cmdstanpy

import os import shutil from cmdstanpy import CmdStanModel, install_cmdstan, set_cmdstan_path # --- Step 1: Ensure CmdStan is installed and path is set --- # This is crucial. If CMDSTAN_PATH environment variable is not set, # CmdStanPy will attempt to download and install CmdStan to '~/.cmdstan'. # You can also manually set the path via set_cmdstan_path(). try: set_cmdstan_path(os.environ.get('CMDSTAN_PATH', '')) except ValueError: print("CmdStan path not explicitly set or found. Attempting automatic installation...") install_cmdstan() print(f"CmdStan installed to: {os.path.join(os.path.expanduser('~'), '.cmdstan')}") # --- Step 2: Define a Stan model --- stan_code = """ data { int<lower=0> N; array[N] int<lower=0,upper=1> y; } parameters { real<lower=0,upper=1> theta; } model { y ~ bernoulli(theta); } """ stan_filepath = "bernoulli.stan" with open(stan_filepath, "w") as f: f.write(stan_code) try: # --- Step 3: Compile the Stan model --- # This creates an executable that CmdStanPy will call. model = CmdStanModel(stan_file=stan_filepath) print(f"Stan model compiled to: {model.exe_file}") # --- Step 4: Prepare data for the model --- data = {'N': 10, 'y': [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]} # --- Step 5: Fit the model using MCMC sampling --- print("Starting MCMC sampling...") fit = model.sample(data=data, chains=2, iter_sampling=500, iter_warmup=500, seed=42) print("MCMC sampling complete.") # --- Step 6: Summarize and inspect results --- print("\nPosterior summary statistics:") print(fit.summary()) # Access posterior draws as a pandas DataFrame # print("\nFirst 5 rows of posterior draws:") # print(fit.draws_pd().head()) except Exception as e: print(f"An error occurred during CmdStanPy execution: {e}") finally: # --- Step 7: Clean up generated files --- if os.path.exists(stan_filepath): os.remove(stan_filepath) # CmdStan installation is usually kept, so no cleanup for it here. print("Cleanup complete.")