CmdStanPy on Colab Starter Code

boilerplate.py

########### INSTALL ###########
# !pip install --upgrade cmdstanpy
import os
import urllib.request
import shutil
# Install pre-built CmdStan binary
# (faster than compiling from source via install_cmdstan() function)
tgz_file = 'colab-cmdstan-2.23.0.tar.gz'
tgz_url = 'https://github.com/stan-dev/cmdstan/releases/download/v2.23.0/colab-cmdstan-2.23.0.tar.gz'
if not os.path.exists(tgz_file):
    urllib.request.urlretrieve(tgz_url, tgz_file)
    shutil.unpack_archive(tgz_file)


# # Specify CmdStan location via environment variable
os.environ['CMDSTAN'] = './cmdstan-2.23.0'
# # Check CmdStan path
from cmdstanpy import CmdStanModel, cmdstan_path
print(cmdstan_path())


########### LOAD MODEL FILE AND SETUP DATA ###########

model_data = {
    "N": 35303,
    "K": 7,
    "P": 2,
    "y": profit_data['y'].values,
    "ITT": profit_data['T'].values,
    "site": profit_data['k'].values,
    "mutau_prior_mean": np.zeros(2),
    "mutau_prior_sigma": np.eye(2)*1e6,
}

model = CmdStanModel(stan_file='/content/drive/MyDrive/MIT6.435/project/basic-microcredit-uninformative-ss.stan')

# Need to run this command if the model is already compiled and loaded from file
# Otherwise the inference step throws an error for me
#!chmod 755 '/content/drive/MyDrive/MIT6.435/project/basic-microcredit-uninformative-ss'

########### GENERATE SAMPLES ###########


fit = model.sample(data=model_data, iter_sampling=10000, cores=2, output_dir='/content/drive/MyDrive/MIT6.435/project')

# fit.summary()