Source code for statsmodels.robust.scale

Support and standalone functions for Robust Linear Models

PJ Huber.  'Robust Statistics' John Wiley and Sons, Inc., New York, 1981.

R Venables, B Ripley. 'Modern Applied Statistics in S'
    Springer, New York, 2002.
from statsmodels.compat.python import callable, range
import numpy as np
from scipy.stats import norm as Gaussian
from . import norms
from import tools

[docs]def mad(a, c=Gaussian.ppf(3/4.), axis=0, center=np.median): # c \approx .6745 """ The Median Absolute Deviation along given axis of an array Parameters ---------- a : array-like Input array. c : float, optional The normalization constant. Defined as scipy.stats.norm.ppf(3/4.), which is approximately .6745. axis : int, optional The defaul is 0. Can also be None. center : callable or float If a callable is provided, such as the default `np.median` then it is expected to be called center(a). The axis argument will be applied via np.apply_over_axes. Otherwise, provide a float. Returns ------- mad : float `mad` = median(abs(`a` - center))/`c` """ a = np.asarray(a) if callable(center): center = np.apply_over_axes(center, a, axis) return np.median((np.fabs(a-center))/c, axis=axis)
def stand_mad(a, c=Gaussian.ppf(3/4.), axis=0): from warnings import warn warn("stand_mad is deprecated and will be removed in 0.7.0. Use mad " "instead.", FutureWarning) return mad(a, c=c, axis=axis)
[docs]class Huber(object): """ Huber's proposal 2 for estimating location and scale jointly. Parameters ---------- c : float, optional Threshold used in threshold for chi=psi**2. Default value is 1.5. tol : float, optional Tolerance for convergence. Default value is 1e-08. maxiter : int, optional0 Maximum number of iterations. Default value is 30. norm : statsmodels.robust.norms.RobustNorm, optional A robust norm used in M estimator of location. If None, the location estimator defaults to a one-step fixed point version of the M-estimator using Huber's T. call Return joint estimates of Huber's scale and location. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> chem_data = np.array([2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03, ... 3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7, 3.7, 3.7, 3.7, ... 3.77, 5.28, 28.95]) >>> sm.robust.scale.huber(chem_data) (array(3.2054980819923693), array(0.67365260010478967)) """ def __init__(self, c=1.5, tol=1.0e-08, maxiter=30, norm=None): self.c = c self.maxiter = maxiter self.tol = tol self.norm = norm tmp = 2 * Gaussian.cdf(c) - 1 self.gamma = tmp + c**2 * (1 - tmp) - 2 * c * Gaussian.pdf(c) def __call__(self, a, mu=None, initscale=None, axis=0): """ Compute Huber's proposal 2 estimate of scale, using an optional initial value of scale and an optional estimate of mu. If mu is supplied, it is not reestimated. Parameters ---------- a : array 1d array mu : float or None, optional If the location mu is supplied then it is not reestimated. Default is None, which means that it is estimated. initscale : float or None, optional A first guess on scale. If initscale is None then the standardized median absolute deviation of a is used. Notes ----- `Huber` minimizes the function sum(psi((a[i]-mu)/scale)**2) as a function of (mu, scale), where psi(x) = np.clip(x, -self.c, self.c) """ a = np.asarray(a) if mu is None: n = a.shape[0] - 1 mu = np.median(a, axis=axis) est_mu = True else: n = a.shape[0] mu = mu est_mu = False if initscale is None: scale = mad(a, axis=axis) else: scale = initscale scale = tools.unsqueeze(scale, axis, a.shape) mu = tools.unsqueeze(mu, axis, a.shape) return self._estimate_both(a, scale, mu, axis, est_mu, n) def _estimate_both(self, a, scale, mu, axis, est_mu, n): """ Estimate scale and location simultaneously with the following pseudo_loop: while not_converged: mu, scale = estimate_location(a, scale, mu), estimate_scale(a, scale, mu) where estimate_location is an M-estimator and estimate_scale implements the check used in Section 5.5 of Venables & Ripley """ for _ in range(self.maxiter): # Estimate the mean along a given axis if est_mu: if self.norm is None: # This is a one-step fixed-point estimator # if self.norm == norms.HuberT # It should be faster than using norms.HuberT nmu = np.clip(a, mu-self.c*scale, mu+self.c*scale).sum(axis) / a.shape[axis] else: nmu = norms.estimate_location(a, scale, self.norm, axis, mu, self.maxiter, self.tol) else: # Effectively, do nothing nmu = mu.squeeze() nmu = tools.unsqueeze(nmu, axis, a.shape) subset = np.less_equal(np.fabs((a - mu)/scale), self.c) card = subset.sum(axis) nscale = np.sqrt(np.sum(subset * (a - nmu)**2, axis) \ / (n * self.gamma - (a.shape[axis] - card) * self.c**2)) nscale = tools.unsqueeze(nscale, axis, a.shape) test1 = np.alltrue(np.less_equal(np.fabs(scale - nscale), nscale * self.tol)) test2 = np.alltrue(np.less_equal(np.fabs(mu - nmu), nscale*self.tol)) if not (test1 and test2): mu = nmu; scale = nscale else: return nmu.squeeze(), nscale.squeeze() raise ValueError('joint estimation of location and scale failed to converge in %d iterations' % self.maxiter)
huber = Huber()
[docs]class HuberScale(object): """ Huber's scaling for fitting robust linear models. Huber's scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the `Huber` class. Parameters ---------- d : float, optional d is the tuning constant for Huber's scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Methods ------- call Return's Huber's scale computed as below Notes -------- Huber's scale is the iterative solution to scale_(i+1)**2 = 1/(n*h)*sum(chi(r/sigma_i)*sigma_i**2 where the Huber function is chi(x) = (x**2)/2 for \|x\| < d chi(x) = (d**2)/2 for \|x\| >= d and the Huber constant h = (n-p)/n*(d**2 + (1-d**2)*\ scipy.stats.norm.cdf(d) - .5 - d*sqrt(2*pi)*exp(-0.5*d**2) """ def __init__(self, d=2.5, tol=1e-08, maxiter=30): self.d = d self.tol = tol self.maxiter = maxiter def __call__(self, df_resid, nobs, resid): h = (df_resid)/nobs*(self.d**2 + (1-self.d**2)*\ Gaussian.cdf(self.d)-.5 - self.d/(np.sqrt(2*np.pi))*\ np.exp(-.5*self.d**2)) s = mad(resid) subset = lambda x: np.less(np.fabs(resid/x),self.d) chi = lambda s: subset(s)*(resid/s)**2/2+(1-subset(s))*(self.d**2/2) scalehist = [np.inf,s] niter = 1 while (np.abs(scalehist[niter-1] - scalehist[niter])>self.tol \ and niter < self.maxiter): nscale = np.sqrt(1/(nobs*h)*np.sum(chi(scalehist[-1]))*\ scalehist[-1]**2) scalehist.append(nscale) niter += 1 #if niter == self.maxiter: # raise ValueError("Huber's scale failed to converge") return scalehist[-1]
hubers_scale = HuberScale()