initial working draft of kameleon mcmc

pymc/step_methods/kernel_metropolis.py

@@ -0,0 +1,267 @@
+'''
+Created on Aug 29, 2014
+
+@author: Heiko Strathmann
+'''
+
+from scipy.spatial.distance import squareform, pdist
+
+from theano import function
+import theano
+from theano.configparser import TheanoConfigParser
+
+import numpy as np
+from pymc.model import modelcontext
+from pymc.step_methods.arraystep import ArrayStep, metrop_select
+from pymc.step_methods.metropolis import tune
+import scipy as sp
+import theano.tensor as T
+
+from ..core import *
+
+
+# To avoid Theano complaining about missing test values
+TheanoConfigParser().compute_test_value = 'off'
+
+
+__all__ = ['KameleonOracle']
+
+
+class KameleonOracle(ArrayStep):
+    """
+    Kernel Adaptive Metropolis-Hastings sampling step with a fixed set of orcale
+    samples. Automatic tuning of the scaling is possible via a simple schedule
+    for a given number of iterations.
+
+    Based on "Kernel Adaptive Metopolis Hastings" by D. Sejdinovic, H. Strathmann,
+    M. Lomeli, C. Andrieu, A. Gretton
+    http://jmlr.org/proceedings/papers/v32/sejdinovic14.html
+
+    See also https://github.com/karlnapf/kameleon-mcmc for experimental code.
+
+    Parameters
+    ----------
+    vars : list
+        List of variables for sampler
+    Z : 2d numpy array
+        Oracle sample to represent target covariance structure
+    proposal_dist : function
+        Function that returns zero-mean deviates when parameterized with
+        S (and n). Defaults to quad_potential.
+    gamma2 : scalar
+        Exploration term in proposal
+    nu2 : scalar
+        Scaling of the covariance part of the proposal
+    kernel : Kernel instance
+        Kernel to use for representing covariance structure in feature space.
+        Must implement interface for kernel and gradient, see for example GaussianKernel
+    model : PyMC Model
+        Optional model for sampling step. Defaults to None (taken from context).
+
+    """
+    def __init__(self, vars=None, Z=None, gamma2=0.1, nu2=1., kernel=None,
+                 tune=True, tune_interval=100, model=None, dist=None):
+        model = modelcontext(model)
+        if vars is None:
+            vars = model.vars
+
+        self.Z = Z
+        self.kernel = kernel
+        self.gamma2 = gamma2
+        self.nu2 = nu2
+        self.tune = tune
+
+        # empty proposal distribution and last likelihood
+        self.q_dist = None
+        self.log_target = -np.inf
+
+        # statistics for tuning scaling
+        self.tune = tune
+        self.tune_interval = tune_interval
+        self.steps_until_tune = tune_interval
+        self.accepted = 0
+
+        super(KameleonOracle, self).__init__(vars, [model.fastlogp])
+
+    def astep(self, q0, logp):
+        # sample from kernel based Gaussian proposal
+        q_dist = self.construct_proposal(q0)
+        q = np.ravel(q_dist.sample())
+
+        # evaluate target log probability
+        logp_q = logp(q)
+
+        # MH accept/reject step
+        if self.q_dist is None:
+            q_new = q
+        else:
+            q_new = metrop_select(logp_q + q_dist.log_pdf(q0) \
+                                  - self.log_pdf_target - self.q_dist.log_pdf(q), q, q0)
+
+        # adapt
+        if self.tune and not self.steps_until_tune:
+            # tune scaling parameter using metropolis  method
+            self.nu2 = tune(self.nu2, self.accepted / float(self.tune_interval))
+            # Reset counter
+            self.steps_until_tune = self.tune_interval
+            self.accepted = 0
+
+        # update log-pdf and proposal distribution object on accept
+        if any(q_new != q0):
+            self.accepted += 1
+            self.q_dist = q_dist
+            self.log_pdf_target = logp_q
+
+        self.steps_until_tune -= 1
+
+        return q_new
+
+    def compute_constants(self, y):
+        """
+        Pre-computes constants of the log density of the proposal distribution,
+        which is Gaussian as p(x|y) ~ N(mu, R)
+        where
+        mu = y-a
+        a = 0
+        R  = gamma^2 I + M M^T
+        M  = 2 [\nabla_x k(x,z_i]|_x=y
+
+        Returns (mu,L_R), where L_R is lower Cholesky factor of R
+        """
+        assert(len(np.shape(y)) == 1)
+
+        # M = 2 [\nabla_x k(x,z_i]|_x=y
+        R = self.gamma2 * np.eye(len(y))
+        if self.Z is not None:
+            M = 2 * self.kernel.gradient(y, self.Z)
+            # R = gamma^2 I + \nu^2 * M H M^T
+            H = np.eye(len(self.Z)) - 1.0 / len(self.Z)
+            R += self.nu2 * M.T.dot(H.dot(M))
+
+        L_R = np.linalg.cholesky(R)
+
+        return y.copy(), L_R
+
+    def construct_proposal(self, y):
+        """
+        Constructs the Kameleon MCMC proposal centred at y, using history Z
+
+        The proposal is a Gaussian based on the kernel values between y and all
+        points in the chain history.
