Performing runtime benchmarks with Python Monitoring Tool Benchy

Hi all,

I've been working on in the last weeks at a little project that I developed called benchy.  The goal of benchy is answer some trivial questions about which code is faster ?  Or which algorithm consumes more memory ?  I know that there are several tools suitable for this task, but I would like to create some performance reports  by myself using Python.   

Why did I create it ?  Since the beginning of the year I decided to rewrite all the code at Crab, a python framework for building recommender systems.  And one of the main components that required some refactoring was the pairwise metrics such as cosine, pearson, euclidean, etc.  I needed to unit test the performance of several versions of code for those functions. But doing this manually ? It's boring. That's why benchy came for!

What benchy can do ?

Benchy is a lightweight Python library for running performance benchmarks over alternative versions of code.  How can we use it ?

Let's see the cosine function, a popular pairwise function for comparing the similarity between two vectors and matrices in recommender systems.

Let's define the benchmarks to test:



With all benchmarks created, we could test a simple benchmark by calling the method run:


The dict associated to the key memory represents the memory performance results. It gives you the number of calls repeat to the statement, the average consumption usage in units . In addition, the key 'runtime' indicates the runtime performance in timing results. It presents the number of calls repeat following the average time to execute it timing in units.

Do you want see a more presentable output ? It is possible calling the method to_rst with the results as parameter:

Benchmark setup

import numpy
X = numpy.random.uniform(1,5,(1000,))

import scipy.spatial.distance as ssd
X = X.reshape(-1,1)
def cosine_distances(X, Y):
    return 1. - ssd.cdist(X, Y, 'cosine')

Benchmark statement

cosine_distances(X, X)

name	repeat	timing	loops	units
scipy.spatial 0.8.0	3	18.36	10	ms

Now let's check which one is faster and which one consumes less memory. Let's create a BenchmarkSuite. It is referred as a container for benchmarks.:

Finally, let's run all the benchmarks together with the BenchmarkRunner. This class can load all the benchmarks from the suite and run each individual analysis and print out interesting reports:



Next, we will plot the relative timings. It is important to measure how faster the other benchmarks are compared to reference one. By calling the method plot_relative:

As you can see the graph aboe the scipy.spatial.distance function is 2129x slower and the sklearn approach is 19x. The best one is the numpy approach. Let's see the absolute timings. Just call the method plot_absolute:

You may notice besides the bar representing the timings, the line plot representing the memory consumption for each statement. The one who consumes the less memory is the nltk.cluster approach!

Finally, benchy also provides a full repport for all benchmarks by calling the method to_rst:

Performance Benchmarks

These historical benchmark graphs were produced with benchy.

Produced on a machine with

• Intel Core i5 950 processor
• Mac Os 10.6
• Python 2.6.5 64-bit
• NumPy 1.6.1

scipy.spatial 0.8.0

Benchmark setup

import numpy
X = numpy.random.uniform(1,5,(1000,))

import scipy.spatial.distance as ssd
X = X.reshape(-1,1)
def cosine_distances(X, Y):
    return 1. - ssd.cdist(X, Y, 'cosine')

Benchmark statement

cosine_distances(X, X)

name	repeat	timing	loops	units
scipy.spatial 0.8.0	3	19.19	10	ms

sklearn 0.13.1

Benchmark setup

import numpy
X = numpy.random.uniform(1,5,(1000,))

from sklearn.metrics.pairwise import cosine_similarity as cosine_distances

Benchmark statement

cosine_distances(X, X)

name	repeat	timing	loops	units
sklearn 0.13.1	3	0.1812	1000	ms

nltk.cluster

Benchmark setup

import numpy
X = numpy.random.uniform(1,5,(1000,))

from nltk import cluster
def cosine_distances(X, Y):
    return 1. - cluster.util.cosine_distance(X, Y)

Benchmark statement

cosine_distances(X, X)

name	repeat	timing	loops	units
nltk.cluster	3	0.01024	1e+04	ms

numpy

Benchmark setup

import numpy
X = numpy.random.uniform(1,5,(1000,))

import numpy, math
def cosine_distances(X, Y):
    return 1. -  numpy.dot(X, Y) / (math.sqrt(numpy.dot(X, X)) *
                                     math.sqrt(numpy.dot(Y, Y)))

Benchmark statement

cosine_distances(X, X)

name	repeat	timing	loops	units
numpy	3	0.009339	1e+05	ms

Final Results

name	repeat	timing	loops	units	timeBaselines
scipy.spatial 0.8.0	3	19.19	10	ms	2055
sklearn 0.13.1	3	0.1812	1000	ms	19.41
nltk.cluster	3	0.01024	1e+04	ms	1.097
numpy	3	0.009339	1e+05	ms	1

Final code!

