Chp_06.ipynb

Kernel: Python 3

%matplotlib inline
import pymc3 as pm
import numpy as np
import pandas as pd
from scipy import stats
# R-like interface, alternatively you can import statsmodels as import statsmodels.api as sm
import statsmodels.formula.api as smf 
import statsmodels.api as sm
import matplotlib.pyplot as plt

%config InlineBackend.figure_format = 'retina'
plt.style.use(['seaborn-colorblind', 'seaborn-darkgrid'])

/home/agustina/anaconda3/lib/python3.5/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
  from pandas.core import datetools

Code 6.1

data = {'species' : ['afarensis', 'africanus', 'habilis', 'boisei', 'rudolfensis', 'ergaster', 'sapiens'],
'brain' : [438, 452, 612, 521, 752, 871, 1350],
'mass' : [37., 35.5, 34.5, 41.5, 55.5, 61.0, 53.5]}
d = pd.DataFrame(data)
d

Code 6.2

m_6_1 = smf.ols('brain ~ mass', data=d).fit()

Code 6.3

1 - m_6_1.resid.var()/d.brain.var()

# m_6_1.summary() check the value for R-squared

0.4901580479490838

Code 6.4

m_6_2 = smf.ols('brain ~ mass + I(mass**2)', data=d).fit()

Code 6.5

m_6_3 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3)', data=d).fit()
m_6_4 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4)', data=d).fit()
m_6_5 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5)', data=d).fit()
m_6_6 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5) + I(mass**6)', data=d).fit()

Code 6.6

m_6_7 = smf.ols('brain ~ 1', data=d).fit()

Code 6.7

d_new = d.drop(d.index[-1])

Code 6.8

f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(8,3))
ax1.scatter(d.mass, d.brain, alpha=0.8)
ax2.scatter(d.mass, d.brain, alpha=0.8)
for i in range(len(d)):
    d_new = d.drop(d.index[-i])
    m0 = smf.ols('brain ~ mass', d_new).fit()
    # need to calculate regression line
    # need to add intercept term explicitly
    x = sm.add_constant(d_new.mass)  # add constant to new data frame with mass
    x_pred = pd.DataFrame({'mass': np.linspace(x.mass.min() - 10, x.mass.max() + 10, 50)})  # create linspace dataframe
    x_pred2 = sm.add_constant(x_pred)  # add constant to newly created linspace dataframe
    y_pred = m0.predict(x_pred2)  # calculate predicted values
    ax1.plot(x_pred, y_pred, 'gray', alpha=.5)
    ax1.set_ylabel('body mass (kg)', fontsize=12);
    ax1.set_xlabel('brain volume (cc)', fontsize=12)
    ax1.set_title('Underfit model')
    
    # fifth order model
    m1 = smf.ols('brain ~ mass + I(mass**2) + I(mass**3) + I(mass**4) + I(mass**5)', data=d_new).fit()
    x = sm.add_constant(d_new.mass)  # add constant to new data frame with mass
    x_pred = pd.DataFrame({'mass': np.linspace(x.mass.min()-10, x.mass.max()+10, 200)})  # create linspace dataframe
    x_pred2 = sm.add_constant(x_pred)  # add constant to newly created linspace dataframe
    y_pred = m1.predict(x_pred2)  # calculate predicted values from fitted model
    ax2.plot(x_pred, y_pred, 'gray', alpha=.5)
    ax2.set_xlim(32,62)
    ax2.set_ylim(-250, 2200)
    ax2.set_ylabel('body mass (kg)', fontsize=12);
    ax2.set_xlabel('brain volume (cc)', fontsize=12)
    ax2.set_title('Overfit model')
plt.show()

Code 6.9

p = (0.3, 0.7)
-sum(p * np.log(p))

0.6108643020548935

Code 6.10

# fit model
m_6_1 = smf.ols('brain ~ mass', data=d).fit()

#compute de deviance by cheating
-2 * m_6_1.llf

94.924989685887581

Code 6.11

# standarize the mass before fitting
d['mass_s'] = d['mass'] - np.mean(d['mass'] / np.std(d['mass']))

with pm.Model() as m_6_8 :
    a = pm.Normal('a', mu=np.mean(d['brain']), sd=10)
    b = pm.Normal('b', mu=0, sd=10)
    sigma = pm.Uniform('sigma', 0, np.std(d['brain']) * 10)
    mu = pm.Deterministic('mu', a + b * d['mass_s'])
    brain = pm.Normal('brain', mu = mu, sd = sigma, observed = d['brain'])
    m_6_8 = pm.sample(1000, tune=1000) 

100%|██████████| 2000/2000 [00:05<00:00, 338.85it/s]

theta = pm.summary(m_6_8)['mean'][:3]

#compute deviance
dev = - 2 * sum(stats.norm.logpdf(d['brain'], loc = theta[0] + theta[1] * d['mass_s']  , scale = theta[2]))
dev 

100.48254797793284

Code 6.12 - 14

The overthinking section corresponding to cells 6.12-14 is not ported because it requires an ad-hoc rethinking package function. Feel free to contribute code to this section.

