We are extending the fklearn capabilities with unsupervised algorithm support. First, we proposed to use k-means clustering implementation from scikit-learn since it is a simple and relevant unsupervised algorithm.
Description of the changes proposed in the pull request
Add kmeans learner using scikit-learn K-means implementation
Add Silhouette Coefficient to evaluate unsupervised results
Add Davies-Bouldin score to evaluate unsupervised results
Add generic_unsupervised_sklearn_evaluator method in order to provide support to scikit-learn unsupervised metrics
Where should the reviewer start?
kmeans_learner method at src/fklearn/training/unsupervised.py. Then, the unsupervised evaluation metrics at src/fklearn/validation/evaluators.py.
Status
READY
Todo list
Background context
We are extending the fklearn capabilities with unsupervised algorithm support. First, we proposed to use k-means clustering implementation from scikit-learn since it is a simple and relevant unsupervised algorithm.
Description of the changes proposed in the pull request
Add kmeans learner using scikit-learn K-means implementation
Add Silhouette Coefficient to evaluate unsupervised results
Add Davies-Bouldin score to evaluate unsupervised results
Add generic_unsupervised_sklearn_evaluator method in order to provide support to scikit-learn unsupervised metrics
Where should the reviewer start?
kmeans_learnermethod atsrc/fklearn/training/unsupervised.py. Then, the unsupervised evaluation metrics atsrc/fklearn/validation/evaluators.py.