Multi Label Confusion Matrix for one-hot-encoded y_test and y_pred

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

This package relies on the usage of following packages:

numpy, matplotlib and itertools

These forementioned packages should already come with Anaconda. If you don't use Anaconda distribution you can install them via pip install [package].

Download, unzip, open a terminal and cd in the unzipped directory, and type:

python setup.py install

At this point, open a python session and type:

import mlcm

If no errors pop up, you are ready to use the package.

The package comes with three functions:

confusion_matrix, plot_confusion_matrix, draw_cm

The first one computes and return the confusion matrix for passed y_test and y_pred, moreover, it prints the accuracy on the test-set:

cm = mlcm.confusion_matrix(y_test, y_pred)

The second one plot the confusion matrix passed as arguments; labels have to be passed also. You also have the possibility to normalize or not the matrix over the test-set observations (default = False).

mlcm.plot_confusion_matrix(cm, classes, normalize=False)

Finally, last function performs not only the calculation of the confusion matrix, but also returns and plots it:

cm = mlcm.draw_cm(y_test, y_pred, classes, normalize=False)

1.0.0

• Mario Damiano - Github: MDamiano - Twitter: @MarioDami - e-mail: [email protected]

This project is distributed under the MIT License - see the LICENSE file for details