Empirical Approximation overview¶

For most models we use sampling MCMC algorithms like Metropolis or NUTS. In PyMC3 we got used to store traces of MCMC samples and then do analysis using them. There is a similar concept for the variational inference submodule in PyMC3: Empirical. This type of approximation stores particles for the SVGD sampler. There is no difference between independent SVGD particles and MCMC samples. Empirical acts as a bridge between MCMC sampling output and full-fledged VI utils like apply_replacements or sample_node. For the interface description, see variational_api_quickstart. Here we will just focus on Emprical and give an overview of specific things for the Empirical approximation

In [1]:

%matplotlib inline
import matplotlib.pyplot as plt
import theano
import numpy as np
import pymc3 as pm
np.random.seed(42)
pm.set_tt_rng(42)


Multimodal density¶

Let’s recall the problem from variational_api_quickstart where we first got a NUTS trace

In [2]:

w = pm.floatX([.2, .8])
mu = pm.floatX([-.3, .5])
sd = pm.floatX([.1, .1])

with pm.Model() as model:
x = pm.NormalMixture('x', w=w, mu=mu, sigma=sd, dtype=theano.config.floatX)
trace = pm.sample(50000)

INFO (theano.gof.compilelock): Waiting for existing lock by process '18726' (I am process '18876')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir
INFO (theano.gof.compilelock): Waiting for existing lock by process '18726' (I am process '18876')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir
INFO (theano.gof.compilelock): Waiting for existing lock by process '18726' (I am process '18876')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir
Auto-assigning NUTS sampler...
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [x]
Sampling 2 chains: 100%|██████████| 101000/101000 [00:51<00:00, 1963.57draws/s]
/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
output = mkl_fft.rfftn_numpy(a, s, axes)
The estimated number of effective samples is smaller than 200 for some parameters.

In [3]:

pm.traceplot(trace);


Great. First having a trace we can create Empirical approx

In [4]:

print(pm.Empirical.__doc__)

**Single Group Full Rank Approximation**

Builds Approximation instance from a given trace,
it has the same interface as variational approximation


In [5]:

with model:
approx = pm.Empirical(trace)

In [6]:

approx

Out[6]:

<pymc3.variational.approximations.Empirical at 0x1c23a8abe0>


This type of approximation has it’s own underlying storage for samples that is theano.shared itself

In [7]:

approx.histogram

Out[7]:

histogram

In [8]:

approx.histogram.get_value()[:10]

Out[8]:

array([[0.44232642],
[0.43486722],
[0.57271322],
[0.58091718],
[0.38298766],
[0.38298766],
[0.33438251],
[0.7098557 ],
[0.61921361],
[0.54169809]])

In [9]:

approx.histogram.get_value().shape

Out[9]:

(100000, 1)


It has exactly the same number of samples that you had in trace before. In our particular case it is 50k. Another thing to notice is that if you have multitrace with more than one chain you’ll get much more samples stored at once. We flatten all the trace for creating Empirical.

This histogram is about how we store samples. The structure is pretty simple: (n_samples, n_dim) The order of these variables is stored internally in the class and in most cases will not be needed for end user

In [10]:

approx.ordering

Out[10]:

<pymc3.blocking.ArrayOrdering at 0x114162c88>


Sampling from posterior is done uniformly with replacements. Call approx.sample(1000) and you’ll get again the trace but the order is not determined. There is no way now to reconstruct the underlying trace again with approx.sample.

In [11]:

new_trace = approx.sample(50000)

INFO (theano.gof.compilelock): Waiting for existing lock by process '18726' (I am process '18876')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir

In [12]:

%timeit new_trace = approx.sample(50000)

1.69 s ± 68.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


After sampling function is compiled sampling bacomes really fast

In [13]:

pm.traceplot(new_trace);


You see there is no order any more but reconstructed density is the same.

2d density¶

In [14]:

mu = pm.floatX([0., 0.])
cov = pm.floatX([[1, .5], [.5, 1.]])
with pm.Model() as model:
pm.MvNormal('x', mu=mu, cov=cov, shape=2)
trace = pm.sample(1000)

Auto-assigning NUTS sampler...
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [x]
Sampling 2 chains: 100%|██████████| 3000/3000 [00:04<00:00, 703.93draws/s]
/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
output = mkl_fft.rfftn_numpy(a, s, axes)

In [15]:

with model:
approx = pm.Empirical(trace)

In [23]:

pm.traceplot(approx.sample(10000));

In [17]:

import seaborn as sns

In [18]:

sns.kdeplot(approx.sample(1000)['x'])

/Users/twiecki/anaconda3/lib/python3.6/site-packages/seaborn/distributions.py:679: UserWarning: Passing a 2D dataset for a bivariate plot is deprecated in favor of kdeplot(x, y), and it will cause an error in future versions. Please update your code.
warnings.warn(warn_msg, UserWarning)
/Users/twiecki/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

Out[18]:

<matplotlib.axes._subplots.AxesSubplot at 0x1c2174d400>


Previously we had a trace_cov function

In [19]:

with model:
print(pm.trace_cov(trace))

[[1.00494398 0.46960795]
[0.46960795 0.97373728]]


Now we can estimate the same covariance using Empirical

In [20]:

print(approx.cov)

Elemwise{true_div,no_inplace}.0


That’s a tensor itself

In [21]:

print(approx.cov.eval())

[[1.0044415  0.46937314]
[0.46937314 0.97325041]]


Estimations are very close and differ due to precision error. We can get the mean in the same way

In [22]:

print(approx.mean.eval())

[-0.00523404 -0.00168637]