.. _mixture:

.. _gmm:

===================================================
Gaussian mixture models
===================================================

.. currentmodule:: sklearn.mixture

`sklearn.mixture` is a package which enables one to learn
Gaussian Mixture Models (diagonal, spherical, tied and full covariance
matrices supported), sample them, and estimate them from
data. Facilities to help determine the appropriate number of
components are also provided.

 .. figure:: ../auto_examples/mixture/images/plot_gmm_pdf_001.png
   :target: ../auto_examples/mixture/plot_gmm_pdf.html
   :align: center
   :scale: 50%

   **Two-component Gaussian mixture model:** *data points, and equi-probability surfaces of 
   the model.*

A Gaussian mixture model is a probabilistic model that assumes all the
data points are generated from a mixture of a finite number of
Gaussian distributions with unknown parameters. One can think of
mixture models as generalizing k-means clustering to incorporate
information about the covariance structure of the data as well as the
centers of the latent Gaussians.

The `scikit-learn` implements different classes to estimate Gaussian
mixture models, that correspond to different estimation strategies,
detailed below.

GMM classifier
===============

The :class:`GMM` object implements the 
:ref:`expectation-maximization <expectation_maximization>` (EM)
algorithm for fitting mixture-of-Gaussian models. It can also draw
confidence ellipsoids for multivariate models, and compute the
Bayesian Information Criterion to assess the number of clusters in the
data. A :meth:`GMM.fit` method is provided that learns a Gaussian
Mixture Model from train data. Given test data, it can assign to each
sample the class of the Gaussian it mostly probably belong to using
the :meth:`GMM.predict` method.

..  
    Alternatively, the probability of each
    sample belonging to the various Gaussians may be retrieved using the
    :meth:`GMM.predict_proba` method.

The :class:`GMM` comes with different options to constrain the covariance
of the difference classes estimated: spherical, diagonal, tied or full
covariance.

.. figure:: ../auto_examples/mixture/images/plot_gmm_classifier_001.png
   :target: ../auto_examples/mixture/plot_gmm_classifier.html
   :align: center
   :scale: 75%

.. topic:: Examples:

    * See :ref:`example_mixture_plot_gmm_classifier.py` for an example of
      using a GMM as a classifier on the iris dataset.

    * See :ref:`example_mixture_plot_gmm_pdf.py` for an example on plotting the 
      density estimation.

Pros and cons of class :class:`GMM`: expectation-maximization inference
------------------------------------------------------------------------

Pros
.....

:Speed: it is the fastest algorithm for learning mixture models

:Agnostic: as this algorithm maximizes only the likelihood, it
  will not bias the means towards zero, or bias the cluster sizes to
  have specific structures that might or might not apply.

Cons
....

:Singularities: when one has insufficiently many points per
   mixture, estimating the covariance matrices becomes difficult,
   and the algorithm is known to diverge and find solutions with
   infinite likelihood unless one regularizes the covariances artificially.

:Number of components: this algorithm will always use all the
   components it has access to, needing held-out data
   or information theoretical criteria to decide how many components to use 
   in the absence of external cues.

Selecting the number of components in a classical GMM 
------------------------------------------------------

The BIC criterion can be used to select the number of components in a GMM
in an efficient way. In theory, it recovers the true number of components
only in the asymptotic regime (i.e. if much data is available).
Note that using a :ref:`DPGMM <dpgmm>` avoids the specification of the
number of components for a Gaussian mixture model.

.. figure:: ../auto_examples/mixture/images/plot_gmm_selection_001.png
   :target: ../auto_examples/mixture/plot_gmm_selection.html
   :align: center
   :scale: 50%

.. topic:: Examples:

    * See :ref:`example_mixture_plot_gmm_selection.py` for an example
      of model selection performed with classical GMM.

.. _expectation_maximization:

Estimation algorithm Expectation-maximization
-----------------------------------------------

The main difficulty in learning Gaussian mixture models from unlabeled
data is that it is one usually doesn't know which points came from
which latent component (if one has access to this information it gets
very easy to fit a separate Gaussian distribution to each set of
points). `Expectation-maximization
<http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm>`_
is a well-fundamented statistical
algorithm to get around this problem by an iterative process. First
one assumes random components (randomly centered on data points,
learned from k-means, or even just normally distributed around the
origin) and computes for each point a probability of being generated by
each component of the model. Then, one tweaks the
parameters to maximize the likelihood of the data given those
assignments. Repeating this process is guaranteed to always converge
to a local optimum. 


VBGMM classifier: variational Gaussian mixtures
================================================

The :class:`VBGMM` object implements a variant of the Gaussian mixture
model with :ref:`variational inference <variational_inference>` algorithms. The API is identical to
:class:`GMM`. It is essentially a middle-ground between :class:`GMM`
and :class:`DPGMM`, as it has some of the properties of the Dirichlet
process.

Pros and cons of class :class:`VBGMM`: variational inference
-------------------------------------------------------------

Pros
.....

:Regularization: due to the incorporation of prior information,
   variational solutions have less pathological special cases than
   expectation-maximization solutions. One can then use full
   covariance matrices in high dimensions or in cases where some
   components might be centered around a single point without
   risking divergence.

Cons
.....

:Bias: to regularize a model one has to add biases. The
   variational algorithm will bias all the means towards the origin
   (part of the prior information adds a "ghost point" in the origin
   to every mixture component) and it will bias the covariances to
   be more spherical. It will also, depending on the concentration
   parameter, bias the cluster structure either towards uniformity
   or towards a rich-get-richer scenario.

:Hyperparameters: this algorithm needs an extra hyperparameter
   that might need experimental tuning via cross-validation.

