first commit

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

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+
+"""
+External, bundled dependencies.
+
+"""

dashboard/flask-server/venv/Lib/site-packages/sklearn/externals/_lobpcg.py

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+"""
+scikit-learn copy of scipy/sparse/linalg/eigen/lobpcg/lobpcg.py v1.8.0
+to be deleted after scipy 1.3.0 becomes a dependency in scikit-lean
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG).
+
+References
+----------
+.. [1] A. V. Knyazev (2001),
+       Toward the Optimal Preconditioned Eigensolver: Locally Optimal
+       Block Preconditioned Conjugate Gradient Method.
+       SIAM Journal on Scientific Computing 23, no. 2,
+       pp. 517-541. :doi:`10.1137/S1064827500366124`
+
+.. [2] A. V. Knyazev, I. Lashuk, M. E. Argentati, and E. Ovchinnikov (2007),
+       Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX)
+       in hypre and PETSc.  :arxiv:`0705.2626`
+
+.. [3] A. V. Knyazev's C and MATLAB implementations:
+       https://github.com/lobpcg/blopex
+"""
+
+import warnings
+import numpy as np
+from scipy.linalg import (inv, eigh, cho_factor, cho_solve,
+                          cholesky, LinAlgError)
+from scipy.sparse.linalg import aslinearoperator
+from numpy import block as bmat
+
+__all__ = ["lobpcg"]
+
+
+def _report_nonhermitian(M, name):
+    """
+    Report if `M` is not a Hermitian matrix given its type.
+    """
+    from scipy.linalg import norm
+
+    md = M - M.T.conj()
+    nmd = norm(md, 1)
+    tol = 10 * np.finfo(M.dtype).eps
+    tol = max(tol, tol * norm(M, 1))
+    if nmd > tol:
+        warnings.warn(
+              f"Matrix {name} of the type {M.dtype} is not Hermitian: "
+              f"condition: {nmd} < {tol} fails.",
+              UserWarning, stacklevel=4
+         )
+
+def _as2d(ar):
+    """
+    If the input array is 2D return it, if it is 1D, append a dimension,
+    making it a column vector.
+    """
+    if ar.ndim == 2:
+        return ar
+    else:  # Assume 1!
+        aux = np.array(ar, copy=False)
+        aux.shape = (ar.shape[0], 1)
+        return aux
+
+
+def _makeOperator(operatorInput, expectedShape):
+    """Takes a dense numpy array or a sparse matrix or
+    a function and makes an operator performing matrix * blockvector
+    products."""
+    if operatorInput is None:
+        return None
+    else:
+        operator = aslinearoperator(operatorInput)
+
+    if operator.shape != expectedShape:
+        raise ValueError("operator has invalid shape")
+
+    return operator
+
+
+def _applyConstraints(blockVectorV, factYBY, blockVectorBY, blockVectorY):
+    """Changes blockVectorV in place."""
+    YBV = np.dot(blockVectorBY.T.conj(), blockVectorV)
+    tmp = cho_solve(factYBY, YBV)
+    blockVectorV -= np.dot(blockVectorY, tmp)
+
+
+def _b_orthonormalize(B, blockVectorV, blockVectorBV=None, retInvR=False):
+    """B-orthonormalize the given block vector using Cholesky."""
+    normalization = blockVectorV.max(axis=0) + np.finfo(blockVectorV.dtype).eps
+    blockVectorV = blockVectorV / normalization
+    if blockVectorBV is None:
+        if B is not None:
+            blockVectorBV = B(blockVectorV)
+        else:
+            blockVectorBV = blockVectorV  # Shared data!!!
+    else:
+        blockVectorBV = blockVectorBV / normalization
+    VBV = blockVectorV.T.conj() @ blockVectorBV
+    try:
+        # VBV is a Cholesky factor from now on...
+        VBV = cholesky(VBV, overwrite_a=True)
+        VBV = inv(VBV, overwrite_a=True)
+        blockVectorV = blockVectorV @ VBV
+        # blockVectorV = (cho_solve((VBV.T, True), blockVectorV.T)).T
+        if B is not None:
+            blockVectorBV = blockVectorBV @ VBV
+            # blockVectorBV = (cho_solve((VBV.T, True), blockVectorBV.T)).T
+        else:
+            blockVectorBV = None
+    except LinAlgError:
+        # raise ValueError('Cholesky has failed')
+        blockVectorV = None
+        blockVectorBV = None
+        VBV = None
+
+    if retInvR:
+        return blockVectorV, blockVectorBV, VBV, normalization
+    else:
+        return blockVectorV, blockVectorBV
+
+
+def _get_indx(_lambda, num, largest):
+    """Get `num` indices into `_lambda` depending on `largest` option."""
+    ii = np.argsort(_lambda)
+    if largest:
+        ii = ii[:-num - 1:-1]
+    else:
+        ii = ii[:num]
+
+    return ii
+
+
+def lobpcg(
+    A,
+    X,
+    B=None,
+    M=None,
+    Y=None,
+    tol=None,
+    maxiter=None,
+    largest=True,
+    verbosityLevel=0,
+    retLambdaHistory=False,
+    retResidualNormsHistory=False,
+):
+    """Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)
+
+    LOBPCG is a preconditioned eigensolver for large symmetric positive
+    definite (SPD) generalized eigenproblems.
