Source code for hail.methods.linear_mixed_model

import numpy as np
import pandas as pd
from hail.utils.misc import plural
from hail.typecheck import *
from hail.utils.java import Env, jnone, jsome
from hail.table import Table


[docs]class LinearMixedModel(object): r"""Class representing a linear mixed model. .. include:: ../_templates/experimental.rst :class:`LinearMixedModel` represents a linear model of the form .. math:: y \sim \mathrm{N}(X \beta, \, \sigma^2 K + \tau^2 I) where - :math:`\mathrm{N}` is a :math:`n`-dimensional normal distribution. - :math:`y` is a known vector of :math:`n` observations. - :math:`X` is a known :math:`n \times p` design matrix for :math:`p` fixed effects. - :math:`K` is a known :math:`n \times n` positive semi-definite kernel. - :math:`I` is the :math:`n \times n` identity matrix. - :math:`\beta` is a :math:`p`-parameter vector of fixed effects. - :math:`\sigma^2` is the variance parameter on :math:`K`. - :math:`\tau^2` is the variance parameter on :math:`I`. In particular, the residuals for the :math:`i^\mathit{th}` and :math:`j^\mathit{th}` observations have covariance :math:`\sigma^2 K_{ij}` for :math:`i \neq j`. This model is equivalent to a `mixed model <https://en.wikipedia.org/wiki/Mixed_model>`__ of the form .. math:: y = X \beta + Z u + \epsilon by setting :math:`K = ZZ^T` where - :math:`Z` is a known :math:`n \times r` design matrix for :math:`r` random effects. - :math:`u` is a :math:`r`-vector of random effects drawn from :math:`\mathrm{N}(0, \sigma^2 I)`. - :math:`\epsilon` is a :math:`n`-vector of random errors drawn from :math:`\mathrm{N}(0, \tau^2 I)`. However, :class:`LinearMixedModel` does not itself realize :math:`K` as a linear kernel with respect to random effects, nor does it take :math:`K` explicitly as input. Rather, via the eigendecomposion :math:`K = U S U^T`, the the class leverages a third, decorrelated form of the model .. math:: Py \sim \mathrm{N}(PX \beta, \, \sigma^2 (\gamma S + I)) where - :math:`P = U^T: \mathbb{R}^n \rightarrow \mathbb{R}^n` is an orthonormal transformation that decorrelates the observations. The rows of :math:`P` are an eigenbasis for :math:`K`. - :math:`S` is the :math:`n \times n` diagonal matrix of corresponding eigenvalues. - :math:`\gamma = \frac{\sigma^2}{\tau^2}` is the ratio of variance parameters. Hence, the triple :math:`(Py, PX, S)` determines the probability of the observations for any choice of model parameters, and is therefore sufficient for inference. This triple, with S encoded as a vector, is the default ("full-rank") initialization of the class. :class:`LinearMixedModel` also provides an efficient strategy to fit the model above with :math:`K` replaced by its rank-:math:`r` approximation :math:`K_r = P_r^T S_r P_r` where - :math:`P_r: \mathbb{R}^n \rightarrow \mathbb{R}^r` has orthonormal rows consisting of the top :math:`r` eigenvectors of :math:`K`. - :math:`S_r` is the :math:`r \times r` diagonal matrix of corresponding non-zero eigenvalues. For this low-rank model, the quintuple :math:`(P_r y, P_r X, S_r, y, X)` is similarly sufficient for inference and corresponds to the "low-rank" initialization of the class. Morally, :math:`y` and :math:`X` are required for low-rank inference because the diagonal :math:`\gamma S + I` is always full-rank. If :math:`K` actually has rank :math:`r`, then :math:`K = K_r` and the low-rank and full-rank models are equivalent. Hence low-rank inference provides