How to generate confusion matrix visualization in python and how to use it in scikit-learn

Confusions matrix are quite useful to understand your classifier problems. scikit-learn allow you to retrieve easily the confusion matrix (metric.confusion_matrix(y_true, y_pred)) but it is hard to read.
An image representation is a great way to look at it like this.

From a confusion matrix, you can derive classification error, precision, recall and extract confusion highlights. mlboost has a simple util class ConfMatrix to do all of this now. Here is an example:

 from mlboost.util.confusion_matrix import ConfMatrix  
 clf.fit(X_train, y_train)  
 pred = clf.predict(X_train)  
 labels = list(set(y_train))  
 labels.sort()  
 cm = ConfMatrix(metrics.confusion_matrix(y_train, pred), labels)  
 cm.save_matrix('conf_matrix.p')  
 cm.get_classification()  
 cm.gen_conf_matrix('conf_matrix')  
 cm.gen_highlights('conf_matrix_highlights')  

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)

1 2 3 4 5 6 7

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. 

Finding the optimal K in kmean: a incremental kmeans in python

I was looking for an good implementation of an incremental k-means where I don't have to set the optimal K. There are interesting papers (x-means, gmeans etc.) but couldn't find any python implementation.

I have decided to write a incremental version on top of sklearn.
The idea is simple:

1. Start at K=x
2. identify worst cluster based on an unsupervised measure (ex: silhouette)
3. Split the worst cluster into 2 clusters
4. measure the global improvement with the new clusters
5. if you get an improvement continue adding clusters

You can find the source code in mlboost/clustering/ikmeans.py
A special thanks to scikit-learn lib to let me prototype this version so fast.