Bayesian data analysis with PyMC3
Thomas Wiecki
Quantopian Inc.
About me¶
- PhD candidate at Brown studying decision making using Bayesian modeling.
- Quantitative Researcher at Quantopian Inc: Building the world's first algorithmic trading platform in the web browser.
Why should you care about Bayesian Data Analysis?¶
In [1]:
from IPython.display import Image
import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'], 'size': 22})
rc('xtick', labelsize=14)
rc('ytick', labelsize=14)
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
%matplotlib inline
In [2]:
Image('backbox_ml.png')
Out[2]:
- Blackbox models not good at conveying what they have learned.
In [4]:
Image('openbox_pp.png')
Out[4]:
Probabilistic Programming¶
- Model unknown causes of a phenomenon as random variables.
- Write a programmatic story of how unknown causes result in observable data.
- Use Bayes formula to invert generative model to infer unknown causes.
Random Variables as Probability Distributions¶
- Represents our beliefs about an unknown state.
- Probability distribution assigns a probability to each possible state.
- Not a single number (e.g. most likely state).
Coin-flipping experiment.¶
- Given multiple flips, what is probability of getting heads?
- Maximum Likelihood answer:
- However:
- Clearly something is missing!
- Quantification of uncertainty.
Moreover...
- Consider a single flip which comes up heads:
- Again, this doesn't seem right.
- Incorporate prior knowledge.
In [2]:
from scipy import stats
# set every possibility to be equally possible
x_coin = np.linspace(0, 1, 101)
$$ \text{Express probability of heads as random variable } \theta$$¶
In [4]:
import prettyplotlib as ppl
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, xlabel=r'Hypothesis for chance of heads',
ylabel=r'Probability of hypothesis',
title=r'Prior probability distribution after no coin tosses')
ppl.plot(ax, x_coin, stats.beta(2, 2).pdf(x_coin), linewidth=3.)
ax.set_xticklabels([r'0\%', r'20\%', r'40\%', r'60\%', r'80\%', r'100\%']);
# fig.savefig('coin1.png')
Out[4]:
In [98]:
stats.beta(2,2).rvs(1)
Out[98]:
In [21]:
import random
import numpy as np
def choose_coin():
return stats.beta(2, 2).rvs(1) # random.uniform(0,1) # np.random.normal(0,1) #
# pylab.hist([choose_coin() for dummy in range(100000)], normed=True, bins=100)
successes = []
returns = 0.
for i in range(10000):
prob_heads = choose_coin()
results = [random.uniform(0,1) < prob_heads for dummy in range(10)]
if results.count(True) == 9:
successes.append(prob_heads)
if random.uniform(0,1) < prob_heads:
returns += -1
else:
returns += 1
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
r = ax.hist(np.array(successes), normed=True, bins=20)
print len(successes)
print "Average return", returns / len(successes)
$$ \theta \sim \text{Beta}(2, 2) $$$$ P(\theta) = \text{Beta}(2, 2) $$
In [108]:
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads',
ylabel='Probability of hypothesis',
title='Posterior probability distribution after first heads')
ppl.plot(ax, x_coin, stats.beta(1, 1).pdf(x_coin)*stats.beta(90, 10).pdf(x_coin), linewidth=3.)
ax.set_xticklabels([r'0\%', r'20\%', r'40\%', r'60\%', r'80\%', r'100\%']);
plt.savefig('coin2.png')
In [ ]:
$$ P(\theta | h=1) = \text{Beta}(3, 2) $$
In [10]:
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads',
ylabel='Probability of hypothesis',
title='Posterior probability distribution after 1 head, 1 tail')
ppl.plot(ax, x_coin, stats.beta(3, 3).pdf(x_coin), linewidth=3.)
ax.set_xticklabels(['0\%', '20\%', '40\%', '60\%', '80\%', '100\%']);
fig.savefig('coin3.png')
In [11]:
Image('coin3.png')
Out[11]:
$$ P(\theta | [h=1, t=1]) = \text{Beta}(3, 3) $$
In [12]:
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, xlabel='Hypothesis for chance of heads',
ylabel='Probability of hypothesis',
title='Posterior probability distribution after 20 heads and 20 tails')
ppl.plot(ax, x_coin, stats.beta(22, 22).pdf(x_coin), linewidth=3.)
