.0, 0.36265956, 2.0],
+            [1.0, 5.0, 1.77045373, 2.0],
+            [0.0, 2.0, 2.55760419, 2.0],
+            [6.0, 8.0, 6.96742194, 4.0],
+            [7.0, 9.0, 18.77445997, 6.0],
+        ]
+    )
+
+    linkage_X_average = np.array(
+        [
+            [3.0, 4.0, 0.36265956, 2.0],
+            [1.0, 5.0, 1.77045373, 2.0],
+            [0.0, 2.0, 2.55760419, 2.0],
+            [6.0, 8.0, 6.55832839, 4.0],
+            [7.0, 9.0, 15.44089605, 6.0],
+        ]
+    )
+
+    n_samples, n_features = np.shape(X)
+    connectivity_X = np.ones((n_samples, n_samples))
+
+    out_X_unstructured = ward_tree(X, return_distance=True)
+    out_X_structured = ward_tree(X, connectivity=connectivity_X, return_distance=True)
+
+    # check that the labels are the same
+    assert_array_equal(linkage_X_ward[:, :2], out_X_unstructured[0])
+    assert_array_equal(linkage_X_ward[:, :2], out_X_structured[0])
+
+    # check that the distances are correct
+    assert_array_almost_equal(linkage_X_ward[:, 2], out_X_unstructured[4])
+    assert_array_almost_equal(linkage_X_ward[:, 2], out_X_structured[4])
+
+    linkage_options = ["complete", "average", "single"]
+    X_linkage_truth = [linkage_X_complete, linkage_X_average]
+    for linkage, X_truth in zip(linkage_options, X_linkage_truth):
+        out_X_unstructured = linkage_tree(X, return_distance=True, linkage=linkage)
+        out_X_structured = linkage_tree(
+            X, connectivity=connectivity_X, linkage=linkage, return_distance=True
+        )
+
+        # check that the labels are the same
+        assert_array_equal(X_truth[:, :2], out_X_unstructured[0])
+        assert_array_equal(X_truth[:, :2], out_X_structured[0])
+
+        # check that the distances are correct
+        assert_array_almost_equal(X_truth[:, 2], out_X_unstructured[4])
+        assert_array_almost_equal(X_truth[:, 2], out_X_structured[4])
+
+
+def test_connectivity_fixing_non_lil():
+    # Check non regression of a bug if a non item assignable connectivity is
+    # provided with more than one component.
+    # create dummy data
+    x = np.array([[0, 0], [1, 1]])
+    # create a mask with several components to force connectivity fixing
+    m = np.array([[True, False], [False, True]])
+    c = grid_to_graph(n_x=2, n_y=2, mask=m)
+    w = AgglomerativeClustering(connectivity=c, linkage="ward")
+    with pytest.warns(UserWarning):
+        w.fit(x)
+
+
+def test_int_float_dict():
+    rng = np.random.RandomState(0)
+    keys = np.unique(rng.randint(100, size=10).astype(np.intp, copy=False))
+    values = rng.rand(len(keys))
+
+    d = IntFloatDict(keys, values)
+    for key, value in zip(keys, values):
+        assert d[key] == value
+
+    other_keys = np.arange(50, dtype=np.intp)[::2]
+    other_values = np.full(50, 0.5)[::2]
+    other = IntFloatDict(other_keys, other_values)
+    # Complete smoke test
+    max_merge(d, other, mask=np.ones(100, dtype=np.intp), n_a=1, n_b=1)
+    average_merge(d, other, mask=np.ones(100, dtype=np.intp), n_a=1, n_b=1)
+
+
+def test_connectivity_callable():
+    rng = np.random.RandomState(0)
+    X = rng.rand(20, 5)
+    connectivity = kneighbors_graph(X, 3, include_self=False)
+    aglc1 = AgglomerativeClustering(connectivity=connectivity)
+    aglc2 = AgglomerativeClustering(
+        connectivity=partial(kneighbors_graph, n_neighbors=3, include_self=False)
+    )
+    aglc1.fit(X)
+    aglc2.fit(X)
+    assert_array_equal(aglc1.labels_, aglc2.labels_)
+
+
+def test_connectivity_ignores_diagonal():
+    rng = np.random.RandomState(0)
+    X = rng.rand(20, 5)
+    connectivity = kneighbors_graph(X, 3, include_self=False)
+    connectivity_include_self = kneighbors_graph(X, 3, include_self=True)
+    aglc1 = AgglomerativeClustering(connectivity=connectivity)
+    aglc2 = AgglomerativeClustering(connectivity=connectivity_include_self)
+    aglc1.fit(X)
+    aglc2.fit(X)
+    assert_array_equal(aglc1.labels_, aglc2.labels_)
+
+
+def test_compute_full_tree():
+    # Test that the full tree is computed if n_clusters is small
+    rng = np.random.RandomState(0)
+    X = rng.randn(10, 2)
+    connectivity = kneighbors_graph(X, 5, include_self=False)
+
+    # When n_clusters is less, the full tree should be built
+    # that is the number of merges should be n_samples - 1
+    agc = AgglomerativeClustering(n_clusters=2, connectivity=connectivity)
+    agc.fit(X)
+    n_samples = X.shape[0]
+    n_nodes = agc.children_.shape[0]
+    assert n_nodes == n_samples - 1
+
+    # When n_clusters is large, greater than max of 100 and 0.02 * n_samples.
+    # we should stop when there are n_clusters.
+    n_clusters = 101
+    X = rng.randn(200, 2)
+    connectivity = kneighbors_graph(X, 10, include_self=False)
+    agc = AgglomerativeClustering(n_clusters=n_clusters, connectivity=connectivity)
+    agc.fit(X)
+    n_samples = X.shape[0]
+    n_nodes = agc.children_.shape[0]
+    assert n_nodes == n_samples - n_clusters
+
+
+def test_n_components():
+    # Test n_components returned by linkage, average and ward tree
+    rng = np.random.RandomState(0)
+    X = rng.rand(5, 5)
+
+    # Connectivity matrix having five components.
+    connectivity = np.eye(5)
+
+    for linkage_func in _TREE_BUILDERS.values():
+        assert ignore_warnings(linkage_func)(X, connectivity=connectivity)[1] == 5
+
+
+def test_agg_n_clusters():
+    # Test that an error is raised when n_clusters <= 0
+
+    rng = np.random.RandomState(0)
+    X = rng.rand(20, 10)
+    for n_clus in [-1, 0]:
+        agc = AgglomerativeClustering(n_clusters=n_clus)
+        msg = "n_clusters should be an integer greater than 0. %s was provided." % str(
+            agc.n_clusters
+        )
+        with pytest.raises(ValueError, match=msg):
+            agc.fit(X)
+
+
+def test_affinity_passed_to_fix_connectivity():
+    # Test that the affinity parameter is actually passed to the pairwise
+    # function
+
+    size = 2
+    rng = np.random.RandomState(0)
+    X = rng.randn(size, size)
+    mask = np.array([True, False, False, True])
+
+    connectivity = grid_to_graph(n_x=size, n_y=size, mask=mask, return_as=np.ndarray)
+
+    class FakeAffinity:
+        def __init__(self):
+            self.counter = 0
+
+        def increment(self, *args, **kwargs):
+            self.counter += 1
+            return self.counter
+
+    fa = FakeAffinity()
+
+    linkage_tree(X, connectivity=connectivity, affinity=fa.increment)
+
+    assert fa.counter == 3
+
+
+@pytest.mark.parametrize("linkage", ["ward", "complete", "average"])
+def test_agglomerative_clustering_with_distance_threshold(linkage):
+    # Check that we obtain the correct number of clusters with
+    # agglomerative clustering with distance_threshold.
