def test_args_sig_figs(self):
sampler_args = SamplerArgs()
cmdstan_path() # sets os.environ['CMDSTAN']
if cmdstan_version_before(2, 25):
with LogCapture() as log:
logging.getLogger()
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=12,
method_args=sampler_args,
)
expect = (
'Argument "sig_figs" invalid for CmdStan versions < 2.25, '
'using version {} in directory {}').format(
os.path.basename(cmdstan_path()),
os.path.dirname(cmdstan_path()),
)
log.check_present(('cmdstanpy', 'WARNING', expect))
else:
cmdstan_args = CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=12,
method_args=sampler_args,
)
cmd = cmdstan_args.compose_command(idx=0,
csv_file='bern-output-1.csv')
self.assertIn('sig_figs=', ' '.join(cmd))
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=-1,
method_args=sampler_args,
)
with self.assertRaises(ValueError):
CmdStanArgs(
model_name='bernoulli',
model_exe='bernoulli.exe',
chain_ids=[1, 2, 3, 4],
sig_figs=20,
method_args=sampler_args,
)
def clean_examples():
cmdstan_examples = os.path.join(cmdstan_path(), "examples")
for root, _, files in os.walk(cmdstan_examples):
for filename in files:
_, ext = os.path.splitext(filename)
if ext.lower() in (".o", ".hpp", ".exe",
"") and (filename != ".gitignore"):
filepath = os.path.join(root, filename)
os.remove(filepath)
print("Deleted {}".format(filepath))
def run_bernoulli_fit():
# specify Stan file, create, compile CmdStanModel object
bernoulli_path = os.path.join(cmdstan_path(), 'examples', 'bernoulli',
'bernoulli.stan')
bernoulli_model = CmdStanModel(stan_file=bernoulli_path)
bernoulli_model.compile()
# specify data, fit the model
bernoulli_data = {'N': 10, 'y': [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]}
# Show progress
bernoulli_fit = bernoulli_model.sample(chains=4,
cores=2,
data=bernoulli_data,
show_progress=True)
# summarize the results (wraps CmdStan `bin/stansummary`):
print(bernoulli_fit.summary())
#%%
import os
from cmdstanpy import cmdstan_path, CmdStanModel
bernoulli_stan = os.path.join(cmdstan_path(), 'examples', 'bernoulli',
'bernoulli.stan')
bernoulli_model = CmdStanModel(stan_file=bernoulli_stan)
bernoulli_model.name
bernoulli_model.stan_file
bernoulli_model.exe_file
print(bernoulli_model.code())
bernoulli_data = os.path.join(cmdstan_path(), 'examples', 'bernoulli',
'bernoulli.data.json')
bern_fit = bernoulli_model.sample(data=bernoulli_data,
output_dir='./model_output')
print(bern_fit)
print(bern_fit.sample.shape)
print(bern_fit.summary())
print(bern_fit.diagnose())
#!/usr/bin/env python
# Python code from Jupyter notebook `cmdstanpy_tutorial.ipynb`
# ### Import CmdStanPy classes and methods
import os.path
import sys
from cmdstanpy import CmdStanModel, cmdstan_path
# ### Instantiate & compile the model
bernoulli_dir = os.path.join(cmdstan_path(), 'examples', 'bernoulli')
stan_file = os.path.join(bernoulli_dir, 'bernoulli.stan')
with open(stan_file, 'r') as f:
print(f.read())
model = CmdStanModel(stan_file=stan_file)
print(model)
# ### Assemble the data
data = {"N": 10, "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]}
# In the CmdStan `examples/bernoulli` directory, there are data files in both `JSON` and `rdump` formats.
# bern_json = os.path.join(bernoulli_dir, 'bernoulli.data.json')
# ### Do Inference
fit = model.sample(data=data)
print(fit)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict
import arviz as az
model_dir = Path("stan_code")
data_dir = Path("data")
# ---- data ---- #
bernoulli_data: Dict = {"N": 10, "y": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]}
data = pd.DataFrame({"y": bernoulli_data["y"]},
index=np.arange(bernoulli_data["N"]))
# ---- model ---- #
bernoulli_stan = Path(cmdstan_path()) / "examples/bernoulli/bernoulli.stan"
bernoulli_model = CmdStanModel(stan_file=bernoulli_stan)
bernoulli_model.compile()
# ---- fitting ---- #
bern_fit: CmdStanMCMC = bernoulli_model.sample(
data=bernoulli_data,
chains=4,
cores=1,
seed=1111,
show_progress=True,
)
# ---- results ---- #
"""samples = multi-dimensional array
all draws from all chains arranged as dimensions:
from pathlib import Path
import cmdstanpy
from cmdstanpy import cmdstan_path, CmdStanModel, CmdStanMCMC
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict
import arviz as az
import os
cmdstanpy.CMDSTAN_PATH = "/Users/tommylees/.cmdstanpy/cmdstan-2.21.0"
print(cmdstan_path())
def clear_pre_existing_files():
os.system(
"""
pchs=`find . -name "*.gch"`;
for f in $pchs; do rm $f; done;
"""
)
os.system(
"""
hpps=`find . -name "*.hpp"`;
for f in $hpps; do rm $f; done;
"""
)
print("Deleted *.hpp and *.gch files")
if __name__ == "__main__":
clear_pre_existing_files()