Multimodality and Resampling

This notebook tests the robustness of modified Liu-West (MLW) resampling to multimodality in the posterior distribution. We use as a test the familiar model $$ \Pr(0 | \omega, t) = \cos^2(\omega t), $$ exploiting that the likelihood function is even in $\omega$ to produce posteriors that always have exactly two modes. Because this structure is not explicitly added to the resampling, but is easy to reason about analytically, it serves as a nice test case.


As usual, we start by configuring division and enabling plotting support.

In [1]:
from __future__ import division, print_function
%matplotlib inline
In [2]:
import numpy as np
import matplotlib.pyplot as plt
except: pass

In order to view the incredibly verbose output from resampler debugging, it is helpful to point Python's logging functionality at a file instead of printing it inside the notebook. We use tempfile to pick a directory in a cross-platform way.

In [3]:
import os, tempfile
logfile = os.path.join(tempfile.gettempdir(), 'multimodal_testing.log')
print("Logging to {}.".format(logfile))

import logging
logging.basicConfig(level=logging.DEBUG, filename=logfile)
Logging to C:\Users\Chris\AppData\Local\Temp\multimodal_testing.log.

Next, we import all the functionality from QInfer that we will need.

In [4]:
import qinfer as qi
C:\Anaconda3\lib\site-packages\IPython\ ShimWarning: The `IPython.parallel` package has been deprecated. You should import from ipyparallel instead.
  "You should import from ipyparallel instead.", ShimWarning)
c:\users\chris\dropbox\software-projects\python-qinfer\src\qinfer\ UserWarning: Could not import IPython parallel. Parallelization support will be disabled.
  "Could not import IPython parallel. "

Model and Prior Definition

We want the simple precession model $\cos^2(\omega t)$ to now extend over $\omega \in [-1, 1]$, so we redefine the are_models_valid method to allow for negative $\omega$.

In [5]:
class NotQuiteSoSimplePrecessionModel(qi.SimplePrecessionModel):
    def are_models_valid(modelparams):
        return (np.abs(modelparams) <= 1)[:, 0]

Having done so, we now set a prior that explicitly includes the degeneracy in the likelihood.

In [6]:
model = NotQuiteSoSimplePrecessionModel()
prior = qi.UniformDistribution([-1, 1])
In [7]:
def trial(a=0.98, h=None, track=True):
    true_params = prior.sample()
    updater = qi.SMCUpdater(model, 1000, prior, resampler=qi.LiuWestResampler(a=a, h=h, debug=True), track_resampling_divergence=track, debug_resampling=True)
    for idx_exp in range(100):
        exp = np.array([(9/8)**idx_exp], dtype=model.expparams_dtype)
        datum = model.simulate_experiment(true_params, exp)
        updater.update(datum, exp)
    # Since this model is always degenerate about the origin, take the mean
    # not of the original particles, but of the absolute value of the particles.
    updater.particle_locations = np.abs(updater.particle_locations)
    if track:
        return [true_params, updater.est_mean()], np.mean(updater.resampling_divergences)
        return [true_params, updater.est_mean()]
In [8]:
def corr(a=0.98, h=None, track=True, n_trials=100):
    trues = np.zeros((n_trials,))
    ests  = np.zeros((n_trials,))
    if track:
        divs = np.zeros((n_trials, ))
    for idx_trial in range(n_trials):
        if track:
            print(idx_trial, end=' ')
            (true, est), div = trial(a, h, track)
            true, est = trial(a, h, track)
        trues[idx_trial] = true
        ests[idx_trial] = est
        if track:
            divs[idx_trial] = div
    if track:
        return trues, ests, divs
        return trues, ests
In [9]:
trues, ests = corr(1, 0.02, False, 100)
In [10]:
bias = np.mean(np.abs(trues) - np.abs(ests))
risk = np.mean(np.abs(np.abs(trues) - np.abs(ests)))
print(bias, risk)
0.00697411114983 0.0123620858504
In [11]:
plt.plot(np.abs(trues), ests, 'k.')
plt.xlim((0, 1))
plt.ylim((0, 1))
(0, 1)
In [12]:
trues, ests = corr(0.98, None, False, 100)
bias = np.mean(np.abs(trues) - np.abs(ests))
In [13]:
plt.plot(np.abs(trues), ests, 'k.')
plt.xlim((0, 1))
plt.ylim((0, 1))
(0, 1)

