!date
import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline
sns.set_context('poster')
sns.set_style('darkgrid')
Thu Jun 11 10:52:33 PDT 2015
Following the approach in http://www.seanet.com/~bradbell/tmb.htm
import pymc3 as pm, theano.tensor as tt, theano.tensor.slinalg, scipy.optimize
Couldn't import dot_parser, loading of dot files will not be possible.
def sim_data(n, p, q, seed):
"""Simulate data to fit with Laplace approximation
Parameters
----------
n : int, rows of data
p : int, number of fixed effect coeffs
q : int, number of random effect coeffs
Results
-------
returns dict PyMC3 model
"""
# sim data for random effect model
np.random.seed(seed) # set random seed for reproducibility
# ground truth for model parameters
beta_true = np.linspace(-1,1,p)
u_true = np.linspace(-1,1,q)
sigma_true = .1
# covariates
X = np.random.normal(size=(n,p))
Z = np.random.uniform(0, 1, size=(n,q))
Z = 1. * (Z < .1)
# observed data
y = np.dot(X, beta_true) + np.dot(Z, u_true) + np.random.normal(0., sigma_true, size=n)
return locals()
data = sim_data(n=1000, p=5, q=500, seed=12345)
def make_model(data):
"""Create a random effects model from simulated data
Parameters
----------
data : dict, as returned by sim_data function
Results
-------
returns PyMC3 model for data
"""
m = pm.Model()
with m:
beta = pm.Normal('beta', mu=0., sd=1., shape=data['p'])
u = pm.Normal('u', mu=0., sd=1., shape=data['q'])
sigma = pm.HalfNormal('sigma', sd=1.)
y_obs = pm.Normal('y_obs', mu=tt.dot(data['X'], beta) + tt.dot(data['Z'], u), sd=sigma, observed=data['y'])
return m
m = make_model(data)
WARNING (theano.gof.compilelock): Overriding existing lock by dead process '32184' (I am process '24906') WARNING:theano.gof.compilelock:Overriding existing lock by dead process '32184' (I am process '24906')
Cannot compute test value: input 0 (<TensorType(float32, matrix)>) of Op Dot22(<TensorType(float32, matrix)>, <TensorType(float32, matrix)>) missing default value
# find MAP
with m:
%time map_pt = pm.find_MAP()
CPU times: user 34.3 s, sys: 268 ms, total: 34.5 s Wall time: 34.7 s
H_uu = pm.hessian(m.logpt, [m.u])
# use Cholesky factorization to calculate determinant
L_H_uu = theano.tensor.slinalg.Cholesky()(H_uu)
# calculate log determinant of H_uu by summing log of diagonal of Cholesky factorization
log_det_H_uu = tt.sum(tt.log(tt.diag(L_H_uu)))
# make that into a theano function
f_log_det_H_uu = m.fn(log_det_H_uu)
/homes/abie/anaconda/envs/testenv/lib/python2.7/site-packages/theano/scan_module/scan_perform_ext.py:133: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility from scan_perform.scan_perform import *
def argmax_u(m, start):
"""Find u that maximizes model posterior for fixed beta, sigma
Parameter
---------
start : dict, with keys for all (transformed) model, e.g.
start={'u':np.zeros(q), 'beta':beta_val, 'sigma_log':np.log(sigma_val)}
Results
-------
return u_hat as an array
"""
with m:
pt = pm.find_MAP(start=start, vars=[m.u])
return pt['u']
u_hat = argmax_u(m, {'u':np.zeros(data['q']), 'beta':.5*np.ones(data['p']), 'sigma_log':np.log(.1)})
u_hat = np.zeros(data['q'])
def approx_marginal_neg_log_posterior(theta):
"""Laplace approximation of the marginal negative log posterior of m
Parameters
----------
theta : array of shape (p+1,), with first p for beta, and final entry for sigma
Results
-------
returns approx -logp as a float
"""
global u_hat
global m
beta = theta[:-1]
sigma = theta[-1]
# prevent optimizer from going astray
if sigma <= 1e-6:
sigma = 1e-6
sigma_log = np.log(sigma)
pt = {'u': u_hat, 'beta': beta, 'sigma_log':sigma_log}
u_hat = argmax_u(m, pt)
pt['u'] = u_hat
det_H_logp = f_log_det_H_uu(pt)
return .5*det_H_logp - m.logp(pt)
approx_marginal_neg_log_posterior(.5*np.ones(data['p']+1))
4704.9488202994035
%time theta = scipy.optimize.fmin_bfgs(approx_marginal_neg_log_posterior, .1*np.ones(data['p']+1))
Warning: Desired error not necessarily achieved due to precision loss. Current function value: 496.161295 Iterations: 66 Function evaluations: 862 Gradient evaluations: 106 CPU times: user 26min 17s, sys: 11.2 s, total: 26min 28s Wall time: 26min 29s
plt.plot(map_pt['u'], u_hat, 'o')
plt.plot([-1,1], [-1,1], 'k--')
plt.xlabel('Random Effect Coeffs from MAP')
plt.ylabel('Random Effect Coeffs from LA')
<matplotlib.text.Text at 0x7fc67d90b250>
plt.plot(map_pt['beta'], theta[:-1], 'o')
plt.plot([-1,1], [-1,1], 'k--')
plt.xlabel('Fixed Effect Coeffs from MAP')
plt.ylabel('Fixed Effect Coeffs from LA')
<matplotlib.text.Text at 0x7fc67d782250>
pd.Series({'Spread of RE from MAP': np.exp(map_pt['sigma_log']),
'Spread of RE from LA': theta[-1]}).plot(kind='barh')
<matplotlib.axes._subplots.AxesSubplot at 0x7fc67d723210>