Install virualenv(-wrapper).
mkvirtualenv political-affiliation-prediction
# or
workon political-affiliation-prediction
pip install -r requirements.dev.txt
and for the frontend
-
Install ruby e.g. via https://github.com/sstephenson/rbenv#homebrew-on-mac-os-x
cd web bundle
In order to train a classifier we download, parse and Bag-of-word transform the speeches and discussions of the German Bundestag from http://www.bundestag.de/plenarprotokolle'
python downloader.py --download --parse
Downloads news articles on politics landing page of various german newspapers and computed pairwise distances between them
python newsreader.py --download --distances
The results are stored in a distances-xxx.json file, but also in distances.json to be served to the web gui.
DEBUG=1 NOJOBS=1 python api.py
cd web
dotenv bundle exec middleman
REMARK: The frontend needs the backend running!
curl --data "text=angriffskrieg" 127.0.0.1:5000/predict
curl --data "text=reiche banken" 127.0.0.1:5000/predict
curl --data "text=herdprämie" 127.0.0.1:5000/predict
curl --data "text=sicherheit" 127.0.0.1:5000/predict
curl --data "url=http://www.zeit.de/politik/ausland/2015-04/iran-verhandlungen-einigung" 127.0.0.1:5000/predict
curl --data "url=http://www.sueddeutsche.de/politik/atomverhandlungen-in-lausanne-das-sind-die-eckpunkte-der-einigung-mit-iran-1.2421243" 127.0.0.1:5000/predict
curl --data "url=http://www.faz.net/aktuell/politik/ausland/europa/einigung-in-lausanne-durchbruch-bei-verhandlungen-ueber-iranisches-atomprogramm-13520160.html" 127.0.0.1:5000/predict
If no classifier.pickle file is in the model folder, the classifier will be retrained The classifier can be explicitly retrained, as e.g. in a python shell:
from classifier import *
clf = Classifier(folder='model',train=True)
In python shell:
from example import test_with_nested_CV
test_with_nested_CV(folder='model')
$ ipython notebook
Then check the notebooks i.e. visualization.ipynb
First build the web project
cd web && dotenv bundle exec middleman build
The build the container
docker build -t kirel/political-affiliation-prediction .
docker push kirel/political-affiliation-prediction
To test the container locally:
docker run -it --rm -p 5000:5000 kirel/political-affiliation-prediction
To deploy:
pip install -r requirements.dev.txt
ansible-playbook ansible/deploy.yml -i ansible/inventory
docker save kirel/political-affiliation-prediction | bzip2 | pv | ssh [email protected] 'bunzip2 | sudo docker load'
Copyright (c) 2015 Daniel Kirsch, Felix Bießmann, released under the MIT license, see LICENSE