WIP: Mutliparameter Models

We also want to try with a multicos model, since that model admits a degeneracy between parameters that is broken by experimental variety. To arrive at a good estimate, then, a resampler must preserve unusual posterior structures that arise in the approach to a unimodal final posterior.

It's worth noting here that the traditional LW parameters work quite well for a better experimental protocol (choosing lots of varied experiments), but that this protocol may not always be available. Thus, MLW is a resource here to compensate for experimental restrictions.

In [14]:
class MultiCosModel(qi.FiniteOutcomeModel):
    def __init__(self, n_terms=2):
        self._n_terms = n_terms
        super(MultiCosModel, self).__init__()
    def n_modelparams(self):
        return self._n_terms
    def is_n_outcomes_constant(self):
        return True
    def n_outcomes(self, expparams):
        return 2
    def are_models_valid(self, modelparams):
        return np.all(np.logical_and(modelparams > 0, modelparams <= 1), axis=1)
    def expparams_dtype(self):
        return [('ts', '{}float'.format(self._n_terms))]
    def likelihood(self, outcomes, modelparams, expparams):
        # We first call the superclass method, which basically
        # just makes sure that call count diagnostics are properly
        # logged.
        super(MultiCosModel, self).likelihood(outcomes, modelparams, expparams)
        # Next, since we have a two-outcome model, everything is defined by
        # Pr(0 | modelparams; expparams), so we find the probability of 0
        # for each model and each experiment.
        # We do so by taking a product along the modelparam index (len 2,
        # indicating omega_1 or omega_2), then squaring the result.
        pr0 =
                # shape (n_models, 1, 2)
                modelparams[:, np.newaxis, :] *
                # shape (n_experiments, 2)
            ), # <- broadcasts to shape (n_models, n_experiments, 2).
            axis=2 # <- product over the final index (len 2)
        ) ** 2 # square each element
        # Now we use pr0_to_likelihood_array to turn this two index array
        # above into the form expected by SMCUpdater and other consumers
        # of likelihood().
        return qi.FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
In [15]:
mp_model = qi.BinomialModel(MultiCosModel(3))
mp_prior = qi.UniformDistribution([[0, 1]]*3)
In [16]:
def multicos_trial(a, h, n_exps=200):
    true = mp_prior.sample()
    mp_updater = qi.SMCUpdater(mp_model, 2000, mp_prior,
        resampler=qi.LiuWestResampler(a=a, h=h, debug=True),
    while True:
        exp = np.array([(20 * np.pi * np.random.random(3), 40)], dtype=mp_model.expparams_dtype)
        # To test the resampler, we take data according to one experiment design until it has to resample, then switch.
        # Since the MLW resampler works quite well, we need to set an upper limit.
        for idx in range(500):
            datum = mp_model.simulate_experiment(true, exp)
            mp_updater.update(datum, exp)
            if len(mp_updater.data_record) >= n_exps:
                return true, mp_updater.est_mean()
            if mp_updater.just_resampled and idx >= 100:
In [17]:
def nanmean(arr):
    return np.nansum(arr) / np.sum(np.isfinite(arr))
In [18]:
def mp_risk(a, h, n_trials=10, n_exps=200):
    mp_errors = np.empty((n_trials,))
    for idx_trial in range(n_trials):
        true, est = multicos_trial(a, h, n_exps)
        mp_errors[idx_trial] = np.sum((true - est)**2)
    n_nan = np.sum(np.isnan(mp_errors))
    if n_nan > 0:
        print("{} NaNs observed.".format(n_nan))
    return nanmean(mp_errors)