ax.set_xticklabels(['0\%', '20\%', '40\%', '60\%', '80\%', '100\%']);
fig.savefig('coin4.png')
In [13]:
Image('coin4.png')
Out[13]:
$$ P(\theta | [h=20, t=20]) = \text{Beta}(22, 22) $$
Bayes Formula¶
$$P(\theta| \text{data}) \propto P(\theta) \ast P(\text{data} |\theta)$$$$\text{posterior} \propto \text{prior} \ast \text{likelihood}$$$\theta$: Parameters of model (chance of getting heads)).
- Except in simple cases, posterior impossible to compute analytically.
- Blackbox approximation algorithm: Markov-Chain Monte Carlo (MCMC) instead draws samples from the posterior.
In [14]:
from scipy import stats
fig = ppl.plt.figure(figsize=(14, 6))
ax1 = fig.add_subplot(121, title='What we want', ylim=(0, .5), xlabel=r'\theta', ylabel=r'P(\theta)')
ppl.plot(ax1, np.linspace(-4, 4, 100), stats.norm.pdf(np.linspace(-3, 3, 100)), linewidth=4.)
ax2 = fig.add_subplot(122, title='What we get', xlim=(-4, 4), ylim=(0, 1800), xlabel=r'\theta', ylabel='\# of samples')
ax2.hist(np.random.randn(10000), bins=20);
In [14]:
Approximating the posterior with MCMC sampling¶
In [15]:
Image('wantget.png')
Out[15]:
PyMC3¶
- Probabilistic Programming framework written in Python.
- Allows for construction of probabilistic models using intuitive syntax.
- Features advanced MCMC samplers.
- Fast: Just-in-time compiled by Theano.
- Extensible: easily incorporates custom MCMC algorithms and unusual probability distributions.
Linear Models¶
- Assumes a linear relationship between two variables.
- E.g. stock price between Gold and Gold Miners.
In [16]:
size = 200
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
# y = a + b*x
true_regression_line = true_intercept + true_slope * x
# add noise
y = true_regression_line + np.random.normal(scale=.5, size=size)
data = dict(x=x, y=y)
In [17]:
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Synthetic data and underlying model')
ppl.scatter(ax, x, y, label='sampled data')
ppl.plot(ax, x, true_regression_line, label='true regression line', linewidth=4.)
ax.legend(loc=2);
fig.savefig('synth_data.png')
In [18]:
Image('synth_data.png')
Out[18]:
Linear Regression¶
$$ y_i = \alpha + \beta \ast x_i + \epsilon $$with $$ \epsilon \sim \mathcal{N}(0, \sigma^2) $$
Probabilistic Reformulation¶
$$ y_i \sim \mathcal{N}(\alpha + \beta \ast x_i, \sigma^2) $$Priors¶
$$ \alpha \sim \mathcal{N}(0, 20^2) $$$$ \beta \sim \mathcal{N}(0, 20^2) $$$$ \sigma \sim \mathcal{U}(0, 20) $$Constructing model in PyMC3¶
In [19]:
import pymc as pm
with pm.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define priors
alpha = pm.Normal('alpha', mu=0, sd=20)
beta = pm.Normal('beta', mu=0, sd=20)
sigma = pm.Uniform('sigma', lower=0, upper=20)
# Define linear regression
y_est = alpha + beta * x
# Define likelihood
likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=y)
# Inference!
start = pm.find_MAP() # Find starting value by optimization
step = pm.NUTS(state=start) # Instantiate MCMC sampling algorithm
trace = pm.sample(2000, step, start=start, progressbar=False) # draw 2000 posterior samples using NUTS sampling
Covenience function glm()¶
In [20]:
with pm.Model() as model:
# specify glm and pass in data. The resulting linear model, its likelihood and
# and all its parameters are automatically added to our model.
pm.glm.glm('y ~ x', data)
step = pm.NUTS() # Instantiate MCMC sampling algorithm
trace = pm.sample(2000, step, progressbar=False) # draw 2000 posterior samples using NUTS sampling
Posterior¶
In [21]:
fig = pm.traceplot(trace, lines={'alpha': 1, 'beta': 2, 'sigma': .5});
In [22]:
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')
ppl.scatter(ax, x, y, label='data')
from pymc import glm
glm.plot_posterior_predictive(trace, samples=100,
label='posterior predictive regression lines')
ppl.plot(ax, x, true_regression_line, label='true regression line', linewidth=5.)