Code 6.15

data = pd.read_csv('Data/cars.csv', sep=',')

with pm.Model() as m_6_15 :
    a = pm.Normal('a', mu=0, sd=100)
    b = pm.Normal('b', mu=0, sd=10)
    sigma = pm.Uniform('sigma', 0, 30)
    mu = pm.Deterministic('mu', a + b * data['speed'])
    dist = pm.Normal('dist', mu=mu, sd=sigma, observed = data['dist'])
    m_6_15 = pm.sample(1000, tune=1000)

100%|██████████| 2000/2000 [00:12<00:00, 161.13it/s]

Code 6.16

n_samples = 1000
n_cases = data.shape[0]
ll = np.zeros((n_cases, n_samples))

for s in range(0, n_samples):
    mu = m_6_15['a'][s] + m_6_15['b'][s] * data['speed']
    p_ = stats.norm.logpdf(data['dist'], loc=mu, scale=m_6_15['sigma'][s])
    ll[:,s] = p_

Code 6.17

from scipy.misc import logsumexp
n_cases = data.shape[0]
lppd = np.zeros((n_cases))
for a in range(1, n_cases):
    lppd[a,] = logsumexp(ll[a,]) - np.log(n_samples)

/home/agustina/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: `logsumexp` is deprecated!
Importing `logsumexp` from scipy.misc is deprecated in scipy 1.0.0. Use `scipy.special.logsumexp` instead.

Code 6.18

pWAIC = np.zeros((n_cases))
for i in range(1, n_cases):
    pWAIC[i,] = np.var(ll[i,])

Code 6.19

- 2 * (sum(lppd) - sum(pWAIC))

412.49046436448634

Code 6.20

waic_vec = - 2 * (lppd - pWAIC)
np.sqrt(n_cases * np.var(waic_vec))

14.88328369492803

Code 6.21

d = pd.read_csv('Data/milk.csv', sep=';')
d['neocortex'] = d['neocortex.perc'] / 100
d.dropna(inplace=True)
d.shape

(17, 9)

Code 6.22

a_start = d['kcal.per.g'].mean()
sigma_start = d['kcal.per.g'].std()

import theano

mass_shared = theano.shared(np.log(d['mass'].values))
neocortex_shared = theano.shared(d['neocortex'].values)

with pm.Model() as m6_11:
    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)
    mu = alpha + 0 * neocortex_shared
    sigma = pm.HalfCauchy('sigma',beta=10, testval=sigma_start)
    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])
    trace_m6_11 = pm.sample(1000, tune=1000)    

with pm.Model() as m6_12:
    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)
    beta = pm.Normal('beta', mu=0, sd=10)
    sigma = pm.HalfCauchy('sigma',beta=10, testval=sigma_start)
    mu = alpha + beta * neocortex_shared
    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])
    trace_m6_12 = pm.sample(1000, tune=1000)
    
with pm.Model() as m6_13:
    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)
    beta = pm.Normal('beta', mu=0, sd=10)
    sigma = pm.HalfCauchy('sigma', beta=10, testval=sigma_start)
    mu = alpha + beta * mass_shared
    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])
    trace_m6_13 = pm.sample(1000, tune=1000)
    
with pm.Model() as m6_14:
    alpha = pm.Normal('alpha', mu=0, sd=10, testval=a_start)
    beta = pm.Normal('beta', mu=0, sd=10, shape=2)
    sigma = pm.HalfCauchy('sigma', beta=10, testval=sigma_start)
    mu = alpha + beta[0] * mass_shared + beta[1] * neocortex_shared
    kcal = pm.Normal('kcal', mu=mu, sd=sigma, observed=d['kcal.per.g'])
    trace_m6_14 = pm.sample(1000, tune=1000)

