Tag: data science

This review was first published at ScraperWiki.

Hadoop in Action by Chuck Lam provides a brief, fairly technical introduction to the Hadoop Big Data ecosystem. Hadoop is an open source implementation of the MapReduce framework originally developed by Google to process huge quantities of web search data. The name MapReduce, refers to dividing up jobs amongst multiple processors (“Mapping”) and then recombining results to provide an answer to the problem (“Reducing”). Hadoop, allows users to process large quantities of data by distributing it across a network of relatively cheap computers. Each computer in the network has a portion of the data to process, and at the end it is combined together to give the final reult. Hadoop provides the infrastructure to enable this. In a sense it is a distributed operated system which provides fundamental services to applications such as Hive and Pig.

At ScraperWiki we’ve had many philosophical discussions about the meaning of Big Data. What size is Big Data? Is it one million lines? Is it one billion lines? Should we express it in terms of gigabytes and terabytes rather than lines?

For many, Big Data is data that requires you use Hadoop or similar to process.

Our view is that Big Data is data big enough to break the tools or hardware you commonly use, so for many of our customers this is a software limit based on Microsoft Excel. Technically Excel can handle a million or so lines but practically speaking life gets uncomfortable as you go above a few tens of thousands of rows.

The largest dataset a customer has come to us with so far is the UK MOT test data – results of the roadworthiness test for every vehicle on the road in the UK, over three years old. This dataset is about 100 million lines and 4 gigabytes per year, it’s available back to 2005 giving a total of approximately 1 billion lines and 32GB. A single year can be readily analysed on an Intel i7 laptop with 8GB RAM, MySQL and Tableau by readily I mean that some indexing jobs can take up to an hour but once indexed most queries are 10 – 20 minutes maximum.

At ScraperWiki a number of us have backgrounds in the physical sciences where we’ve been interested in computational intensive simulations involving clusters of commodity hardware which pre-date Hadoop (Beowulf), or big data from the Large Hadron Collider. Physical scientists have long been interested in parallel computing where the amount of data to move around is small but the amount of calculation to do is large. The point here is that parallel computing is possible for a subset of problems where a task can be divided up into smaller chunks of data and processing to be distributed amongst multiple processors or machines. In the case of photorealistic computer graphics rendering this might be frames of video, or a portion of a whole scene. Software like Matlab, Fortran and computer graphics renderers can parallelise certain operations with relative ease. The difficulty has always been turning your big computing problem into one of those “certain operations”. The Large Hadron Collider is an example more suited to the Hadoop style approach, the data flows are enormous but the calculations performed on that data are, comparatively less troublesome.

Hadoop in Action spends a significant amount of time discussing the core Hadoop system and MapReduce processing framework. I must admit to finding this part rather dull. I perked up when we reached Pig, described as a data processing language and Hive, a SQL-like system. One gets the impression the Pig system was built around a naming convention pushed too far: the Pig commandline is called Grunt and the language used by Pig is Pig Latin. Pig and Hive look like systems where I could sit down and run some queries with a language looks like my old friend, SQL.

The book finishes with some case studies, these are an image conversion problem, machine learning and data processing at China Mobile, the Stumbleupon social bookmarking system and providing search for IBM’s intranet. In the latter three cases users were migrating from SQL based systems running on monolithic hardware. To give an idea of scale: China Mobile collect terabytes of data per day across 100s of millions of customers, the IBM intranet has something like 100 million pages and 16 million documents and Stumbupon has 25 million users clicking their Stumble buttons about 1 billion times in a month.

Overall, a handy introduction to Hadoop although perhaps oddly pitched – it’s probably too technical for most managers, not technical enough for system administrators and with insufficient applications for data scientists. If you want to get hands on experience of Hadoop, then the Hortonworks Sandbox provides a pre-packaged virtual machine with a web interface for you to try out the various technologies.

If you want us to help you get value out of your big data or even Big Data, please get in touch!

This review was first published at ScraperWiki.

As well as developing scrapers and a data platform, at ScraperWiki we also do data analysis. Some of this is just because we’re interested, other times it’s because clients don’t have the tools or the time to do the analysis they want themselves. Often the problem is with the size of the data. Excel is the universal solvent for data analysis problems – go look at any survey of data scientists. But Excel has it’s limitations. There are the technical limitations of something like a million rows maximum size but well before this size Excel becomes a pain to use.

There is another path – the programming route. As a physical scientist of moderate age I’ve followed these two data analysis paths in parallel. Excel for the quick look see and some presentation. Programming for bigger tasks, tasks I want to do repeatedly and types of data Excel simply can’t handle – like image data. For me the programming path started with FORTRAN and the NAG libraries, from which I moved into Matlab. FORTRAN is pure, traditional programming born in the days when you had to light your own computing fire. Matlab and competitors like Mathematica, R and IDL follow a slightly different path. At their core they are specialist programming languages but they come embedded in graphical environments which can be used interactively. You type code at a prompt and stuff happens, plots pop up and so forth. You can capture this interaction and put it into scripts/programs, or simply write programs from scratch.