+    rng = np.random.RandomState(0)
+    mask = np.ones([10, 10], dtype=bool)
+    n_samples = 100
+    X = rng.randn(n_samples, 50)
+    connectivity = grid_to_graph(*mask.shape)
+    # test when distance threshold is set to 10
+    distance_threshold = 10
+    for conn in [None, connectivity]:
+        clustering = AgglomerativeClustering(
+            n_clusters=None,
+            distance_threshold=distance_threshold,
+            connectivity=conn,
+            linkage=linkage,
+        )
+        clustering.fit(X)
+        clusters_produced = clustering.labels_
+        num_clusters_produced = len(np.unique(clustering.labels_))
+        # test if the clusters produced match the point in the linkage tree
+        # where the distance exceeds the threshold
+        tree_builder = _TREE_BUILDERS[linkage]
+        children, n_components, n_leaves, parent, distances = tree_builder(
+            X, connectivity=conn, n_clusters=None, return_distance=True
+        )
+        num_clusters_at_threshold = (
+            np.count_nonzero(distances >= distance_threshold) + 1
+        )
+        # test number of clusters produced
+        assert num_clusters_at_threshold == num_clusters_produced
+        # test clusters produced
+        clusters_at_threshold = _hc_cut(
+            n_clusters=num_clusters_produced, children=children, n_leaves=n_leaves
+        )
+        assert np.array_equiv(clusters_produced, clusters_at_threshold)
+
+
+def test_small_distance_threshold():
+    rng = np.random.RandomState(0)
+    n_samples = 10
+    X = rng.randint(-300, 300, size=(n_samples, 3))
+    # this should result in all data in their own clusters, given that
+    # their pairwise distances are bigger than .1 (which may not be the case
+    # with a different random seed).
+    clustering = AgglomerativeClustering(
+        n_clusters=None, distance_threshold=1.0, linkage="single"
+    ).fit(X)
+    # check that the pairwise distances are indeed all larger than .1
+    all_distances = pairwise_distances(X, metric="minkowski", p=2)
+    np.fill_diagonal(all_distances, np.inf)
+    assert np.all(all_distances > 0.1)
+    assert clustering.n_clusters_ == n_samples
+
+
+def test_cluster_distances_with_distance_threshold():
+    rng = np.random.RandomState(0)
+    n_samples = 100
+    X = rng.randint(-10, 10, size=(n_samples, 3))
+    # check the distances within the clusters and with other clusters
+    distance_threshold = 4
+    clustering = AgglomerativeClustering(
+        n_clusters=None, distance_threshold=distance_threshold, linkage="single"
+    ).fit(X)
+    labels = clustering.labels_
+    D = pairwise_distances(X, metric="minkowski", p=2)
+    # to avoid taking the 0 diagonal in min()
+    np.fill_diagonal(D, np.inf)
+    for label in np.unique(labels):
+        in_cluster_mask = labels == label
+        max_in_cluster_distance = (
+            D[in_cluster_mask][:, in_cluster_mask].min(axis=0).max()
+        )
+        min_out_cluster_distance = (
+            D[in_cluster_mask][:, ~in_cluster_mask].min(axis=0).min()
+        )
+        # single data point clusters only have that inf diagonal here
+        if in_cluster_mask.sum() > 1:
+            assert max_in_cluster_distance < distance_threshold
+        assert min_out_cluster_distance >= distance_threshold
+
+
+@pytest.mark.parametrize("linkage", ["ward", "complete", "average"])
+@pytest.mark.parametrize(
+    ("threshold", "y_true"), [(0.5, [1, 0]), (1.0, [1, 0]), (1.5, [0, 0])]
+)
+def test_agglomerative_clustering_with_distance_threshold_edge_case(
+    linkage, threshold, y_true
+):
+    # test boundary case of distance_threshold matching the distance
+    X = [[0], [1]]
+    clusterer = AgglomerativeClustering(
+        n_clusters=None, distance_threshold=threshold, linkage=linkage
+    )
+    y_pred = clusterer.fit_predict(X)
+    assert adjusted_rand_score(y_true, y_pred) == 1
+
+
+def test_dist_threshold_invalid_parameters():
+    X = [[0], [1]]
+    with pytest.raises(ValueError, match="Exactly one of "):
+        AgglomerativeClustering(n_clusters=None, distance_threshold=None).fit(X)
+
+    with pytest.raises(ValueError, match="Exactly one of "):
+        AgglomerativeClustering(n_clusters=2, distance_threshold=1).fit(X)
+
+    X = [[0], [1]]
+    with pytest.raises(ValueError, match="compute_full_tree must be True if"):
+        AgglomerativeClustering(
+            n_clusters=None, distance_threshold=1, compute_full_tree=False
+        ).fit(X)
+
+
+def test_invalid_shape_precomputed_dist_matrix():
+    # Check that an error is raised when affinity='precomputed'
+    # and a non square matrix is passed (PR #16257).
+    rng = np.random.RandomState(0)
+    X = rng.rand(5, 3)
+    with pytest.raises(
+        ValueError,
+        match=r"Distance matrix should be square, got matrix of shape \(5, 3\)",
+    ):
+        AgglomerativeClustering(affinity="precomputed", linkage="complete").fit(X)
+
+
+def test_precomputed_connectivity_affinity_with_2_connected_components():
+    """Check that connecting components works when connectivity and
+    affinity are both precomputed and the number of connected components is
+    greater than 1. Non-regression test for #16151.
+    """
+
+    connectivity_matrix = np.array(
+        [
+            [0, 1, 1, 0, 0],
+            [0, 0, 1, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 1],
+            [0, 0, 0, 0, 0],
+        ]
+    )
+    # ensure that connectivity_matrix has two connected components
+    assert connected_components(connectivity_matrix)[0] == 2
+
+    rng = np.random.RandomState(0)
+    X = rng.randn(5, 10)
+
+    X_dist = pairwise_distances(X)
+    clusterer_precomputed = AgglomerativeClustering(
+        affinity="precomputed", connectivity=connectivity_matrix, linkage="complete"
+    )
+    msg = "Completing it to avoid stopping the tree early"
+    with pytest.warns(UserWarning, match=msg):
+        clusterer_precomputed.fit(X_dist)
+
+    clusterer = AgglomerativeClustering(
+        connectivity=connectivity_matrix, linkage="complete"
+    )
+    with pytest.warns(UserWarning, match=msg):
+        clusterer.fit(X)
+
+    assert_array_equal(clusterer.labels_, clusterer_precomputed.labels_)
+    assert_array_equal(clusterer.children_, clusterer_precomputed.children_)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_mean_shift.py

@@ -0,0 +1,213 @@
+"""
+Testing for mean shift clustering methods
+
+"""
+
+import numpy as np
+import warnings
+import pytest
+
+from scipy import sparse
+
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import assert_allclose
+
+from sklearn.cluster import MeanShift
+from sklearn.cluster import mean_shift
+from sklearn.cluster import estimate_bandwidth
+from sklearn.cluster import get_bin_seeds
+from sklearn.datasets import make_blobs
+from sklearn.metrics import v_measure_score
+
+
+n_clusters = 3
+centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
+X, _ = make_blobs(
+    n_samples=300,
+    n_features=2,
+    centers=centers,
+    cluster_std=0.4,
+    shuffle=True,
+    random_state=11,
+)
+
+
+def test_estimate_bandwidth():
+    # Test estimate_bandwidth
+    bandwidth = estimate_bandwidth(X, n_samples=200)
+    assert 0.9 <= bandwidth <= 1.5
+
+
+def test_estimate_bandwidth_1sample():
+    # Test estimate_bandwidth when n_samples=1 and quantile<1, so that
+    # n_neighbors is set to 1.
+    bandwidth = estimate_bandwidth(X, n_samples=1, quantile=0.3)
+    assert bandwidth == pytest.approx(0.0, abs=1e-5)
+
+
+@pytest.mark.parametrize(
+    "bandwidth, cluster_all, expected, first_cluster_label",
+    [(1.2, True, 3, 0), (1.2, False, 4, -1)],
+)
+def test_mean_shift(bandwidth, cluster_all, expected, first_cluster_label):
+    # Test MeanShift algorithm
+    ms = MeanShift(bandwidth=bandwidth, cluster_all=cluster_all)
+    labels = ms.fit(X).labels_
+    labels_unique = np.unique(labels)
+    n_clusters_ = len(labels_unique)
+    assert n_clusters_ == expected
+    assert labels_unique[0] == first_cluster_label
+
+    cluster_centers, labels_mean_shift = mean_shift(X, cluster_all=cluster_all)
+    labels_mean_shift_unique = np.unique(labels_mean_shift)
+    n_clusters_mean_shift = len(labels_mean_shift_unique)
+    assert n_clusters_mean_shift == expected
+    assert labels_mean_shift_unique[0] == first_cluster_label
+
+
+def test_mean_shift_negative_bandwidth():
+    bandwidth = -1
+    ms = MeanShift(bandwidth=bandwidth)
+    msg = r"bandwidth needs to be greater than zero or None," r" got -1\.000000"
+    with pytest.raises(ValueError, match=msg):
+        ms.fit(X)
+
+
+def test_estimate_bandwidth_with_sparse_matrix():
+    # Test estimate_bandwidth with sparse matrix
+    X = sparse.lil_matrix((1000, 1000))
+    msg = "A sparse matrix was passed, but dense data is required."
+    with pytest.raises(TypeError, match=msg):
+        estimate_bandwidth(X)
+
+
+def test_parallel():
+    centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
+    X, _ = make_blobs(
+        n_samples=50,
+        n_features=2,
+        centers=centers,
+        cluster_std=0.4,
+        shuffle=True,
+        random_state=11,
+    )
+
+    ms1 = MeanShift(n_jobs=2)
+    ms1.fit(X)
+
+    ms2 = MeanShift()
+    ms2.fit(X)
+
+    assert_array_almost_equal(ms1.cluster_centers_, ms2.cluster_centers_)
+    assert_array_equal(ms1.labels_, ms2.labels_)
+
+
+def test_meanshift_predict():
+    # Test MeanShift.predict
+    ms = MeanShift(bandwidth=1.2)
+    labels = ms.fit_predict(X)
+    labels2 = ms.predict(X)
+    assert_array_equal(labels, labels2)
+
+
+def test_meanshift_all_orphans():
+    # init away from the data, crash with a sensible warning
+    ms = MeanShift(bandwidth=0.1, seeds=[[-9, -9], [-10, -10]])
+    msg = "No point was within bandwidth=0.1"
+    with pytest.raises(ValueError, match=msg):
+        ms.fit(
+            X,
+        )
+
+
+def test_unfitted():
+    # Non-regression: before fit, there should be not fitted attributes.
+    ms = MeanShift()
+    assert not hasattr(ms, "cluster_centers_")
+    assert not hasattr(ms, "labels_")
+
+
+def test_cluster_intensity_tie():
+    X = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]])
+    c1 = MeanShift(bandwidth=2).fit(X)
+
+    X = np.array([[4, 7], [3, 5], [3, 6], [1, 1], [2, 1], [1, 0]])
+    c2 = MeanShift(bandwidth=2).fit(X)
+    assert_array_equal(c1.labels_, [1, 1, 1, 0, 0, 0])
+    assert_array_equal(c2.labels_, [0, 0, 0, 1, 1, 1])
+
+
+def test_bin_seeds():
+    # Test the bin seeding technique which can be used in the mean shift
+    # algorithm
+    # Data is just 6 points in the plane
+    X = np.array(
+        [[1.0, 1.0], [1.4, 1.4], [1.8, 1.2], [2.0, 1.0], [2.1, 1.1], [0.0, 0.0]]
+    )
+
+    # With a bin coarseness of 1.0 and min_bin_freq of 1, 3 bins should be
+    # found
+    ground_truth = {(1.0, 1.0), (2.0, 1.0), (0.0, 0.0)}
+    test_bins = get_bin_seeds(X, 1, 1)
+    test_result = set(tuple(p) for p in test_bins)
+    assert len(ground_truth.symmetric_difference(test_result)) == 0
+
+    # With a bin coarseness of 1.0 and min_bin_freq of 2, 2 bins should be
+    # found
+    ground_truth = {(1.0, 1.0), (2.0, 1.0)}
+    test_bins = get_bin_seeds(X, 1, 2)
+    test_result = set(tuple(p) for p in test_bins)
+    assert len(ground_truth.symmetric_difference(test_result)) == 0
+
+    # With a bin size of 0.01 and min_bin_freq of 1, 6 bins should be found
+    # we bail and use the whole data here.
+    with warnings.catch_warnings(record=True):
+        test_bins = get_bin_seeds(X, 0.01, 1)
+    assert_array_almost_equal(test_bins, X)
+
+    # tight clusters around [0, 0] and [1, 1], only get two bins
+    X, _ = make_blobs(
+        n_samples=100,
+        n_features=2,
+        centers=[[0, 0], [1, 1]],
+        cluster_std=0.1,
+        random_state=0,
+    )
+    test_bins = get_bin_seeds(X, 1)
+    assert_array_equal(test_bins, [[0, 0], [1, 1]])
+
+
+@pytest.mark.parametrize("max_iter", [1, 100])
+def test_max_iter(max_iter):
+    clusters1, _ = mean_shift(X, max_iter=max_iter)
+    ms = MeanShift(max_iter=max_iter).fit(X)
+    clusters2 = ms.cluster_centers_
+
+    assert ms.n_iter_ <= ms.max_iter
+    assert len(clusters1) == len(clusters2)
+
+    for c1, c2 in zip(clusters1, clusters2):
+        assert np.allclose(c1, c2)
+
+
+def test_mean_shift_zero_bandwidth():
+    # Check that mean shift works when the estimated bandwidth is 0.
+    X = np.array([1, 1, 1, 2, 2, 2, 3, 3]).reshape(-1, 1)
+
+    # estimate_bandwidth with default args returns 0 on this dataset
+    bandwidth = estimate_bandwidth(X)
+    assert bandwidth == 0
+
+    # get_bin_seeds with a 0 bin_size should return the dataset itself
+    assert get_bin_seeds(X, bin_size=bandwidth) is X
+
+    # MeanShift with binning and a 0 estimated bandwidth should be equivalent
+    # to no binning.
+    ms_binning = MeanShift(bin_seeding=True, bandwidth=None).fit(X)
+    ms_nobinning = MeanShift(bin_seeding=False).fit(X)
+    expected_labels = np.array([0, 0, 0, 1, 1, 1, 2, 2])
+
+    assert v_measure_score(ms_binning.labels_, expected_labels) == 1
+    assert v_measure_score(ms_nobinning.labels_, expected_labels) == 1
+    assert_allclose(ms_binning.cluster_centers_, ms_nobinning.cluster_centers_)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_optics.py

@@ -0,0 +1,806 @@
+# Authors: Shane Grigsby <refuge@rocktalus.com>
+#          Adrin Jalali <adrin.jalali@gmail.com>
+# License: BSD 3 clause
+import numpy as np
+import pytest
+import warnings
+
+from sklearn.datasets import make_blobs
+from sklearn.cluster import OPTICS
+from sklearn.cluster._optics import _extend_region, _extract_xi_labels
+from sklearn.exceptions import DataConversionWarning
+from sklearn.metrics.cluster import contingency_matrix
+from sklearn.metrics.pairwise import pairwise_distances
+from sklearn.cluster import DBSCAN
+from sklearn.utils import shuffle
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import assert_allclose
+
+from sklearn.cluster.tests.common import generate_clustered_data
+
+
+rng = np.random.RandomState(0)
+n_points_per_cluster = 10
+C1 = [-5, -2] + 0.8 * rng.randn(n_points_per_cluster, 2)
+C2 = [4, -1] + 0.1 * rng.randn(n_points_per_cluster, 2)
+C3 = [1, -2] + 0.2 * rng.randn(n_points_per_cluster, 2)
+C4 = [-2, 3] + 0.3 * rng.randn(n_points_per_cluster, 2)
+C5 = [3, -2] + 1.6 * rng.randn(n_points_per_cluster, 2)
+C6 = [5, 6] + 2 * rng.randn(n_points_per_cluster, 2)
+X = np.vstack((C1, C2, C3, C4, C5, C6))
+
+
+@pytest.mark.parametrize(
+    ("r_plot", "end"),
+    [
+        [[10, 8.9, 8.8, 8.7, 7, 10], 3],
+        [[10, 8.9, 8.8, 8.7, 8.6, 7, 10], 0],
+        [[10, 8.9, 8.8, 8.7, 7, 6, np.inf], 4],
+        [[10, 8.9, 8.8, 8.7, 7, 6, np.inf], 4],
+    ],
+)
+def test_extend_downward(r_plot, end):
+    r_plot = np.array(r_plot)
+    ratio = r_plot[:-1] / r_plot[1:]
+    steep_downward = ratio >= 1 / 0.9
+    upward = ratio < 1
+
+    e = _extend_region(steep_downward, upward, 0, 2)
+    assert e == end
+
+
+@pytest.mark.parametrize(
+    ("r_plot", "end"),
+    [
+        [[1, 2, 2.1, 2.2, 4, 8, 8, np.inf], 6],
+        [[1, 2, 2.1, 2.2, 2.3, 4, 8, 8, np.inf], 0],
+        [[1, 2, 2.1, 2, np.inf], 0],
+        [[1, 2, 2.1, np.inf], 2],
+    ],
+)
+def test_extend_upward(r_plot, end):
+    r_plot = np.array(r_plot)
+    ratio = r_plot[:-1] / r_plot[1:]
+    steep_upward = ratio <= 0.9
+    downward = ratio > 1
+
+    e = _extend_region(steep_upward, downward, 0, 2)
+    assert e == end
+
+
+@pytest.mark.parametrize(
+    ("ordering", "clusters", "expected"),
+    [
+        [[0, 1, 2, 3], [[0, 1], [2, 3]], [0, 0, 1, 1]],
+        [[0, 1, 2, 3], [[0, 1], [3, 3]], [0, 0, -1, 1]],
+        [[0, 1, 2, 3], [[0, 1], [3, 3], [0, 3]], [0, 0, -1, 1]],
+        [[3, 1, 2, 0], [[0, 1], [3, 3], [0, 3]], [1, 0, -1, 0]],
+    ],
+)
+def test_the_extract_xi_labels(ordering, clusters, expected):
+    labels = _extract_xi_labels(ordering, clusters)
+
+    assert_array_equal(labels, expected)
+
+
+def test_extract_xi(global_dtype):
+    # small and easy test (no clusters around other clusters)
+    # but with a clear noise data.
+    rng = np.random.RandomState(0)
+    n_points_per_cluster = 5
+
+    C1 = [-5, -2] + 0.8 * rng.randn(n_points_per_cluster, 2)
+    C2 = [4, -1] + 0.1 * rng.randn(n_points_per_cluster, 2)
+    C3 = [1, -2] + 0.2 * rng.randn(n_points_per_cluster, 2)
+    C4 = [-2, 3] + 0.3 * rng.randn(n_points_per_cluster, 2)
+    C5 = [3, -2] + 0.6 * rng.randn(n_points_per_cluster, 2)
+    C6 = [5, 6] + 0.2 * rng.randn(n_points_per_cluster, 2)
+
+    X = np.vstack((C1, C2, C3, C4, C5, np.array([[100, 100]]), C6)).astype(
+        global_dtype, copy=False
+    )
+    expected_labels = np.r_[[2] * 5, [0] * 5, [1] * 5, [3] * 5, [1] * 5, -1, [4] * 5]
+    X, expected_labels = shuffle(X, expected_labels, random_state=rng)
+
+    clust = OPTICS(
+        min_samples=3, min_cluster_size=2, max_eps=20, cluster_method="xi", xi=0.4
+    ).fit(X)
+    assert_array_equal(clust.labels_, expected_labels)
+
+    # check float min_samples and min_cluster_size
+    clust = OPTICS(
+        min_samples=0.1, min_cluster_size=0.08, max_eps=20, cluster_method="xi", xi=0.4
+    ).fit(X)
+    assert_array_equal(clust.labels_, expected_labels)
+
+    X = np.vstack((C1, C2, C3, C4, C5, np.array([[100, 100]] * 2), C6)).astype(
+        global_dtype, copy=False
+    )
+    expected_labels = np.r_[
+        [1] * 5, [3] * 5, [2] * 5, [0] * 5, [2] * 5, -1, -1, [4] * 5
+    ]
+    X, expected_labels = shuffle(X, expected_labels, random_state=rng)
+
+    clust = OPTICS(
+        min_samples=3, min_cluster_size=3, max_eps=20, cluster_method="xi", xi=0.3
+    ).fit(X)
+    # this may fail if the predecessor correction is not at work!
+    assert_array_equal(clust.labels_, expected_labels)
+
+    C1 = [[0, 0], [0, 0.1], [0, -0.1], [0.1, 0]]
+    C2 = [[10, 10], [10, 9], [10, 11], [9, 10]]
+    C3 = [[100, 100], [100, 90], [100, 110], [90, 100]]
+    X = np.vstack((C1, C2, C3)).astype(global_dtype, copy=False)
+    expected_labels = np.r_[[0] * 4, [1] * 4, [2] * 4]
+    X, expected_labels = shuffle(X, expected_labels, random_state=rng)
+
+    clust = OPTICS(
+        min_samples=2, min_cluster_size=2, max_eps=np.inf, cluster_method="xi", xi=0.04
+    ).fit(X)
+    assert_array_equal(clust.labels_, expected_labels)
+
+
+def test_cluster_hierarchy_(global_dtype):
+    rng = np.random.RandomState(0)
+    n_points_per_cluster = 100
+    C1 = [0, 0] + 2 * rng.randn(n_points_per_cluster, 2).astype(
+        global_dtype, copy=False
+    )
+    C2 = [0, 0] + 50 * rng.randn(n_points_per_cluster, 2).astype(
+        global_dtype, copy=False
+    )
+    X = np.vstack((C1, C2))
+    X = shuffle(X, random_state=0)
+
+    clusters = OPTICS(min_samples=20, xi=0.1).fit(X).cluster_hierarchy_
+    assert clusters.shape == (2, 2)
+    diff = np.sum(clusters - np.array([[0, 99], [0, 199]]))
+    assert diff / len(X) < 0.05
+
+
+def test_correct_number_of_clusters():
+    # in 'auto' mode
+
+    n_clusters = 3
+    X = generate_clustered_data(n_clusters=n_clusters)
+    # Parameters chosen specifically for this task.
+    # Compute OPTICS
+    clust = OPTICS(max_eps=5.0 * 6.0, min_samples=4, xi=0.1)
+    clust.fit(X)
+    # number of clusters, ignoring noise if present
+    n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
+    assert n_clusters_1 == n_clusters
+
+    # check attribute types and sizes
+    assert clust.labels_.shape == (len(X),)
+    assert clust.labels_.dtype.kind == "i"
+
+    assert clust.reachability_.shape == (len(X),)
+    assert clust.reachability_.dtype.kind == "f"
+
+    assert clust.core_distances_.shape == (len(X),)
+    assert clust.core_distances_.dtype.kind == "f"
+
+    assert clust.ordering_.shape == (len(X),)
+    assert clust.ordering_.dtype.kind == "i"
+    assert set(clust.ordering_) == set(range(len(X)))
+
+
+def test_minimum_number_of_sample_check():
+    # test that we check a minimum number of samples
+    msg = "min_samples must be no greater than"
+
+    # Compute OPTICS
+    X = [[1, 1]]
+    clust = OPTICS(max_eps=5.0 * 0.3, min_samples=10, min_cluster_size=1)
+
+    # Run the fit
+    with pytest.raises(ValueError, match=msg):
+        clust.fit(X)
+
+
+def test_bad_extract():
+    # Test an extraction of eps too close to original eps
+    msg = "Specify an epsilon smaller than 0.15. Got 0.3."
+    centers = [[1, 1], [-1, -1], [1, -1]]
+    X, labels_true = make_blobs(
+        n_samples=750, centers=centers, cluster_std=0.4, random_state=0
+    )
+
+    # Compute OPTICS
+    clust = OPTICS(max_eps=5.0 * 0.03, cluster_method="dbscan", eps=0.3, min_samples=10)
+    with pytest.raises(ValueError, match=msg):
+        clust.fit(X)
+
+
+def test_bad_reachability():
+    msg = "All reachability values are inf. Set a larger max_eps."
+    centers = [[1, 1], [-1, -1], [1, -1]]
+    X, labels_true = make_blobs(
+        n_samples=750, centers=centers, cluster_std=0.4, random_state=0
+    )
+
+    with pytest.warns(UserWarning, match=msg):
+        clust = OPTICS(max_eps=5.0 * 0.003, min_samples=10, eps=0.015)
+        clust.fit(X)
+
+
+def test_nowarn_if_metric_bool_data_bool():
+    # make sure no warning is raised if metric and data are both boolean
+    # non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/18996
+
+    pairwise_metric = "rogerstanimoto"
+    X = np.random.randint(2, size=(5, 2), dtype=bool)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DataConversionWarning)
+
+        OPTICS(metric=pairwise_metric).fit(X)
+
+
+def test_warn_if_metric_bool_data_no_bool():
+    # make sure a *single* conversion warning is raised if metric is boolean
+    # but data isn't
+    # non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/18996
+
+    pairwise_metric = "rogerstanimoto"
+    X = np.random.randint(2, size=(5, 2), dtype=np.int32)
+    msg = f"Data will be converted to boolean for metric {pairwise_metric}"
+
+    with pytest.warns(DataConversionWarning, match=msg) as warn_record:
+        OPTICS(metric=pairwise_metric).fit(X)
+        assert len(warn_record) == 1
+
+
+def test_nowarn_if_metric_no_bool():
+    # make sure no conversion warning is raised if
+    # metric isn't boolean, no matter what the data type is
+    pairwise_metric = "minkowski"
+    X_bool = np.random.randint(2, size=(5, 2), dtype=bool)
+    X_num = np.random.randint(2, size=(5, 2), dtype=np.int32)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DataConversionWarning)
+
+        # fit boolean data
+        OPTICS(metric=pairwise_metric).fit(X_bool)
+        # fit numeric data
+        OPTICS(metric=pairwise_metric).fit(X_num)
+
+
+def test_close_extract():
+    # Test extract where extraction eps is close to scaled max_eps
+
+    centers = [[1, 1], [-1, -1], [1, -1]]
+    X, labels_true = make_blobs(
+        n_samples=750, centers=centers, cluster_std=0.4, random_state=0
+    )
+
+    # Compute OPTICS
+    clust = OPTICS(max_eps=1.0, cluster_method="dbscan", eps=0.3, min_samples=10).fit(X)
+    # Cluster ordering starts at 0; max cluster label = 2 is 3 clusters
+    assert max(clust.labels_) == 2
+
+
+@pytest.mark.parametrize("eps", [0.1, 0.3, 0.5])
+@pytest.mark.parametrize("min_samples", [3, 10, 20])
+def test_dbscan_optics_parity(eps, min_samples, global_dtype):
+    # Test that OPTICS clustering labels are <= 5% difference of DBSCAN
+
+    centers = [[1, 1], [-1, -1], [1, -1]]
+    X, labels_true = make_blobs(
+        n_samples=750, centers=centers, cluster_std=0.4, random_state=0
+    )
+
+    X = X.astype(global_dtype, copy=False)
+
+    # calculate optics with dbscan extract at 0.3 epsilon
+    op = OPTICS(min_samples=min_samples, cluster_method="dbscan", eps=eps).fit(X)
+
+    # calculate dbscan labels
+    db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
+
+    contingency = contingency_matrix(db.labels_, op.labels_)
+    agree = min(
+        np.sum(np.max(contingency, axis=0)), np.sum(np.max(contingency, axis=1))
+    )
+    disagree = X.shape[0] - agree
+
+    percent_mismatch = np.round((disagree - 1) / X.shape[0], 2)
+
+    # verify label mismatch is <= 5% labels
+    assert percent_mismatch <= 0.05
+
+
+def test_min_samples_edge_case(global_dtype):
+    C1 = [[0, 0], [0, 0.1], [0, -0.1]]
+    C2 = [[10, 10], [10, 9], [10, 11]]
+    C3 = [[100, 100], [100, 96], [100, 106]]
+    X = np.vstack((C1, C2, C3)).astype(global_dtype, copy=False)
+
+    expected_labels = np.r_[[0] * 3, [1] * 3, [2] * 3]
+    clust = OPTICS(min_samples=3, max_eps=7, cluster_method="xi", xi=0.04).fit(X)
+    assert_array_equal(clust.labels_, expected_labels)
+
+    expected_labels = np.r_[[0] * 3, [1] * 3, [-1] * 3]
+    clust = OPTICS(min_samples=3, max_eps=3, cluster_method="xi", xi=0.04).fit(X)
+    assert_array_equal(clust.labels_, expected_labels)
+
+    expected_labels = np.r_[[-1] * 9]
+    with pytest.warns(UserWarning, match="All reachability values"):
+        clust = OPTICS(min_samples=4, max_eps=3, cluster_method="xi", xi=0.04).fit(X)
+        assert_array_equal(clust.labels_, expected_labels)
+
+
+# try arbitrary minimum sizes
+@pytest.mark.parametrize("min_cluster_size", range(2, X.shape[0] // 10, 23))
+def test_min_cluster_size(min_cluster_size, global_dtype):
+    redX = X[::2].astype(global_dtype, copy=False)  # reduce for speed
+    clust = OPTICS(min_samples=9, min_cluster_size=min_cluster_size).fit(redX)
+    cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
+    if cluster_sizes.size:
+        assert min(cluster_sizes) >= min_cluster_size
+    # check behaviour is the same when min_cluster_size is a fraction
+    clust_frac = OPTICS(
+        min_samples=9, min_cluster_size=min_cluster_size / redX.shape[0]
+    )
+    clust_frac.fit(redX)
+    assert_array_equal(clust.labels_, clust_frac.labels_)
+
+
+@pytest.mark.parametrize("min_cluster_size", [0, -1, 1.1, 2.2])
+def test_min_cluster_size_invalid(min_cluster_size):
+    clust = OPTICS(min_cluster_size=min_cluster_size)
+    with pytest.raises(ValueError, match="must be a positive integer or a "):
+        clust.fit(X)
+
+
+def test_min_cluster_size_invalid2():
+    clust = OPTICS(min_cluster_size=len(X) + 1)
+    with pytest.raises(ValueError, match="must be no greater than the "):
+        clust.fit(X)
+
+
+def test_processing_order():
+    # Ensure that we consider all unprocessed points,
+    # not only direct neighbors. when picking the next point.
+    Y = [[0], [10], [-10], [25]]
+    clust = OPTICS(min_samples=3, max_eps=15).fit(Y)
+    assert_array_equal(clust.reachability_, [np.inf, 10, 10, 15])
+    assert_array_equal(clust.core_distances_, [10, 15, np.inf, np.inf])
+    assert_array_equal(clust.ordering_, [0, 1, 2, 3])
+
+
+def test_compare_to_ELKI():
+    # Expected values, computed with (future) ELKI 0.7.5 using:
+    # java -jar elki.jar cli -dbc.in csv -dbc.filter FixedDBIDsFilter
+    #   -algorithm clustering.optics.OPTICSHeap -optics.minpts 5
+    # where the FixedDBIDsFilter gives 0-indexed ids.
+    r1 = [
+        np.inf,
+        1.0574896366427478,
+        0.7587934993548423,
+        0.7290174038973836,
+        0.7290174038973836,
+        0.7290174038973836,
+        0.6861627576116127,
+        0.7587934993548423,
+        0.9280118450166668,
+        1.1748022534146194,
+        3.3355455741292257,
+        0.49618389254482587,
+        0.2552805046961355,
+        0.2552805046961355,
+        0.24944622248445714,
+        0.24944622248445714,
+        0.24944622248445714,
+        0.2552805046961355,
+        0.2552805046961355,
+        0.3086779122185853,
+        4.163024452756142,
+        1.623152630340929,
+        0.45315840475822655,
+        0.25468325192031926,
+        0.2254004358159971,
+        0.18765711877083036,
+        0.1821471333893275,
+        0.1821471333893275,
+        0.18765711877083036,
+        0.18765711877083036,
+        0.2240202988740153,
+        1.154337614548715,
+        1.342604473837069,
+        1.323308536402633,
+        0.8607514948648837,
+        0.27219111215810565,
+        0.13260875220533205,
+        0.13260875220533205,
+        0.09890587675958984,
+        0.09890587675958984,
+        0.13548790801634494,
+        0.1575483940837384,
+        0.17515137170530226,
+        0.17575920159442388,
+        0.27219111215810565,
+        0.6101447895405373,
+        1.3189208094864302,
+        1.323308536402633,
+        2.2509184159764577,
+        2.4517810628594527,
+        3.675977064404973,
+        3.8264795626020365,
+        2.9130735341510614,
+        2.9130735341510614,
+        2.9130735341510614,
+        2.9130735341510614,
+        2.8459300127258036,
+        2.8459300127258036,
+        2.8459300127258036,
+        3.0321982337972537,
+    ]
+    o1 = [
+        0,
+        3,
+        6,
+        4,
+        7,
+        8,
+        2,
+        9,
+        5,
+        1,
+        31,
+        30,
+        32,
+        34,
+        33,
+        38,
+        39,
+        35,
+        37,
+        36,
+        44,
+        21,
+        23,
+        24,
+        22,
+        25,
+        27,
+        29,
+        26,
+        28,
+        20,
+        40,
+        45,
+        46,
+        10,
+        15,
+        11,
+        13,
+        17,
+        19,
+        18,
+        12,
+        16,
+        14,
+        47,
+        49,
+        43,
+        48,
+        42,
+        41,
+        53,
+        57,
+        51,
+        52,
+        56,
+        59,
+        54,
+        55,
+        58,
+        50,
+    ]
+    p1 = [
+        -1,
+        0,
+        3,
+        6,
+        6,
+        6,
+        8,
+        3,
+        7,
+        5,
+        1,
+        31,
+        30,
+        30,
+        34,
+        34,
+        34,
+        32,
+        32,
+        37,
+        36,
+        44,
+        21,
+        23,
+        24,
+        22,
+        25,
+        25,
+        22,
+        22,
+        22,
+        21,
+        40,
+        45,
+        46,
+        10,
+        15,
+        15,
+        13,
+        13,
+        15,
+        11,
+        19,
+        15,
+        10,
+        47,
+        12,
+        45,
+        14,
+        43,
+        42,
+        53,
+        57,
+        57,
+        57,
+        57,
+        59,
+        59,
+        59,
+        58,
+    ]
+
+    # Tests against known extraction array
+    # Does NOT work with metric='euclidean', because sklearn euclidean has
+    # worse numeric precision. 'minkowski' is slower but more accurate.
+    clust1 = OPTICS(min_samples=5).fit(X)
+
+    assert_array_equal(clust1.ordering_, np.array(o1))
+    assert_array_equal(clust1.predecessor_[clust1.ordering_], np.array(p1))
+    assert_allclose(clust1.reachability_[clust1.ordering_], np.array(r1))
+    # ELKI currently does not print the core distances (which are not used much
+    # in literature, but we can at least ensure to have this consistency:
+    for i in clust1.ordering_[1:]:
+        assert clust1.reachability_[i] >= clust1.core_distances_[clust1.predecessor_[i]]
+
+    # Expected values, computed with (future) ELKI 0.7.5 using
+    r2 = [
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        0.27219111215810565,
+        0.13260875220533205,
+        0.13260875220533205,
+        0.09890587675958984,
+        0.09890587675958984,
+        0.13548790801634494,
+        0.1575483940837384,
+        0.17515137170530226,
+        0.17575920159442388,
+        0.27219111215810565,
+        0.4928068613197889,
+        np.inf,
+        0.2666183922512113,
+        0.18765711877083036,
+        0.1821471333893275,
+        0.1821471333893275,
+        0.1821471333893275,
+        0.18715928772277457,
+        0.18765711877083036,
+        0.18765711877083036,
+        0.25468325192031926,
+        np.inf,
+        0.2552805046961355,
+        0.2552805046961355,
+        0.24944622248445714,
+        0.24944622248445714,
+        0.24944622248445714,
+        0.2552805046961355,
+        0.2552805046961355,
+        0.3086779122185853,
+        0.34466409325984865,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+        np.inf,
+    ]
+    o2 = [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        15,
+        11,
+        13,
+        17,
+        19,
+        18,
+        12,
+        16,
+        14,
+        47,
+        46,
+        20,
+        22,
+        25,
+        23,
+        27,
+        29,
+        24,
+        26,
+        28,
+        21,
+        30,
+        32,
+        34,
+        33,
+        38,
+        39,
+        35,
+        37,
+        36,
+        31,
+        40,
+        41,
+        42,
+        43,
+        44,
+        45,
+        48,
+        49,
+        50,
+        51,
+        52,
+        53,
+        54,
+        55,
+        56,
+        57,
+        58,
+        59,
+    ]
+    p2 = [
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        10,
+        15,
+        15,
+        13,
+        13,
+        15,
+        11,
+        19,
+        15,
+        10,
+        47,
+        -1,
+        20,
+        22,
+        25,
+        25,
+        25,
+        25,
+        22,
+        22,
+        23,
+        -1,
+        30,
+        30,
+        34,
+        34,
+        34,
+        32,
+        32,
+        37,
+        38,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+        -1,
+    ]
+    clust2 = OPTICS(min_samples=5, max_eps=0.5).fit(X)
+
+    assert_array_equal(clust2.ordering_, np.array(o2))
+    assert_array_equal(clust2.predecessor_[clust2.ordering_], np.array(p2))
+    assert_allclose(clust2.reachability_[clust2.ordering_], np.array(r2))
+
+    index = np.where(clust1.core_distances_ <= 0.5)[0]
+    assert_allclose(clust1.core_distances_[index], clust2.core_distances_[index])
+
+
+def test_wrong_cluster_method():
+    clust = OPTICS(cluster_method="superfancy")
+    with pytest.raises(ValueError, match="cluster_method should be one of "):
+        clust.fit(X)
+
+
+def test_extract_dbscan(global_dtype):
+    # testing an easy dbscan case. Not including clusters with different
+    # densities.
+    rng = np.random.RandomState(0)
+    n_points_per_cluster = 20
+    C1 = [-5, -2] + 0.2 * rng.randn(n_points_per_cluster, 2)
+    C2 = [4, -1] + 0.2 * rng.randn(n_points_per_cluster, 2)
+    C3 = [1, 2] + 0.2 * rng.randn(n_points_per_cluster, 2)
+    C4 = [-2, 3] + 0.2 * rng.randn(n_points_per_cluster, 2)
+    X = np.vstack((C1, C2, C3, C4)).astype(global_dtype, copy=False)
+
+    clust = OPTICS(cluster_method="dbscan", eps=0.5).fit(X)
+    assert_array_equal(np.sort(np.unique(clust.labels_)), [0, 1, 2, 3])
+
+
+def test_precomputed_dists(global_dtype):
+    redX = X[::2].astype(global_dtype, copy=False)
+    dists = pairwise_distances(redX, metric="euclidean")
+    clust1 = OPTICS(min_samples=10, algorithm="brute", metric="precomputed").fit(dists)
+    clust2 = OPTICS(min_samples=10, algorithm="brute", metric="euclidean").fit(redX)
+
+    assert_allclose(clust1.reachability_, clust2.reachability_)
+    assert_array_equal(clust1.labels_, clust2.labels_)

dashboard/flask-server/venv/Lib/site-packages/sklearn/cluster/tests/test_spectral.py

@@ -0,0 +1,414 @@
+"""Testing for Spectral Clustering methods"""
+import re
+
+import numpy as np
+from scipy import sparse
+from scipy.linalg import LinAlgError
+
+import pytest
+
+import pickle
+
+from sklearn.utils import check_random_state
+from sklearn.utils._testing import assert_array_equal
+
+from sklearn.cluster import SpectralClustering, spectral_clustering
+from sklearn.cluster._spectral import discretize, cluster_qr
+from sklearn.feature_extraction import img_to_graph
+from sklearn.metrics import pairwise_distances
+from sklearn.metrics import adjusted_rand_score
+from sklearn.metrics.pairwise import kernel_metrics, rbf_kernel
+from sklearn.neighbors import NearestNeighbors
+from sklearn.datasets import make_blobs
+
+try:
+    from pyamg import smoothed_aggregation_solver  # noqa
+
+    amg_loaded = True
+except ImportError:
+    amg_loaded = False
+
+centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
+X, _ = make_blobs(
+    n_samples=60,
+    n_features=2,
+    centers=centers,
+    cluster_std=0.4,
+    shuffle=True,
+    random_state=0,
+)
+
+
+@pytest.mark.parametrize("eigen_solver", ("arpack", "lobpcg"))
+@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
+def test_spectral_clustering(eigen_solver, assign_labels):
+    S = np.array(
+        [
+            [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+            [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+            [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+            [0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
+        ]
+    )
+
+    for mat in (S, sparse.csr_matrix(S)):
+        model = SpectralClustering(
+            random_state=0,
+            n_clusters=2,
+            affinity="precomputed",
+            eigen_solver=eigen_solver,
+            assign_labels=assign_labels,
+        ).fit(mat)
+        labels = model.labels_
+        if labels[0] == 0:
+            labels = 1 - labels
+
+        assert adjusted_rand_score(labels, [1, 1, 1, 0, 0, 0, 0]) == 1
+
+        model_copy = pickle.loads(pickle.dumps(model))
+        assert model_copy.n_clusters == model.n_clusters
+        assert model_copy.eigen_solver == model.eigen_solver
+        assert_array_equal(model_copy.labels_, model.labels_)
+
+
+def test_spectral_unknown_mode():
+    # Test that SpectralClustering fails with an unknown mode set.
+    centers = np.array(
+        [
+            [0.0, 0.0, 0.0],
+            [10.0, 10.0, 10.0],
+            [20.0, 20.0, 20.0],
+        ]
+    )
+    X, true_labels = make_blobs(
+        n_samples=100, centers=centers, cluster_std=1.0, random_state=42
+    )
+    D = pairwise_distances(X)  # Distance matrix
+    S = np.max(D) - D  # Similarity matrix
+    S = sparse.coo_matrix(S)
+    with pytest.raises(ValueError):
+        spectral_clustering(S, n_clusters=2, random_state=0, eigen_solver="<unknown>")
+
+
+def test_spectral_unknown_assign_labels():
+    # Test that SpectralClustering fails with an unknown assign_labels set.
+    centers = np.array(
+        [
+            [0.0, 0.0, 0.0],
+            [10.0, 10.0, 10.0],
+            [20.0, 20.0, 20.0],
+        ]
+    )
+    X, true_labels = make_blobs(
+        n_samples=100, centers=centers, cluster_std=1.0, random_state=42
+    )
+    D = pairwise_distances(X)  # Distance matrix
+    S = np.max(D) - D  # Similarity matrix
+    S = sparse.coo_matrix(S)
+    with pytest.raises(ValueError):
+        spectral_clustering(S, n_clusters=2, random_state=0, assign_labels="<unknown>")
+
+
+@pytest.mark.parametrize(
+    "input, params, err_type, err_msg",
+    [
+        (X, {"n_clusters": -1}, ValueError, "n_clusters == -1, must be >= 1"),
+        (X, {"n_clusters": 0}, ValueError, "n_clusters == 0, must be >= 1"),
+        (
+            X,
+            {"n_clusters": 1.5},
+            TypeError,
+            "n_clusters must be an instance of int, not float",
+        ),
+        (X, {"n_init": -1}, ValueError, "n_init == -1, must be >= 1"),
+        (X, {"n_init": 0}, ValueError, "n_init == 0, must be >= 1"),
+        (
+            X,
+            {"n_init": 1.5},
+            TypeError,
+            "n_init must be an instance of int, not float",
+        ),
+        (X, {"gamma": -1}, ValueError, "gamma == -1, must be >= 1"),
+        (X, {"gamma": 0}, ValueError, "gamma == 0, must be >= 1"),
+        (X, {"n_neighbors": -1}, ValueError, "n_neighbors == -1, must be >= 1"),
+        (X, {"n_neighbors": 0}, ValueError, "n_neighbors == 0, must be >= 1"),
+        (
+            X,
+            {"eigen_tol": -1, "eigen_solver": "arpack"},
+            ValueError,
+            "eigen_tol == -1, must be >= 0",
+        ),
+        (X, {"degree": -1}, ValueError, "degree == -1, must be >= 1"),
+        (X, {"degree": 0}, ValueError, "degree == 0, must be >= 1"),
+    ],
+)
+def test_spectral_params_validation(input, params, err_type, err_msg):
+    """Check the parameters validation in `SpectralClustering`."""
+    est = SpectralClustering(**params)
+    with pytest.raises(err_type, match=err_msg):
+        est.fit(input)
+
+
+@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
+def test_spectral_clustering_sparse(assign_labels):
+    X, y = make_blobs(
+        n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
+    )
+
+    S = rbf_kernel(X, gamma=1)
+    S = np.maximum(S - 1e-4, 0)
+    S = sparse.coo_matrix(S)
+
+    labels = (
+        SpectralClustering(
+            random_state=0,
+            n_clusters=2,
+            affinity="precomputed",
+            assign_labels=assign_labels,
+        )
+        .fit(S)
+        .labels_
+    )
+    assert adjusted_rand_score(y, labels) == 1
+
+
+def test_precomputed_nearest_neighbors_filtering():
+    # Test precomputed graph filtering when containing too many neighbors
+    X, y = make_blobs(
+        n_samples=200, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
+    )
+
+    n_neighbors = 2
+    results = []
+    for additional_neighbors in [0, 10]:
+        nn = NearestNeighbors(n_neighbors=n_neighbors + additional_neighbors).fit(X)
+        graph = nn.kneighbors_graph(X, mode="connectivity")
+        labels = (
+            SpectralClustering(
+                random_state=0,
+                n_clusters=2,
+                affinity="precomputed_nearest_neighbors",
+                n_neighbors=n_neighbors,
+            )
+            .fit(graph)
+            .labels_
+        )
+        results.append(labels)
+
+    assert_array_equal(results[0], results[1])
+
+
+def test_affinities():
+    # Note: in the following, random_state has been selected to have
+    # a dataset that yields a stable eigen decomposition both when built
+    # on OSX and Linux
+    X, y = make_blobs(
+        n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
+    )
+    # nearest neighbors affinity
+    sp = SpectralClustering(n_clusters=2, affinity="nearest_neighbors", random_state=0)
+    with pytest.warns(UserWarning, match="not fully connected"):
+        sp.fit(X)
+    assert adjusted_rand_score(y, sp.labels_) == 1
+
+    sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0)
+    labels = sp.fit(X).labels_
+    assert adjusted_rand_score(y, labels) == 1
+
+    X = check_random_state(10).rand(10, 5) * 10
+
+    kernels_available = kernel_metrics()
+    for kern in kernels_available:
+        # Additive chi^2 gives a negative similarity matrix which
+        # doesn't make sense for spectral clustering
+        if kern != "additive_chi2":
+            sp = SpectralClustering(n_clusters=2, affinity=kern, random_state=0)
+            labels = sp.fit(X).labels_
+            assert (X.shape[0],) == labels.shape
+
+    sp = SpectralClustering(n_clusters=2, affinity=lambda x, y: 1, random_state=0)
+    labels = sp.fit(X).labels_
+    assert (X.shape[0],) == labels.shape
+
+    def histogram(x, y, **kwargs):
+        # Histogram kernel implemented as a callable.
