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How to use _combine_constraints method in pandera

Best Python code snippet using pandera_python

truncation.py

Source:truncation.py

...185    if chi_max is not None:186        # keep at most chi_max values187        good2 = np.zeros(len(piv), dtype=np.bool_)188        good2[-chi_max:] = True189        good = _combine_constraints(good, good2, "chi_max")190    if chi_min is not None and chi_min > 1:191        # keep at most chi_max values192        good2 = np.ones(len(piv), dtype=np.bool_)193        good2[-chi_min + 1:] = False194        good = _combine_constraints(good, good2, "chi_min")195    if deg_tol:196        # don't cut between values (cut-1, cut) with ``log(S[cut]/S[cut-1]) < deg_tol``197        # this is equivalent to198        # ``(S[cut] - S[cut-1])/S[cut-1] < exp(deg_tol) - 1 = deg_tol + O(deg_tol^2)``199        good2 = np.empty(len(piv), np.bool_)200        good2[0] = True201        good2[1:] = np.greater_equal(logS[1:] - logS[:-1], deg_tol)202        good = _combine_constraints(good, good2, "degeneracy_tol")203    if svd_min is not None:204        # keep only values S[i] >= svd_min205        good2 = np.greater_equal(logS, np.log(svd_min))206        good = _combine_constraints(good, good2, "svd_min")207    if trunc_cut is not None:208        good2 = (np.cumsum(S[piv]**2) > trunc_cut * trunc_cut)209        good = _combine_constraints(good, good2, "trunc_cut")210    cut = np.nonzero(good)[0][0]  # smallest possible cut: keep as many S as allowed211    mask = np.zeros(len(S), dtype=np.bool_)212    np.put(mask, piv[cut:], True)213    norm_new = np.linalg.norm(S[mask])214    return mask, norm_new, TruncationError.from_S(S[np.logical_not(mask)]),215def svd_theta(theta, trunc_par, qtotal_LR=[None, None], inner_labels=['vR', 'vL']):216    """Performs SVD of a matrix `theta` (= the wavefunction) and truncates it.217    Perform a singular value decomposition (SVD) with :func:`~tenpy.linalg.np_conserved.svd`218    and truncates with :func:`truncate`.219    The result is an approximation220    ``theta ~= tensordot(U.scale_axis(S*renormalization, 1), VH, axes=1)``221    Parameters222    ----------223    theta : :class:`~tenpy.linalg.np_conserved.Array`, shape ``(M, N)``224        The matrix, on which the singular value decomposition (SVD) is performed.225        Usually, `theta` represents the wavefunction, such that the SVD is a Schmidt decomposition.226    trunc_par : dict227        truncation parameters as described in :func:`truncate`.228    qtotalLR : (charges, charges)229        The total charges for the returned `U` and `VH`.230    inner_labels : (string, string)231        Labels for the `U` and `VH` on the newly-created bond.232    Returns233    -------234    U : :class:`~tenpy.linalg.np_conserved.Array`235        Matrix with left singular vectors as columns.236        Shape ``(M, M)`` or ``(M, K)`` depending on `full_matrices`.237    S : 1D ndarray238        The singluar values of the array.239        If no `cutoff` is given, it has lenght ``min(M, N)``.240        Normalized to ``np.linalg.norm(S)==1``.241    VH : :class:`~tenpy.linalg.np_conserved.Array`242        Matrix with right singular vectors as rows.243        Shape ``(N, N)`` or ``(K, N)`` depending on `full_matrices`.244    err : :class:`TruncationError`245        The truncation error introduced.246    renormalization : float247        Factor, by which S was renormalized.248    """249    U, S, VH = npc.svd(theta,250                       full_matrices=False,251                       compute_uv=True,252                       qtotal_LR=qtotal_LR,253                       inner_labels=inner_labels)254    renormalization = np.linalg.norm(S)255    S = S / renormalization256    piv, new_norm, err = truncate(S, trunc_par)257    new_len_S = np.sum(piv, dtype=np.int_)258    if new_len_S * 100 < len(S) and (trunc_par['chi_max'] is None259                                     or new_len_S != trunc_par['chi_max']):260        msg = "Catastrophic reduction in chi: {0:d} -> {1:d}".format(len(S), new_len_S)261        # NANs are excluded in npc.svd262        UHU = npc.tensordot(U.conj(), U, axes=[[0], [0]])263        msg += " |U^d U - 1| = {0:f}".format(npc.norm(UHU - npc.eye_like(UHU)))264        VHV = npc.tensordot(VH, VH.conj(), axes=[[1], [1]])265        msg += " |V V - 1| = {0:f}".format(npc.norm(VHV - npc.eye_like(VHV)))266        warnings.warn(msg, stacklevel=2)267    S = S[piv] / new_norm268    renormalization *= new_norm269    U.iproject(piv, axes=1)  # U = U[:, piv]270    VH.iproject(piv, axes=0)  # VH = VH[piv, :]271    return U, S, VH, err, renormalization272def _combine_constraints(good1, good2, warn):273    """return logical_and(good1, good2) if there remains at least one `True` entry.274    Otherwise print a warning and return just `good1`.275    """276    res = np.logical_and(good1, good2)277    if np.any(res):278        return res279    warnings.warn("truncation: can't satisfy constraint for " + warn, stacklevel=3)...

test_translator.py

Source:test_translator.py

...20    m = three_parameter_problem.m21    assert y.size == m22def test_translate_equality_constraints(three_parameter_problem):23    translator = Translator(three_parameter_problem)24    translated_eqcon = translator._combine_constraints("eq")25    # Get the Jacobian of the translated eqcon26    x = np.zeros(translated_eqcon.dim)27    jac1 = translated_eqcon.jac(x)[:, :-1]28    # Get the Jacobian of the original constraint29    x1 = np.zeros(three_parameter_problem.shape[0])30    x2 = np.zeros(three_parameter_problem.shape[1])31    jac0 = three_parameter_problem.constraints[0].jac(x1, x2)32    assert np.isclose(jac1, jac0).all()33def test_translate_inequality_constraint(three_parameter_problem):34    translator = Translator(three_parameter_problem)35    translated_incon = translator._combine_constraints("ineq")36    n1 = three_parameter_problem.shape[0]37    # Get Jacobian of the original inequality constraint38    y = np.zeros(three_parameter_problem.constraints[1].dim)39    jac0 = three_parameter_problem.constraints[1].jac(y)40    # Get corresponding part of the Jacobian of the translated inequality constraint.41    x = np.zeros(translated_incon.dim)42    jac1 = translated_incon.jac(x)43    assert np.isclose(jac1[:, n1:-1], jac0).all()44def test_translate_unconstrained(unconstrained_problem):45    translator = Translator(unconstrained_problem)46    eqcon = translator._combine_constraints("eq")47    incon = translator._combine_constraints("ineq")48    assert isinstance(eqcon, NullConstraint)...

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