100%|██████████| 2000/2000 [00:08<00:00, 243.55it/s]
 87%|████████▋ | 1747/2000 [02:33<00:22, 11.39it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 1 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.
  % (self._chain_id, n_diverging))
100%|█████████▉| 1999/2000 [02:45<00:00, 12.10it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 2 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.
  % (self._chain_id, n_diverging))
100%|██████████| 2000/2000 [02:45<00:00, 12.10it/s]
100%|██████████| 2000/2000 [00:28<00:00, 69.65it/s]
100%|██████████| 2000/2000 [04:31<00:00,  7.38it/s]/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:452: UserWarning: The acceptance probability in chain 0 does not match the target. It is 0.681715916019, but should be close to 0.8. Try to increase the number of tuning steps.
  % (self._chain_id, mean_accept, target_accept))
/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 0 contains 25 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.
  % (self._chain_id, n_diverging))

/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/step_methods/hmc/nuts.py:468: UserWarning: Chain 1 contains 5 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.
  % (self._chain_id, n_diverging))

Code 6.23

pm.waic(trace_m6_14, m6_14)

/home/agustina/anaconda3/lib/python3.5/site-packages/pymc3/stats.py:220: UserWarning: For one or more samples the posterior variance of the
        log predictive densities exceeds 0.4. This could be indication of
        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details
        
  """)

WAIC_r(WAIC=-17.054227823698863, WAIC_se=4.8600477062566103, p_WAIC=2.9504889764816951)

Code 6.24

compare_df = pm.compare([trace_m6_11, trace_m6_12, trace_m6_13, trace_m6_14], 
                        [m6_11, m6_12, m6_13, m6_14], 
                        method='pseudo-BMA')

compare_df.loc[:,'model'] = pd.Series(['m6.11', 'm6.12', 'm6.13', 'm6.14'])
compare_df = compare_df.set_index('model')
compare_df

Code 6.25

pm.compareplot(compare_df);

Code 6.26

diff = np.random.normal(loc=6.7, scale=7.26, size=100000)
sum(diff[diff<0]) / 100000

-0.69650384567058254

Code 6.27

Compare function already checks number of observations to be equal.

coeftab = pd.DataFrame({'m6_11': pm.summary(trace_m6_11)['mean'],
                        'm6_12': pm.summary(trace_m6_12)['mean'],
                        'm6_13': pm.summary(trace_m6_13)['mean'],
                        'm6_14': pm.summary(trace_m6_14)['mean']})
coeftab

Code 6.28

traces = [trace_m6_11, trace_m6_12, trace_m6_13, trace_m6_14]   
models = [m6_11, m6_12, m6_13, m6_14]

plt.figure(figsize=(10, 8))
pm.forestplot(traces, plot_kwargs={'fontsize':14});

Code 6.29

kcal_per_g = np.repeat(0, 30) # empty outcome
neocortex = np.linspace(0.5, 0.8, 30) # sequence of neocortex
mass = np.repeat(4.5, 30)     # average mass

mass_shared.set_value(np.log(mass))
neocortex_shared.set_value(neocortex)
post_pred = pm.sample_ppc(trace_m6_14, samples=10000, model=m6_14)

100%|██████████| 10000/10000 [00:37<00:00, 264.13it/s]

Code 6.30

milk_ensemble = pm.sample_ppc_w(traces, 10000, 
                                models, weights=compare_df.weight.sort_index(ascending=True))

100%|██████████| 10000/10000 [00:28<00:00, 355.45it/s]

plt.figure(figsize=(8, 6))

plt.plot(neocortex, post_pred['kcal'].mean(0), ls='--', color='C2')
hpd_post_pred = pm.hpd(post_pred['kcal'])
plt.plot(neocortex,hpd_post_pred[:,0], ls='--', color='C2')
plt.plot(neocortex,hpd_post_pred[:,], ls='--', color='C2')

plt.plot(neocortex, milk_ensemble['kcal'].mean(0), color='C0')
hpd_av = pm.hpd(milk_ensemble['kcal'])
plt.fill_between(neocortex, hpd_av[:,0], hpd_av[:,1], alpha=0.1, color='C0')

plt.scatter(d['neocortex'], d['kcal.per.g'], facecolor='None', edgecolors='C0')

plt.ylim(0.3, 1)
plt.xlabel('neocortex', fontsize=16)
plt.ylabel('kcal.per.g', fontsize=16);

import sys, IPython, scipy, matplotlib, platform
print("This notebook was createad on a computer %s running %s and using:\nPython %s\nIPython %s\nPyMC3 %s\nNumPy %s\nPandas %s\nSciPy %s\nMatplotlib %s\n" % (platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))

This notebook was createad on a computer x86_64 running debian stretch/sid and using:
Python 3.5.4
IPython 4.1.2
PyMC3 3.2
NumPy 1.13.3
Pandas 0.21.0
SciPy 1.0.0
Matplotlib 2.0.2