a more efficient, equally-exact algorithm for fitting the full-rank model. This situation arises, for example, when :math:`K` is the linear kernel of a mixed model with fewer random effects than observations. Even when :math:`K` has full rank, using a lower-rank approximation may be an effective from of regularization, in addition to boosting computational efficiency. **Initialization** With full-rank initialization by :math:`(Py, PX, S)`, the following class attributes are set: .. list-table:: :header-rows: 1 * - Attribute - Type - Value * - `low_rank` - bool - ``False`` * - `n` - int - Number of observations :math:`n` * - `f` - int - Number of fixed effects :math:`p` * - `r` - int - Effective number of random effects, must equal :math:`n` * - `py` - numpy.ndarray - Rotated response vector :math:`P y` with shape :math:`(n)` * - `px` - numpy.ndarray - Rotated design matrix :math:`P X` with shape :math:`(n, p)`. * - `s` - numpy.ndarray - Eigenvalues vector :math:`S` of :math:`K` with shape :math:`(n)` With low-rank initialization by :math:`(P_r y, P_r X, S_r, y, X)`, the following class attributes are set: .. list-table:: :header-rows: 1 * - Attribute - Type - Value * - `low_rank` - bool - ``True`` * - `n` - int - Number of observations :math:`n` * - `f` - int - Number of fixed effects :math:`p` * - `r` - int - Effective number of random effects, must be less than :math:`n` * - `py` - numpy.ndarray - Projected response vector :math:`P_r y` with shape :math:`(r)` * - `px` - numpy.ndarray - Projected design matrix :math:`P_r X` with shape :math:`(r, p)` * - `s` - numpy.ndarray - Eigenvalues vector :math:`S_r` of :math:`K_r` with shape :math:`(r)` * - `y` - numpy.ndarray - Response vector with shape :math:`(n)` * - `x` - numpy.ndarray - Design matrix with shape :math:`(n, p)` **Fitting the model** :meth:`fit` uses `restricted maximum likelihood <https://en.wikipedia.org/wiki/Restricted_maximum_likelihood>`__ (REML) to estimate :math:`(\beta, \sigma^2, \tau^2)`, adding the following attributes at this estimate. .. list-table:: :header-rows: 1 * - Attribute - Type - Value * - `beta` - numpy.ndarray - :math:`\beta` * - `sigma_sq` - float - :math:`\sigma^2` * - `tau_sq` - float - :math:`\tau^2` * - `gamma` - float - :math:`\gamma = \frac{\sigma^2}{\tau^2}` * - `log_gamma` - float - :math:`\log{\gamma}` * - `h_sq` - float - :math:`\mathit{h}^2 = \frac{\sigma^2}{\sigma^2 + \tau^2}` * - `h_sq_standard_error` - float - asymptotic estimate of standard error for :math:`\mathit{h}^2` Estimation proceeds by minimizing the function :meth:`compute_neg_log_reml` with respect to the parameter :math:`\log{\gamma}` governing the (log) ratio of the variance parameters :math:`\sigma^2` and :math:`\tau^2`. For any fixed ratio, the REML estimate and log likelihood have closed-form solutions. **Testing alternative models** The model is also equivalent to its augmentation .. math:: y \sim \mathrm{N}\left(x_\star\beta_\star + X \beta, \, \sigma^2 K + \tau^2 I\right) by an additional covariate of interest :math:`x_\star` under the null hypothesis that the corresponding fixed effect :math:`\beta_\star` is zero. After running :meth:`fit` to fit the null model, the methods :meth:`fit_alternatives` and :meth:`fit_alternatives_numpy` may be used to test the null hypothesis :math:`\beta_\star = 0` versus the alternative hypothesis :math:`\beta_\star \neq 0` for each :math:`n`-vector :math:`x_\star` in a collection of augmentations. Testing