.. _variational_inference:

Estimation algorithm: variational inference
---------------------------------------------

Variational inference is an extension of expectation-maximization that
maximizes a lower bound on model evidence (including
priors) instead of data likelihood.  The principle behind
variational methods is the same as expectation-maximization (that is
both are iterative algorithms that alternate between finding the
probabilities for each point to be generated by each mixture and
fitting the mixtures to these assigned points), but variational
methods add regularization by integrating information from prior
distributions. This avoids the singularities often found in
expectation-maximization solutions but introduces some subtle biases
to the model. Inference is often notably slower, but not usually as
much so as to render usage unpractical.

Due to its Bayesian nature, the variational algorithm needs more
hyper-parameters than expectation-maximization, the most
important of these being the concentration parameter `alpha`. Specifying
a high value of alpha leads more often to uniformly-sized mixture
components, while specifying small (between 0 and 1) values will lead
to some mixture components getting almost all the points while most
mixture components will be centered on just a few of the remaining
points.

.. _dpgmm:

DPGMM classifier: Infinite Gaussian mixtures
============================================

The :class:`DPGMM` object implements a variant of the Gaussian mixture
model with a variable (but bounded) number of components using the
Dirichlet Process. The API is identical to :class:`GMM`. 
This class doesn't require the user to choose the number of
components, and at the expense of extra computational time the user
only needs to specify a loose upper bound on this number and a
concentration parameter.

.. |plot_gmm| image:: ../auto_examples/mixture/images/plot_gmm_001.png
   :target: ../auto_examples/mixture/plot_gmm.html
   :scale: 48%

.. |plot_gmm_sin| image:: ../auto_examples/mixture/images/plot_gmm_sin_001.png
   :target: ../auto_examples/mixture/plot_gmm_sin.html
   :scale: 48%

.. centered:: |plot_gmm| |plot_gmm_sin|


The examples above compare Gaussian mixture models with fixed number of
components, to DPGMM models. **On the left** the GMM is fitted with 5
components on a dataset composed of 2 clusters. We can see that the DPGMM is
able to limit itself to only 2 components whereas the GMM fits the data fit too
many components. Note that with very little observations, the DPGMM can take a
conservative stand, and fit only one component. **On the right** we are fitting
a dataset not well-depicted by a mixture of Gaussian. Adjusting the `alpha`
parameter of the DPGMM controls the number of components used to fit this
data.

.. topic:: Examples:

    * See :ref:`example_mixture_plot_gmm.py` for an example on plotting the
      confidence ellipsoids for both :class:`GMM` and :class:`DPGMM`.

    * :ref:`example_mixture_plot_gmm_sin.py` shows using :class:`GMM` and
      :class:`DPGMM` to fit a sine wave

Pros and cons of class :class:`DPGMM`: Diriclet process mixture model
----------------------------------------------------------------------

Pros
.....

:Less sensitivity to the number of parameters: unlike finite
   models, which will almost always use all components as much as
   they can, and hence will produce wildly different solutions for
   different numbers of components, the Dirichlet process solution
   won't change much with changes to the parameters, leading to more
   stability and less tuning.

:No need to specify the number of components: only an upper bound of
   this number needs to be provided. Note however that the DPMM is not
   a formal model selection procedure, and thus provides no guarantee
   on the result.

Cons
.....

:Speed: the extra parametrization necessary for variational
   inference and for the structure of the Dirichlet process can and
   will make inference slower, although not by much.

:Bias: as in variational techniques, but only more so, there are
   many implicit biases in the Dirichlet process and the inference
   algorithms, and whenever there is a mismatch between these biases
   and the data it might be possible to fit better models using a
   finite mixture.

.. _dirichlet_process:

The Dirichlet Process
---------------------

Here we describe variational inference algorithms on Dirichlet process
mixtures. The Dirichlet process is a prior probability distribution on
*clusterings with an infinite, unbounded, number of partitions*.
Variational techniques let us incorporate this prior structure on
Gaussian mixture models at almost no penalty in inference time, comparing
with a finite Gaussian mixture model.

An important question is how can the Dirichlet process use an
infinite, unbounded number of clusters and still be consistent. While
a full explanation doesn't fit this manual, one can think of its
`chinese restaurant process
<http://en.wikipedia.org/wiki/Chinese_restaurant_process>`_ 
analogy to help understanding it. The
chinese restaurant process is a generative story for the Dirichlet
process. Imagine a chinese restaurant with an infinite number of
tables, at first all empty. When the first customer of the day
arrives, he sits at the first table. Every following customer will
then either sit on an occupied table with probability proportional to
the number of customers in that table or sit in an entirely new table
with probability proportional to the concentration parameter
`alpha`. After a finite number of customers has sat, it is easy to see
that only finitely many of the infinite tables will ever be used, and
the higher the value of alpha the more total tables will be used. So
the Dirichlet process does clustering with an unbounded number of
mixture components by assuming a very asymmetrical prior structure
over the assignments of points to components that is very concentrated
(this property is known as rich-get-richer, as the full tables in the
Chinese restaurant process only tend to get fuller as the simulation
progresses).

Variational inference techniques for the Dirichlet process still work
with a finite approximation to this infinite mixture model, but
instead of having to specify a priori how many components one wants to
use, one just specifies the concentration parameter and an upper bound
on the number of mixture components (this upper bound, assuming it is
higher than the "true" number of components, affects only algorithmic
complexity, not the actual number of components used).

.. topic:: Derivation:

   * See `here <dp-derivation.html>`_ the full derivation of this
     algorithm.

.. toctree::
    :hidden:

    dp-derivation.rst