+
+    Parameters
+    ----------
+    A : {sparse matrix, dense matrix, LinearOperator}
+        The symmetric linear operator of the problem, usually a
+        sparse matrix.  Often called the "stiffness matrix".
+    X : ndarray, float32 or float64
+        Initial approximation to the ``k`` eigenvectors (non-sparse). If `A`
+        has ``shape=(n,n)`` then `X` should have shape ``shape=(n,k)``.
+    B : {dense matrix, sparse matrix, LinearOperator}, optional
+        The right hand side operator in a generalized eigenproblem.
+        By default, ``B = Identity``.  Often called the "mass matrix".
+    M : {dense matrix, sparse matrix, LinearOperator}, optional
+        Preconditioner to `A`; by default ``M = Identity``.
+        `M` should approximate the inverse of `A`.
+    Y : ndarray, float32 or float64, optional
+        n-by-sizeY matrix of constraints (non-sparse), sizeY < n
+        The iterations will be performed in the B-orthogonal complement
+        of the column-space of Y. Y must be full rank.
+    tol : scalar, optional
+        Solver tolerance (stopping criterion).
+        The default is ``tol=n*sqrt(eps)``.
+    maxiter : int, optional
+        Maximum number of iterations.  The default is ``maxiter = 20``.
+    largest : bool, optional
+        When True, solve for the largest eigenvalues, otherwise the smallest.
+    verbosityLevel : int, optional
+        Controls solver output.  The default is ``verbosityLevel=0``.
+    retLambdaHistory : bool, optional
+        Whether to return eigenvalue history.  Default is False.
+    retResidualNormsHistory : bool, optional
+        Whether to return history of residual norms.  Default is False.
+
+    Returns
+    -------
+    w : ndarray
+        Array of ``k`` eigenvalues
+    v : ndarray
+        An array of ``k`` eigenvectors.  `v` has the same shape as `X`.
+    lambdas : list of ndarray, optional
+        The eigenvalue history, if `retLambdaHistory` is True.
+    rnorms : list of ndarray, optional
+        The history of residual norms, if `retResidualNormsHistory` is True.
+
+    Notes
+    -----
+    If both ``retLambdaHistory`` and ``retResidualNormsHistory`` are True,
+    the return tuple has the following format
+    ``(lambda, V, lambda history, residual norms history)``.
+
+    In the following ``n`` denotes the matrix size and ``m`` the number
+    of required eigenvalues (smallest or largest).
+
+    The LOBPCG code internally solves eigenproblems of the size ``3m`` on every
+    iteration by calling the "standard" dense eigensolver, so if ``m`` is not
+    small enough compared to ``n``, it does not make sense to call the LOBPCG
+    code, but rather one should use the "standard" eigensolver, e.g. numpy or
+    scipy function in this case.
+    If one calls the LOBPCG algorithm for ``5m > n``, it will most likely break
+    internally, so the code tries to call the standard function instead.
+
+    It is not that ``n`` should be large for the LOBPCG to work, but rather the
+    ratio ``n / m`` should be large. It you call LOBPCG with ``m=1``
+    and ``n=10``, it works though ``n`` is small. The method is intended
+    for extremely large ``n / m``.
+
+    The convergence speed depends basically on two factors:
+
+    1. How well relatively separated the seeking eigenvalues are from the rest
+       of the eigenvalues. One can try to vary ``m`` to make this better.
+
+    2. How well conditioned the problem is. This can be changed by using proper
+       preconditioning. For example, a rod vibration test problem (under tests
+       directory) is ill-conditioned for large ``n``, so convergence will be
+       slow, unless efficient preconditioning is used. For this specific
+       problem, a good simple preconditioner function would be a linear solve
+       for `A`, which is easy to code since A is tridiagonal.
+
+    References
+    ----------
+    .. [1] A. V. Knyazev (2001),
+           Toward the Optimal Preconditioned Eigensolver: Locally Optimal
+           Block Preconditioned Conjugate Gradient Method.
+           SIAM Journal on Scientific Computing 23, no. 2,
+           pp. 517-541. :doi:`10.1137/S1064827500366124`
+
+    .. [2] A. V. Knyazev, I. Lashuk, M. E. Argentati, and E. Ovchinnikov
+           (2007), Block Locally Optimal Preconditioned Eigenvalue Xolvers
+           (BLOPEX) in hypre and PETSc. :arxiv:`0705.2626`
+
+    .. [3] A. V. Knyazev's C and MATLAB implementations:
+           https://github.com/lobpcg/blopex
+
+    Examples
+    --------
+
+    Solve ``A x = lambda x`` with constraints and preconditioning.
