run_exp(rank)
#dump('parser_model_'+str(NDRAWS)+'_iterations', trace)
with parser_model:
trace = load('../../data/parser_model_' + str(NDRAWS) + '_iterations')
pm.diagnostics.gelman_rubin(trace)
traceplot(trace)
plt.savefig('../../figures/parser_' + str(NDRAWS) + '_trace.eps')
plt.savefig('../../figures/parser_' + str(NDRAWS) + '_trace.png')
plt.savefig('../../figures/parser_' + str(NDRAWS) + '_trace.pdf')
#plt.show()
mu_rt = pd.DataFrame(trace['mu_rt'])
yerr_rt = [(mu_rt.mean()-mu_rt.quantile(0.025)),\
(mu_rt.quantile(0.975)-mu_rt.mean())]
def generate_parser_model_figure():
fig, ax1 = plt.subplots(ncols=1, nrows=1)
fig.set_size_inches(5.5, 3.5)
# plot 1: RTs
ax1.errorbar(RT, mu_rt.mean(), yerr=yerr_rt, marker='o', linestyle='')
ax1.plot(np.linspace(300, 500, 10), np.linspace(300, 500, 10),\
color='red', linestyle=':')
#ax1.set_title('Fan model: Observed vs. predicted RTs')
ax1.set_title('')
ax1.set_xlabel('Observed RTs (ms)')
ax1.set_ylabel('Predicted RTs (ms)')
ax1.grid(b=True, which='minor', color='w', linewidth=1.0)
#dump('../data/lex_dec_pyactr_no_imaginal', trace)
with lex_decision_with_bayes:
trace = load('../data/lex_dec_pyactr_no_imaginal')
pm.diagnostics.gelman_rubin(trace)
pm.traceplot(trace)
plt.savefig('../figures/lex_dec_model_pyactr_no_imaginal_trace.eps')
plt.savefig('../figures/lex_dec_model_pyactr_no_imaginal_trace.png')
plt.savefig('../figures/lex_dec_model_pyactr_no_imaginal_trace.pdf')
#plt.show()
mu_rt = pd.DataFrame(trace['mu_rt']) * 1000
RT = RT * 1000
yerr_rt = [(mu_rt.mean() - mu_rt.quantile(0.025)),
(mu_rt.quantile(0.975) - mu_rt.mean())]
mu_prob = pd.DataFrame(trace['mu_prob'])
yerr_prob = [(mu_prob.mean() - mu_prob.quantile(0.025)),
(mu_prob.quantile(0.975) - mu_prob.mean())]
def generate_lex_dec_pyactr_no_imaginal_figure():
fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2)
fig.set_size_inches(5.5, 5.5)
# plot 1: RTs
ax1.errorbar(RT, mu_rt.mean(), yerr=yerr_rt, marker='o', linestyle='')
ax1.plot(np.linspace(500, 800, 10),
np.linspace(500, 800, 10),
color='red',
# accuracy likelihood
odds_reciprocal = tt.exp(-(activation_from_time - threshold)/noise)
mu_prob = Deterministic('mu_prob', 1/(1 + odds_reciprocal))
prob_observed = Normal('prob_observed', mu=mu_prob, sd=0.01,\
observed=ACCURACY)
# we start the sampling
#step = Metropolis()
#db = SQLite('lex_dec_pyactr_chain_no_imaginal.sqlite')
#trace = sample(draws=60000, trace=db, njobs=1, step=step, init='auto')
with lex_decision_with_bayes:
trace = load('./data/lex_dec_pyactr_chain_no_imaginal.sqlite')
trace = trace[10500:]
mu_rt = pd.DataFrame(trace['mu_rt'])
yerr_rt = [(mu_rt.mean()-mu_rt.quantile(0.025)),\
(mu_rt.quantile(0.975)-mu_rt.mean())]
mu_prob = pd.DataFrame(trace['mu_prob'])
yerr_prob = [(mu_prob.mean()-mu_prob.quantile(0.025)),\
(mu_prob.quantile(0.975)-mu_prob.mean())]
def generate_lex_dec_pyactr_no_imaginal_figure():
fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2)
fig.set_size_inches(6.0, 8.5)
# plot 1: RTs
ax1.errorbar(RT, mu_rt.mean(), yerr=yerr_rt, marker='o', linestyle='')
ax1.plot(np.linspace(500, 800, 10), np.linspace(500, 800, 10),\
color='red', linestyle=':')
ax1.set_title('Lex. dec. model (pyactr, no imaginal): RTs')
ax1.set_xlabel('Observed RTs (ms)')