Python plot_ssm Examples

Example #1

    iter=8000,
    warmup=2000,
    control=control,
    thin=6
)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of mu
tools.plot_ssm(mcmc_sample,
               sales_df_3['date'],
               'local liner trend',
               'sales',
               'mu',
               sales_df_3['sales'])

# plot ssm of delta
tools.plot_ssm(mcmc_sample,
               sales_df_3['date'],
               'drift',
               'delta',
               'delta')

Example #2

    sm = pickle.load(open('%s.pkl' % filename, 'rb'))
    # sm = pystan.StanModel(file='5-4-1-simple-reg.stan')
else:
    # a model using prior for mu and sigma.
    sm = pystan.StanModel(file='%s.stan' % filename)

control = {'adapt_delta': 0.8, 'max_treedepth': 10}

mcmc_result = sm.sampling(data=stan_data,
                          seed=1,
                          chains=4,
                          iter=8000,
                          warmup=2000,
                          control=control,
                          thin=6)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of probs
tools.plot_ssm(mcmc_sample, np.arange(0, len(boat_df['x'])),
               'autoregressive local trend', 'prob.', 'probs', boat_df['x'])

Example #3

    sm = pickle.load(open('%s.pkl' % filename, 'rb'))
    # sm = pystan.StanModel(file='5-4-1-simple-reg.stan')
else:
    # a model using prior for mu and sigma.
    sm = pystan.StanModel(file='%s.stan' % filename)

control = {'adapt_delta': 0.8, 'max_treedepth': 16}

mcmc_result = sm.sampling(data=stan_data,
                          seed=1,
                          chains=4,
                          iter=8000,
                          warmup=2000,
                          control=control,
                          thin=6)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of mu
tools.plot_ssm(mcmc_sample, sales_df_5['date'], 'autoregressive local trend',
               'sales', 'alpha', sales_df_5['sales'])

Example #4

File: 5-3-2.py Project: MasazI/python-r-stan-bayesian-model

    control=control,
    thin=1
)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('5-3-2-local-level-interpolation.pkl'):
    with open('5-3-2-local-level-interpolation.pkl', 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract()

# plot ssm
tools.plot_ssm(mcmc_sample,
               sales_df['date'],
               'local level model',
               'sales',
               'mu',
               sales_df['sales'])

# plot ssm about prediction
tools.plot_ssm(mcmc_sample,
               sales_df['date'],
               'local level model',
               'sales',
               'y_pred',
               sales_df['sales'])

Example #5

File: 5-6.py Project: MasazI/python-r-stan-bayesian-model

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of alpha (trend and cycle)
tools.plot_ssm(mcmc_sample,
               sales_df_4['date'],
               'all state',
               'sales',
               'alpha',
               sales_df_4['sales'])

# plot ssm of mu (only trend)
tools.plot_ssm(mcmc_sample,
               sales_df_4['date'],
               'only trend',
               'sales',
               'mu',
               sales_df_4['sales'])

# plot ssm of gamma (only cycle)
tools.plot_ssm(mcmc_sample,
               sales_df_4['date'],
               'only cycle',

Example #6

File: 5-9.py Project: MasazI/python-r-stan-bayesian-model

mcmc_result = sm.sampling(data=stan_data,
                          seed=1,
                          chains=4,
                          iter=3000,
                          warmup=2000,
                          control=control,
                          thin=6)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of probs
tools.plot_ssm(mcmc_sample, fish_df['date'], 'expectation of state', 'prob.',
               'lambda_exp', fish_df['fish_num'])

tools.plot_ssm(mcmc_sample, fish_df['date'],
               'expectation of state without random', 'prob.', 'lambda_smooth',
               fish_df['fish_num'])

tools.plot_ssm(mcmc_sample, fish_df['date'],
               'expectation of state without random and fixed temperature',
               'prob.', 'lambda_smooth_fix', fish_df['fish_num'])

Example #7

if os.path.exists('%s.pkl' % filename):
    sm = pickle.load(open('%s.pkl' % filename, 'rb'))
    # sm = pystan.StanModel(file='5-4-1-simple-reg.stan')
else:
    # a model using prior for mu and sigma.
    sm = pystan.StanModel(file='%s.stan' % filename)

control = {'adapt_delta': 0.99, 'max_treedepth': 16}

mcmc_result = sm.sampling(data=stan_data,
                          seed=1,
                          chains=4,
                          iter=8000,
                          warmup=1200,
                          control=control,
                          thin=6)

print(mcmc_result)
mcmc_result.plot()
plt.show()

# saving compiled model
if not os.path.exists('%s.pkl' % filename):
    with open('%s.pkl' % filename, 'wb') as f:
        pickle.dump(sm, f)

mcmc_sample = mcmc_result.extract(permuted=True)

# plot ssm of mu
tools.plot_ssm(mcmc_sample, sales_df_3['date'], 'smooth trend model', 'sales',
               'mu', sales_df_3['sales'])