This Notebook is basically an excuse to demo poisson regression using PyMC3, both manually and using the glm library to demo interactions using the patsy library. We will create some dummy data, poisson distributed according to a linear model, and try to recover the coefficients of that linear model through inference.
For more statistical detail see:
This very basic model is insipired by a project by Ian Osvald, which is concerend with understanding the various effects of external environmental factors upon the allergic sneezing of a test subject.
$> less conda_env_pymc3_examples.yml
name: pymc3_examples
channels:
- defaults
dependencies:
- python=3.5
- jupyter
- ipywidgets
- numpy
- scipy
- matplotlib
- pandas
- pytables
- scikit-learn
- statsmodels
- seaborn
- patsy
- requests
- pip
- pip:
- regex
$> conda env create --file conda_env_pymc3_examples.yml
$> source activate pymc3_examples
$> pip install --process-dependency-links git+https://github.com/pymc-devs/pymc3
In [1]:
## Interactive magics
%matplotlib inline
In [2]:
import sys
import warnings
warnings.filterwarnings('ignore')
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import patsy as pt
from scipy import optimize
# pymc3 libraries
import pymc3 as pm
import theano as thno
import theano.tensor as T
sns.set(style="darkgrid", palette="muted")
pd.set_option('display.mpl_style', 'default')
plt.rcParams['figure.figsize'] = 14, 6
np.random.seed(0)
In [3]:
def strip_derived_rvs(rvs):
'''Convenience fn: remove PyMC3-generated RVs from a list'''
ret_rvs = []
for rv in rvs:
if not (re.search('_log',rv.name) or re.search('_interval',rv.name)):
ret_rvs.append(rv)
return ret_rvs
def plot_traces_pymc(trcs, varnames=None):
''' Convenience fn: plot traces with overlaid means and values '''
nrows = len(trcs.varnames)
if varnames is not None:
nrows = len(varnames)
ax = pm.traceplot(trcs, varnames=varnames, figsize=(12,nrows*1.4),
lines={k: v['mean'] for k, v in
pm.df_summary(trcs,varnames=varnames).iterrows()})
for i, mn in enumerate(pm.df_summary(trcs, varnames=varnames)['mean']):
ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data',
xytext=(5,10), textcoords='offset points', rotation=90,
va='bottom', fontsize='large', color='#AA0022')
This dummy dataset is created to emulate some data created as part of a study into quantified self, and the real data is more complicated than this. Ask Ian Osvald if you'd like to know more https://twitter.com/ianozsvald
nsneeze (int)alcohol (boolean)nomeds (boolean)Create 4000 days of data: daily counts of sneezes which are poisson distributed w.r.t alcohol consumption and antihistamine usage
In [4]:
# decide poisson theta values
theta_noalcohol_meds = 1 # no alcohol, took an antihist
theta_alcohol_meds = 3 # alcohol, took an antihist
theta_noalcohol_nomeds = 6 # no alcohol, no antihist
theta_alcohol_nomeds = 36 # alcohol, no antihist
# create samples
q = 1000
df = pd.DataFrame({
'nsneeze': np.concatenate((np.random.poisson(theta_noalcohol_meds, q),
np.random.poisson(theta_alcohol_meds, q),
np.random.poisson(theta_noalcohol_nomeds, q),
np.random.poisson(theta_alcohol_nomeds, q))),
'alcohol': np.concatenate((np.repeat(False, q),
np.repeat(True, q),
np.repeat(False, q),
np.repeat(True, q))),
'nomeds': np.concatenate((np.repeat(False, q),
np.repeat(False, q),
np.repeat(True, q),
np.repeat(True, q)))})
In [5]:
df.tail()
Out[5]:
In [6]:
df.groupby(['alcohol','nomeds']).mean().unstack()
Out[6]:
In [7]:
g = sns.factorplot(x='nsneeze', row='nomeds', col='alcohol', data=df,
kind='count', size=4, aspect=1.5)
Observe:
nomeds == False and alcohol == False (top-left, akak antihistamines WERE used, alcohol was NOT drunk) the mean of the poisson distribution of sneeze counts is low.alcohol == True (top-right) increases the sneeze count nsneeze slightlynomeds == True (lower-left) increases the sneeze count nsneeze furtheralcohol == True and nomeds == True (lower-right) increases the sneeze count nsneeze a lot, increasing both the mean and variance.Our model here is a very simple Poisson regression, allowing for interaction of terms:
