Simplifying clustering visualization with mlboost

Are you looking for a simple way to visualized your supervised or semi-supervised data clusters with different dimension reduction algorithms like PCA, LDA, isomap, LLE ,mds, random trees, spectral embedding  etc.?
Here is an output example on 4 newsgroups dataset.

If you are following sklearn loading standard, with mlboost, you can do it by changing 2 lines of code (line #5 and #6) or modify this example. (python yourvisu.py -m y)

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import sys from mlboost.clustering import visu # add your data loading function that return data_train and data_test from X import LOAD_DATASET_Y visu.add_loading_dataset_fct('y', LOAD_DATASET_Y) visu.main(sys.argv[1:])

Btw, if you click on the legend, it will remove the class as you can see here when I remove the green class 2. In the context of semi-supervised, simply set samples class to "?" (dataset.target[i]). 

 

Without scikit-learn and matplotlib, it won't be that easy to experiment visualization. 

kmeans with configurable distance function: How to hack sklearn.kmeans to use a different distance function?

Like others, I was looking for a good k-means implementation where I can set the distance function.

Unfortunately, sklearn.kmeans doesn't allow to change the cost function. Euclidean is hard coded. 

As pointed out by the sklearn team, it is quite complex to generalize due to:

•  mean is not always the average and is most of the time not trivial (http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf  see table 8.2 page 501)
• generalization isn't always compatible with optimized version

So if your distance function is cosine which has the same mean as euclidean, you can monkey patch  sklearn.cluster.k_means_.eucledian_distances this way: (put this code before calling kmean.fit(X).

from sklearn.metrics.pairwise import cosine_similarity
def new_euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False) 
    return cosine_similarity(X,Y)

# monkey patch (ensure cosine dist function is used)
from sklearn.cluster import k_means_k_means_.euclidean_distances 

k_means_.euclidean_distances = new_euclidean_distances 

Warning: you need to normalize your input vectors.