ax.legend(loc=0);
fig.savefig('ppc1.png')
In [22]:
In [23]:
Image('ppc1.png')
Out[23]:
Robust Regression¶
In [24]:
# Add outliers
x_out = np.append(x, [.1, .15, .2, .25, .25])
y_out = np.append(y, [8, 6, 9, 7, 9])
data_out = dict(x=x_out, y=y_out)
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')
ppl.scatter(ax, x_out, y_out, label='data')
Out[24]:
In [25]:
with pm.Model() as model:
glm.glm('y ~ x', data_out)
trace = pm.sample(2000, pm.NUTS(), progressbar=False)
In [26]:
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')
ppl.scatter(ax, x_out, y_out, label='data')
glm.plot_posterior_predictive(trace, samples=100,
label='posterior predictive regression lines')
ppl.plot(ax, x, true_regression_line,
label='true regression line', linewidth=5.)
plt.legend(loc=0);
fig.savefig('ppc2.png')
In [27]:
Image('ppc2.png')
Out[27]:
In [28]:
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
normal_dist = pm.Normal.dist(mu=0, sd=1)
t_dist = pm.T.dist(mu=0, lam=1, nu=1)
x_eval = np.linspace(-8, 8, 300)
ppl.plot(ax, x_eval, pm.theano.tensor.exp(normal_dist.logp(x_eval)).eval(), label='Normal', linewidth=2.)
ppl.plot(ax, x_eval, pm.theano.tensor.exp(t_dist.logp(x_eval)).eval(), label='Student T', linewidth=2.)
plt.xlabel('x')
plt.ylabel('Probability density')
plt.legend();
fig.savefig('t-dist.png')
Fit strongly biased by outliers¶
- Normal distribution has very light tails.
- -> sensitive to outliers.
- Instead, use Student T distribution with heavier tails.
In [29]:
Image('t-dist.png')
Out[29]:
In [30]:
with pm.Model() as model_robust:
family = pm.glm.families.T()
pm.glm.glm('y ~ x', data_out, family=family)
trace_robust = pm.sample(2000, pm.NUTS(), progressbar=False)
In [31]:
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, xlabel='Value of gold', ylabel='Value of gold miners', title='Posterior predictive regression lines')
ppl.scatter(ax, x_out, y_out)
glm.plot_posterior_predictive(trace_robust, samples=100,
label='posterior predictive regression lines')
ppl.plot(ax, x, true_regression_line,
label='true regression line', linewidth=5.)
plt.legend();
fig.savefig('ppc3.png')
In [32]:
Image('ppc3.png')
Out[32]:
Real-world example: Algorithmic Trading¶
- Pairtrading is a famous technique that plays two stocks against each other.
- For this to work, stocks must be correlated (cointegrated).
- One common example is the price of gold (GLD) and the price of gold mining operations (GDX).
In [33]:
import zipline
import pytz
from datetime import datetime
fig = plt.figure(figsize=(8, 4))
prices = zipline.data.load_from_yahoo(stocks=['GLD', 'GDX'],
end=datetime(2013, 8, 1, 0, 0, 0, 0, pytz.utc)).dropna()[:1000]
prices.plot();
In [34]:
fig = plt.figure(figsize=(9, 6))
ax = fig.add_subplot(111, xlabel='Price GDX in \$', ylabel='Price GLD in \$')
colors = np.linspace(0.1, 1, len(prices))
mymap = plt.get_cmap("winter")
sc = ax.scatter(prices.GDX, prices.GLD, c=colors, cmap=mymap, lw=0)
cb = plt.colorbar(sc)
cb.ax.set_yticklabels([str(p.date()) for p in prices[::len(prices)//10].index]);
fig.savefig('price_corr.png')