[MRG+1] Bayesian Gaussian Mixture (Integration of GSoC2015 -- second step)

doc/modules/classes.rst

@@ -954,8 +954,8 @@ See the :ref:`metrics` section of the user guide for further details.
    :template: class.rst
 
    mixture.GaussianMixture
+   mixture.BayesianGaussianMixture
    mixture.DPGMM
-   mixture.VBGMM
 
 
 .. _multiclass_ref:

doc/modules/mixture.rst

@@ -133,40 +133,13 @@ parameters to maximize the likelihood of the data given those
 assignments. Repeating this process is guaranteed to always converge
 to a local optimum.
 
-.. _vbgmm:
+.. _bgmm:
 
-VBGMM: variational Gaussian mixtures
-====================================
+Bayesian Gaussian Mixture
+=========================
 
-The :class:`VBGMM` object implements a variant of the Gaussian mixture
-model with :ref:`variational inference <variational_inference>` algorithms.
-
-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.
+The :class:`BayesianGaussianMixture` object implements a variant of the Gaussian
+mixture model with variational inference algorithms.
 
 .. _variational_inference:
 
@@ -175,7 +148,7 @@ 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
+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
@@ -195,6 +168,47 @@ to some mixture components getting almost all the points while most
 mixture components will be centered on just a few of the remaining
 points.
 
+.. figure:: ../auto_examples/mixture/images/sphx_glr_plot_bayesian_gaussian_mixture_001.png
+   :target: ../auto_examples/mixture/plot_bayesian_gaussian_mixture.html
+   :align: center
+   :scale: 50%
+
+.. topic:: Examples:
+
+    * See :ref:`plot_bayesian_gaussian_mixture.py` for a comparaison of
+      the results of the ``BayesianGaussianMixture`` for different values
+      of the parameter ``alpha``.
+
+Pros and cons of variational inference with :class:BayesianGaussianMixture
+--------------------------------------------------------------------------
+
+Pros
+.....
+
+:Regularization: due to the incorporation of prior information,
+   variational solutions have less pathological special cases than
+   expectation-maximization solutions.
+
+:Automatic selection: when `dirichlet_concentration_prior` is small enough and
+`n_components` is larger than what is found necessary by the model, the
+Variational Bayesian mixture model has a natural tendency to set some mixture
+weights values close to zero. This makes it possible to let the model choose a
+suitable number of effective components automatically.
+
+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.
+
 .. _dpgmm:
 
 DPGMM: Infinite Gaussian mixtures

doc/whats_new.rst

@@ -64,13 +64,13 @@ Model Selection Enhancements and API Changes
 
   - **Parameters ``n_folds`` and ``n_iter`` renamed to ``n_splits``**
 
-    Some parameter names have changed: 
-    The ``n_folds`` parameter in :class:`model_selection.KFold`, 
-    :class:`model_selection.LabelKFold`, and 
+    Some parameter names have changed:
+    The ``n_folds`` parameter in :class:`model_selection.KFold`,
+    :class:`model_selection.LabelKFold`, and
     :class:`model_selection.StratifiedKFold` is now renamed to ``n_splits``.
     The ``n_iter`` parameter in :class:`model_selection.ShuffleSplit`,
-    :class:`model_selection.LabelShuffleSplit`, 
-    and :class:`model_selection.StratifiedShuffleSplit` is now renamed 
+    :class:`model_selection.LabelShuffleSplit`,
+    and :class:`model_selection.StratifiedShuffleSplit` is now renamed
     to ``n_splits``.
 
 
@@ -141,8 +141,8 @@ New features
      <https://github.com/scikit-learn/scikit-learn/pull/6954>`_) by `Nelson
      Liu`_
 
-   - Added new cross-validation splitter 
-     :class:`model_selection.TimeSeriesSplit` to handle time series data. 
+   - Added new cross-validation splitter
+     :class:`model_selection.TimeSeriesSplit` to handle time series data.
      (`#6586
      <https://github.com/scikit-learn/scikit-learn/pull/6586>`_) by `YenChen
      Lin`_
@@ -396,10 +396,19 @@ API changes summary
    - Access to public attributes ``.X_`` and ``.y_`` has been deprecated in
      :class:`isotonic.IsotonicRegression`. By `Jonathan Arfa`_.
 
