# Normalizing Flows Overview¶

Normalizing Flows is a rich family of distributions. They were described by Rezende and Mohamed, and their experiments proved the importance of studying them further. Some extensions like that of Tomczak and Welling made partially/full rank Gaussian approximations for high dimensional spaces computationally tractable.

This notebook reveals some tips and tricks for using normalizing flows effectively in PyMC3.

:

%matplotlib inline
from collections import Counter

import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import seaborn as sns
import theano
import theano.tensor as tt

pm.set_tt_rng(42)
np.random.seed(42)


## Theory¶

Normalizing flows is a series of invertible transformations on an initial distribution.

$z_K = f_K \circ \dots \circ f_2 \circ f_1(z_0)$

In this case, we can compute a tractable density for the flow.

$\ln q_K(z_K) = \ln q_0(z_0) - \sum_{k=1}^{K}\ln \left|\frac{\partial f_k}{\partial z_{k-1}}\right|$

Here, every $$f_k$$ is a parametric function with a well-defined determinant. The transformation used is up to the user; for example, the simplest flow is an affine transform:

$z = loc(scale(z_0)) = \mu + \sigma * z_0$

In this case, we get a mean field approximation if $$z_0 \sim \mathcal{N}(0, 1)$$

## Flow Formulas¶

In PyMC3 there are flexible ways to define flows with formulas. There are currently 5 types defined:

• Loc (loc): $$z' = z + \mu$$

• Scale (scale): $$z' = \sigma * z$$

• Planar (planar): $$z' = z + u * \tanh(w^T z + b)$$

• Radial (radial): $$z' = z + \beta (\alpha + ||z-z_r||)^{-1}(z-z_r)$$

• Householder (hh): $$z' = H z$$

Formulae can be composed as a string, e.g. 'scale-loc', 'scale-hh*4-loc', 'panar*10'. Each step is separated with '-', and repeated flows are defined with '*' in the form of '<flow>*<#repeats>'.

Flow-based approximations in PyMC3 are based on the NormalizingFlow class, with corresponding inference classes named using the NF abbreviation (analogous to how ADVI and SVGD are treated in PyMC3).

Concretely, an approximation is represented by:

:

pm.NormalizingFlow

:

pymc3.variational.approximations.NormalizingFlow


While an inference class is:

:

pm.NFVI

:

pymc3.variational.inference.NFVI


## Flow patterns¶

Composing flows requires some understanding of the target output. Flows that are too complex might not converge, whereas if they are too simple, they may not accurately estimate the posterior.

Let’s start simply:

:

with pm.Model() as dummy:

N = pm.Normal("N", shape=(100,))


### Mean Field connectivity¶

Let’s apply the transformation corresponding to the mean-field family to begin with:

:

pm.NormalizingFlow("scale-loc", model=dummy)

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f15893ecf10>


### Full Rank Normal connectivity¶

We can get a full rank model with dense covariance matrix using householder flows (hh). One hh flow adds exactly one rank to the covariance matrix, so for a full rank matrix we need K=ndim householder flows. hh flows are volume-preserving, so we need to change the scaling if we want our posterior to have unit variance for the latent variables.

After we specify the covariance with a combination of 'scale-hh*K', we then add location shift with the loc flow. We now have a full-rank analog:

:

pm.NormalizingFlow("scale-hh*100-loc", model=dummy)

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f1589303450>


A more interesting case is when we do not expect a lot of interactions within the posterior. In this case, where our covariance is expected to be sparse, we can constrain it by defining a low rank approximation family.

This has the additional benefit of reducing the computational cost of approximating the model.

:

pm.NormalizingFlow("scale-hh*10-loc", model=dummy)

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f1586b51210>


Parameters can be initialized randomly, using the jitter argument to specify the scale of the randomness.

:

pm.NormalizingFlow("scale-hh*10-loc", model=dummy, jitter=0.001)  # LowRank

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f15869b8890>


• Planar (planar): $$z' = z + u * \tanh(w^T z + b)$$

• Radial (radial): $$z' = z + \beta (\alpha + ||z-z_r||)^{-1}(z-z_r)$$

Planar flows are useful for splitting the incoming distribution into two parts, which allows multimodal distributions to be modeled.

