Source code for statsmodels.regression.dimred

import warnings

import numpy as np
import pandas as pd

from statsmodels.base import model
import statsmodels.base.wrapper as wrap
from statsmodels.tools.sm_exceptions import ConvergenceWarning


class _DimReductionRegression(model.Model):
    """
    A base class for dimension reduction regression methods.
    """

    def __init__(self, endog, exog, **kwargs):
        super().__init__(endog, exog, **kwargs)

    def _prep(self, n_slice):

        # Sort the data by endog
        ii = np.argsort(self.endog)
        x = self.exog[ii, :]

        # Whiten the data
        x -= x.mean(0)
        covx = np.dot(x.T, x) / x.shape[0]
        covxr = np.linalg.cholesky(covx)
        x = np.linalg.solve(covxr, x.T).T
        self.wexog = x
        self._covxr = covxr

        # Split the data into slices
        self._split_wexog = np.array_split(x, n_slice)


[docs] class SlicedInverseReg(_DimReductionRegression): """ Sliced Inverse Regression (SIR) Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates References ---------- KC Li (1991). Sliced inverse regression for dimension reduction. JASA 86, 316-342. """
[docs] def fit(self, slice_n=20, **kwargs): """ Estimate the EDR space using Sliced Inverse Regression. Parameters ---------- slice_n : int, optional Target number of observations per slice """ # Sample size per slice if len(kwargs) > 0: msg = "SIR.fit does not take any extra keyword arguments" warnings.warn(msg) # Number of slices n_slice = self.exog.shape[0] // slice_n self._prep(n_slice) mn = [z.mean(0) for z in self._split_wexog] n = [z.shape[0] for z in self._split_wexog] mn = np.asarray(mn) n = np.asarray(n) # Estimate Cov E[X | Y=y] mnc = np.dot(mn.T, n[:, None] * mn) / n.sum() a, b = np.linalg.eigh(mnc) jj = np.argsort(-a) a = a[jj] b = b[:, jj] params = np.linalg.solve(self._covxr.T, b) results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results)
def _regularized_objective(self, A): # The objective function for regularized SIR p = self.k_vars covx = self._covx mn = self._slice_means ph = self._slice_props v = 0 A = np.reshape(A, (p, self.ndim)) # The penalty for k in range(self.ndim): u = np.dot(self.pen_mat, A[:, k]) v += np.sum(u * u) # The SIR objective function covxa = np.dot(covx, A) q, _ = np.linalg.qr(covxa) qd = np.dot(q, np.dot(q.T, mn.T)) qu = mn.T - qd v += np.dot(ph, (qu * qu).sum(0)) return v def _regularized_grad(self, A): # The gradient of the objective function for regularized SIR p = self.k_vars ndim = self.ndim covx = self._covx n_slice = self.n_slice mn = self._slice_means ph = self._slice_props A = A.reshape((p, ndim)) # Penalty gradient gr = 2 * np.dot(self.pen_mat.T, np.dot(self.pen_mat, A)) A = A.reshape((p, ndim)) covxa = np.dot(covx, A) covx2a = np.dot(covx, covxa) Q = np.dot(covxa.T, covxa) Qi = np.linalg.inv(Q) jm = np.zeros((p, ndim)) qcv = np.linalg.solve(Q, covxa.T) ft = [None] * (p * ndim) for q in range(p): for r in range(ndim): jm *= 0 jm[q, r] = 1 umat = np.dot(covx2a.T, jm) umat += umat.T umat = -np.dot(Qi, np.dot(umat, Qi)) fmat = np.dot(np.dot(covx, jm), qcv) fmat += np.dot(covxa, np.dot(umat, covxa.T)) fmat += np.dot(covxa, np.linalg.solve(Q, np.dot(jm.T, covx))) ft[q*ndim + r] = fmat ch = np.linalg.solve(Q, np.dot(covxa.T, mn.T)) cu = mn - np.dot(covxa, ch).T for i in range(n_slice): u = cu[i, :] v = mn[i, :] for q in range(p): for r in range(ndim): f = np.dot(u, np.dot(ft[q*ndim + r], v)) gr[q, r] -= 2 * ph[i] * f return gr.ravel()
