def test_bernoulli_data(self):
data_dict = {'N': 10, 'y': [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]}
stan = os.path.join(datafiles_path, 'bernoulli.stan')
output = os.path.join(TMPDIR, 'test3-bernoulli-output')
model = compile_model(stan)
post_sample = sample(model, data=data_dict, csv_output_file=output)
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
txt_file = ''.join([os.path.splitext(csv_file)[0], '.txt'])
self.assertTrue(os.path.exists(csv_file))
self.assertTrue(os.path.exists(txt_file))
def test_bernoulli(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
post_sample = sample(model,
chains=4,
cores=2,
seed=12345,
sampling_iters=200,
data=jdata)
df = summary(post_sample)
self.assertTrue(df.shape == (2, 9))
def test_bernoulli(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
post_sample = sample(model,
chains=4,
cores=2,
seed=12345,
sampling_iters=200,
data=jdata)
post_sample.assemble_sample()
df = get_drawset(post_sample)
self.assertEqual(df.shape, (post_sample.chains * post_sample.draws,
len(post_sample.column_names)))
def diagnose_no_problems(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
post_sample = sample(model,
chains=4,
cores=2,
seed=12345,
sampling_iters=200,
data=jdata)
capturedOutput = io.StringIO()
sys.stdout = capturedOutput
diagnose(post_sample)
sys.stdout = sys.__stdout__
self.assertEqual(capturedOutput.getvalue(), 'No problems detected.\n')
def test_bernoulli_1(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
output = os.path.join(datafiles_path, 'test1-bernoulli-output')
post_sample = sample(
model,
chains=4,
cores=2,
seed=12345,
sampling_iters=100,
data=jdata,
csv_output_file=output,
max_treedepth=11,
adapt_delta=0.95,
)
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
txt_file = ''.join([os.path.splitext(csv_file)[0], '.txt'])
self.assertTrue(os.path.exists(csv_file))
self.assertTrue(os.path.exists(txt_file))
self.assertEqual(post_sample.chains, 4)
self.assertEqual(post_sample.draws, 100)
column_names = [
'lp__',
'accept_stat__',
'stepsize__',
'treedepth__',
'n_leapfrog__',
'divergent__',
'energy__',
'theta',
]
self.assertEqual(post_sample.column_names, tuple(column_names))
post_sample.assemble_sample()
self.assertEqual(post_sample.sample.shape, (100, 4, len(column_names)))
self.assertEqual(post_sample.metric_type, 'diag_e')
self.assertEqual(post_sample.stepsize.shape, (4, ))
self.assertEqual(post_sample.metric.shape, (4, 1))
def test_bernoulli_2(self):
# tempfile for outputs
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
post_sample = sample(model,
chains=4,
cores=2,
seed=12345,
sampling_iters=100,
data=jdata,
max_treedepth=11,
adapt_delta=0.95)
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
txt_file = ''.join([os.path.splitext(csv_file)[0], '.txt'])
self.assertTrue(os.path.exists(csv_file))
self.assertTrue(os.path.exists(txt_file))
def test_bernoulli(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
exe = os.path.join(datafiles_path, 'bernoulli')
if not os.path.exists(exe):
compile_model(stan)
model = Model(stan, exe_file=exe)
jdata = os.path.join(datafiles_path, 'bernoulli.data.json')
post_sample = sample(model,
chains=4,
cores=2,
seed=12345,
sampling_iters=200,
data=jdata)
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
txt_file = ''.join([os.path.splitext(csv_file)[0], '.txt'])
self.assertTrue(os.path.exists(csv_file))
self.assertTrue(os.path.exists(txt_file))
basename = 'bern_save_csvfiles_test'
save_csvfiles(post_sample, datafiles_path, basename) # good
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
self.assertTrue(os.path.exists(csv_file))
with self.assertRaisesRegex(Exception, 'cannot save'):
save_csvfiles(post_sample, os.path.join('no', 'such', 'dir'),
basename)
with self.assertRaisesRegex(Exception, 'file exists'):
save_csvfiles(post_sample, datafiles_path, basename)
save_csvfiles(post_sample, basename=basename) # default dir
for i in range(post_sample.chains):
csv_file = post_sample.csv_files[i]
self.assertTrue(os.path.exists(csv_file))
for i in range(post_sample.chains): # cleanup
os.remove(post_sample.csv_files[i])
def test_missing_input(self):
stan = os.path.join(datafiles_path, 'bernoulli.stan')
output = os.path.join(TMPDIR, 'test4-bernoulli-output')
model = compile_model(stan)
with self.assertRaisesRegex(Exception, 'Error during sampling'):
post_sample = sample(model, csv_output_file=output)
