For a more visual tutorial go to Biolab’s blog

Input data

This section describes how to load the data in Orange3-Recommendation.

Data format

Orange can read files in native tab-delimited format, or can load data from any of the major standard spreadsheet file type, like CSV and Excel. Native format starts with a header row with feature (column) names. Second header row gives the attribute type, which can be continuous, discrete, string or time. The third header line contains meta information to identify dependent features (class), irrelevant features (ignore) or meta features (meta). Here are the first few lines from a data set ratings.tab:

tid      user        movie       score
string   discrete    discrete    continuous
meta     row=1       col=1       class
1        Breza       HarrySally  2
2        Dana        Cvetje      5
3        Cene        Prometheus  5
4        Ksenija     HarrySally  4
5        Albert      Matrix      4

The third row is mandatory in this kind of datasets, in order to know which attributes correspond to the users (row=1) and which ones to the items (col=1). For the case of big datasets, users and items must be specified as a continuous attributes due to efficiency issues. Here are the first few lines from a data set MovieLens100K.tab:

user            movie         score         tid
continuous      continuous    continuous    time
row=1           col=1         class         meta
196             242           3             881250949
186             302           3             891717742
22              377           1             878887116
244             51            2             880606923
166             346           1             886397596
298             474           4             884182806

Loading data

Datasets can be loaded as follow:

import Orange
data = Orange.data.Table("ratings.tab")

In the add-on, several toy datasets are included: ratings.tab, movielens100k.tab, binary_data.tab, epinions_train.tab, epinions_test.tab,... and a few more.

Getting started

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

 # Load data and train the model
 data = Orange.data.Table('movielens100k.tab')
 learner = BRISMFLearner(num_factors=15, num_iter=25, learning_rate=0.07, lmbda=0.1)
 recommender = learner(data)

 # Make predictions
 prediction = recommender(data[:3])
 [ 3.79505151  3.75096513  1.293013 ]

The first three lines of code, import the Orange module, the BRISMF factorization model and loads the MovieLens100K dataset. In the next lines we instantiate the model (learner = BRISMFLearner(...)) and we fit the model with the loaded data.

Finally, we predict the ratings for the first three pairs (user, item) in the loaded dataset.

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)
[[ 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]]

This time, we’ve fill an array with the indices of the users to which make the predictions for all the items.

If we want as an output just the first k elements (do not confuse with top best items), we have to add the parameter top=INTEGER to the function

prediction = recommender.predict_items(indices_users, top=2)
[[ 1.34743879  4.61513578]
 [ 1.16652757  4.5516808]
 [ 2.74395768  4.04859096]]


Finally, we want to known which of a list of recommender performs better on our dataset. Therefore, we perform cross-validation over a list of learners.

The first thing we need to do is to make a list of all the learners that we want to cross-validate.

from orangecontrib.recommendation import GlobalAvgLearner,
global_avg = GlobalAvgLearner()
items_avg = ItemAvgLearner()
users_avg = UserAvgLearner()
useritem_baseline = UserItemBaselineLearner()
brismf = BRISMFLearner(num_factors=15, num_iter=25, learning_rate=0.07, lmbda=0.1)
learners = [global_avg, items_avg, users_avg, useritem_baseline, brismf]

Once, we have the list of learners and the data loaded, we score the methods. For the case, we have scored the recommendation two measures for goodnes of fit, which they’re later printed. To measure the error of the scoring, you can use all the functions defined in Orange.evaluation.

res = Orange.evaluation.CrossValidation(data, learners, k=5)
rmse = Orange.evaluation.RMSE(res)
r2 = Orange.evaluation.R2(res)

print("Learner  RMSE  R2")
for i in range(len(learners)):
    print("{:8s} {:.2f} {:5.2f}".format(learners[i].name, rmse[i], r2[i]))
Learner                   RMSE  R2
  - Global average        1.13 -0.00
  - Item average          1.03  0.16
  - User average          1.04  0.14
  - User-Item Baseline    0.98  0.25
  - BRISMF                0.96  0.28