+
+    >>> import numpy as np
+    >>> from scipy.sparse import spdiags, issparse
+    >>> from scipy.sparse.linalg import lobpcg, LinearOperator
+    >>> n = 100
+    >>> vals = np.arange(1, n + 1)
+    >>> A = spdiags(vals, 0, n, n)
+    >>> A.toarray()
+    array([[  1.,   0.,   0., ...,   0.,   0.,   0.],
+           [  0.,   2.,   0., ...,   0.,   0.,   0.],
+           [  0.,   0.,   3., ...,   0.,   0.,   0.],
+           ...,
+           [  0.,   0.,   0., ...,  98.,   0.,   0.],
+           [  0.,   0.,   0., ...,   0.,  99.,   0.],
+           [  0.,   0.,   0., ...,   0.,   0., 100.]])
+
+    Constraints:
+
+    >>> Y = np.eye(n, 3)
+
+    Initial guess for eigenvectors, should have linearly independent
+    columns. Column dimension = number of requested eigenvalues.
+
+    >>> rng = np.random.default_rng()
+    >>> X = rng.random((n, 3))
+
+    Preconditioner in the inverse of A in this example:
+
+    >>> invA = spdiags([1./vals], 0, n, n)
+
+    The preconditiner must be defined by a function:
+
+    >>> def precond( x ):
+    ...     return invA @ x
+
+    The argument x of the preconditioner function is a matrix inside `lobpcg`,
+    thus the use of matrix-matrix product ``@``.
+
+    The preconditioner function is passed to lobpcg as a `LinearOperator`:
+
+    >>> M = LinearOperator(matvec=precond, matmat=precond,
+    ...                    shape=(n, n), dtype=np.float64)
+
+    Let us now solve the eigenvalue problem for the matrix A:
+
+    >>> eigenvalues, _ = lobpcg(A, X, Y=Y, M=M, largest=False)
+    >>> eigenvalues
+    array([4., 5., 6.])
+
+    Note that the vectors passed in Y are the eigenvectors of the 3 smallest
+    eigenvalues. The results returned are orthogonal to those.
+
+    """
+    blockVectorX = X
+    blockVectorY = Y
+    residualTolerance = tol
+    if maxiter is None:
+        maxiter = 20
+
+    if blockVectorY is not None:
+        sizeY = blockVectorY.shape[1]
+    else:
+        sizeY = 0
+
+    # Block size.
+    if len(blockVectorX.shape) != 2:
+        raise ValueError("expected rank-2 array for argument X")
+
+    n, sizeX = blockVectorX.shape
+
+    if verbosityLevel:
+        aux = "Solving "
+        if B is None:
+            aux += "standard"
+        else:
+            aux += "generalized"
+        aux += " eigenvalue problem with"
+        if M is None:
+            aux += "out"
+        aux += " preconditioning\n\n"
+        aux += "matrix size %d\n" % n
+        aux += "block size %d\n\n" % sizeX
+        if blockVectorY is None:
+            aux += "No constraints\n\n"
+        else:
+            if sizeY > 1:
+                aux += "%d constraints\n\n" % sizeY
+            else:
+                aux += "%d constraint\n\n" % sizeY
+        print(aux)
+
+    A = _makeOperator(A, (n, n))
+    B = _makeOperator(B, (n, n))
+    M = _makeOperator(M, (n, n))
+
+    if (n - sizeY) < (5 * sizeX):
+        warnings.warn(
+            f"The problem size {n} minus the constraints size {sizeY} "
+            f"is too small relative to the block size {sizeX}. "
+            f"Using a dense eigensolver instead of LOBPCG.",
+            UserWarning, stacklevel=2
+        )
+
+        sizeX = min(sizeX, n)
+
+        if blockVectorY is not None:
+            raise NotImplementedError(
+                "The dense eigensolver does not support constraints."
+            )
+
+        # Define the closed range of indices of eigenvalues to return.
+        if largest:
+            eigvals = (n - sizeX, n - 1)
+        else:
+            eigvals = (0, sizeX - 1)
+
+        A_dense = A(np.eye(n, dtype=A.dtype))
+        B_dense = None if B is None else B(np.eye(n, dtype=B.dtype))
+
+        vals, vecs = eigh(A_dense,
+                          B_dense,
+                          eigvals=eigvals,
+                          check_finite=False)
+        if largest:
+            # Reverse order to be compatible with eigs() in 'LM' mode.
+            vals = vals[::-1]
+            vecs = vecs[:, ::-1]
+
+        return vals, vecs
+
+    if (residualTolerance is None) or (residualTolerance <= 0.0):
+        residualTolerance = np.sqrt(1e-15) * n
+
+    # Apply constraints to X.
+    if blockVectorY is not None:
+
+        if B is not None:
+            blockVectorBY = B(blockVectorY)
+        else:
+            blockVectorBY = blockVectorY
+
+        # gramYBY is a dense array.
+        gramYBY = np.dot(blockVectorY.T.conj(), blockVectorBY)
+        try:
+            # gramYBY is a Cholesky factor from now on...
+            gramYBY = cho_factor(gramYBY)
+        except LinAlgError as e:
+            raise ValueError("Linearly dependent constraints") from e
+
+        _applyConstraints(blockVectorX, gramYBY, blockVectorBY, blockVectorY)
+
+    ##
+    # B-orthonormalize X.
+    blockVectorX, blockVectorBX = _b_orthonormalize(B, blockVectorX)
+    if blockVectorX is None:
+        raise ValueError("Linearly dependent initial approximations")
+
+    ##
+    # Compute the initial Ritz vectors: solve the eigenproblem.
+    blockVectorAX = A(blockVectorX)
+    gramXAX = np.dot(blockVectorX.T.conj(), blockVectorAX)
+
+    _lambda, eigBlockVector = eigh(gramXAX, check_finite=False)
+    ii = _get_indx(_lambda, sizeX, largest)
+    _lambda = _lambda[ii]
+
+    eigBlockVector = np.asarray(eigBlockVector[:, ii])
+    blockVectorX = np.dot(blockVectorX, eigBlockVector)
+    blockVectorAX = np.dot(blockVectorAX, eigBlockVector)
+    if B is not None:
+        blockVectorBX = np.dot(blockVectorBX, eigBlockVector)
+
+    ##
+    # Active index set.
+    activeMask = np.ones((sizeX,), dtype=bool)
+
+    lambdaHistory = [_lambda]
+    residualNormsHistory = []
+
+    previousBlockSize = sizeX
+    ident = np.eye(sizeX, dtype=A.dtype)
+    ident0 = np.eye(sizeX, dtype=A.dtype)
+
+    ##
+    # Main iteration loop.
+
+    blockVectorP = None  # set during iteration
+    blockVectorAP = None
+    blockVectorBP = None
+
+    iterationNumber = -1
+    restart = True
+    explicitGramFlag = False
+    while iterationNumber < maxiter:
+        iterationNumber += 1
+        if verbosityLevel > 0:
+            print("-"*50)
+            print(f"iteration {iterationNumber}")
+
+        if B is not None:
+            aux = blockVectorBX * _lambda[np.newaxis, :]
+        else:
+            aux = blockVectorX * _lambda[np.newaxis, :]
+
+        blockVectorR = blockVectorAX - aux
+
+        aux = np.sum(blockVectorR.conj() * blockVectorR, 0)
+        residualNorms = np.sqrt(aux)
+
+        residualNormsHistory.append(residualNorms)
+
+        ii = np.where(residualNorms > residualTolerance, True, False)
+        activeMask = activeMask & ii
+        if verbosityLevel > 2:
+            print(activeMask)
+
+        currentBlockSize = activeMask.sum()
+        if currentBlockSize != previousBlockSize:
+            previousBlockSize = currentBlockSize
+            ident = np.eye(currentBlockSize, dtype=A.dtype)
+
+        if currentBlockSize == 0:
+            break
+
+        if verbosityLevel > 0:
+            print(f"current block size: {currentBlockSize}")
+            print(f"eigenvalue(s):\n{_lambda}")
+            print(f"residual norm(s):\n{residualNorms}")
+        if verbosityLevel > 10:
+            print(eigBlockVector)
+
+        activeBlockVectorR = _as2d(blockVectorR[:, activeMask])
+
+        if iterationNumber > 0:
+            activeBlockVectorP = _as2d(blockVectorP[:, activeMask])
+            activeBlockVectorAP = _as2d(blockVectorAP[:, activeMask])
+            if B is not None:
+                activeBlockVectorBP = _as2d(blockVectorBP[:, activeMask])
+
+        if M is not None:
+            # Apply preconditioner T to the active residuals.
+            activeBlockVectorR = M(activeBlockVectorR)
+
+        ##
+        # Apply constraints to the preconditioned residuals.
+        if blockVectorY is not None:
+            _applyConstraints(activeBlockVectorR,
+                              gramYBY,
+                              blockVectorBY,
+                              blockVectorY)
+
+        ##
+        # B-orthogonalize the preconditioned residuals to X.
+        if B is not None:
+            activeBlockVectorR = activeBlockVectorR - (
+                blockVectorX @
+                (blockVectorBX.T.conj() @ activeBlockVectorR)
+            )
+        else:
+            activeBlockVectorR = activeBlockVectorR - (
+                blockVectorX @
+                (blockVectorX.T.conj() @ activeBlockVectorR)
+            )
+
+        ##
+        # B-orthonormalize the preconditioned residuals.
+        aux = _b_orthonormalize(B, activeBlockVectorR)
+        activeBlockVectorR, activeBlockVectorBR = aux
+
+        if activeBlockVectorR is None:
+            warnings.warn(
+                f"Failed at iteration {iterationNumber} with accuracies "
+                f"{residualNorms}\n not reaching the requested "
+                f"tolerance {residualTolerance}.",
+                UserWarning, stacklevel=2
+            )
+            break
+        activeBlockVectorAR = A(activeBlockVectorR)
+
+        if iterationNumber > 0:
+            if B is not None:
+                aux = _b_orthonormalize(
+                    B, activeBlockVectorP, activeBlockVectorBP, retInvR=True
+                )
+                activeBlockVectorP, activeBlockVectorBP, invR, normal = aux
+            else:
+                aux = _b_orthonormalize(B, activeBlockVectorP, retInvR=True)
+                activeBlockVectorP, _, invR, normal = aux
+            # Function _b_orthonormalize returns None if Cholesky fails
+            if activeBlockVectorP is not None:
+                activeBlockVectorAP = activeBlockVectorAP / normal
+                activeBlockVectorAP = np.dot(activeBlockVectorAP, invR)
+                restart = False
+            else:
+                restart = True
+
+        ##
+        # Perform the Rayleigh Ritz Procedure:
+        # Compute symmetric Gram matrices:
+
+        if activeBlockVectorAR.dtype == "float32":
+            myeps = 1
+        elif activeBlockVectorR.dtype == "float32":
+            myeps = 1e-4
+        else:
+            myeps = 1e-8
+
+        if residualNorms.max() > myeps and not explicitGramFlag:
+            explicitGramFlag = False
+        else:
+            # Once explicitGramFlag, forever explicitGramFlag.
+            explicitGramFlag = True
+
+        # Shared memory assingments to simplify the code
+        if B is None:
+            blockVectorBX = blockVectorX
+            activeBlockVectorBR = activeBlockVectorR
+            if not restart:
+                activeBlockVectorBP = activeBlockVectorP
+
+        # Common submatrices:
+        gramXAR = np.dot(blockVectorX.T.conj(), activeBlockVectorAR)
+        gramRAR = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorAR)
+
+        if explicitGramFlag:
+            gramRAR = (gramRAR + gramRAR.T.conj()) / 2
+            gramXAX = np.dot(blockVectorX.T.conj(), blockVectorAX)
+            gramXAX = (gramXAX + gramXAX.T.conj()) / 2
+            gramXBX = np.dot(blockVectorX.T.conj(), blockVectorBX)
+            gramRBR = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorBR)
+            gramXBR = np.dot(blockVectorX.T.conj(), activeBlockVectorBR)
+        else:
+            gramXAX = np.diag(_lambda)
+            gramXBX = ident0
+            gramRBR = ident
+            gramXBR = np.zeros((sizeX, currentBlockSize), dtype=A.dtype)
+
+        def _handle_gramA_gramB_verbosity(gramA, gramB):
+            if verbosityLevel > 0:
+                _report_nonhermitian(gramA, "gramA")
+                _report_nonhermitian(gramB, "gramB")
+            if verbosityLevel > 10:
+                # Note: not documented, but leave it in here for now
+                np.savetxt("gramA.txt", gramA)
+                np.savetxt("gramB.txt", gramB)
+
+        if not restart:
+            gramXAP = np.dot(blockVectorX.T.conj(), activeBlockVectorAP)
+            gramRAP = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorAP)
+            gramPAP = np.dot(activeBlockVectorP.T.conj(), activeBlockVectorAP)
+            gramXBP = np.dot(blockVectorX.T.conj(), activeBlockVectorBP)
+            gramRBP = np.dot(activeBlockVectorR.T.conj(), activeBlockVectorBP)
+            if explicitGramFlag:
+                gramPAP = (gramPAP + gramPAP.T.conj()) / 2
+                gramPBP = np.dot(activeBlockVectorP.T.conj(),
+                                 activeBlockVectorBP)
+            else:
+                gramPBP = ident
+
+            gramA = bmat(
+                [
+                    [gramXAX, gramXAR, gramXAP],
+                    [gramXAR.T.conj(), gramRAR, gramRAP],
+                    [gramXAP.T.conj(), gramRAP.T.conj(), gramPAP],
+                ]
+            )
+            gramB = bmat(
+                [
+                    [gramXBX, gramXBR, gramXBP],
+                    [gramXBR.T.conj(), gramRBR, gramRBP],
+                    [gramXBP.T.conj(), gramRBP.T.conj(), gramPBP],
+                ]
+            )
+
+            _handle_gramA_gramB_verbosity(gramA, gramB)
+
+            try:
+                _lambda, eigBlockVector = eigh(gramA,
+                                               gramB,
+                                               check_finite=False)
+            except LinAlgError:
+                # try again after dropping the direction vectors P from RR
+                restart = True
+
+        if restart:
+            gramA = bmat([[gramXAX, gramXAR], [gramXAR.T.conj(), gramRAR]])
+            gramB = bmat([[gramXBX, gramXBR], [gramXBR.T.conj(), gramRBR]])
+
+            _handle_gramA_gramB_verbosity(gramA, gramB)
+
+            try:
+                _lambda, eigBlockVector = eigh(gramA,
+                                               gramB,
+                                               check_finite=False)
+            except LinAlgError as e:
+                raise ValueError("eigh has failed in lobpcg iterations") from e
+
+        ii = _get_indx(_lambda, sizeX, largest)
+        if verbosityLevel > 10:
+            print(ii)
+            print(f"lambda:\n{_lambda}")
+
+        _lambda = _lambda[ii]
+        eigBlockVector = eigBlockVector[:, ii]
+
+        lambdaHistory.append(_lambda)
+
+        if verbosityLevel > 10:
+            print(f"lambda:\n{_lambda}")
+        #         # Normalize eigenvectors!