Outside the physical sciences, data analysis often means databases. Physical scientists are largely interested in numbers, other sciences and business analysts are often interested in a mixture of numbers and categorical things. For example, in analysing the performance of a drug you may be interested in the dose (i.e. a number) but also in categorical features of the patient such as gender and their symptoms. Databases, and analysis packages such as R and SAS are better suited to this type of data. Business analysts appear to move from Excel to Tableau as their data get bigger and more complex. Tableau gives easy visualisation of database shaped data. It provides connectors to many different databases. My workflow at ScraperWiki is often Python to SQL database to Tableau.

Python for Data Analysis by Wes McKinney draws these threads together. The book is partly about the range of tools which make Python an alternative to systems like R, Matlab and their ilk and partly a guide to McKinney’s own contribution to this area: the pandas library. Pandas brings R-like dataframes and database-like operations to Python. It helps keep all your data analysis needs in one big Python-y tent. Dataframes are 2-dimensional tables of data whose rows and columns have indexes which can be numeric but are typically text. The pandas library provides a great deal of functionality to process Dataframes, in particular enabling filtering and grouping calculations which are reminiscent of the SQL database workflow. The indexes can be hierarchical. As well as the 2-dimensional Dataframe, pandas also provides 1-dimensional Series and a 3-dimensional Panel data structures.

I’ve already been using pandas in the Python part of my workflow. It’s excellent for importing data, and simplifies the process of reshaping data for upload to a SQL database and onwards to visualisation in Tableau. I’m also finding it can be used to help replace some of the more exploratory analysis I do in Tableau and SQL.

Outside of pandas the key technologies McKinney introduces are the ipython interactive console and the NumPy library. I mentioned the ipython notebook in my previous book review. ipython gives Python the interactive analysis capabilities of systems like Matlab. The NumPy library is a high performance library providing simple multi-dimensional arrays, comforting those who grew up with a FORTRAN background.

Why switch from commercial offerings like Matlab to the Python ecosystem? Partly it’s cost, the pricing model for Matlab has a moderately expensive core (i.e. $1000) with further functionality in moderately expensive toolboxes (more $1000s). Furthermore, the most painful and complex thing I did at my previous (very large) employer was represent users in the contractual interactions between my company and Mathworks to license Matlab and its associated tool boxes for hundreds of employees spread across the globe. These days Python offers me a wider range of high quality toolboxes, at it’s core it’s a respectable programming language with all the features and tooling that brings. If my code doesn’t run it’s because I wrote it wrong, not because my colleague in Shanghai has grabbed the last remaining network license for a key toolbox. R still offers statistical analysis with greater gravitas and some really nice, publication quality plotting but it does not have the air of a general purpose programming language.

The parts of Python for Data Analysis which I found most interesting, and engaging, were the examples of pandas code in “live” usage. Early in the book this includes analysis of first names for babies in the US over time, with later examples from the financial sector – in which the author worked. Much of the rest is very heavy on showing code snippets which is distracting from a straightforward reading of the book.  In some senses Mining the Social Web has really spoiled me – I now expect a book like this to come with an Ipython Notebook!

This review was first published at ScraperWiki.

The twitter search and follower tools are amongst the most popular on the ScraperWiki platform so we are looking to provide more value in this area. To this end I’ve been reading “Mining the Social Web” by Matthew A. Russell.

In the first instance the book looks like a run through the APIs for various social media services (Twitter, Facebook, LinkedIn, Google+, GitHub etc) but after the first couple of chapters on Twitter and Facebook it becomes obvious that it is more subtle than that. Each chapter also includes material on a data mining technique; for Twitter it is simply counting things. The Facebook chapter introduces graph analysis, a theme extended in the chapter on GitHub. Google+ is used as a framework to introduce term frequency-inverse document frequency (TF-IDF), an information retrieval technique and a basic, but effective, way to process natural language. Web pages scraping is used as a means to introduce some more ideas about natural language processing and summarisation. Mining mailboxes uses a subset of the Enron mail corpus to introduces MongoDB as a document storage system. The final chapter is a twitter cookbook which includes lots of short recipes for simple twitter related activities but no further analysis. The coverage of each topic isn’t deep but it is practical – introducing the key libraries to do tasks. And it’s alive with suggests for further work, and references to help with that.

The examples in the book are provided as IPython Notebooks which are supplied, along with a Notebook server on a virtual machine, from a GitHub repository. IPython notebooks are interactive Python sessions run through a browser interface. Content is divided into cells which can either be code or simple descriptive text. A code cell can be executed and the output from the code appears in an output cell. These notebooks are a really nice way to present example code since the code has some context. The virtual machine approach is also a great innovation since configuring Python libraries and the IPython server itself, in a platform agnostic manner, is really difficult and this solution bypasses most of those problems. The system makes it incredibly easy to run the example code for yourself, almost too easy in fact, I found myself clicking blindly through some of the example code. Potentially the book could have been presented simply as an IPython notebook, this is likely not economically practical but it would be nice to collect the links to further reading there where they would be more usable. The GitHub repository also provides a great place for interaction with the author: I filed a couple of issues regarding setting the system up and he responded unerringly quickly – as he did for many other readers. Also I discovered incidentally, through being subscribed to the repository, that one of the people I follow on Twitter (and a guest blogger here) was also reading the book. An interesting example of the social web in action!