uses the likelihood ratio test with both the null and alternative models constrained by the REML estimate of :math:`\log{\gamma}` under the null hypothesis. The test statistic :math:`\chi^2` equals :math:`n` times the log ratio of the squared residuals and follows a chi-squared distribution with one degree of freedom. When testing alternatives, full-rank inference only requires the vector :math:`P x_\star`, whereas low-rank inference requires both :math:`P_r x_\star` and :math:`x_\star`. Parameters ---------- py: :class:`numpy.ndarray` Projected response vector :math:`P_r y` with shape :math:`(r)`. px: :class:`numpy.ndarray` Projected design matrix :math:`P_r X` with shape :math:`(r, p)`. s: :class:`numpy.ndarray` Eigenvalues vector :math:`S` with shape :math:`(r)`. y: :class:`numpy.ndarray`, optional Response vector with shape :math:`(n)`. Include for low-rank inference. x: :class:`numpy.ndarray`, optional Design matrix with shape :math:`(n, p)`. Include for low-rank inference. """ @typecheck_method(py=np.ndarray, px=np.ndarray, s=np.ndarray, y=nullable(np.ndarray), x=nullable(np.ndarray)) def __init__(self, py, px, s, y=None, x=None): if y is None and x is None: low_rank = False elif y is not None and x is not None: low_rank = True else: raise ValueError('for low-rank, set both y and x; for full-rank, do not set y or x.') assert py.ndim == 1 assert px.ndim == 2 assert s.ndim == 1 r, f = px.shape if f == 0: raise ValueError('LinearMixedModel must have at least one fixed effect.') # could relax assert py.size == r assert s.size == r if low_rank: assert y.ndim == 1 assert x.ndim == 2 assert x.shape == (y.size, f) assert y.size > r n = y.size else: n = r self.low_rank = low_rank self.n = n self.f = f self.r = r self.py = py self.px = px self.s = s self.y = y self.x = x self._check_dof() self.beta = None self.sigma_sq = None self.tau_sq = None self.gamma = None self.log_gamma = None self.h_sq = None self.h_sq_standard_error = None self.optimize_result = None self._fitted = False if low_rank: self._yty = y @ y self._xty = x.T @ y self._xtx = x.T @ x self._dof = n - f self._d = None self._ydy = None self._xdy = None self._xdx = None self._dof_alt = n - (f + 1) self._d_alt = None self._ydy_alt = None self._xdy_alt = np.zeros(f + 1) self._xdx_alt = np.zeros((f + 1, f + 1)) self._residual_sq = None self._scala_model = None def _reset(self): self._fitted = False self.beta = None self.sigma_sq = None self.tau_sq = None self.gamma = None self.log_gamma = None self.h_sq = None self.h_sq_standard_error = None self.optimize_result = None
[docs] def compute_neg_log_reml(self, log_gamma, return_parameters=False): r"""Compute negative log REML constrained to a fixed value of :math:`\log{\gamma}`. This function computes the triple :math:`(\beta, \sigma^2, \tau^2)` with :math:`\gamma = \frac{\sigma^2}{\tau^2}` at which the restricted likelihood is maximized and returns the negative of the log of the restricted likelihood at these parameters, shifted by a constant whose value is independent of the input. To compute the actual negative log REML, add .. math:: \frac{1}{2}\left((n - p)(1 + \log(2\pi)) - \log(\det(X^T X)\right) to the returned value. Parameters ---------- log_gamma: :obj:`float` Value of :math:`\log{\gamma}`. return_parameters: If ``True``, also return :math:`\beta`, :math:`\sigma^2`, and :math:`\tau^2`. Returns ------- :obj:`float` or (:obj:`float`, :class:`numpy.ndarray`, :obj:`float`, :obj:`float`) If `return_parameters` is ``False``, returns (shifted) negative log REML. Otherwise, returns (shifted) negative log REML, :math:`\beta`, :math:`\sigma^2`, and :math:`\tau^2`. """ from scipy.linalg import solve, LinAlgError gamma = np.exp(log_gamma) d = 1 / (self.s + 1 / gamma) logdet_d = np.sum(np.log(d)) + (self.n - self.r) * log_gamma if self.low_rank: d -= gamma dpy = d * self.py ydy = self.py @ dpy + gamma * self._yty xdy = self.px.T @ dpy + gamma * self._xty xdx = (self.px.T * d) @ self.px + gamma * self._xtx else: dpy = d * self.py ydy = self.py @ dpy xdy = self.px.T @ dpy xdx = (self.px.T * d) @ self.px try: beta = solve(xdx, xdy, assume_a='pos') residual_sq = ydy - xdy.T @ beta sigma_sq = residual_sq / self._dof tau_sq = sigma_sq / gamma neg_log_reml = (np.linalg.slogdet(xdx)[1] - logdet_d + self._dof * np.log(sigma_sq)) / 2 self._d, self._ydy, self._xdy, self._xdx = d, ydy, xdy, xdx # used in fit if return_parameters: return neg_log_reml, beta, sigma_sq, tau_sq else: return neg_log_reml except LinAlgError as e:
raise Exception(f'linear algebra error while solving for REML estimate') from e
[docs] @typecheck_method(log_gamma=nullable(float), bounds=tupleof(numeric), tol=float, maxiter=int) def fit(self, log_gamma=None, bounds=(-8.0, 8.0), tol=1e-8, maxiter=500): r"""Find the triple :math:`(\beta, \sigma^2, \tau^2)` maximizing REML. This method sets the attributes `beta`, `sigma_sq`, `tau_sq`, `gamma`, `log_gamma`, and `h_sq` as described in the top-level class documentation. If `log_gamma` is provided, :meth:`fit` finds the REML solution with :math:`\log{\gamma}` constrained to this value. Otherwise, :meth:`fit` searches for the value of :math:`\log{\gamma}` that minimizes :meth:`compute_neg_log_reml`, and also sets the attribute `optimize_result` of type `scipy.optimize.OptimizeResult <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html>`__. Parameters ---------- log_gamma: :obj:`float`, optional If provided, the solution is constrained to have this value of :math:`\log{\gamma}`. bounds: :obj:`float`, :obj:`float` Lower and upper bounds for :math:`\log{\gamma}`. tol: :obj:`float` Absolute tolerance for optimizing :math:`\log{\gamma}`. maxiter: :obj:`float` Maximum number of iterations for optimizing :math:`\log{\gamma}`. """ if self._fitted: self._reset() if log_gamma: self.log_gamma = log_gamma else: from scipy.optimize import minimize_scalar self.optimize_result = minimize_scalar( self.compute_neg_log_reml, method='bounded', bounds=bounds, options={'xatol': tol, 'maxiter': maxiter}) if self.optimize_result.success: if self.optimize_result.x - bounds[0] < 0.01: raise Exception("failed to fit log_gamma: optimum within 0.01 of lower bound.") elif bounds[1] - self.optimize_result.x < tol: raise Exception("failed to fit log_gamma: optimum within 0.01 of upper bound.") else: self.log_gamma = self.optimize_result.x else: raise Exception(f'failed to fit log_gamma:\n {self.optimize_result}') _, self.beta, self.sigma_sq, self.tau_sq = self.compute_neg_log_reml(self.log_gamma, return_parameters=True) self.gamma = np.exp(self.log_gamma) self.h_sq = self.sigma_sq / (self.sigma_sq + self.tau_sq) self._residual_sq = self.sigma_sq * self._dof self._d_alt = self._d self._ydy_alt = self._ydy self._xdy_alt[1:] = self._xdy self._xdx_alt[1:, 1:] = self._xdx self._fitted = True