+        #         aux = np.sum( eigBlockVector.conj() * eigBlockVector, 0 )
+        #         eigVecNorms = np.sqrt( aux )
+        #         eigBlockVector = eigBlockVector / eigVecNorms[np.newaxis, :]
+        #         eigBlockVector, aux = _b_orthonormalize( B, eigBlockVector )
+
+        if verbosityLevel > 10:
+            print(eigBlockVector)
+
+        # Compute Ritz vectors.
+        if B is not None:
+            if not restart:
+                eigBlockVectorX = eigBlockVector[:sizeX]
+                eigBlockVectorR = eigBlockVector[sizeX:
+                                                 sizeX + currentBlockSize]
+                eigBlockVectorP = eigBlockVector[sizeX + currentBlockSize:]
+
+                pp = np.dot(activeBlockVectorR, eigBlockVectorR)
+                pp += np.dot(activeBlockVectorP, eigBlockVectorP)
+
+                app = np.dot(activeBlockVectorAR, eigBlockVectorR)
+                app += np.dot(activeBlockVectorAP, eigBlockVectorP)
+
+                bpp = np.dot(activeBlockVectorBR, eigBlockVectorR)
+                bpp += np.dot(activeBlockVectorBP, eigBlockVectorP)
+            else:
+                eigBlockVectorX = eigBlockVector[:sizeX]
+                eigBlockVectorR = eigBlockVector[sizeX:]
+
+                pp = np.dot(activeBlockVectorR, eigBlockVectorR)
+                app = np.dot(activeBlockVectorAR, eigBlockVectorR)
+                bpp = np.dot(activeBlockVectorBR, eigBlockVectorR)
+
+            if verbosityLevel > 10:
+                print(pp)
+                print(app)
+                print(bpp)
+
+            blockVectorX = np.dot(blockVectorX, eigBlockVectorX) + pp
+            blockVectorAX = np.dot(blockVectorAX, eigBlockVectorX) + app
+            blockVectorBX = np.dot(blockVectorBX, eigBlockVectorX) + bpp
+
+            blockVectorP, blockVectorAP, blockVectorBP = pp, app, bpp
+
+        else:
+            if not restart:
+                eigBlockVectorX = eigBlockVector[:sizeX]
+                eigBlockVectorR = eigBlockVector[sizeX:
+                                                 sizeX + currentBlockSize]
+                eigBlockVectorP = eigBlockVector[sizeX + currentBlockSize:]
+
+                pp = np.dot(activeBlockVectorR, eigBlockVectorR)
+                pp += np.dot(activeBlockVectorP, eigBlockVectorP)
+
+                app = np.dot(activeBlockVectorAR, eigBlockVectorR)
+                app += np.dot(activeBlockVectorAP, eigBlockVectorP)
+            else:
+                eigBlockVectorX = eigBlockVector[:sizeX]
+                eigBlockVectorR = eigBlockVector[sizeX:]
+
+                pp = np.dot(activeBlockVectorR, eigBlockVectorR)
+                app = np.dot(activeBlockVectorAR, eigBlockVectorR)
+
+            if verbosityLevel > 10:
+                print(pp)
+                print(app)
+
+            blockVectorX = np.dot(blockVectorX, eigBlockVectorX) + pp
+            blockVectorAX = np.dot(blockVectorAX, eigBlockVectorX) + app
+
+            blockVectorP, blockVectorAP = pp, app
+
+    if B is not None:
+        aux = blockVectorBX * _lambda[np.newaxis, :]
+
+    else:
+        aux = blockVectorX * _lambda[np.newaxis, :]
+
+    blockVectorR = blockVectorAX - aux
+
+    aux = np.sum(blockVectorR.conj() * blockVectorR, 0)
+    residualNorms = np.sqrt(aux)
+
+    if np.max(residualNorms) > residualTolerance:
+        warnings.warn(
+            f"Exited at iteration {iterationNumber} with accuracies \n"
+            f"{residualNorms}\n"
+            f"not reaching the requested tolerance {residualTolerance}.",
+            UserWarning, stacklevel=2
+        )
+
+    # Future work: Need to add Postprocessing here:
+    # Making sure eigenvectors "exactly" satisfy the blockVectorY constrains?