[docs] def fit_regularized(self, ndim=1, pen_mat=None, slice_n=20, maxiter=100, gtol=1e-3, **kwargs): """ Estimate the EDR space using regularized SIR. Parameters ---------- ndim : int The number of EDR directions to estimate pen_mat : array_like A 2d array such that the squared Frobenius norm of `dot(pen_mat, dirs)`` is added to the objective function, where `dirs` is an orthogonal array whose columns span the estimated EDR space. slice_n : int, optional Target number of observations per slice maxiter :int The maximum number of iterations for estimating the EDR space. gtol : float If the norm of the gradient of the objective function falls below this value, the algorithm has converged. Returns ------- A results class instance. Notes ----- If each row of `exog` can be viewed as containing the values of a function evaluated at equally-spaced locations, then setting the rows of `pen_mat` to [[1, -2, 1, ...], [0, 1, -2, 1, ..], ...] will give smooth EDR coefficients. This is a form of "functional SIR" using the squared second derivative as a penalty. References ---------- L. Ferre, A.F. Yao (2003). Functional sliced inverse regression analysis. Statistics: a journal of theoretical and applied statistics 37(6) 475-488. """ if len(kwargs) > 0: msg = "SIR.fit_regularized does not take keyword arguments" warnings.warn(msg) if pen_mat is None: raise ValueError("pen_mat is a required argument") start_params = kwargs.get("start_params", None) # Sample size per slice slice_n = kwargs.get("slice_n", 20) # Number of slices n_slice = self.exog.shape[0] // slice_n # Sort the data by endog ii = np.argsort(self.endog) x = self.exog[ii, :] x -= x.mean(0) covx = np.cov(x.T) # Split the data into slices split_exog = np.array_split(x, n_slice) mn = [z.mean(0) for z in split_exog] n = [z.shape[0] for z in split_exog] mn = np.asarray(mn) n = np.asarray(n) self._slice_props = n / n.sum() self.ndim = ndim self.k_vars = covx.shape[0] self.pen_mat = pen_mat self._covx = covx self.n_slice = n_slice self._slice_means = mn if start_params is None: params = np.zeros((self.k_vars, ndim)) params[0:ndim, 0:ndim] = np.eye(ndim) params = params else: if start_params.shape[1] != ndim: msg = "Shape of start_params is not compatible with ndim" raise ValueError(msg) params = start_params params, _, cnvrg = _grass_opt(params, self._regularized_objective, self._regularized_grad, maxiter, gtol) if not cnvrg: g = self._regularized_grad(params.ravel()) gn = np.sqrt(np.dot(g, g)) msg = "SIR.fit_regularized did not converge, |g|=%f" % gn warnings.warn(msg) results = DimReductionResults(self, params, eigs=None) return DimReductionResultsWrapper(results)
[docs] class PrincipalHessianDirections(_DimReductionRegression): """ Principal Hessian Directions (PHD) Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates Returns ------- A model instance. Call `fit` to obtain a results instance, from which the estimated parameters can be obtained. References ---------- KC Li (1992). On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another application of Stein's lemma. JASA 87:420. """
[docs] def fit(self, **kwargs): """ Estimate the EDR space using PHD. Parameters ---------- resid : bool, optional If True, use least squares regression to remove the linear relationship between each covariate and the response, before conducting PHD. Returns ------- A results instance which can be used to access the estimated parameters. """ resid = kwargs.get("resid", False) y = self.endog - self.endog.mean() x = self.exog - self.exog.mean(0) if resid: from statsmodels.regression.linear_model import OLS r = OLS(y, x).fit() y = r.resid cm = np.einsum('i,ij,ik->jk', y, x, x) cm /= len(y) cx = np.cov(x.T) cb = np.linalg.solve(cx, cm) a, b = np.linalg.eig(cb) jj = np.argsort(-np.abs(a)) a = a[jj] params = b[:, jj] results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results)
[docs] class SlicedAverageVarianceEstimation(_DimReductionRegression): """ Sliced Average Variance Estimation (SAVE) Parameters ---------- endog : array_like (1d) The dependent variable exog : array_like (2d) The covariates bc : bool, optional If True, use the bias-corrected CSAVE method of Li and Zhu. References ---------- RD Cook. SAVE: A method for dimension reduction and graphics in regression. http://www.stat.umn.edu/RegGraph/RecentDev/save.pdf Y Li, L-X Zhu (2007). Asymptotics for sliced average variance estimation. The Annals of Statistics. https://arxiv.org/pdf/0708.0462.pdf """ def __init__(self, endog, exog, **kwargs): super(SAVE, self).__init__(endog, exog, **kwargs) self.bc = False if "bc" in kwargs and kwargs["bc"] is True: self.bc = True
