First exampleΒΆ

This example will show you how to run your very first experiment using Cornac. It consists in training and evaluating the Probabilistic Matrix Factorization (PMF) recommender model.

#Importing required modules from Cornac.
from cornac.models import PMF
from cornac.experiment import Experiment
from cornac.evaluation_strategies import Split
from cornac import metrics

#Importing some additional useful modules.
from scipy.io import loadmat

#Loading and preparing the Amazon office data,
#Available in the GitHub repository, inside folder 'data/'.
office= loadmat("path to office.mat")
mat_office = office['mat']

#Instantiate a pfm recommender model.
#Please refer to the documentation for details on parameter settings.
rec_pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001, init_params={'U':None,'V':None})

#Instantiate an evaluation strategy.
es_split = Split(data = mat_office, prop_test=0.2, prop_validation=0.0, good_rating=4)

#Instantiate evaluation metrics.
rec = metrics.Recall(m=20)
pre = metrics.Precision(m=20)
mae = metrics.MAE()
rmse = metrics.RMSE()

#Instantiate and then run an experiment.
res_pmf = Experiment(es_split, [rec_pmf], metrics=[mae,rmse,pre,rec])
res_pmf.run()

#Get average results.
res_pmf.res_avg