# scikit-learn and Game of Thrones

# scikit-learn and Game of Thrones

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In my last post, I showed how to find similar Game of Thrones episodes based on the characters that appear in different episodes. This allowed us to find similar episodes on an episode by episode basis, but I was curious whether there were **groups of similar episodes** that we could identify.

scikit-learn provides several clustering algorithms that can run over our episode vectors and hopefully find clusters of similar episodes. A clustering algorithm groups similar documents together, where similarity is based on calculating a ‘distance’ between documents. Documents separated by a small distance would be in the same cluster, whereas if there’s a large distance between episodes then they’d probably be in different clusters.

The simplest variant is K-means clustering:

The KMeans algorithm clusters data by trying to separate samples in *n* groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified.

The output from the algorithm is a list of labels which correspond to the cluster assigned to each episode.

Let’s give it a try on the Game of Thrones episodes. We’ll start with the 2-dimensional array of episodes/character appearances that we created in the previous post.

```
>>> all.shape
(60, 638)
>>> all
array([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]])
```

We have a 60 (episodes) x 638 (characters) array which we can now plug into the K-means clustering algorithm:

```
>>> from sklearn.cluster import KMeans
>>> n_clusters = 3
>>> km = KMeans(n_clusters=n_clusters, init='k-means++', max_iter=100, n_init=1)
>>> cluster_labels = km.fit_predict(all)
>>> cluster_labels
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)
```

cluster_labels is an array containing a label for each episode in the all array. The spread of these labels is as follows:

```
>>> import numpy as np
>>> np.bincount(cluster_labels)
array([19, 12, 29])
```

i.e. 19 episodes in cluster 0, 12 in cluster 1, and 29 in cluster 2.

## How Do We Know If the Clustering Is Any Good?

Ideally we’d have some labeled training data which we could compare our labels against, but since we don’t we can measure the effectiveness of our clustering by calculating inter-centroidal separation and intra-cluster variance, i.e. how close are the episodes to other episodes in the same cluster vs how close are they to episodes in the closest different cluster.

scikit-learn gives us a function that we can use to calculate this score – the silhouette coefficient.

The output of this function is a score between -1 and 1.

- A score of 1 means that our clustering has worked well and a document is far away from the boundary of another cluster.
- A score of -1 means that our document should have been placed in another cluster.
- A score of 0 means that the document is very close to the decision boundary between two clusters.

I tried calculating this coefficient for some different values of K. This is what I found:

```
from sklearn import metrics
for n_clusters in range(2, 10):
km = KMeans(n_clusters=n_clusters, init='k-means++', max_iter=100, n_init=1)
cluster_labels = km.fit_predict(all)
silhouette_avg = metrics.silhouette_score(all, cluster_labels, sample_size=1000)
sample_silhouette_values = metrics.silhouette_samples(all, cluster_labels)
print n_clusters, silhouette_avg
2 0.0798610142955
3 0.0648416081725
4 0.0390877994786
5 0.020165277756
6 0.030557856406
7 0.0389677156458
8 0.0590721834989
9 0.0466170527996
```

The best score we manage here is 0.07 when we set the number of clusters to 2. Even our highest score is much lower than the lowest score on the documentation page!

I tried it out with some higher values of K but only saw a score over 0.5 once I put the number of clusters to 40 which would mean 1 or 2 episodes per cluster at most.

At the moment our episode arrays contain 638 elements so they’re too long to visualize on a 2D silhouette plot. We’d need to apply a dimensionality reduction algorithm before doing that.

In summary, it looks like character co-occurrence isn’t a good way to cluster episodes. I’m curious what would happen if we flip the array on its head and try and cluster the characters instead, but that’s for another day.

If anyone spots anything that I’ve missed when reading the output of the algorithm let me know in the comments. I’m just learning by experimentation at the moment.

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Published at DZone with permission of Mark Needham , DZone MVB. See the original article here.

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