def run_stan_model(stan_file, data, **kwargs):
"""
Convenience function to compile, sample and diagnose a Stan model.
Notes
-----
For prior predictive sampling (or to otherwise
simulate data), pass `fixed_param=True`.
https://cmdstanpy.readthedocs.io/en/latest/sample.html#example-generate-data-fixed-param-true
"""
model = CmdStanModel(stan_file=stan_file)
model.compile()
fit = model.sample(data=data, **kwargs)
fit.diagnose()
return model, fit
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())
json_data = {
"J": J - Start,
"x_distance": x_distance[Start:J].tolist(),
"y_successes": y_successes_shrunk[Start:J].tolist(),
"n_attempts": n_attempts_shrunk[Start:J].tolist()
}
fig, (ax2, ax) = plt.subplots(1, 2)
ax.set_facecolor('grey')
ax.set_ylabel('chance in 1')
ax.set_xlabel('putt distance (feet)')
stan_program = CmdStanModel(stan_file='stan/logist.stan')
stan_program.compile()
fit = stan_program.sample(data=json_data, csv_basename='./puttbetlog')
#print(fit.summary())
logistic_color = 'y'
runGolf(fit, 'stan/logist.stan', ax, logistic_color)
a_draws = fit.get_drawset(['a_intercept']).to_numpy()
b_draws = fit.get_drawset(['b_slope']).to_numpy()
ax2.hist(a_draws, color=logistic_color, label='a_intercept')
ax2.hist(b_draws, label='b_slope')
ax2.legend(loc='upper right')
ax2.set_ylabel('number of draws')
stan_program = CmdStanModel(stan_file='stan/mechai.stan')
stan_program.compile()
fit2 = stan_program.sample(data=json_data, csv_basename='./puttbetmech')
runGolf(fit2, 'stan/mechai.stan', ax, 'g')
from cmdstanpy import CmdStanModel
from prep import build_stan_data
data = build_stan_data("050401")
model = CmdStanModel(model_name="xrt", stan_file="simple_xrt.stan")
model.compile(force=True)
fit = model.sample(
data=data,
chains=1,
seed=1234,
iter_warmup=200,
iter_sampling=200,
show_progress=True,
)
model_dir = Path("stan_code")
data_dir = Path("data")
# ---- data ---- #
eight_school_data = {
"J": 8,
"y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),
"sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),
}
# ---- model ---- #
stan_file = model_dir / "schools.stan"
stan_model = CmdStanModel(stan_file=stan_file)
stan_model.compile()
# ---- fitting ---- #
stan_fit = stan_model.sample(data=eight_school_data)
# ---- results ---- #
cmdstanpy_data = az.from_cmdstanpy(
posterior=stan_fit,
posterior_predictive="y_hat",
observed_data={"y": eight_school_data["y"]},
log_likelihood="log_lik",
coords={"school": np.arange(eight_school_data["J"])},
dims={
"theta": ["school"],
"y": ["school"],
"log_lik": ["school"],
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:
(draws, chains, columns).
"""