[docs] def fit(self, **kwargs): """ Estimate the EDR space. Parameters ---------- slice_n : int Number of observations per slice """ # Sample size per slice slice_n = kwargs.get("slice_n", 50) # Number of slices n_slice = self.exog.shape[0] // slice_n self._prep(n_slice) cv = [np.cov(z.T) for z in self._split_wexog] ns = [z.shape[0] for z in self._split_wexog] p = self.wexog.shape[1] if not self.bc: # Cook's original approach vm = 0 for w, cvx in zip(ns, cv): icv = np.eye(p) - cvx vm += w * np.dot(icv, icv) vm /= len(cv) else: # The bias-corrected approach of Li and Zhu # \Lambda_n in Li, Zhu av = 0 for c in cv: av += np.dot(c, c) av /= len(cv) # V_n in Li, Zhu vn = 0 for x in self._split_wexog: r = x - x.mean(0) for i in range(r.shape[0]): u = r[i, :] m = np.outer(u, u) vn += np.dot(m, m) vn /= self.exog.shape[0] c = np.mean(ns) k1 = c * (c - 1) / ((c - 1)**2 + 1) k2 = (c - 1) / ((c - 1)**2 + 1) av2 = k1 * av - k2 * vn vm = np.eye(p) - 2 * sum(cv) / len(cv) + av2 a, b = np.linalg.eigh(vm) jj = np.argsort(-a) a = a[jj] b = b[:, jj] params = np.linalg.solve(self._covxr.T, b) results = DimReductionResults(self, params, eigs=a) return DimReductionResultsWrapper(results)
[docs] class DimReductionResults(model.Results): """ Results class for a dimension reduction regression. Notes ----- The `params` attribute is a matrix whose columns span the effective dimension reduction (EDR) space. Some methods produce a corresponding set of eigenvalues (`eigs`) that indicate how much information is contained in each basis direction. """ def __init__(self, model, params, eigs): super().__init__( model, params) self.eigs = eigs
class DimReductionResultsWrapper(wrap.ResultsWrapper): _attrs = { 'params': 'columns', } _wrap_attrs = _attrs wrap.populate_wrapper(DimReductionResultsWrapper, # noqa:E305 DimReductionResults) def _grass_opt(params, fun, grad, maxiter, gtol): """ Minimize a function on a Grassmann manifold. Parameters ---------- params : array_like Starting value for the optimization. fun : function The function to be minimized. grad : function The gradient of fun. maxiter : int The maximum number of iterations. gtol : float Convergence occurs when the gradient norm falls below this value. Returns ------- params : array_like The minimizing value for the objective function. fval : float The smallest achieved value of the objective function. cnvrg : bool True if the algorithm converged to a limit point. Notes ----- `params` is 2-d, but `fun` and `grad` should take 1-d arrays `params.ravel()` as arguments. Reference --------- A Edelman, TA Arias, ST Smith (1998). The geometry of algorithms with orthogonality constraints. SIAM J Matrix Anal Appl. http://math.mit.edu/~edelman/publications/geometry_of_algorithms.pdf """ p, d = params.shape params = params.ravel() f0 = fun(params) cnvrg = False for _ in range(maxiter): # Project the gradient to the tangent space g = grad(params) g -= np.dot(g, params) * params / np.dot(params, params) if np.sqrt(np.sum(g * g)) < gtol: cnvrg = True break gm = g.reshape((p, d)) u, s, vt = np.linalg.svd(gm, 0) paramsm = params.reshape((p, d)) pa0 = np.dot(paramsm, vt.T) def geo(t): # Parameterize the geodesic path in the direction # of the gradient as a function of a real value t. pa = pa0 * np.cos(s * t) + u * np.sin(s * t) return np.dot(pa, vt).ravel() # Try to find a downhill step along the geodesic path. step = 2. while step > 1e-10: pa = geo(-step) f1 = fun(pa) if f1 < f0: params = pa f0 = f1 break step /= 2 params = params.reshape((p, d)) return params, f0, cnvrg class CovarianceReduction(_DimReductionRegression): """ Dimension reduction for covariance