Similarly, a radial flow changes density around a specific reference point.

## Simulated data example¶

There were 4 potential functions illustrated in the original paper, which we can replicate here. Inference can be unstable in multimodal cases, but there are strategies for dealing with them.

First, let’s specify the potential functions:

:

def w1(z):
return tt.sin(2.0 * np.pi * z / 4.0)

def w2(z):
return 3.0 * tt.exp(-0.5 * ((z - 1.0) / 0.6) ** 2)

def w3(z):
return 3.0 * (1 + tt.exp(-(z - 1.0) / 0.3)) ** -1

def pot1(z):
z = z.T
return 0.5 * ((z.norm(2, axis=0) - 2.0) / 0.4) ** 2 - tt.log(
tt.exp(-0.5 * ((z - 2.0) / 0.6) ** 2) + tt.exp(-0.5 * ((z + 2.0) / 0.6) ** 2)
)

def pot2(z):
z = z.T
return 0.5 * ((z - w1(z)) / 0.4) ** 2 + 0.1 * tt.abs_(z)

def pot3(z):
z = z.T
return -tt.log(
tt.exp(-0.5 * ((z - w1(z)) / 0.35) ** 2)
+ tt.exp(-0.5 * ((z - w1(z) + w2(z)) / 0.35) ** 2)
) + 0.1 * tt.abs_(z)

def pot4(z):
z = z.T
return -tt.log(
tt.exp(-0.5 * ((z - w1(z)) / 0.4) ** 2)
+ tt.exp(-0.5 * ((z - w1(z) + w3(z)) / 0.35) ** 2)
) + 0.1 * tt.abs_(z)

z = tt.matrix("z")
z.tag.test_value = pm.floatX([[0.0, 0.0]])
pot1f = theano.function([z], pot1(z))
pot2f = theano.function([z], pot2(z))
pot3f = theano.function([z], pot3(z))
pot4f = theano.function([z], pot4(z))

:

def contour_pot(potf, ax=None, title=None, xlim=5, ylim=5):
grid = pm.floatX(np.mgrid[-xlim:xlim:100j, -ylim:ylim:100j])
grid_2d = grid.reshape(2, -1).T
cmap = plt.get_cmap("inferno")
if ax is None:
_, ax = plt.subplots(figsize=(12, 9))
pdf1e = np.exp(-potf(grid_2d))
contour = ax.contourf(grid, grid, pdf1e.reshape(100, 100), cmap=cmap)
if title is not None:
ax.set_title(title, fontsize=16)
return ax

:

fig, ax = plt.subplots(2, 2, figsize=(12, 12))
ax = ax.flatten()
contour_pot(
pot1f,
ax,
"pot1",
)
contour_pot(pot2f, ax, "pot2")
contour_pot(pot3f, ax, "pot3")
contour_pot(pot4f, ax, "pot4")
fig.tight_layout() ## Reproducing first potential function¶

:

from pymc3.distributions.dist_math import bound

def cust_logp(z):
# return bound(-pot1(z), z>-5, z<5)
return -pot1(z)

with pm.Model() as pot1m:
pm.DensityDist("pot1", logp=cust_logp, shape=(2,))


### NUTS¶

Let’s use NUTS first. Just to have a look how good is it’s approximation.

Note you may need to rerun the model a couple of times, as the sampler/estimator might not fully explore function due to multimodality.

:

pm.set_tt_rng(42)
np.random.seed(42)
with pot1m:
trace = pm.sample(
1000,
init="auto",
cores=2,
start=[dict(pot1=np.array([-2, 0])), dict(pot1=np.array([2, 0]))],
)

Auto-assigning NUTS sampler...
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [pot1]

100.00% [4000/4000 00:04<00:00 Sampling 2 chains, 102 divergences]
Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 6 seconds.
There were 102 divergences after tuning. Increase target_accept or reparameterize.
The acceptance probability does not match the target. It is 0.30246096720680476, but should be close to 0.8. Try to increase the number of tuning steps.
The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.
The estimated number of effective samples is smaller than 200 for some parameters.

