from sklearn.datasets import make_blobs

X, y = make_blobs(random_state=42)
X.shape

plt.scatter(X[:, 0], X[:, 1]);

"""
Explanation: SciPy 2016 Scikit-learn Tutorial
Unsupervised Learning Part 2 -- Clustering
Clustering is the task of gathering samples into groups of similar
samples according to some predefined similarity or distance (dissimilarity)
measure, such as the Euclidean distance.
In this section we will explore a basic clustering task on some synthetic and real-world datasets.
Here are some common applications of clustering algorithms:

Compression for data reduction
Summarizing data as a reprocessing step for recommender systems
Similarly:
grouping related web news (e.g. Google News) and web search results
grouping related stock quotes for investment portfolio management
building customer profiles for market analysis
Building a code book of prototype samples for unsupervised feature extraction

Let's start by creating a simple, 2-dimensional, synthetic dataset:
End of explanation
"""


from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3, random_state=42)

"""
Explanation: In the scatter plot above, we can see three separate groups of data points and we would like to recover them using clustering -- think of "discovering" the class labels that we already take for granted in a classification task.
Even if the groups are obvious in the data, it is hard to find them when the data lives in a high-dimensional space, which we can't visualize in a single histogram or scatterplot.
Now we will use one of the simplest clustering algorithms, K-means.
This is an iterative algorithm which searches for three cluster
centers such that the distance from each point to its cluster is
minimized. The standard implementation of K-means uses the Euclidean distance, which is why we want to make sure that all our variables are measured on the same scale if we are working with real-world datastets. In the previous notebook, we talked about one technique to achieve this, namely, standardization.
Question: what would you expect the output to look like?
End of explanation
"""


labels = kmeans.fit_predict(X)

labels

all(y == labels)

"""
Explanation: We can get the cluster labels either by calling fit and then accessing the 
labels_ attribute of the K means estimator, or by calling fit_predict.
Either way, the result contains the ID of the cluster that each point is assigned to.
End of explanation
"""


plt.scatter(X[:, 0], X[:, 1], c=labels);

"""
Explanation: Let's visualize the assignments that have been found
End of explanation
"""


plt.scatter(X[:, 0], X[:, 1], c=y);

"""
Explanation: Compared to the true labels:
End of explanation
"""


from sklearn.metrics import confusion_matrix, accuracy_score

print('Accuracy score:', accuracy_score(y, labels))
print(confusion_matrix(y, labels))

np.mean(y == labels)

"""
Explanation: Here, we are probably satisfied with the clustering results. But in general we might want to have a more quantitative evaluation. How about comparing our cluster labels with the ground truth we got when generating the blobs?
End of explanation
"""


from sklearn.metrics import adjusted_rand_score

adjusted_rand_score(y, labels)

"""
Explanation: Exercise
After looking at the "True" label array y, and the scatterplot and labels above, can you figure out why our computed accuracy is 0.0, not 1.0, and can you fix it?

Even though we recovered the partitioning of the data into clusters perfectly, the cluster IDs we assigned were arbitrary,
and we can not hope to recover them. Therefore, we must use a different scoring metric, such as adjusted_rand_score, which is invariant to permutations of the labels:
End of explanation
"""


kmeans = KMeans(n_clusters=2, random_state=42)
labels = kmeans.fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=labels);

"""
Explanation: One of the "short-comings" of K-means is that we have to specify the number of clusters, which we often don't know apriori. For example, let's have a look what happens if we set the number of clusters to 2 in our synthetic 3-blob dataset:
End of explanation
"""


distortions = []
for i in range(1, 11):
    km = KMeans(n_clusters=i, 
                random_state=0)
    km.fit(X)
    distortions.append(km.inertia_)
plt.plot(range(1, 11), distortions, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')

plt.show()

"""
Explanation: The Elbow Method
The Elbow method is a "rule-of-thumb" approach to finding the optimal number of clusters. Here, we look at the cluster dispersion for different values of k:
End of explanation
"""


from sklearn.datasets import make_blobs

X, y = make_blobs(random_state=170, n_samples=600)
rng = np.random.RandomState(74)

transformation = rng.normal(size=(2, 2))
X = np.dot(X, transformation)

y_pred = KMeans(n_clusters=3).fit_predict(X)

plt.scatter(X[:, 0], X[:, 1], c=y_pred)

"""
Explanation: Then, we pick the value that resembles the "pit of an elbow." As we can see, this would be k=3 in this case, which makes sense given our visual expection of the dataset previously.
Clustering comes with assumptions: A clustering algorithm finds clusters by making assumptions with samples should be grouped together. Each algorithm makes different assumptions and the quality and interpretability of your results will depend on whether the assumptions are satisfied for your goal. For K-means clustering, the model is that all clusters have equal, spherical variance.
In general, there is no guarantee that structure found by a clustering algorithm has anything to do with what you were interested in.
We can easily create a dataset that has non-isotropic clusters, on which kmeans will fail:
End of explanation
"""


from sklearn.datasets import load_digits
digits = load_digits()
# ...

#%load solutions/08B_digits_clustering.py

"""
Explanation: Some Notable Clustering Routines
The following are two well-known clustering algorithms. 

sklearn.cluster.KMeans: <br/>
    The simplest, yet effective clustering algorithm. Needs to be provided with the
    number of clusters in advance, and assumes that the data is normalized as input
    (but use a PCA model as preprocessor).
sklearn.cluster.MeanShift: <br/>
    Can find better looking clusters than KMeans but is not scalable to high number of samples.
sklearn.cluster.DBSCAN: <br/>
    Can detect irregularly shaped clusters based on density, i.e. sparse regions in
    the input space are likely to become inter-cluster boundaries. Can also detect
    outliers (samples that are not part of a cluster).
sklearn.cluster.AffinityPropagation: <br/>
    Clustering algorithm based on message passing between data points.
sklearn.cluster.SpectralClustering: <br/>
    KMeans applied to a projection of the normalized graph Laplacian: finds
    normalized graph cuts if the affinity matrix is interpreted as an adjacency matrix of a graph.
sklearn.cluster.Ward: <br/>
    Ward implements hierarchical clustering based on the Ward algorithm,
    a variance-minimizing approach. At each step, it minimizes the sum of
    squared differences within all clusters (inertia criterion).

Of these, Ward, SpectralClustering, DBSCAN and Affinity propagation can also work with precomputed similarity matrices.
<img src="figures/cluster_comparison.png" width="900">
Exercise: digits clustering
Perform K-means clustering on the digits data, searching for ten clusters.
Visualize the cluster centers as images (i.e. reshape each to 8x8 and use
plt.imshow)  Do the clusters seem to be correlated with particular digits? What is the adjusted_rand_score?
Visualize the projected digits as in the last notebook, but this time use the
cluster labels as the color.  What do you notice?
End of explanation
"""