+   - The old :class:`VBGMM` is deprecated in favor of the new
+     :class:`BayesianGaussianMixture`. The new class solves the computational
+     problems of the old class and computes the Variational Bayesian Gaussian
+     mixture faster than before.
+     Ref :ref:`b` for more information.
+     (`#6651 <https://github.com/scikit-learn/scikit-learn/pull/6651>`_) by
+     `Wei Xue`_ and `Thierry Guillemot`_.
+
    - The old :class:`GMM` is deprecated in favor of the new
      :class:`GaussianMixture`. The new class computes the Gaussian mixture
      faster than before and some of computational problems have been solved.
-     By `Wei Xue`_ and `Thierry Guillemot`_.
+     (`#6666 <https://github.com/scikit-learn/scikit-learn/pull/6666>`_) by
+     `Wei Xue`_ and `Thierry Guillemot`_.
 
    - The ``grid_scores_`` attribute of :class:`model_selection.GridSearchCV`
      and :class:`model_selection.RandomizedSearchCV` is deprecated in favor of
@@ -409,7 +418,7 @@ API changes summary
      `Raghav R V`_.
 
    - The parameters ``n_iter`` or ``n_folds`` in old CV splitters are replaced
-     by the new parameter ``n_splits`` since it can provide a consistent 
+     by the new parameter ``n_splits`` since it can provide a consistent
      and unambiguous interface to represent the number of train-test splits.
      (`#7187 <https://github.com/scikit-learn/scikit-learn/pull/7187>`_)
      by `YenChen Lin`_.

examples/mixture/plot_bayesian_gaussian_mixture.py

@@ -0,0 +1,115 @@
+"""
+======================================================
+Bayesian Gaussian Mixture Concentration Prior Analysis
+======================================================
+
+Plot the resulting ellipsoids of a mixture of three Gaussians with EM and
+variational Bayesian Gaussian Mixture for three different values of on the prior
+the dirichlet concentration.
+
+For all models, the Variationnal Bayesian Gaussian Mixture adapts its number of
+mixture automatically. The parameter `dirichlet_concentration_prior` has a
+direct link with the resulting number of components. Specifying a high value of
+`dirichlet_concentration_prior` leads more often to uniformly-sized mixture
+components, while specifying small (under 0.1) 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.
+"""
+# Author: Wei Xue <xuewei4d@gmail.com>
+#         Thierry Guillemot <thierry.guillemot.work@gmail.com>
+# License: BSD 3 clause
+
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+from sklearn.mixture import BayesianGaussianMixture
+
+print(__doc__)
+
+
+def plot_ellipses(ax, weights, means, covars):
+    for n in range(means.shape[0]):
+        v, w = np.linalg.eigh(covars[n][:2, :2])
+        u = w[0] / np.linalg.norm(w[0])
+        angle = np.arctan2(u[1], u[0])
+        angle = 180 * angle / np.pi  # convert to degrees
+        v = 2 * np.sqrt(2) * np.sqrt(v)
+        ell = mpl.patches.Ellipse(means[n, :2], v[0], v[1], 180 + angle)
+        ell.set_clip_box(ax.bbox)
+        ell.set_alpha(weights[n])
+        ax.add_artist(ell)
+
+
+def plot_results(ax1, ax2, estimator, dirichlet_concentration_prior, X, y, plot_title=False):
+    estimator.dirichlet_concentration_prior = dirichlet_concentration_prior
+    estimator.fit(X)
+    ax1.set_title("Bayesian Gaussian Mixture for "
+                  r"$dc_0=%.1e$" % dirichlet_concentration_prior)
+    # ax1.axis('equal')
+    ax1.scatter(X[:, 0], X[:, 1], s=5, marker='o', color=colors[y], alpha=0.8)
+    ax1.set_xlim(-2., 2.)
+    ax1.set_ylim(-3., 3.)
+    ax1.set_xticks(())
+    ax1.set_yticks(())
+    plot_ellipses(ax1, estimator.weights_, estimator.means_,
+                  estimator.covariances_)
+
+    ax2.get_xaxis().set_tick_params(direction='out')
+    ax2.yaxis.grid(True, alpha=0.7)
+    for k, w in enumerate(estimator.weights_):
+        ax2.bar(k - .45, w, width=0.9, color='royalblue', zorder=3)
+        ax2.text(k, w + 0.007, "%.1f%%" % (w * 100.),
+                 horizontalalignment='center')
+    ax2.set_xlim(-.6, 2 * n_components - .4)
+    ax2.set_ylim(0., 1.1)
+    ax2.tick_params(axis='y', which='both', left='off',
+                    right='off', labelleft='off')
+    ax2.tick_params(axis='x', which='both', top='off')
+
+    if plot_title:
+        ax1.set_ylabel('Estimated Mixtures')
+        ax2.set_ylabel('Weight of each component')
+
+# Parameters
+random_state = 2
+n_components, n_features = 3, 2
+colors = np.array(['mediumseagreen', 'royalblue', 'r', 'gold',
+                   'orchid', 'indigo', 'darkcyan', 'tomato'])
+dirichlet_concentration_prior = np.logspace(-3, 3, 3)
+covars = np.array([[[.7, .0], [.0, .1]],
+                   [[.5, .0], [.0, .1]],
+                   [[.5, .0], [.0, .1]]])
+samples = np.array([200, 500, 200])
+means = np.array([[.0, -.70],
+                  [.0, .0],
+                  [.0, .70]])
+
+
+# Here we put beta_prior to 0.8 to minimize the influence of the prior for this
+# dataset
+estimator = BayesianGaussianMixture(n_components=2 * n_components,
+                                    init_params='random', max_iter=1500,
+                                    mean_precision_prior=.8, tol=1e-9,
+                                    random_state=random_state)
+
+# Generate data
+rng = np.random.RandomState(random_state)
+X = np.vstack([
+    rng.multivariate_normal(means[j], covars[j], samples[j])
+    for j in range(n_components)])
+y = np.concatenate([j * np.ones(samples[j], dtype=int)
+                    for j in range(n_components)])
+
+# Plot Results
+plt.figure(figsize=(4.7 * 3, 8))
+plt.subplots_adjust(bottom=.04, top=0.95, hspace=.05, wspace=.05,
+                    left=.03, right=.97)
+
+gs = gridspec.GridSpec(3, len(dirichlet_concentration_prior))
+for k, dc in enumerate(dirichlet_concentration_prior):
+    plot_results(plt.subplot(gs[0:2, k]), plt.subplot(gs[2, k]),
+                 estimator, dc, X, y, plot_title=k == 0)
+
+plt.show()

sklearn/mixture/__init__.py

@@ -8,6 +8,7 @@
 from .dpgmm import DPGMM, VBGMM
 
 from .gaussian_mixture import GaussianMixture
+from .bayesian_mixture import BayesianGaussianMixture
 
 
 __all__ = ['DPGMM',
@@ -17,4 +18,5 @@
            'distribute_covar_matrix_to_match_covariance_type',
            'log_multivariate_normal_density',
            'sample_gaussian',
-           'GaussianMixture']
+           'GaussianMixture',
+           'BayesianGaussianMixture']

sklearn/mixture/base.py

@@ -237,7 +237,6 @@ def fit(self, X, y=None):
 
         return self
 
-    @abstractmethod
     def _e_step(self, X):
         """E step.
 
@@ -248,12 +247,14 @@ def _e_step(self, X):
         Returns
         -------
         log_prob_norm : array, shape (n_samples,)
-            log p(X)
+            Logarithm of the probability of X.
 
         log_responsibility : array, shape (n_samples, n_components)
-            logarithm of the responsibilities
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of X.
         """
-        pass
+        log_prob_norm, log_resp = self._estimate_log_prob_resp(X)
+        return np.mean(log_prob_norm), log_resp
 
     @abstractmethod
     def _m_step(self, X, log_resp):
@@ -264,6 +265,8 @@ def _m_step(self, X, log_resp):
         X : array-like, shape (n_samples, n_features)
 
         log_resp : array-like, shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of X.
         """
         pass