# Unsupervised learning: seeking representations of the data¶

## Clustering: grouping observations together¶

The problem solved in clustering

Given the iris dataset, if we knew that there were 3 types of iris, but
did not have access to a taxonomist to label them: we could try a
**clustering task**: split the observations into well-separated group
called *clusters*.

### K-means clustering¶

Note that there exist a lot of different clustering criteria and associated
algorithms. The simplest clustering algorithm is
*K-means*.

```
>>> from sklearn import cluster, datasets
>>> iris = datasets.load_iris()
>>> X_iris = iris.data
>>> y_iris = iris.target
>>> k_means = cluster.KMeans(n_clusters=3)
>>> k_means.fit(X_iris)
KMeans(copy_x=True, init='k-means++', ...
>>> print(k_means.labels_[::10])
[1 1 1 1 1 0 0 0 0 0 2 2 2 2 2]
>>> print(y_iris[::10])
[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]
```

Warning

There is absolutely no guarantee of recovering a ground truth. First, choosing the right number of clusters is hard. Second, the algorithm is sensitive to initialization, and can fall into local minima, although in the sklearn package we play many tricks to mitigate this issue.

Bad initialization |
8 clusters |
Ground truth |

**Don’t over-interpret clustering results**

**Application example: vector quantization**

Clustering in general and KMeans, in particular, can be seen as a way of choosing a small number of exemplars to compress the information. The problem is sometimes known as vector quantization. For instance, this can be used to posterize an image:

```
>>> import scipy as sp
>>> try:
... lena = sp.lena()
... except AttributeError:
... from scipy import misc
... lena = misc.lena()
>>> X = lena.reshape((-1, 1)) # We need an (n_sample, n_feature) array
>>> k_means = cluster.KMeans(n_clusters=5, n_init=1)
>>> k_means.fit(X)
KMeans(copy_x=True, init='k-means++', ...
>>> values = k_means.cluster_centers_.squeeze()
>>> labels = k_means.labels_
>>> lena_compressed = np.choose(labels, values)
>>> lena_compressed.shape = lena.shape
```

Raw image | K-means quantization | Equal bins | Image histogram |

### Hierarchical agglomerative clustering: Ward¶

A *Hierarchical clustering* method is a type of cluster analysis
that aims to build a hierarchy of clusters. In general, the various approaches
of this technique are either:

Agglomerative- bottom-up approaches, orDivisive- top-down approaches.

For estimating a large number of clusters, top-down approaches are both
statistically ill-posed and slow due to it starting with all observations
as one cluster, which it splits recursively. Agglomerative
hierarchical-clustering is a bottom-up approach that successively merges
observations together and is particularly useful when the clusters of interest
are made of only a few observations. *Ward* clustering minimizes a criterion
similar to k-means in a bottom-up approach. When the number of clusters is large,
it is much more computationally efficient than k-means.

#### Connectivity-constrained clustering¶

With Ward clustering, it is possible to specify which samples can be clustered together by giving a connectivity graph. Graphs in the scikit are represented by their adjacency matrix. Often, a sparse matrix is used. This can be useful, for instance, to retrieve connected regions (sometimes also referred to as connected components) when clustering an image:

```
###############################################################################
# Generate data
lena = sp.misc.lena()
# Downsample the image by a factor of 4
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
X = np.reshape(lena, (-1, 1))
###############################################################################
# Define the structure A of the data. Pixels connected to their neighbors.
connectivity = grid_to_graph(*lena.shape)
###############################################################################
# Compute clustering
print("Compute structured hierarchical clustering...")
st = time.time()
n_clusters = 15 # number of regions
ward = Ward(n_clusters=n_clusters, connectivity=connectivity).fit(X)
label = np.reshape(ward.labels_, lena.shape)
print("Elapsed time: ", time.time() - st)
print("Number of pixels: ", label.size)
print("Number of clusters: ", np.unique(label).size)
```

#### Feature agglomeration¶

We have seen that sparsity could be used to mitigate the curse of
dimensionality, *i.e* an insufficient amount of observations compared to the
number of features. Another approach is to merge together similar
features: **feature agglomeration**. This approach can be implemented by
clustering in the feature direction, in other words clustering the
transposed data.

```
>>> digits = datasets.load_digits()
>>> images = digits.images
>>> X = np.reshape(images, (len(images), -1))
>>> connectivity = grid_to_graph(*images[0].shape)
>>> agglo = cluster.WardAgglomeration(connectivity=connectivity,
... n_clusters=32)
>>> agglo.fit(X)
WardAgglomeration(compute_full_tree='auto',...
>>> X_reduced = agglo.transform(X)
>>> X_approx = agglo.inverse_transform(X_reduced)
>>> images_approx = np.reshape(X_approx, images.shape)
```

transform and inverse_transform methods

Some estimators expose a transform method, for instance to reduce the dimensionality of the dataset.

## Decompositions: from a signal to components and loadings¶

**Components and loadings**

If X is our multivariate data, then the problem that we are trying to solve
is to rewrite it on a different observational basis: we want to learn
loadings L and a set of components C such that *X = L C*.
Different criteria exist to choose the components

### Principal component analysis: PCA¶

*Principal component analysis (PCA)* selects the successive components that
explain the maximum variance in the signal.

The point cloud spanned by the observations above is very flat in one
direction: one of the three univariate features can almost be exactly
computed using the other two. PCA finds the directions in which the data is
not *flat*

When used to *transform* data, PCA can reduce the dimensionality of the
data by projecting on a principal subspace.

```
>>> # Create a signal with only 2 useful dimensions
>>> x1 = np.random.normal(size=100)
>>> x2 = np.random.normal(size=100)
>>> x3 = x1 + x2
>>> X = np.c_[x1, x2, x3]
>>> from sklearn import decomposition
>>> pca = decomposition.PCA()
>>> pca.fit(X)
PCA(copy=True, n_components=None, whiten=False)
>>> print(pca.explained_variance_)
[ 2.18565811e+00 1.19346747e+00 8.43026679e-32]
>>> # As we can see, only the 2 first components are useful
>>> pca.n_components = 2
>>> X_reduced = pca.fit_transform(X)
>>> X_reduced.shape
(100, 2)
```

### Independent Component Analysis: ICA¶

*Independent component analysis (ICA)* selects components so that the distribution of their loadings carries
a maximum amount of independent information. It is able to recover
**non-Gaussian** independent signals:

```
>>> # Generate sample data
>>> time = np.linspace(0, 10, 2000)
>>> s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal
>>> s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal
>>> S = np.c_[s1, s2]
>>> S += 0.2 * np.random.normal(size=S.shape) # Add noise
>>> S /= S.std(axis=0) # Standardize data
>>> # Mix data
>>> A = np.array([[1, 1], [0.5, 2]]) # Mixing matrix
>>> X = np.dot(S, A.T) # Generate observations
>>> # Compute ICA
>>> ica = decomposition.FastICA()
>>> S_ = ica.fit_transform(X) # Get the estimated sources
>>> A_ = ica.mixing_.T
>>> np.allclose(X, np.dot(S_, A_) + ica.mean_)
True
```