Example #1
0
def fit_l1_cvxopt_cp(
        f, score, start_params, args, kwargs, disp=False, maxiter=100,
        callback=None, retall=False, full_output=False, hess=None):
    """
    Solve the l1 regularized problem using cvxopt.solvers.cp

    Specifically:  We convert the convex but non-smooth problem

    .. math:: \\min_\\beta f(\\beta) + \\sum_k\\alpha_k |\\beta_k|

    via the transformation to the smooth, convex, constrained problem in twice
    as many variables (adding the "added variables" :math:`u_k`)

    .. math:: \\min_{\\beta,u} f(\\beta) + \\sum_k\\alpha_k u_k,

    subject to

    .. math:: -u_k \\leq \\beta_k \\leq u_k.

    Parameters
    ----------
    All the usual parameters from LikelhoodModel.fit
    alpha : non-negative scalar or numpy array (same size as parameters)
        The weight multiplying the l1 penalty term
    trim_mode : 'auto, 'size', or 'off'
        If not 'off', trim (set to zero) parameters that would have been zero
            if the solver reached the theoretical minimum.
        If 'auto', trim params using the Theory above.
        If 'size', trim params if they have very small absolute value
    size_trim_tol : float or 'auto' (default = 'auto')
        For use when trim_mode === 'size'
    auto_trim_tol : float
        For sue when trim_mode == 'auto'.  Use
    qc_tol : float
        Print warning and don't allow auto trim when (ii) in "Theory" (above)
        is violated by this much.
    qc_verbose : Boolean
        If true, print out a full QC report upon failure
    abstol : float
        absolute accuracy (default: 1e-7).
    reltol : float
        relative accuracy (default: 1e-6).
    feastol : float
        tolerance for feasibility conditions (default: 1e-7).
    refinement : int
        number of iterative refinement steps when solving KKT equations
        (default: 1).
    """
    start_params = np.array(start_params).ravel('F')

    ## Extract arguments
    # k_params is total number of covariates, possibly including a leading constant.
    k_params = len(start_params)
    # The start point
    x0 = np.append(start_params, np.fabs(start_params))
    x0 = matrix(x0, (2 * k_params, 1))
    # The regularization parameter
    alpha = np.array(kwargs['alpha_rescaled']).ravel('F')
    # Make sure it's a vector
    alpha = alpha * np.ones(k_params)
    assert alpha.min() >= 0

    ## Wrap up functions for cvxopt
    f_0 = lambda x: _objective_func(f, x, k_params, alpha, *args)
    Df = lambda x: _fprime(score, x, k_params, alpha)
    G = _get_G(k_params)  # Inequality constraint matrix, Gx \leq h
    h = matrix(0.0, (2 * k_params, 1))  # RHS in inequality constraint
    H = lambda x, z: _hessian_wrapper(hess, x, z, k_params)

    ## Define the optimization function
    def F(x=None, z=None):
        if x is None:
            return 0, x0
        elif z is None:
            return f_0(x), Df(x)
        else:
            return f_0(x), Df(x), H(x, z)

    ## Convert optimization settings to cvxopt form
    solvers.options['show_progress'] = disp
    solvers.options['maxiters'] = maxiter
    if 'abstol' in kwargs:
        solvers.options['abstol'] = kwargs['abstol']
    if 'reltol' in kwargs:
        solvers.options['reltol'] = kwargs['reltol']
    if 'feastol' in kwargs:
        solvers.options['feastol'] = kwargs['feastol']
    if 'refinement' in kwargs:
        solvers.options['refinement'] = kwargs['refinement']

    ### Call the optimizer
    results = solvers.cp(F, G, h)
    x = np.asarray(results['x']).ravel()
    params = x[:k_params]

    ### Post-process
    # QC
    qc_tol = kwargs['qc_tol']
    qc_verbose = kwargs['qc_verbose']
    passed = l1_solvers_common.qc_results(
        params, alpha, score, qc_tol, qc_verbose)
    # Possibly trim
    trim_mode = kwargs['trim_mode']
    size_trim_tol = kwargs['size_trim_tol']
    auto_trim_tol = kwargs['auto_trim_tol']
    params, trimmed = l1_solvers_common.do_trim_params(
        params, k_params, alpha, score, passed, trim_mode, size_trim_tol,
        auto_trim_tol)

    ### Pack up return values for statsmodels
    # TODO These retvals are returned as mle_retvals...but the fit wasn't ML
    if full_output:
        fopt = f_0(x)
        gopt = float('nan')  # Objective is non-differentiable
        hopt = float('nan')
        iterations = float('nan')
        converged = 'True' if results['status'] == 'optimal'\
            else results['status']
        retvals = {
            'fopt': fopt, 'converged': converged, 'iterations': iterations,
            'gopt': gopt, 'hopt': hopt, 'trimmed': trimmed}
    else:
        x = np.array(results['x']).ravel()
        params = x[:k_params]

    ### Return results
    if full_output:
        return params, retvals
    else:
        return params
Example #2
0
def fit_l1_slsqp(
        f, score, start_params, args, kwargs, disp=False, maxiter=1000,
        callback=None, retall=False, full_output=False, hess=None):
    """
    Solve the l1 regularized problem using scipy.optimize.fmin_slsqp().

    Specifically:  We convert the convex but non-smooth problem

    .. math:: \\min_\\beta f(\\beta) + \\sum_k\\alpha_k |\\beta_k|

    via the transformation to the smooth, convex, constrained problem in twice
    as many variables (adding the "added variables" :math:`u_k`)

    .. math:: \\min_{\\beta,u} f(\\beta) + \\sum_k\\alpha_k u_k,

    subject to

    .. math:: -u_k \\leq \\beta_k \\leq u_k.

    Parameters
    ----------
    All the usual parameters from LikelhoodModel.fit
    alpha : non-negative scalar or numpy array (same size as parameters)
        The weight multiplying the l1 penalty term
    trim_mode : 'auto, 'size', or 'off'
        If not 'off', trim (set to zero) parameters that would have been zero
            if the solver reached the theoretical minimum.
        If 'auto', trim params using the Theory above.
        If 'size', trim params if they have very small absolute value
    size_trim_tol : float or 'auto' (default = 'auto')
        For use when trim_mode === 'size'
    auto_trim_tol : float
        For sue when trim_mode == 'auto'.  Use
    qc_tol : float
        Print warning and don't allow auto trim when (ii) in "Theory" (above)
        is violated by this much.
    qc_verbose : Boolean
        If true, print out a full QC report upon failure
    acc : float (default 1e-6)
        Requested accuracy as used by slsqp
    """
    start_params = np.array(start_params).ravel('F')

    ### Extract values
    # k_params is total number of covariates,
    # possibly including a leading constant.
    k_params = len(start_params)
    # The start point
    x0 = np.append(start_params, np.fabs(start_params))
    # alpha is the regularization parameter
    alpha = np.array(kwargs['alpha_rescaled']).ravel('F')
    # Make sure it's a vector
    alpha = alpha * np.ones(k_params)
    assert alpha.min() >= 0
    # Convert display parameters to scipy.optimize form
    disp_slsqp = _get_disp_slsqp(disp, retall)
    # Set/retrieve the desired accuracy
    acc = kwargs.setdefault('acc', 1e-10)

    ### Wrap up for use in fmin_slsqp
    func = lambda x_full: _objective_func(f, x_full, k_params, alpha, *args)
    f_ieqcons_wrap = lambda x_full: _f_ieqcons(x_full, k_params)
    fprime_wrap = lambda x_full: _fprime(score, x_full, k_params, alpha)
    fprime_ieqcons_wrap = lambda x_full: _fprime_ieqcons(x_full, k_params)

    ### Call the solver
    results = fmin_slsqp(
        func, x0, f_ieqcons=f_ieqcons_wrap, fprime=fprime_wrap, acc=acc,
        iter=maxiter, disp=disp_slsqp, full_output=full_output,
        fprime_ieqcons=fprime_ieqcons_wrap)
    params = np.asarray(results[0][:k_params])

    ### Post-process
    # QC
    qc_tol = kwargs['qc_tol']
    qc_verbose = kwargs['qc_verbose']
    passed = l1_solvers_common.qc_results(
        params, alpha, score, qc_tol, qc_verbose)
    # Possibly trim
    trim_mode = kwargs['trim_mode']
    size_trim_tol = kwargs['size_trim_tol']
    auto_trim_tol = kwargs['auto_trim_tol']
    params, trimmed = l1_solvers_common.do_trim_params(
        params, k_params, alpha, score, passed, trim_mode, size_trim_tol,
        auto_trim_tol)

    ### Pack up return values for statsmodels optimizers
    # TODO These retvals are returned as mle_retvals...but the fit wasn't ML.
    # This could be confusing someday.
    if full_output:
        x_full, fx, its, imode, smode = results
        fopt = func(np.asarray(x_full))
        converged = 'True' if imode == 0 else smode
        iterations = its
        gopt = float('nan')     # Objective is non-differentiable
        hopt = float('nan')
        retvals = {
            'fopt': fopt, 'converged': converged, 'iterations': iterations,
            'gopt': gopt, 'hopt': hopt, 'trimmed': trimmed}

    ### Return
    if full_output:
        return params, retvals
    else:
        return params