In [1]:
!date

Wed Mar  6 12:56:23 PST 2013
In [2]:
import ipynb_style
ipynb_style.clean()
Out[2]:

Stan is not an acronym, it is a BUGS-competitor MCMC package produced by Andrew Gelman and company. It has come up enough now that I need to give it a try. There is also some enthusiasm about packaging it for python, a la rstan.

http://mc-stan.org/

In [11]:
!wget https://stan.googlecode.com/files/stan-src-1.1.1.tgz

--2013-03-06 13:00:09--  https://stan.googlecode.com/files/stan-src-1.1.1.tgz
Resolving stan.googlecode.com... 74.125.141.82, 2607:f8b0:400e:c02::52
Connecting to stan.googlecode.com|74.125.141.82|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 24059353 (23M) [application/x-gzip]
Saving to: “stan-src-1.1.1.tgz”

100%[======================================>] 24,059,353  7.61M/s   in 3.0s    

2013-03-06 13:00:12 (7.61 MB/s) - “stan-src-1.1.1.tgz” saved [24059353/24059353]
In [1]:
#!tar -xvzf stan-src-1.1.1.tgz

It seems that there is a complete copy of Boost in there, as well as a copy of Eigen.

In [2]:
cd stan-src-1.1.1/

/snfs2/HOME/abie/new_dm/stan-src-1.1.1
In [5]:
!make bin/libstan.a

g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O3 -o bin/stan/agrad/agrad.o src/stan/agrad/agrad.cpp
src/stan/agrad/agrad.hpp:2191: warning: ‘void stan::agrad::free_memory()’ defined but not used
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O3 -o bin/stan/math/matrix.o src/stan/math/matrix.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O3 -o bin/stan/agrad/matrix.o src/stan/agrad/matrix.cpp
src/stan/agrad/agrad.hpp:2191: warning: ‘void stan::agrad::free_memory()’ defined but not used
ar -rs bin/libstan.a bin/stan/agrad/agrad.o bin/stan/math/matrix.o bin/stan/agrad/matrix.o
ar: creating bin/libstan.a
In [6]:
!make bin/stanc

g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O3 -o bin/stan/command/stanc.o src/stan/command/stanc.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/statement_grammar_inst.o src/stan/gm/grammars/statement_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/whitespace_grammar_inst.o src/stan/gm/grammars/whitespace_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/expression_grammar_inst.o src/stan/gm/grammars/expression_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/var_decls_grammar_inst.o src/stan/gm/grammars/var_decls_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/statement_2_grammar_inst.o src/stan/gm/grammars/statement_2_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/term_grammar_inst.o src/stan/gm/grammars/term_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/grammars/program_grammar_inst.o src/stan/gm/grammars/program_grammar_inst.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O0 -o bin/stan/gm/ast_def.o src/stan/gm/ast_def.cpp
ar -rs bin/libstanc.a bin/stan/gm/grammars/statement_grammar_inst.o bin/stan/gm/grammars/whitespace_grammar_inst.o bin/stan/gm/grammars/expression_grammar_inst.o bin/stan/gm/grammars/var_decls_grammar_inst.o bin/stan/gm/grammars/statement_2_grammar_inst.o bin/stan/gm/grammars/term_grammar_inst.o bin/stan/gm/grammars/program_grammar_inst.o bin/stan/gm/ast_def.o
ar: creating bin/libstanc.a
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE  -lpthread  -O0 -o bin/stanc bin/stan/command/stanc.o -Lbin -lstanc
In [7]:
!cat src/models/basic_estimators/bernoulli.stan

data { 
  int<lower=0> N; 
  int<lower=0,upper=1> y[N];
} 
parameters {
  real<lower=0,upper=1> theta;
} 
model {
  theta ~ beta(1,1);
  for (n in 1:N) 
    y[n] ~ bernoulli(theta);
}
In [9]:
!cat src/models/basic_estimators/bernoulli.data.R

N <- 10
y <- c(0,1,0,0,0,0,0,0,0,1)
In [10]:
!make src/models/basic_estimators/bernoulli

--- Precompiling src/stan/model/model_header.hpp for g++ ---
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -O3 -o src/stan/model/model_header.hpp.gch src/stan/model/model_header.hpp
In file included from src/stan/prob/distributions/multivariate/discrete.hpp:5,
                 from src/stan/prob/distributions/multivariate.hpp:5,
                 from src/stan/prob/distributions.hpp:5,
                 from src/stan/model/model_header.hpp:31:
src/stan/agrad/agrad.hpp:2191: warning: ‘void stan::agrad::free_memory()’ defined but not used

--- Translating Stan graphical model to C++ code ---
bin/stanc src/models/basic_estimators/bernoulli.stan --o=src/models/basic_estimators/bernoulli.cpp
Model name=bernoulli_model
Input file=src/models/basic_estimators/bernoulli.stan
Output file=src/models/basic_estimators/bernoulli.cpp
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE   -c -O3 -o src/models/basic_estimators/bernoulli.o src/models/basic_estimators/bernoulli.cpp
src/stan/agrad/agrad.hpp:2191: warning: ‘void stan::agrad::free_memory()’ defined but not used
g++ -I src -I lib/eigen_3.1.2 -I lib/boost_1.52.0 -Wall -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE  -lpthread  -O3 -o src/models/basic_estimators/bernoulli src/models/basic_estimators/bernoulli.o -Lbin -lstan
In [11]:
cd src/models/basic_estimators