+        """
+        mu, L = self.compute_constants(y)
+
+        return Gaussian(mu, L, is_cholesky=True)
+
+class Gaussian():
+    """
+    Helper class to sample from and evaluate log-pdf of a multivariate Gaussian,
+    using efficient Cholesky based representation (Cholesky only computed once)
+    """
+    def __init__(self, mu, Sigma, is_cholesky=False):
+        self.mu = mu
+        self.is_cholesky = is_cholesky
+
+        if self.is_cholesky:
+            self.L = Sigma
+        else:
+            self.L = np.linalg.cholesky(Sigma)
+
+        self.dimension = len(mu)
+
+    def log_pdf(self, X):
+        # duck typing for shape
+        if len(np.shape(X)) == 1:
+            X = X.reshape(1, len(X))
+
+        log_determinant_part = -sum(np.log(np.diag(self.L)))
+
+        quadratic_parts = np.zeros(len(X))
+        for i in range(len(X)):
+            x = X[i] - self.mu
+
+            # solve y=K^(-1)x = L^(-T)L^(-1)x
+            y = sp.linalg.solve_triangular(self.L, x.T, lower=True)
+            y = sp.linalg.solve_triangular(self.L.T, y, lower=False)
+            quadratic_parts[i] = -0.5 * x.dot(y)
+
+        const_part = -0.5 * len(self.L) * np.log(2 * np.pi)
+
+        return const_part + log_determinant_part + quadratic_parts
+
+    def sample(self, n=1):
+        V = np.random.randn(self.dimension, n)
+
+        # map to our desired Gaussian and transpose to have row-wise vectors
+        return self.L.dot(V).T + self.mu
+
+def theano_sq_dists_mat_expr(X, Y):
+    return (-2 * X.dot(Y.T).T + T.sum(X ** 2, 1).T).T + T.sum(Y ** 2, 1)
+
+def theano_gaussian_kernel_expr(sq_dists, sigma):
+    return T.exp(-sq_dists / (2.*sigma ** 2))
+
+def theano_sq_dists_vec_expr(x, Y):
+        # element wise vector norm
+        Y_norm = T.sum(Y ** 2, 1)
+        xY_terms = x.T.dot(Y.T)
+
+        # expanded sq euclidean distance
+        return T.sum(x ** 2) + Y_norm - 2 * xY_terms
+
+class GaussianKernel():
+    """
+    Helper class to represent a Gaussian kernel, with methods to compute kernel
+    function and its gradient wrt the left argument.
+    Uses Theano's autodiff for computing kernel gradients.
+    """
+
+    # compile theano functions
+    X = T.dmatrix('X')
+    x = T.dvector('x')
+    Y = T.dmatrix('Y')
+    sigma = T.dscalar('sigma')
+
+    # kernel expressions as for left input being matrix or vector
+    sq_dist_mat_expr = theano_sq_dists_mat_expr(X, Y)
+    sq_dist_vec_expr = theano_sq_dists_vec_expr(x, Y)
+    K_expr = theano_gaussian_kernel_expr(sq_dist_mat_expr, sigma)
+    k_expr = theano_gaussian_kernel_expr(sq_dist_vec_expr, sigma)
+
+    # compile
+    theano_kernel_mat = function(inputs=[X, Y, sigma], outputs=K_expr)
+    theano_kernel_vec = function(inputs=[x, Y, sigma], outputs=k_expr)
+    theano_kernel_vec_grad_x = function(inputs=[x, Y, sigma],
+                                             outputs=theano.gradient.jacobian(k_expr, x))
+
+    @staticmethod
+    def gaussian_median_heuristic(X):
+        dists = squareform(pdist(X, 'sqeuclidean'))
+        median_dist = np.median(dists[dists > 0])
+        sigma = np.sqrt(0.5 * median_dist)
+        return sigma
+
+    def __init__(self, width):
+        self.width = width
+
+    def kernel(self, X, Y=None):
+        """
+        Computes the standard Gaussian kernel k(x,y)=exp(-0.5* ||x-y||**2 / sigma**2)
+
+        X - 2d array, samples on right hand side
+        Y - 2d array, samples on left hand side, can be None in which case its replaced by X
+        """
+        return self.theano_kernel(X, Y, self.width)
+
+    def gradient(self, x, Y):
+        """
+        Computes the gradient of the Gaussian kernel wrt. to the left argument, i.e.
+        k(x,y)=exp(-0.5* ||x-y||**2 / sigma**2), which is
+        \nabla_x k(x,y)=1.0/sigma**2 k(x,y)(y-x)
+        Given a set of row vectors Y, this computes the
+        gradient for every pair (x,y) for y in Y.
+
+        x - single sample on right hand side (1d array)
+        Y - samples on left hand side (2d array)
+        """
+        return self.theano_kernel_vec_grad_x(x, Y, self.width)