Evernote-classify is a fork of Geeknote which adds automatic classification of your Evernote notes into notebooks. At the moment the program only provides recommendations of last five recently updated notes.

Download the repository:

$ git clone git://github.com/alexksikes/geeknote-classify.git
$ cd geeknote-classify

Install the excellent scikit-learn machine learning library:

$ pip install -U scikit-learn

First you need to login using geeknote:

$ python geeknote.py login

Then train a classifier:

$ python geeknote.py train

By default "train" will only fetch the last 300 recently updated notes from your account. Use "--counts" to override this behavior. You can also save the fetched notes with the "--save-notes" command (useful for offline testing, see below).

You can now see the recommendation of the classifier:

$ python geeknote.py classify

Two datasets are provided. The first one comes from Teddy Note and the second one from a sample of the 20 newsgroup dataset.

You can directly test these datasets:

$ python classifier.py ./classifier_dataset/teddy.train ./classifier_dataset/teddy.test

You should get something like that:

...
Making a dataset from the notes saved in classifier_dataset/teddy.train.
Training ...
Estimating model performance with 5 folds cross validation ...
Accuracy: 0.93 (+/- 0.02)
Making a dataset for the notes saved in classifier_dataset/teddy.test.
Evaluating the classifier on this dataset.
Showing the suggestions (current notebook >> suggested notebook).
 0 : 07.12.2012  travel >> travel    An Cabo Verde Windsurf Center
 1 : 07.12.2012  debate >> debate    Should U.S. Continue Aid to Pakistan? - Council on Foreign Relations
 2 : 07.12.2012  debate >> debate    Writing From a Global Perspective
 3 : 07.12.2012  useful >> travel    Getting Started
 4 : 23.03.2012  debate >> debate    The Long War Journal - Charts on US Strikes in Pakistan

Additionally you can login into "sandbox_classify" and "sandbox_classify2" (password is the same as login). "sandbox_classify" is the account holding the Teddy Note dataset. "sandbox_classify2" holds the 20 newsgroup dataset.

• sync notes instead of downloading
• 2-way sync of files (gnsync is only from local to remote)
• not just the 5 updated notes ... more options
• more datasets to play with
• better documentation (doctest)
• use logging instead of print
• use scikit standard namespace
• train on the classified notes, suggest on the unclassified ones
• auto assign to notebooks
• integrate with existing unit testing framework
• grid search of best param of classifier
• feature selection
• notes with diff kinds of content (not just text)