Speeding up the implementation of Stochastic Gradient Ascent in Python

[breamoreboy at gmail.com]

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.