Name: orange3-recommendation
Owner: Bioinformatics Laboratory
Description: ? :thumbsdown: Add-on for Orange3 to support recommender systems.
Created: 2016-08-16 13:09:51.0
Updated: 2017-11-30 10:39:44.0
Pushed: 2016-11-24 21:14:45.0
Size: 12529
Language: Python
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Orange3 Recommendation is a Python module that extends Orange3 to include support for recommender systems.
For more information, see our documentation
Orange3-Recommendation is tested to work under Python 3.
The required dependencies to build the software are Numpy >= 1.9.0 and Scikit-Learn >= 0.16
This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:
python setup.py install --user
To install for all users on Unix/Linux::
python setup.py build
sudo python setup.py install
For development mode use:
python setup.py develop
All modules can be found inside orangecontrib.recommendation.*. Thus, to import all modules we can type:
from orangecontrib.recommendation import *
Rating pairs (user, item):
Let's presume that we want to load a dataset, train it and predict its first three pairs of (id_user, id_item)
import Orange
from orangecontrib.recommendation import BRISMFLearner
data = Orange.data.Table('movielens100k.tab')
learner = BRISMFLearner(num_factors=15, num_iter=25, learning_rate=0.07, lmbda=0.1)
recommender = learner(data)
prediction = recommender(data[:3])
print(prediction)
>>> [ 3.79505151 3.75096513 1.293013 ]
Recommend items for set of users:
Now we want to get all the predictions (all items) for a set of users:
import numpy as np
indices_users = np.array([4, 12, 36])
prediction = recommender.predict_items(indices_users)
print(prediction)
>>> [[ 1.34743879 4.61513578 3.90757263 ..., 3.03535099 4.08221699 4.26139511]
[ 1.16652757 4.5516808 3.9867497 ..., 2.94690548 3.67274108 4.1868596 ]
[ 2.74395768 4.04859096 4.04553826 ..., 3.22923456 3.69682699 4.95043435]]
See performance section in the documentation.