matrices (CORE). Parameters ---------- endog : array_like The dependent variable, treated as group labels exog : array_like The independent variables. dim : int The dimension of the subspace onto which the covariance matrices are projected. Returns ------- A model instance. Call `fit` on the model instance to obtain a results instance, which contains the fitted model parameters. Notes ----- This is a likelihood-based dimension reduction procedure based on Wishart models for sample covariance matrices. The goal is to find a projection matrix P so that C_i | P'C_iP and C_j | P'C_jP are equal in distribution for all i, j, where the C_i are the within-group covariance matrices. The model and methodology are as described in Cook and Forzani. The optimization method follows Edelman et. al. References ---------- DR Cook, L Forzani (2008). Covariance reducing models: an alternative to spectral modeling of covariance matrices. Biometrika 95:4. A Edelman, TA Arias, ST Smith (1998). The geometry of algorithms with orthogonality constraints. SIAM J Matrix Anal Appl. http://math.mit.edu/~edelman/publications/geometry_of_algorithms.pdf """ def __init__(self, endog, exog, dim): super().__init__(endog, exog) covs, ns = [], [] df = pd.DataFrame(self.exog, index=self.endog) for _, v in df.groupby(df.index): covs.append(v.cov().values) ns.append(v.shape[0]) self.nobs = len(endog) # The marginal covariance covm = 0 for i, _ in enumerate(covs): covm += covs[i] * ns[i] covm /= self.nobs self.covm = covm self.covs = covs self.ns = ns self.dim = dim def loglike(self, params): """ Evaluate the log-likelihood Parameters ---------- params : array_like The projection matrix used to reduce the covariances, flattened to 1d. Returns the log-likelihood. """ p = self.covm.shape[0] proj = params.reshape((p, self.dim)) c = np.dot(proj.T, np.dot(self.covm, proj)) _, ldet = np.linalg.slogdet(c) f = self.nobs * ldet / 2 for j, c in enumerate(self.covs): c = np.dot(proj.T, np.dot(c, proj)) _, ldet = np.linalg.slogdet(c) f -= self.ns[j] * ldet / 2 return f def score(self, params): """ Evaluate the score function. Parameters ---------- params : array_like The projection matrix used to reduce the covariances, flattened to 1d. Returns the score function evaluated at 'params'. """ p = self.covm.shape[0] proj = params.reshape((p, self.dim)) c0 = np.dot(proj.T, np.dot(self.covm, proj)) cP = np.dot(self.covm, proj) g = self.nobs * np.linalg.solve(c0, cP.T).T for j, c in enumerate(self.covs): c0 = np.dot(proj.T, np.dot(c, proj)) cP = np.dot(c, proj) g -= self.ns[j] * np.linalg.solve(c0, cP.T).T return g.ravel() def fit(self, start_params=None, maxiter=200, gtol=1e-4): """ Fit the covariance reduction model. Parameters ---------- start_params : array_like Starting value for the projection matrix. May be rectangular, or flattened. maxiter : int The maximum number of gradient steps to take. gtol : float Convergence criterion for the gradient norm. Returns ------- A results instance that can be used to access the fitted parameters. """ p = self.covm.shape[0] d = self.dim # Starting value for params if start_params is None: params = np.zeros((p, d)) params[0:d, 0:d] = np.eye(d) params = params else: params = start_params # _grass_opt is designed for minimization, we are doing maximization # here so everything needs to be flipped. params, llf, cnvrg = _grass_opt(params, lambda x: -self.loglike(x), lambda x: -self.score(x), maxiter, gtol) llf *= -1 if not cnvrg: g = self.score(params.ravel()) gn = np.sqrt(np.sum(g * g)) msg = "CovReduce optimization did not converge, |g|=%f" % gn warnings.warn(msg, ConvergenceWarning) results = DimReductionResults(self, params, eigs=None) results.llf = llf return DimReductionResultsWrapper(results) # aliases for expert users SIR = SlicedInverseReg PHD = PrincipalHessianDirections SAVE = SlicedAverageVarianceEstimation CORE = CovarianceReduction

Last update: Oct 03, 2024