I might say this micro-project is still a prototype, however  I tried to build it to be easily extensible. I have several ideas to extend it, but feel free to fork it and send suggestions and bug fixes.  This project was inspired by the open-source project vbench, a framework for performance benchmarks over your source repository's history. I recommend!

For me, benchy will assist me to test several pairwise alternative functions in Crab. :)  Soon I will publish the performance results that we got with the pairwise functions that we built for Crab :)

I hope you enjoyed,

Regards,

Marcel Caraciolo

Guide to Recommender Systems Book Online

Hi all,

This year one of my goals is to write a book such as a guide to teach recommender systems for programmers. I know there are several textbooks that focus on providing a theorical foundation for recommender systems, and as result, may seem difficult to understand. For programmers that want to learn how to start to use or understand the components of a recommender system, this book is what they are looking for.  

This guide follows a learn-by-doing approach. Therefore, I will use theory and apply it through the exercises and experiment with Python code.  I hope when you complete the book you will be able to understand how to build a recommender system and give you the first steps to apply them at your own systems. The textbook is laid out as a series of small steps that will guide you for undestanding the recommender system techniques. 

This book is available for download for free under a Creative Commons license. This project is also leaded by my colleague Ricardo Caspirro, who will review and translate it to portuguese language.

Below I provide the table of contents of the book.

Guide to Recommender Systems

The link for the online guide is available here.

http://muricoca.github.com/recommendation-lectures/index.html

Table of Contents

Chapter 01: Introduction to Recommender Systems

Finding out what recommender system is and what problems it solves. And a fast review of what you will be able to do when you finish this book.

Chapter 02: Collaborative Filtering

This chapter focus on how you can use the state-of-the-art techniques of collaborative filtering that makes automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from similar users (user-based) or similar items( item-based).

Chapter 03: Content Based Filtering

Recommender systems that  suggest an item to a user based upon a description of the item and a profile of the user's interests. Although thedetails of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. 

Chapter 04: Hybrid Based Filtering

This chapter will focus how to pick the best features of collaborative and of content and mix them to build hybrid recommender systems. It will present the current work on this field and an example of  how it works and how you can decide the best strategy to select.

Chapter 05:  Model - Based Recommenders

Techniques that will include memory-based techniques or data mining techniques such as association analysis, symbolic data analysis and classification/clustering techniques will be covered in this chapter.

Chapter 06:  Evaluation of Recommender Systems

This chapter starts with a short description on how to evaluate the recommender systems and the commonly used metrics for compare the recommender algorithms in the development and deployment stages.

Chapter 07:  Recommender Systems and Distributed-Computing

Recommender Systems suffer with sparse matrices where the user x items preferences are sparsed (lots of missing values - preferences). It results on large datasets with millions of items, users and preferences.  For this task it is considered to use distributed computing techniques such as map-reduce to distribute the recommendations. This chapter will cover those topics.

Chapter 08:  Study Case

It will present a study case of a mobile recommender system for recommend users to another users using several techniques showed above and how we tested and deployed it.

Chapter 09:  Recommender Systems the Next Generation

This chapter brings the next generation of recommender systems, describing what the research is going after in several fields such as ubiquity, semmantics, etc.

Chapter 10:  Meeting Python-RecSys Framework

It will present the Python-RecSys framework for building recommender systems with Python in a easy way. It will describe how to build or test already implemented techniques or develop new ones and deploy them with frameworks Web and REST.

This book is under development, please let me know if there are any suggestions or corrections to make over one of those chapters. If you see that there is a topic that needs an extra chapter or a topic that I am missing, please also let me know and comment.

I hope you enjoy this work, specially the developers!

Regards,

Marcel Caraciolo

Presentation at VII Brazilian Symposium of Collaborative Systems (SBSC) about Recommender Systems

Hi all,

I am sharing the slides from my keynote at VII Brazilian Symposium of Collaborative Systems (SBSC) where I presented my current work at recommender systems focusing in social networks.

My work "Content Recommendation based on Data Mining in Adaptive Social Networks"  presents how I built the recommender system at the educational social network AtéPassar and the current results behind it. It is a novel project in brazilian social networks specially because I worked hard at the explanations that come along with each recommendation.

Content Recommendation Based on Data Mining in Adaptive Social Networks

View more presentations from Marcel Caraciolo

Soon I will provide the paper.  The special part of this event was the track only for recommender systems! I had the opportunity to meet brazilian researchers and developers interested in this field.

I hope I will participate again next year! It is a great event for the researchers who miss those events focused on recommender systems.

Regards,

Marcel