:

dftrace = pm.trace_to_dataframe(trace)
sns.jointplot(dftrace.iloc[:, 0], dftrace.iloc[:, 1], kind="kde")

:

<seaborn.axisgrid.JointGrid at 0x7f1585cfb3d0> ### Normalizing flows¶

As a first (naive) try with flows, we will keep things simple: Let’s use just 2 planar flows and see what we get:

:

with pot1m:
inference = pm.NFVI("planar*2", jitter=1)

## Plotting starting distribution
dftrace = pm.trace_to_dataframe(inference.approx.sample(1000))
sns.jointplot(dftrace.iloc[:, 0], dftrace.iloc[:, 1], kind="kde"); It is illustrative to track gradients as well as parameters. In this setup, different sampling points can give different gradients because a single sampled point tends to collapse to a mode.

Here are the parameters of the model:

:

inference.approx.params

:

[b, u, w, b, u, w]


We also require an objective:

:

inference.objective(nmc=None)

:

Elemwise{mul,no_inplace}.0


Theano can be used to calcuate the gradient of the objective with respect to the parameters:

:

with theano.configparser.change_flags(compute_test_value="off"):

:

[Elemwise{add,no_inplace}.0,


If we want to keep track of the gradient changes during the inference, we warp them in a pymc3 callback:

:

from collections import OrderedDict, defaultdict
from itertools import count

@theano.configparser.change_flags(compute_test_value="off")
def get_tracker(inference):
numbers = defaultdict(count)
params = inference.approx.params
names = ["%s_%d" % (v.name, next(numbers[v.name])) for v in inference.approx.params]
return pm.callbacks.Tracker(
**OrderedDict(
[(name, v.eval) for name, v in zip(names, params)]
)
)

tracker = get_tracker(inference)

:

tracker.whatchdict

:

{'b_0': <bound method Variable.eval of b>,
'u_0': <bound method Variable.eval of u>,
'w_0': <bound method Variable.eval of w>,
'b_1': <bound method Variable.eval of b>,
'u_1': <bound method Variable.eval of u>,
'w_1': <bound method Variable.eval of w>,

:

inference.fit(30000, obj_optimizer=pm.adagrad_window(learning_rate=0.01), callbacks=[tracker])

100.00% [30000/30000 02:01<00:00 Average Loss = -0.87404]
Finished [100%]: Average Loss = -0.89074

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f157f5ee5d0>

:

dftrace = pm.trace_to_dataframe(inference.approx.sample(1000))
sns.jointplot(dftrace.iloc[:, 0], dftrace.iloc[:, 1], kind="kde")

:

<seaborn.axisgrid.JointGrid at 0x7f156ba27a50> :

plt.plot(inference.hist); As you can see, the objective history is not very informative here. This is where the gradient tracker can be more informative.

:

# fmt: off
trackername = ['u_0', 'w_0', 'b_0', 'u_1', 'w_1', 'b_1',
# fmt: on

def plot_tracker_results(tracker):
fig, ax = plt.subplots(len(tracker.hist) // 2, 2, figsize=(16, len(tracker.hist) // 2 * 2.3))
ax = ax.flatten()
# names = list(tracker.hist.keys())
names = trackername
gnames = names[len(names) // 2 :]
names = names[: len(names) // 2]
pairnames = zip(names, gnames)

i = names.index(name)
left = ax[i * 2]
right = ax[i * 2 + 1]
params = np.asarray(tracker[name])
if params.ndim == 1:
params = params[:, None]
params = params.T
left.set_title("Param trace of %s" % name)
s = params.shape
for j, (v, g) in enumerate(zip(params, grads)):
left.plot(v, "-")
right.plot(g, "o", alpha=1 / s / 10)
left.legend([name + "_%d" % j for j in range(len(names))])
right.legend([gname + "_%d" % j for j in range(len(names))])

for vn, gn in pairnames:
fig.tight_layout()

:

plot_tracker_results(tracker); Inference is often unstable, some parameters are not well fitted as they poorly influence the resulting posterior.

In a multimodal setting, the dominant mode might well change from run to run.

### Going deeper¶

We can try to improve our approximation by adding flows; in the original paper they used both 8 and 32. Let’s try using 8 here.

:

with pot1m:
inference = pm.NFVI("planar*8", jitter=1.0)

dftrace = pm.trace_to_dataframe(inference.approx.sample(1000))
sns.jointplot(dftrace.iloc[:, 0], dftrace.iloc[:, 1], kind="kde"); We can try for a more robust fit by allocating more samples to obj_n_mc in fit, which controls the number of Monte Carlo samples used to approximate the gradient.

:

inference.fit(
25000,
obj_n_mc=100,
callbacks=[pm.callbacks.CheckParametersConvergence()],
)

100.00% [25000/25000 02:39<00:00 Average Loss = -1.7774]
Finished [100%]: Average Loss = -1.7772

:

<pymc3.variational.approximations.NormalizingFlow at 0x7f156b117290>

:

dftrace = pm.trace_to_dataframe(inference.approx.sample(1000))
sns.jointplot(dftrace.iloc[:, 0], dftrace.iloc[:, 1], kind="kde")

:

<seaborn.axisgrid.JointGrid at 0x7f15666e2b10> This is a noticeable improvement. Here, we see that flows are able to characterize the multimodality of a given posterior, but as we have seen, they are hard to fit. The initial point of the optimization matters in general for the multimodal case.

### MCMC vs NFVI¶

Let’s use another potential function, and compare the sampling using NUTS to what we get with NF:

:

def cust_logp(z):
return -pot4(z)

with pm.Model() as pot_m:
pm.DensityDist("pot_func", logp=cust_logp, shape=(2,))

:

with pot_m:
traceNUTS = pm.sample(3000, tune=1000, target_accept=0.9, cores=2)

Auto-assigning NUTS sampler...
Multiprocess sampling (2 chains in 2 jobs)
NUTS: [pot_func]

100.00% [8000/8000 00:16<00:00 Sampling 2 chains, 34 divergences]
Sampling 2 chains for 1_000 tune and 3_000 draw iterations (2_000 + 6_000 draws total) took 16 seconds.
There were 32 divergences after tuning. Increase target_accept or reparameterize.
The acceptance probability does not match the target. It is 0.5722625904068807, but should be close to 0.9. Try to increase the number of tuning steps.
There were 2 divergences after tuning. Increase target_accept or reparameterize.
The acceptance probability does not match the target. It is 0.8154901665548793, but should be close to 0.9. Try to increase the number of tuning steps.
The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.
The estimated number of effective samples is smaller than 200 for some parameters.

:

formula = "planar*10"
with pot_m:
inference = pm.NFVI(formula, jitter=0.1)

traceNF = inference.approx.sample(5000)

100.00% [25000/25000 01:28<00:00 Average Loss = -2.5039]
Finished [100%]: Average Loss = -2.5043

:

fig, ax = plt.subplots(1, 3, figsize=(18, 6))
contour_pot(pot4f, ax, "Target Potential Function")

ax.scatter(traceNUTS["pot_func"][:, 0], traceNUTS["pot_func"][:, 1], c="r", alpha=0.05)
ax.set_xlim(-5, 5)
ax.set_ylim(-5, 5)
ax.set_title("NUTS")

ax.scatter(traceNF["pot_func"][:, 0], traceNF["pot_func"][:, 1], c="b", alpha=0.05)
ax.set_xlim(-5, 5)
ax.set_ylim(-5, 5)
ax.set_title("NF with " + formula); :

%load_ext watermark
%watermark -n -u -v -iv -w

pymc3   3.9.0
theano  1.0.4
numpy   1.18.5
seaborn 0.10.1
last updated: Mon Jun 15 2020

CPython 3.7.7
IPython 7.15.0
watermark 2.0.2