On Wednesday, January 17, 2018 at 2:30:13 PM UTC, Leo wrote: > Hello everyone, > > I am implementing a time-dependent Recommender System which applies BPR > (Bayesian Personalized Ranking), where Stochastic Gradient Ascent is used to > learn the parameters of the model. Such that, one iteration involves sampling > randomly the quadruple (i.e. userID, positive_item, negative_item, > epoch_index) for n times, where n is the total number of positive feedbacks > (i.e. the number of ratings given to all items). But, as my implementation > takes too much time for learning the parameters (since it requires 100 > iterations to learn the parameters), I was wondering if there is a way to > improve my code and speed up the learning process of the parameters. > > Please find the code of my implementation (the update function named as > updateFactors is the one that learns the parameters, and such that I guess is > the one that should be improved in order to speed up the process of learning > the parameter values) in the following link: > https://codereview.stackexchange.com/questions/183707/speeding-up-the-implementation-of-stochastic-gradient-ascent-in-python
You have a fair chunk of code so I've only taken a quick glance so this could just be the tip of the iceberg. You're clearly using Python 2 as you have print statements, not functions. If you can upgrade to Python 3 is it is superior, see e.g. https://www.youtube.com/watch?v=f_6vDi7ywuA for i in range(1, len(self.userItems[userID])): is a code smell, consider using for userItem in self.userItems[userID]: if len(pos_per_user[userID]) == 0: is written if pos_per_user[userID]: if userID == None: should be if userID is None: Profile your code with https://docs.python.org/3/library/profile.html to find where the hot spots are. Read https://wiki.python.org/moin/PythonSpeed/PerformanceTips for tips on how to up your code performance. -- Kindest regards. Mark Lawrence. -- https://mail.python.org/mailman/listinfo/python-list