self.h_sq_standard_error = self._estimate_h_sq_standard_error() def _estimate_h_sq_standard_error(self): assert self._fitted epsilon = 1e-4 # parabolic interpolation radius lg = self.log_gamma + np.array([-epsilon, 0.0, epsilon]) h2 = 1 / (1 + np.exp(lg)) nll = [self.compute_neg_log_reml(lgi) for lgi in lg] if nll[1] > nll[0] or nll[1] > nll[2]: i = 0 if nll[1] > nll[0] else 2 raise Exception(f'Minimum of negative log likelihood fit as {nll[1]} at log_gamma={lg[1]},' f'\n but found smaller value of {nll[i]} at log_gamma={lg[i]}.' f'\n Investigate by plotting the negative log likelihood function.') # Asymptotically near MLE, nLL = a * x^2 + b * x + c with a = 1 / (2 * se^2) # By Lagrange interpolation: a = ((h2[2] * (nll[1] - nll[0]) + h2[1] * (nll[0] - nll[2]) + h2[0] * (nll[2] - nll[1])) / ((h2[1] - h2[0]) * (h2[0] - h2[2]) * (h2[2] - h2[1]))) return 1 / np.sqrt(2 * a)
[docs] @typecheck_method(pa_t_path=str, a_t_path=nullable(str), partition_size=nullable(int)) def fit_alternatives(self, pa_t_path, a_t_path=None, partition_size=None): r"""Fit and test alternative model for each augmented design matrix in parallel. Notes ----- The resulting table has the following fields: .. list-table:: :header-rows: 1 * - Field - Type - Value * - `idx` - int64 - Index of augmented design matrix. * - `beta` - float64 - :math:`\beta_\star` * - `sigma_sq` - float64 - :math:`\sigma^2` * - `chi_sq` - float64 - :math:`\chi^2` * - `p_value` - float64 - p-value :math:`(P_r A)^T` and :math:`A^T` (if given) must have the same number of rows (augmentations). These rows are grouped into partitions for parallel processing. The number of partitions equals the ceiling of ``n_rows / partition_size``, and should be at least the number or cores to make use of all cores. By default, there is one partition per row of blocks in :math:`(P_r A)^T`. Setting the partition size to an exact (rather than approximate) divisor or multiple of the block size reduces superfluous shuffling of data. The number of columns in each block matrix must be less than :math:`2^{31}`. Warning ------- The block matrices must be stored in row-major format, as results from :meth:`.BlockMatrix.write` with ``force_row_major=True`` and from :meth:`.BlockMatrix.write_from_entry_expr`. Otherwise, this method will produce an error message. Parameters ---------- pa_t_path: :obj:`str` Path to block matrix :math:`(P_r A)^T` with shape :math:`(m, r)`. Each row is a projected augmentation :math:`P_r x_\star` of :math:`P_r X`. a_t_path: :obj:`str`, optional Path to block matrix :math:`A^T` with shape :math:`(m, n)`. Each row is an augmentation :math:`x_\star` of :math:`X`. Include for low-rank inference. partition_size: :obj:`int`, optional Number of rows to process per partition. Default given by block size of :math:`(P_r A)^T`. Returns ------- :class:`.Table` Table of results for each augmented design matrix. """ from hail.table import Table self._check_dof(self.f + 1) if self.low_rank and a_t_path is None: raise ValueError('model is low-rank so a_t is required.') elif not (self.low_rank or a_t_path is None): raise ValueError('model is full-rank so a_t must not be set.') if self._scala_model is None: self._set_scala_model() if partition_size is None: block_size = Env.hail().linalg.BlockMatrix.readMetadata(Env.hc()._jhc, pa_t_path).blockSize() partition_size = block_size elif partition_size <= 0: raise ValueError(f'partition_size must be positive, found {partition_size}') jpa_t = Env.hail().linalg.RowMatrix.readBlockMatrix(Env.hc()._jhc, pa_t_path, jsome(partition_size)) if a_t_path is None: maybe_ja_t = jnone() else: maybe_ja_t = jsome( Env.hail().linalg.RowMatrix.readBlockMatrix(Env.hc()._jhc, a_t_path, jsome(partition_size)))