+        assert kwargs == {}  # no kernel_params that we didn't ask for
+        return np.minimum(x, y).sum()
+
+    sp = SpectralClustering(n_clusters=2, affinity=histogram, random_state=0)
+    labels = sp.fit(X).labels_
+    assert (X.shape[0],) == labels.shape
+
+    # raise error on unknown affinity
+    sp = SpectralClustering(n_clusters=2, affinity="<unknown>")
+    with pytest.raises(ValueError):
+        sp.fit(X)
+
+
+def test_cluster_qr():
+    # cluster_qr by itself should not be used for clustering generic data
+    # other than the rows of the eigenvectors within spectral clustering,
+    # but cluster_qr must still preserve the labels for different dtypes
+    # of the generic fixed input even if the labels may be meaningless.
+    random_state = np.random.RandomState(seed=8)
+    n_samples, n_components = 10, 5
+    data = random_state.randn(n_samples, n_components)
+    labels_float64 = cluster_qr(data.astype(np.float64))
+    # Each sample is assigned a cluster identifier
+    assert labels_float64.shape == (n_samples,)
+    # All components should be covered by the assignment
+    assert np.array_equal(np.unique(labels_float64), np.arange(n_components))
+    # Single precision data should yield the same cluster assignments
+    labels_float32 = cluster_qr(data.astype(np.float32))
+    assert np.array_equal(labels_float64, labels_float32)
+
+
+def test_cluster_qr_permutation_invariance():
+    # cluster_qr must be invariant to sample permutation.
+    random_state = np.random.RandomState(seed=8)
+    n_samples, n_components = 100, 5
+    data = random_state.randn(n_samples, n_components)
+    perm = random_state.permutation(n_samples)
+    assert np.array_equal(
+        cluster_qr(data)[perm],
+        cluster_qr(data[perm]),
+    )
+
+
+@pytest.mark.parametrize("n_samples", [50, 100, 150, 500])
+def test_discretize(n_samples):
+    # Test the discretize using a noise assignment matrix
+    random_state = np.random.RandomState(seed=8)
+    for n_class in range(2, 10):
+        # random class labels
+        y_true = random_state.randint(0, n_class + 1, n_samples)
+        y_true = np.array(y_true, float)
+        # noise class assignment matrix
+        y_indicator = sparse.coo_matrix(
+            (np.ones(n_samples), (np.arange(n_samples), y_true)),
+            shape=(n_samples, n_class + 1),
+        )
+        y_true_noisy = y_indicator.toarray() + 0.1 * random_state.randn(
+            n_samples, n_class + 1
+        )
+        y_pred = discretize(y_true_noisy, random_state=random_state)
+        assert adjusted_rand_score(y_true, y_pred) > 0.8
+
+
+# TODO: Remove when pyamg does replaces sp.rand call with np.random.rand
+# https://github.com/scikit-learn/scikit-learn/issues/15913
+@pytest.mark.filterwarnings(
+    "ignore:scipy.rand is deprecated:DeprecationWarning:pyamg.*"
+)
+# TODO: Remove when pyamg removes the use of np.float
+@pytest.mark.filterwarnings(
+    "ignore:`np.float` is a deprecated alias:DeprecationWarning:pyamg.*"
+)
+# TODO: Remove when pyamg removes the use of pinv2
+@pytest.mark.filterwarnings(
+    "ignore:scipy.linalg.pinv2 is deprecated:DeprecationWarning:pyamg.*"
+)
+def test_spectral_clustering_with_arpack_amg_solvers():
+    # Test that spectral_clustering is the same for arpack and amg solver
+    # Based on toy example from plot_segmentation_toy.py
+
+    # a small two coin image
+    x, y = np.indices((40, 40))
+
+    center1, center2 = (14, 12), (20, 25)
+    radius1, radius2 = 8, 7
+
+    circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1**2
+    circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2**2
+
+    circles = circle1 | circle2
+    mask = circles.copy()
+    img = circles.astype(float)
+
+    graph = img_to_graph(img, mask=mask)
+    graph.data = np.exp(-graph.data / graph.data.std())
+
+    labels_arpack = spectral_clustering(
+        graph, n_clusters=2, eigen_solver="arpack", random_state=0
+    )
+
+    assert len(np.unique(labels_arpack)) == 2
+
+    if amg_loaded:
+        labels_amg = spectral_clustering(
+            graph, n_clusters=2, eigen_solver="amg", random_state=0
+        )
+        assert adjusted_rand_score(labels_arpack, labels_amg) == 1
+    else:
+        with pytest.raises(ValueError):
+            spectral_clustering(graph, n_clusters=2, eigen_solver="amg", random_state=0)
+
+
+def test_n_components():
+    # Test that after adding n_components, result is different and
+    # n_components = n_clusters by default
+    X, y = make_blobs(
+        n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
+    )
+    sp = SpectralClustering(n_clusters=2, random_state=0)
+    labels = sp.fit(X).labels_
+    # set n_components = n_cluster and test if result is the same
+    labels_same_ncomp = (
+        SpectralClustering(n_clusters=2, n_components=2, random_state=0).fit(X).labels_
+    )
+    # test that n_components=n_clusters by default
+    assert_array_equal(labels, labels_same_ncomp)
+
+    # test that n_components affect result
+    # n_clusters=8 by default, and set n_components=2
+    labels_diff_ncomp = (
+        SpectralClustering(n_components=2, random_state=0).fit(X).labels_
+    )
+    assert not np.array_equal(labels, labels_diff_ncomp)
+
+
+@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
+def test_verbose(assign_labels, capsys):
+    # Check verbose mode of KMeans for better coverage.
+    X, y = make_blobs(
+        n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
+    )
+
+    SpectralClustering(n_clusters=2, random_state=42, verbose=1).fit(X)
+
+    captured = capsys.readouterr()
+
+    assert re.search(r"Computing label assignment using", captured.out)
+
+    if assign_labels == "kmeans":
+        assert re.search(r"Initialization complete", captured.out)
+        assert re.search(r"Iteration [0-9]+, inertia", captured.out)
+
+
+def test_spectral_clustering_np_matrix_raises():
+    """Check that spectral_clustering raises an informative error when passed
+    a np.matrix. See #10993"""
+    X = np.matrix([[0.0, 2.0], [2.0, 0.0]])
+
+    msg = r"spectral_clustering does not support passing in affinity as an np\.matrix"
+    with pytest.raises(TypeError, match=msg):
+        spectral_clustering(X)
+
+
+def test_spectral_clustering_not_infinite_loop(capsys, monkeypatch):
+    """Check that discretize raises LinAlgError when svd never converges.
+
+    Non-regression test for #21380
+    """
+
+    def new_svd(*args, **kwargs):
+        raise LinAlgError()
+
+    monkeypatch.setattr(np.linalg, "svd", new_svd)
+    vectors = np.ones((10, 4))
+
+    with pytest.raises(LinAlgError, match="SVD did not converge"):
+        discretize(vectors)