dime-sampler 1.0.0
DIME MCMC sampling for python
License: MIT

dime_sampler
============

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**Differential-Independence Mixture Ensemble ("DIME") MCMC sampling for Python** 

This is the Python implementation of the DIME sampler proposed in `Ensemble MCMC Sampling for Robust Bayesian Inference <https://gregorboehl.com/live/dime_mcmc_boehl.pdf>`_ *(Gregor Boehl, 2022, SSRN No. 4250395)*. It provides the ``DIMEMove`` as a drop-in replacement for the great `emcee <https://github.com/dfm/emcee>`_ MCMC package.

The sampler has a series of advantages over conventional samplers:

#. DIME MCMC is a (very fast) gradient-free **global multi-start optimizer** and, at the same time, a **MCMC sampler** that converges to the posterior distribution. This makes any posterior mode density maximization prior to MCMC sampling superfluous.
#. The DIME sampler is pretty robust for odd **shaped, multimodal, black-box distributions**.
#. DIME MCMC is **parallelizable**: many chains can run in parallel, and the necessary number of draws decreases almost one-to-one with the number of chains.
#. DIME proposals are generated from an **endogenous and adaptive proposal distribution**, thereby providing close-to-optimal proposal distributions for black box target distributions without the need for manual fine-tuning.
    
There is a nice `set of slides <https://gregorboehl.com/revealjs/emc>`_ on my website which explains the DIME principle.

.. figure:: https://github.com/gboehl/dime/blob/main/docs/dist.png?raw=true
  :width: 800
  :alt: Sample and target distribution
  
  Figure: A trimodal example distribution in 35 dimensions

There exist complementary stand-alone implementations in `Julia language <https://github.com/gboehl/DIMESampler.jl>`_ and `in matlab <https://github.com/gboehl/dime-mcmc-matlab>`_.

Installation
------------

Installing the `repository version <https://pypi.org/project/dime_sampler/>`_ from PyPi is as simple as typing

.. code-block:: bash

   pip install dime_sampler

in your terminal or Anaconda Prompt. 


Usage
-----

The package provides a direct drop-in replacement for `emcee <https://github.com/dfm/emcee>`_:

.. code-block:: python

    import emcee
    from dime_sampler import DIMEMove

    move = DIMEMove()

    ...
    def log_prob(x):
      ...
    # define your density function, the number of chains `nchain` etc...
    ...

    sampler = emcee.EnsembleSampler(nchain, ndim, log_prob, moves=move)
    ...
    # off you go sampling

The rest of the usage is analog to emcee. See below for getting a quickstart or have a look `this tutorial <https://emcee.readthedocs.io/en/stable/tutorials/quickstart/>`_ for details. The parameters specific to the ``DIMEMove`` are documented `here <https://dime-sampler.readthedocs.io/en/latest/modules.html#>`_.


Tutorial
--------

Lets look at an example. Let's define a nice and challenging distribution (it's the distribution from the figure above):

.. code-block:: python

    # some import
    import emcee
    import numpy as np
    import scipy.stats as ss
    from dime_sampler import DIMEMove
    from dime_sampler.test_all import _create_test_func, _marginal_pdf_test_func

    # make it reproducible
    np.random.seed(0)

    # define distribution
    m = 2
    cov_scale = 0.05
    weight = (0.33, .1)
    ndim = 35
    initvar = np.sqrt(2)

    log_prob = _create_test_func(ndim, weight, m, cov_scale)

``log_prob`` will now return the log-PDF of a 35-dimensional Gaussian mixture with **three separate modes**.

Next, define the initial ensemble. In a Bayesian setup, a good initial ensemble would be a sample from the prior distribution. Here, we will go for a sample from a rather flat Gaussian distribution.

.. code-block:: python

    # number of chains and number of iterations
    nchain = ndim * 5
    niter = 5000

    # initial ensemble
    initmean = np.zeros(ndim)
    initcov = np.eye(ndim) * np.sqrt(2)
    initchain = ss.multivariate_normal(mean=initmean, cov=initcov).rvs(nchain)

Setting the number of parallel chains to ``5*ndim`` is a sane default. For highly irregular distributions with several modes you should use more chains. Very simple distributions can go with less.

Now let the sampler run for 5000 iterations.

.. code-block:: python

    move = DIMEMove(aimh_prob=0.1, df_proposal_dist=10)
    sampler = emcee.EnsembleSampler(nchain, ndim, log_prob, moves=move)
    sampler.run_mcmc(initchain, int(niter), progress=True)

The setting of ``aimh_prob`` is the actual default value. For less complex distributions (e.g. distributions closer to Gaussian) a higher value can be chosen, which accelerates burn-in. The value ``df_proposal_dist`` sets the degrees of freedom for the proposal distribution of the independence move. ``10`` is a sane default and it is rather unlikely that this value must be changed.

The following code creates the figure above, which is a plot of the marginal distribution along the first dimension (remember that this actually is a 35-dimensional distribution).

.. code-block:: python

    # import matplotlib
    import matplotlib.pyplot as plt

    # get elements
    chain = sampler.get_chain()
    lprob = sampler.get_log_prob()

    # plotting
    fig, ax = plt.subplots(figsize=(9,6))
    ax.hist(chain[-niter//2 :, :, 0].flatten(), bins=50, density=True, alpha=0.2, label="Sample")
    xlim = ax.get_xlim()
    x = np.linspace(xlim[0], xlim[1], 100)
    ax.plot(x, ss.norm(scale=np.sqrt(initvar)).pdf(x), "--", label="Initialization")
    ax.plot(x, ss.t(df=10, loc=move.prop_mean[0], scale=move.prop_cov[0, 0] ** 0.5).pdf(x), ":", label="Final proposals")
    ax.plot(x, _marginal_pdf_test_func(x, cov_scale, m, weight), label="Target")
    ax.legend()

To ensure proper mixing, let us also have a look at the MCMC traces, again focussing on the first dimension.

.. code-block:: python

    fig, ax = plt.subplots(figsize=(9,6))
    ax.plot(chain[:, :, 0], alpha=0.05, c="C0")

.. image:: https://github.com/gboehl/dime_sampler/blob/main/docs/traces.png?raw=true
  :width: 800
  :alt: MCMC traces

Note how chains are also switching between the three modes because of the global proposal kernel.

While DIME is an MCMC sampler, it can straightforwardly be used as a global optimization routine. To this end, specify some broad starting region (in a non-Bayesian setup there is no prior) and let the sampler run for an extended number of iterations. Finally, assess whether the maximum value per ensemble did not change much in the last few hundred iterations. In a normal Bayesian setup, plotting the associated log-likelihood over time also helps to assess convergence to the posterior distribution.

.. code-block:: python

    fig, ax = plt.subplots(figsize=(9,6))
    ax.plot(lprob, alpha=0.05, c="C0")
    ax.plot(np.arange(niter), np.max(lprob) * np.ones(niter), "--", c="C1")

.. image:: https://github.com/gboehl/dime_sampler/blob/main/docs/lprobs.png?raw=true
  :width: 800
  :alt: Log-likelihoods

References
----------

If you are using this software in your research, please cite

.. code-block:: bibtex

    @techreport{boehl2022mcmc,
    author={Gregor Boehl},
    title={Ensemble MCMC Sampling for Robust Bayesian Inference},
    journal={Available at SSRN 4250395},
    year={2022}
    }