/snfs2/HOME/abie/new_dm/stan-src-1.1.1/src/models/basic_estimators
In [12]:
!./bernoulli --data=bernoulli.data.R

STAN SAMPLING COMMAND
data = bernoulli.data.R
init = random initialization
init tries = 1
samples = samples.csv
append_samples = 0
save_warmup = 0
seed = 1225776884 (randomly generated)
chain_id = 1 (default)
iter = 2000
warmup = 1000
thin = 1 (default)
equal_step_sizes = 0
leapfrog_steps = -1
max_treedepth = 10
epsilon = -1
epsilon_pm = 0
delta = 0.5
gamma = 0.05

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In [14]:
!cat samples.csv

# Samples Generated by Stan
#
# stan_version_major=1
# stan_version_minor=1
# stan_version_patch=1
# data=bernoulli.data.R
# init=random initialization
# append_samples=0
# save_warmup=0
# seed=1225776884
# chain_id=1
# iter=2000
# warmup=1000
# thin=1
# equal_step_sizes=0
# leapfrog_steps=-1
# max_treedepth=10
# epsilon=-1
# epsilon_pm=0
# delta=0.5
# gamma=0.05
#
lp__,treedepth__,stepsize__,theta
# (mcmc::nuts_diag) adaptation finished
# step size=1.43736
# parameter step size multipliers:
# 1
-6.85846,1,1.43736,0.194532
-7.96891,1,1.43736,0.469048
-8.72278,1,1.43736,0.0673013
-6.7601,1,1.43736,0.269756
-7.37097,1,1.43736,0.13024
-7.37097,1,1.43736,0.13024
-6.7549,1,1.43736,0.264852
-7.39808,1,1.43736,0.40677
-6.88107,1,1.43736,0.189492
-6.88107,1,1.43736,0.189492
-6.81933,1,1.43736,0.204886
-6.75235,1,1.43736,0.238493
-6.75235,1,1.43736,0.238493
-7.45007,1,1.43736,0.413319
-7.45007,2,1.43736,0.413319
-7.45007,1,1.43736,0.413319
-7.48737,1,1.43736,0.121639
-7.48737,1,1.43736,0.121639
-7.48737,1,1.43736,0.121639
-6.93027,1,1.43736,0.329996
-6.93027,1,1.43736,0.329996
-6.99768,1,1.43736,0.169292
-6.99768,1,1.43736,0.169292
-6.99768,1,1.43736,0.169292
-6.99768,1,1.43736,0.169292
-6.99768,1,1.43736,0.169292
-6.74802,1,1.43736,0.250156
-6.74802,1,1.43736,0.250156
-6.8032,1,1.43736,0.292974
-6.8032,1,1.43736,0.292974
-6.94372,1,1.43736,0.333044
-6.77186,1,1.43736,0.277932
-7.08457,1,1.43736,0.360528
-7.08457,1,1.43736,0.360528
-7.08457,1,1.43736,0.360528
-7.86399,1,1.43736,0.0996016
-7.86399,1,1.43736,0.0996016
-7.86399,1,1.43736,0.0996016
-7.02929,1,1.43736,0.350525
-7.02929,1,1.43736,0.350525
-6.83544,1,1.43736,0.200311
-6.83544,1,1.43736,0.200311
-6.95814,1,1.43736,0.17534
-6.74827,1,1.43736,0.25282
-6.74827,1,1.43736,0.25282
-6.74827,1,1.43736,0.25282
-6.74827,1,1.43736,0.25282
-6.74827,1,1.43736,0.25282
-6.7981,1,1.43736,0.290876
-6.75251,1,1.43736,0.261967
-6.75251,1,1.43736,0.261967
-6.75597,1,1.43736,0.265974
-6.75597,1,1.43736,0.265974
-6.75597,1,1.43736,0.265974
-6.74835,1,1.43736,0.253218
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
-6.81502,1,1.43736,0.297502
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-7.2293,1,1.43736,0.142457
-6.88947,1,1.43736,0.187746
In [40]:
import pandas as pd
df = pd.read_csv('samples.csv', skiprows=27, header=None)
df.columns = 'lp__,treedepth__,stepsize__,theta'.split(',')
In [41]:
df.head()
Out[41]:
lp__ treedepth__ stepsize__ theta
0 -6.85846 1 1.43736 0.194532
1 -7.96891 1 1.43736 0.469048
2 -8.72278 1 1.43736 0.067301
3 -6.76010 1 1.43736 0.269756
4 -7.37097 1 1.43736 0.130240
In [42]:
plot(df.theta)
Out[42]:
[<matplotlib.lines.Line2D at 0x3490550>]
In [45]:
acorr(df.theta, detrend=detrend_mean, maxlags=100);

It seems to work, that is good.

In [46]:
!date

Wed Mar  6 15:00:53 PST 2013
In [ ]: