# In Depth: Gaussian Mixture Models

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The *k*-means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application. In particular, the non-probabilistic nature of *k*-means and its use of simple distance-from-cluster-center to assign cluster membership leads to poor performance for many real-world situations. In this section we will take a look at Gaussian mixture models (GMMs), which can be viewed as an extension of the ideas behind *k*-means, but can also be a powerful tool for estimation beyond simple clustering.

We begin with the standard imports:In [1]:

%matplotlibinlineimportmatplotlib.pyplotaspltimportseabornassns; sns.set()importnumpyasnp

## Motivating GMM: Weaknesses of k-Means

Let’s take a look at some of the weaknesses of *k*-means and think about how we might improve the cluster model. As we saw in the previous section, given simple, well-separated data, *k*-means finds suitable clustering results.

For example, if we have simple blobs of data, the *k*-means algorithm can quickly label those clusters in a way that closely matches what we might do by eye:In [2]:

# Generate some datafromsklearn.datasets.samples_generatorimportmake_blobs X, y_true = make_blobs(n_samples=400, centers=4, cluster_std=0.60, random_state=0) X = X[:, ::-1]# flip axes for better plotting

In [3]:

# Plot the data with K Means Labelsfromsklearn.clusterimportKMeans kmeans = KMeans(4, random_state=0) labels = kmeans.fit(X).predict(X) plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');

From an intuitive standpoint, we might expect that the clustering assignment for some points is more certain than others: for example, there appears to be a very slight overlap between the two middle clusters, such that we might not have complete confidence in the cluster assigment of points between them. Unfortunately, the *k*-means model has no intrinsic measure of probability or uncertainty of cluster assignments (although it may be possible to use a bootstrap approach to estimate this uncertainty). For this, we must think about generalizing the model.

One way to think about the *k*-means model is that it places a circle (or, in higher dimensions, a hyper-sphere) at the center of each cluster, with a radius defined by the most distant point in the cluster. This radius acts as a hard cutoff for cluster assignment within the training set: any point outside this circle is not considered a member of the cluster. We can visualize this cluster model with the following function:In [4]:

fromsklearn.clusterimportKMeansfromscipy.spatial.distanceimportcdistdefplot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None): labels = kmeans.fit_predict(X)# plot the input dataax = axorplt.gca() ax.axis('equal') ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)# plot the representation of the KMeans modelcenters = kmeans.cluster_centers_ radii = [cdist(X[labels == i], [center]).max()fori, centerinenumerate(centers)]forc, rinzip(centers, radii): ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))

In [5]:

kmeans = KMeans(n_clusters=4, random_state=0) plot_kmeans(kmeans, X)

An important observation for *k*-means is that these cluster models *must be circular*: *k*-means has no built-in way of accounting for oblong or elliptical clusters. So, for example, if we take the same data and transform it, the cluster assignments end up becoming muddled:In [6]:

rng = np.random.RandomState(13) X_stretched = np.dot(X, rng.randn(2, 2)) kmeans = KMeans(n_clusters=4, random_state=0) plot_kmeans(kmeans, X_stretched)

By eye, we recognize that these transformed clusters are non-circular, and thus circular clusters would be a poor fit. Nevertheless, *k*-means is not flexible enough to account for this, and tries to force-fit the data into four circular clusters. This results in a mixing of cluster assignments where the resulting circles overlap: see especially the bottom-right of this plot. One might imagine addressing this particular situation by preprocessing the data with PCA (see In Depth: Principal Component Analysis), but in practice there is no guarantee that such a global operation will circularize the individual data.

These two disadvantages of *k*-means—its lack of flexibility in cluster shape and lack of probabilistic cluster assignment—mean that for many datasets (especially low-dimensional datasets) it may not perform as well as you might hope.

You might imagine addressing these weaknesses by generalizing the *k*-means model: for example, you could measure uncertainty in cluster assignment by comparing the distances of each point to *all* cluster centers, rather than focusing on just the closest. You might also imagine allowing the cluster boundaries to be ellipses rather than circles, so as to account for non-circular clusters. It turns out these are two essential components of a different type of clustering model, Gaussian mixture models.

## Generalizing E–M: Gaussian Mixture Models

A Gaussian mixture model (GMM) attempts to find a mixture of multi-dimensional Gaussian probability distributions that best model any input dataset. In the simplest case, GMMs can be used for finding clusters in the same manner as *k*-means:In [7]:

fromsklearn.mixtureimportGMM gmm = GMM(n_components=4).fit(X) labels = gmm.predict(X) plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');

But because GMM contains a probabilistic model under the hood, it is also possible to find probabilistic cluster assignments—in Scikit-Learn this is done using the `predict_proba`

method. This returns a matrix of size `[n_samples, n_clusters]`

which measures the probability that any point belongs to the given cluster:In [8]:

probs = gmm.predict_proba(X) print(probs[:5].round(3))

[[ 0. 0. 0.475 0.525] [ 0. 1. 0. 0. ] [ 0. 1. 0. 0. ] [ 0. 0. 0. 1. ] [ 0. 1. 0. 0. ]]

We can visualize this uncertainty by, for example, making the size of each point proportional to the certainty of its prediction; looking at the following figure, we can see that it is precisely the points at the boundaries between clusters that reflect this uncertainty of cluster assignment:In [9]:

size = 50 * probs.max(1) ** 2# square emphasizes differencesplt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=size);

Under the hood, a Gaussian mixture model is very similar to *k*-means: it uses an expectation–maximization approach which qualitatively does the following:

- Choose starting guesses for the location and shape
- Repeat until converged:
*E-step*: for each point, find weights encoding the probability of membership in each cluster*M-step*: for each cluster, update its location, normalization, and shape based on*all*data points, making use of the weights

The result of this is that each cluster is associated not with a hard-edged sphere, but with a smooth Gaussian model. Just as in the *k*-means expectation–maximization approach, this algorithm can sometimes miss the globally optimal solution, and thus in practice multiple random initializations are used.

Let’s create a function that will help us visualize the locations and shapes of the GMM clusters by drawing ellipses based on the GMM output:In [10]:

frommatplotlib.patchesimportEllipsedefdraw_ellipse(position, covariance, ax=None, **kwargs):"""Draw an ellipse with a given position and covariance"""ax = axorplt.gca()# Convert covariance to principal axesifcovariance.shape == (2, 2): U, s, Vt = np.linalg.svd(covariance) angle = np.degrees(np.arctan2(U[1, 0], U[0, 0])) width, height = 2 * np.sqrt(s)else: angle = 0 width, height = 2 * np.sqrt(covariance)# Draw the Ellipsefornsiginrange(1, 4): ax.add_patch(Ellipse(position, nsig * width, nsig * height, angle, **kwargs))defplot_gmm(gmm, X, label=True, ax=None): ax = axorplt.gca() labels = gmm.fit(X).predict(X)iflabel: ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)else: ax.scatter(X[:, 0], X[:, 1], s=40, zorder=2) ax.axis('equal') w_factor = 0.2 / gmm.weights_.max()forpos, covar, winzip(gmm.means_, gmm.covars_, gmm.weights_): draw_ellipse(pos, covar, alpha=w * w_factor)

With this in place, we can take a look at what the four-component GMM gives us for our initial data:In [11]:

gmm = GMM(n_components=4, random_state=42) plot_gmm(gmm, X)

Similarly, we can use the GMM approach to fit our stretched dataset; allowing for a full covariance the model will fit even very oblong, stretched-out clusters:In [12]:

gmm = GMM(n_components=4, covariance_type='full', random_state=42) plot_gmm(gmm, X_stretched)

This makes clear that GMM addresses the two main practical issues with *k*-means encountered before.

### Choosing the covariance type

If you look at the details of the preceding fits, you will see that the `covariance_type`

option was set differently within each. This hyperparameter controls the degrees of freedom in the shape of each cluster; it is essential to set this carefully for any given problem. The default is `covariance_type="diag"`

, which means that the size of the cluster along each dimension can be set independently, with the resulting ellipse constrained to align with the axes. A slightly simpler and faster model is `covariance_type="spherical"`

, which constrains the shape of the cluster such that all dimensions are equal. The resulting clustering will have similar characteristics to that of *k*-means, though it is not entirely equivalent. A more complicated and computationally expensive model (especially as the number of dimensions grows) is to use `covariance_type="full"`

, which allows each cluster to be modeled as an ellipse with arbitrary orientation.

We can see a visual representation of these three choices for a single cluster within the following figure:

## GMM as *Density Estimation*

Though GMM is often categorized as a clustering algorithm, fundamentally it is an algorithm for *density estimation*. That is to say, the result of a GMM fit to some data is technically not a clustering model, but a generative probabilistic model describing the distribution of the data.

As an example, consider some data generated from Scikit-Learn’s `make_moons`

function, which we saw in In Depth: K-Means Clustering:In [13]:

fromsklearn.datasetsimportmake_moons Xmoon, ymoon = make_moons(200, noise=.05, random_state=0) plt.scatter(Xmoon[:, 0], Xmoon[:, 1]);

If we try to fit this with a two-component GMM viewed as a clustering model, the results are not particularly useful:In [14]:

gmm2 = GMM(n_components=2, covariance_type='full', random_state=0) plot_gmm(gmm2, Xmoon)

But if we instead use many more components and ignore the cluster labels, we find a fit that is much closer to the input data:In [15]:

gmm16 = GMM(n_components=16, covariance_type='full', random_state=0) plot_gmm(gmm16, Xmoon, label=False)