return Table(self._scala_model.fit(jpa_t, maybe_ja_t))
[docs] @typecheck_method(pa=np.ndarray, a=nullable(np.ndarray)) def fit_alternatives_numpy(self, pa, a=None): r"""Fit and test alternative model for each augmented design matrix. Notes ----- The resulting table has the following fields: .. list-table:: :header-rows: 1 * - Field - Type - Value * - `idx` - int64 - Index of augmented design matrix. * - `beta` - float64 - :math:`\beta_\star` * - `sigma_sq` - float64 - :math:`\sigma^2` * - `chi_sq` - float64 - :math:`\chi^2` * - `p_value` - float64 - p-value Parameters ---------- pa: :class:`numpy.ndarray` Projected matrix :math:`P_r A` of alternatives with shape :math:`(r, m)`. Each column is a projected augmentation :math:`P_r x_\star` of :math:`P_r X`. a: :class:`numpy.ndarray`, optional Matrix :math:`A` of alternatives with shape :math:`(n, m)`. Each column is an augmentation :math:`x_\star` of :math:`X`. Required for low-rank inference. Returns ------- :class:`.Table` Table of results for each augmented design matrix. """ self._check_dof(self.f + 1) if not self._fitted: raise Exception("null model is not fit. Run 'fit' first.") n_cols = pa.shape[1] assert pa.shape[0] == self.r if self.low_rank: assert a.shape[0] == self.n and a.shape[1] == n_cols data = [(i,) + self._fit_alternative_numpy(pa[:, i], a[:, i]) for i in range(n_cols)] else: data = [(i,) + self._fit_alternative_numpy(pa[:, i], None) for i in range(n_cols)] df = pd.DataFrame.from_records(data, columns=['idx', 'beta', 'sigma_sq', 'chi_sq', 'p_value'])
return Table.from_pandas(df, key='idx') def _fit_alternative_numpy(self, pa, a): from scipy.linalg import solve, LinAlgError from scipy.stats.distributions import chi2 gamma = self.gamma dpa = self._d_alt * pa # single thread => no need to copy ydy = self._ydy_alt xdy = self._xdy_alt xdx = self._xdx_alt if self.low_rank: xdy[0] = self.py @ dpa + gamma * (self.y @ a) xdx[0, 0] = pa @ dpa + gamma * (a @ a) xdx[0, 1:] = self.px.T @ dpa + gamma * (self.x.T @ a) else: xdy[0] = self.py @ dpa xdx[0, 0] = pa @ dpa xdx[0, 1:] = self.px.T @ dpa try: beta = solve(xdx, xdy, assume_a='pos') # only uses upper triangle residual_sq = ydy - xdy.T @ beta sigma_sq = residual_sq / self._dof_alt chi_sq = self.n * np.log(self._residual_sq / residual_sq) # division => precision p_value = chi2.sf(chi_sq, 1) return beta[0], sigma_sq, chi_sq, p_value except LinAlgError: return tuple(4 * [float('nan')]) def _set_scala_model(self): from hail.utils.java import Env from hail.linalg import _jarray_from_ndarray, _breeze_from_ndarray if not self._fitted: raise Exception("null model is not fit. Run 'fit' first.") self._scala_model = Env.hail().stats.LinearMixedModel.apply( Env.hc()._jhc, self.gamma, self._residual_sq, _jarray_from_ndarray(self.py), _breeze_from_ndarray(self.px), _jarray_from_ndarray(self._d_alt), self._ydy_alt, _jarray_from_ndarray(self._xdy_alt), _breeze_from_ndarray(self._xdx_alt), jsome(_jarray_from_ndarray(self.y)) if self.low_rank else jnone(), jsome(_breeze_from_ndarray(self.x)) if self.low_rank else jnone() ) def _check_dof(self, f=None): if f is None: f = self.f dof = self.n - f if dof <= 0: raise ValueError(f"{self.n} {plural('observation', self.n)} with {f} fixed {plural('effect', f)}"
f"implies {dof} {plural('degree', dof)} of freedom. Must be positive.")