Skip to main content

Python interface to CmdStan

Project description

CmdStanPy

codecov

CmdStanPy is a lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms. It supports both development and production workflows. Because model development and testing may require many iterations, the defaults favor development mode and therefore output files are stored on a temporary filesystem. Non-default options allow all aspects of a run to be specified so that scripts can be used to distributed analysis jobs across nodes and machines.

CmdStanPy is distributed via PyPi: https://pypi.org/project/cmdstanpy/

or Conda Forge: https://anaconda.org/conda-forge/cmdstanpy

Goals

  • Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.

  • Provide access to all CmdStan inference methods.

  • Easy to install,

    • minimal Python library dependencies: numpy, pandas
    • Python code doesn't interface directly with c++, only calls compiled executables
  • Modular - CmdStanPy produces a MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.

  • Low memory overhead - by default, minimal memory used above that required by CmdStanPy; objects run CmdStan programs and track CmdStan input and output files.

Source Repository

CmdStanPy and CmdStan are available from GitHub: https://github.com/stan-dev/cmdstanpy and https://github.com/stan-dev/cmdstan

Docs

The latest release documentation is hosted on https://mc-stan.org/cmdstanpy, older release versions are available from readthedocs: https://cmdstanpy.readthedocs.io

Licensing

The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.

Example

import os
from cmdstanpy import cmdstan_path, CmdStanModel

# specify locations of Stan program file and data
stan_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
data_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json')

# instantiate a model; compiles the Stan program by default
model = CmdStanModel(stan_file=stan_file)

# obtain a posterior sample from the model conditioned on the data
fit = model.sample(chains=4, data=data_file)

# summarize the results (wraps CmdStan `bin/stansummary`):
fit.summary()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cmdstanpy-1.0.7.tar.gz (100.1 kB view details)

Uploaded Source

Built Distribution

cmdstanpy-1.0.7-py3-none-any.whl (80.7 kB view details)

Uploaded Python 3

File details

Details for the file cmdstanpy-1.0.7.tar.gz.

File metadata

  • Download URL: cmdstanpy-1.0.7.tar.gz
  • Upload date:
  • Size: 100.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for cmdstanpy-1.0.7.tar.gz
Algorithm Hash digest
SHA256 032cdba9f54aba9e292e5fc980372834a162e6d7b841f3bb28ab77a4235ee0df
MD5 73223eb8d5b0500b8d8d0a5e2ee59009
BLAKE2b-256 f2758b992f2c456eaf4de7264c18444e2dd1fc1d087e246a602252384fb1dfca

See more details on using hashes here.

File details

Details for the file cmdstanpy-1.0.7-py3-none-any.whl.

File metadata

  • Download URL: cmdstanpy-1.0.7-py3-none-any.whl
  • Upload date:
  • Size: 80.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for cmdstanpy-1.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 590c7babe711aff7bd9ad6fe6c4a3f92da425258448262491c019afc789e1107
MD5 dc49dcd30cd187c4814f2e6e581d451c
BLAKE2b-256 6ebf887c3e0d9495fa93ca9247d02200fcf9e3c15a28ea33e2ef0cebbb190521

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page