+    # Making sure eigenvecotrs are "exactly" othonormalized by final "exact" RR
+    # Keeping the best iterates in case of divergence
+
+    if verbosityLevel > 0:
+        print(f"Final eigenvalue(s):\n{_lambda}")
+        print(f"Final residual norm(s):\n{residualNorms}")
+
+    if retLambdaHistory:
+        if retResidualNormsHistory:
+            return _lambda, blockVectorX, lambdaHistory, residualNormsHistory
+        else:
+            return _lambda, blockVectorX, lambdaHistory
+    else:
+        if retResidualNormsHistory:
+            return _lambda, blockVectorX, residualNormsHistory
+        else:
+            return _lambda, blockVectorX

dashboard/flask-server/venv/Lib/site-packages/sklearn/externals/_numpy_compiler_patch.py

@@ -0,0 +1,139 @@
+# Copyright (c) 2005-2022, NumPy Developers.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+
+#     * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+
+#     * Redistributions in binary form must reproduce the above
+#        copyright notice, this list of conditions and the following
+#        disclaimer in the documentation and/or other materials provided
+#        with the distribution.
+
+#     * Neither the name of the NumPy Developers nor the names of any
+#        contributors may be used to endorse or promote products derived
+#        from this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import os
+import sys
+import subprocess
+import re
+from distutils.errors import DistutilsExecError
+
+from numpy.distutils import log
+
+
+def is_sequence(seq):
+    if isinstance(seq, str):
+        return False
+    try:
+        len(seq)
+    except Exception:
+        return False
+    return True
+
+
+def forward_bytes_to_stdout(val):
+    """
+    Forward bytes from a subprocess call to the console, without attempting to
+    decode them.
+
+    The assumption is that the subprocess call already returned bytes in
+    a suitable encoding.
+    """
+    if hasattr(sys.stdout, "buffer"):
+        # use the underlying binary output if there is one
+        sys.stdout.buffer.write(val)
+    elif hasattr(sys.stdout, "encoding"):
+        # round-trip the encoding if necessary
+        sys.stdout.write(val.decode(sys.stdout.encoding))
+    else:
+        # make a best-guess at the encoding
+        sys.stdout.write(val.decode("utf8", errors="replace"))
+
+
+def CCompiler_spawn(self, cmd, display=None, env=None):
+    """
+    Execute a command in a sub-process.
+
+    Parameters
+    ----------
+    cmd : str
+        The command to execute.
+    display : str or sequence of str, optional
+        The text to add to the log file kept by `numpy.distutils`.
+        If not given, `display` is equal to `cmd`.
+    env: a dictionary for environment variables, optional
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    DistutilsExecError
+        If the command failed, i.e. the exit status was not 0.
+
+    """
+    env = env if env is not None else dict(os.environ)
+    if display is None:
+        display = cmd
+        if is_sequence(display):
+            display = " ".join(list(display))
+    log.info(display)
+    try:
+        if self.verbose:
+            subprocess.check_output(cmd, env=env)
+        else:
+            subprocess.check_output(cmd, stderr=subprocess.STDOUT, env=env)
+    except subprocess.CalledProcessError as exc:
+        o = exc.output
+        s = exc.returncode
+    except OSError as e:
+        # OSError doesn't have the same hooks for the exception
+        # output, but exec_command() historically would use an
+        # empty string for EnvironmentError (base class for
+        # OSError)
+        # o = b''
+        # still that would make the end-user lost in translation!
+        o = f"\n\n{e}\n\n\n"
+        try:
+            o = o.encode(sys.stdout.encoding)
+        except AttributeError:
+            o = o.encode("utf8")
+        # status previously used by exec_command() for parent
+        # of OSError
+        s = 127
+    else:
+        # use a convenience return here so that any kind of
+        # caught exception will execute the default code after the
+        # try / except block, which handles various exceptions
+        return None
+
+    if is_sequence(cmd):
+        cmd = " ".join(list(cmd))
+
+    if self.verbose:
+        forward_bytes_to_stdout(o)
+
+    if re.search(b"Too many open files", o):
+        msg = "\nTry rerunning setup command until build succeeds."
+    else:
+        msg = ""
+    raise DistutilsExecError(
+        'Command "%s" failed with exit status %d%s' % (cmd, s, msg)
+    )

dashboard/flask-server/venv/Lib/site-packages/sklearn/externals/_packaging/_structures.py

@@ -0,0 +1,90 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/_structures.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+class InfinityType:
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+class NegativeInfinityType:
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()

dashboard/flask-server/venv/Lib/site-packages/sklearn/externals/_packaging/version.py

@@ -0,0 +1,527 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/version.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import collections
+import itertools
+import re
+import warnings
+from typing import Callable, Iterator, List, Optional, SupportsInt, Tuple, Union
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+__all__ = ["parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN"]
+
+InfiniteTypes = Union[InfinityType, NegativeInfinityType]
+PrePostDevType = Union[InfiniteTypes, Tuple[str, int]]
+SubLocalType = Union[InfiniteTypes, int, str]
+LocalType = Union[
+    NegativeInfinityType,
+    Tuple[
+        Union[
+            SubLocalType,
+            Tuple[SubLocalType, str],
+            Tuple[NegativeInfinityType, SubLocalType],
+        ],
+        ...,
+    ],
+]
+CmpKey = Tuple[
+    int, Tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType
+]
+LegacyCmpKey = Tuple[int, Tuple[str, ...]]