$$ \theta = exp(\beta X)$$$$ Y_{sneeze\_count} ~ Poisson(\theta)$$Create linear model for interaction of terms
In [8]:
fml = 'nsneeze ~ alcohol + antihist + alcohol:antihist' # full patsy formulation
In [9]:
fml = 'nsneeze ~ alcohol * nomeds' # lazy, alternative patsy formulation
Create Design Matrices
In [10]:
(mx_en, mx_ex) = pt.dmatrices(fml, df, return_type='dataframe', NA_action='raise')
In [11]:
pd.concat((mx_ex.head(3),mx_ex.tail(3)))
Out[11]:
Create Model
In [12]:
with pm.Model() as mdl_fish:
# define priors, weakly informative Normal
b0 = pm.Normal('b0_intercept', mu=0, sd=10)
b1 = pm.Normal('b1_alcohol[T.True]', mu=0, sd=10)
b2 = pm.Normal('b2_nomeds[T.True]', mu=0, sd=10)
b3 = pm.Normal('b3_alcohol[T.True]:nomeds[T.True]', mu=0, sd=10)
# define linear model and exp link function
theta = (b0 +
b1 * mx_ex['alcohol[T.True]'] +
b2 * mx_ex['nomeds[T.True]'] +
b3 * mx_ex['alcohol[T.True]:nomeds[T.True]'])
## Define Poisson likelihood
y = pm.Poisson('y', mu=np.exp(theta), observed=mx_en['nsneeze'].values)
Sample Model
In [13]:
with mdl_fish:
trc_fish = pm.sample(2000, tune=1000, njobs=4)[1000:]
View Diagnostics
In [14]:
rvs_fish = [rv.name for rv in strip_derived_rvs(mdl_fish.unobserved_RVs)]
plot_traces_pymc(trc_fish, varnames=rvs_fish)
Observe:
In [15]:
np.exp(pm.df_summary(trc_fish, varnames=rvs_fish)[['mean','hpd_2.5','hpd_97.5']])
Out[15]:
Observe:
1. exp(b0_intercept): mean=1.02 cr=[0.96, 1.08]
Roughly linear baseline count when no alcohol and meds, as per the generated data:
theta_noalcohol_meds = 1 (as set above)
theta_noalcohol_meds = exp(b0_intercept)
= 1
2. exp(b1_alcohol): mean=2.88 cr=[2.69, 3.09]
non-zero positive effect of adding alcohol, a ~3x multiplier of
baseline sneeze count, as per the generated data:
theta_alcohol_meds = 3 (as set above)
theta_alcohol_meds = exp(b0_intercept + b1_alcohol)
= exp(b0_intercept) * exp(b1_alcohol)
= 1 * 3 = 3
3. exp(b2_nomeds[T.True]): mean=5.76 cr=[5.40, 6.17]
larger, non-zero positive effect of adding nomeds, a ~6x multiplier of
baseline sneeze count, as per the generated data:
theta_noalcohol_nomeds = 6 (as set above)
theta_noalcohol_nomeds = exp(b0_intercept + b2_nomeds)
= exp(b0_intercept) * exp(b2_nomeds)
= 1 * 6 = 6
4. exp(b3_alcohol[T.True]:nomeds[T.True]): mean=2.12 cr=[1.98, 2.30]
small, positive interaction effect of alcohol and meds, a ~2x multiplier of
baseline sneeze count, as per the generated data:
theta_alcohol_nomeds = 36 (as set above)
theta_alcohol_nomeds = exp(b0_intercept + b1_alcohol + b2_nomeds + b3_alcohol:nomeds)
= exp(b0_intercept) * exp(b1_alcohol) * exp(b2_nomeds * b3_alcohol:nomeds)
= 1 * 3 * 6 * 2 = 36Create Model
Alternative automatic formulation using pmyc.glm
In [16]:
with pm.Model() as mdl_fish_alt:
pm.glm.GLM.from_formula(fml, df, family=pm.glm.families.Poisson())
Sample Model
In [17]:
with mdl_fish_alt:
trc_fish_alt = pm.sample(4000, tune=2000)[2000:]
View Traces
In [18]:
rvs_fish_alt = [rv.name for rv in strip_derived_rvs(mdl_fish_alt.unobserved_RVs)]
plot_traces_pymc(trc_fish_alt, varnames=rvs_fish_alt)
In [19]:
np.exp(pm.df_summary(trc_fish_alt, varnames=rvs_fish_alt)[['mean','hpd_2.5','hpd_97.5']])
Out[19]:
Observe:
mu coeff is for the overall mean of the dataset and has an extreme skew, if we look at the median value ...
In [20]:
np.percentile(trc_fish_alt['mu'], [25,50,75])
Out[20]:
... of 9.45 with a range [25%, 75%] of [4.17, 24.18], we see this is pretty close to the overall mean of:
In [21]:
df['nsneeze'].mean()
Out[21]:
Example originally contributed by Jonathan Sedar 2016-05-15 github.com/jonsedar