+VersionComparisonMethod = Callable[
+    [Union[CmpKey, LegacyCmpKey], Union[CmpKey, LegacyCmpKey]], bool
+]
+
+_Version = collections.namedtuple(
+    "_Version", ["epoch", "release", "dev", "pre", "post", "local"]
+)
+
+
+def parse(version: str) -> Union["LegacyVersion", "Version"]:
+    """
+    Parse the given version string and return either a :class:`Version` object
+    or a :class:`LegacyVersion` object depending on if the given version is
+    a valid PEP 440 version or a legacy version.
+    """
+    try:
+        return Version(version)
+    except InvalidVersion:
+        return LegacyVersion(version)
+
+
+class InvalidVersion(ValueError):
+    """
+    An invalid version was found, users should refer to PEP 440.
+    """
+
+
+class _BaseVersion:
+    _key: Union[CmpKey, LegacyCmpKey]
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+class LegacyVersion(_BaseVersion):
+    def __init__(self, version: str) -> None:
+        self._version = str(version)
+        self._key = _legacy_cmpkey(self._version)
+
+        warnings.warn(
+            "Creating a LegacyVersion has been deprecated and will be "
+            "removed in the next major release",
+            DeprecationWarning,
+        )
+
+    def __str__(self) -> str:
+        return self._version
+
+    def __repr__(self) -> str:
+        return f"<LegacyVersion('{self}')>"
+
+    @property
+    def public(self) -> str:
+        return self._version
+
+    @property
+    def base_version(self) -> str:
+        return self._version
+
+    @property
+    def epoch(self) -> int:
+        return -1
+
+    @property
+    def release(self) -> None:
+        return None
+
+    @property
+    def pre(self) -> None:
+        return None
+
+    @property
+    def post(self) -> None:
+        return None
+
+    @property
+    def dev(self) -> None:
+        return None
+
+    @property
+    def local(self) -> None:
+        return None
+
+    @property
+    def is_prerelease(self) -> bool:
+        return False
+
+    @property
+    def is_postrelease(self) -> bool:
+        return False
+
+    @property
+    def is_devrelease(self) -> bool:
+        return False
+
+
+_legacy_version_component_re = re.compile(r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE)
+
+_legacy_version_replacement_map = {
+    "pre": "c",
+    "preview": "c",
+    "-": "final-",
+    "rc": "c",
+    "dev": "@",
+}
+
+
+def _parse_version_parts(s: str) -> Iterator[str]:
+    for part in _legacy_version_component_re.split(s):
+        part = _legacy_version_replacement_map.get(part, part)
+
+        if not part or part == ".":
+            continue
+
+        if part[:1] in "0123456789":
+            # pad for numeric comparison
+            yield part.zfill(8)
+        else:
+            yield "*" + part
+
+    # ensure that alpha/beta/candidate are before final
+    yield "*final"
+
+
+def _legacy_cmpkey(version: str) -> LegacyCmpKey:
+
+    # We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch
+    # greater than or equal to 0. This will effectively put the LegacyVersion,
+    # which uses the defacto standard originally implemented by setuptools,
+    # as before all PEP 440 versions.
+    epoch = -1
+
+    # This scheme is taken from pkg_resources.parse_version setuptools prior to
+    # it's adoption of the packaging library.
+    parts: List[str] = []
+    for part in _parse_version_parts(version.lower()):
+        if part.startswith("*"):
+            # remove "-" before a prerelease tag
+            if part < "*final":
+                while parts and parts[-1] == "*final-":
+                    parts.pop()
+
+            # remove trailing zeros from each series of numeric parts
+            while parts and parts[-1] == "00000000":
+                parts.pop()
+
+        parts.append(part)
+
+    return epoch, tuple(parts)
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?                           # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P<pre>                                          # pre-release
+            [-_\.]?
+            (?P<pre_l>(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P<pre_n>[0-9]+)?
+        )?
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?P<post_l>post|rev|r)
+                [-_\.]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?
+        (?P<dev>                                          # dev release
+            [-_\.]?
+            (?P<dev_l>dev)
+            [-_\.]?
+            (?P<dev_n>[0-9]+)?
+        )?
+    )
+    (?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+
+    def __init__(self, version: str) -> None:
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        return f"<Version('{self}')>"
+
+    def __str__(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        _epoch: int = self._version.epoch
+        return _epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        _release: Tuple[int, ...] = self._version.release
+        return _release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        _pre: Optional[Tuple[str, int]] = self._version.pre
+        return _pre
+
+    @property
+    def post(self) -> Optional[int]:
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: str, number: Union[str, bytes, SupportsInt]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[Tuple[SubLocalType]],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: PrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: PrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: PrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: LocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local

dashboard/flask-server/venv/Lib/site-packages/sklearn/externals/conftest.py

@@ -0,0 +1,7 @@
+# Do not collect any tests in externals. This is more robust than using
+# --ignore because --ignore needs a path and it is not convenient to pass in
+# the externals path (very long install-dependent path in site-packages) when
+# using --pyargs
+def pytest_ignore_collect(path, config):
+    return True
+