Mining the social web covers some material I had not come across in my earlier machine learning/ data mining reading. There are a couple of chapters containing material on graph theory using data from Facebook and GitHub data. In the way of benefitting from reading about the same material in different places, Russell highlights that cluster and de-duplication are of course facets of the same subject.

I read with interest the section on using a MongoDB database as a store for tweets and other data in the form of JSON objects. Currently I am bemused by MongoDB. The ScraperWiki platform uses it to store user profile information. I have occasional recourse to try to look things up there. I’ve struggled to see the benefit of MongoDB over a SQL database. Particularly having watched two of my colleagues spend a morning working out how to do a what would be a simple SQL join in MongoDB. Mining the social web has made me wonder about giving MongoDB another chance.

The penultimate chapter is a discussion of the semantic web, introducing both microformats as well as RDF technology, although the discussion is much less concrete than earlier chapters. Microformats are HTML elements which hold semantic information about a page using an agreed schema, to give an example: the geo microformat encodes geographic information. In the absence of such a microformat, geographic information such as latitude and longitude could be encoded in pretty much any way, making it necessary to either use custom scrapers on a page by page basis or complex heuristics to infer the presence of such information. RDF is one of the underpinning technologies for the semantic web: a shorthand for a worldwide web marked up such that machines can understand the meaning of webpages. This touches on the EU Newsreader project on which we are collaborators, and which seeks to generate this type of semantic mark up for news articles using natural language processing.

Overall, definitely worth reading. We’re interested in extending our tools for social media and with this book in hand I’m confident we can do it and be aware of more possibilities.

This review was first published at ScraperWiki.

I’ve been doing more reading on machine learning, this time in the form of Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank and Mark A. Hall. This comes by recommendation of my academic colleagues on the Newsreader project, who rely heavily on machine learning techniques to do natural language processing.

Data mining is about finding structure in data, the algorithms for doing this are found in the field of machine learning. The classic example is Iris flower dataset. This dataset contains measurements of parts of a flower for three different species of Iris, the challenge is to build a system which classifies a flower to its species by its measurements. More practical examples are in the diagnosis of machine faults, credit assessment, detection of oil slicks, customer support analysis, marketing and sales.

Previously I’ve reviewed Machine Learning in Action by Peter Harrington. Data Mining is a somewhat different book. The core contents are quite similar: background to machine learning, evaluating your results and a run through the core algorithms. Machine Learning in Action is a pretty quick run through the field touching on many subjects, with toy demonstrations built from scratch in Python. Data Mining, running to almost 600 pages, is a much more thorough reference. There is a place for both types of book, even on the same bookshelf.

Data Mining is written by three members of the University of Waikato’s Computer Science Department and is based around the Weka machine learning system developed there. Weka is a complete framework, written in Java, which implements the algorithms described in the book as well as some others. Weka can be accessed via the command-line or using a GUI. As well as the machine learning algorithms there are systems for preparing data, evaluating and visualising results. A collection of well-known demonstration data sets are included. I’ve no reason to doubt the quality of the implementations in Weka, the GUI interface is functional, occasionally puzzling and not particularly slick. The book stands alone from the Weka framework but the framework provides a good playground to try out the techniques discussed in the book. Weka seems to be entirely suitable for conducting serious analysis. This approach is in contrast to the approach of Harrington in Machine Learning in Action who provides toy implementations of algorithms in Python.

The first two parts of the book provide an overview of machine learning, followed by a more detailed look at how the key algorithms are implemented. The third section is dedicated to Weka, whilst the first two sections refer to it but do not rely on it. The third section is divided into a discussion of Weka, covering all its key features and then a tutorial. I found this a bit confusing since the first part has the air of a tutorial, but isn’t, and the tutorial part keeps referring back to the overview section for its screenshots.

With some knowledge already in machine learning, the things I learned from this book:

• better methods, and subtleties in measuring the performance of machine learning algorithms;
• the success of the one-rule algorithm, essentially a decision tree which gets the maximum benefit from a single rule. It turns such an approach is surprisingly effective and only bettered a little, if at all, by more sophisticated algorithms;
• getting enough, clean data to do machine learning is often a problem;
• where to learn more!

The first edition of this book was published in 1999; my review is of the third edition. The book does show some signs of age, Machine Learning in Action  was written as a response to a poll published at the International Conference on Data Mining 2006 on the 10 most important machine learning algorithms (see the paper here). Whilst Data Mining mentions this survey, it is as something of an afterthought and the authors seem bemused by the inclusion of the PageRank algorithm used by Google to rank web pages in search results. They mention the Moa framework for data stream mining although do not discuss it in any detail. Moa focuses on techniques for large datasets.

In summary: a well-written, well-structured and readable book on machine learning algorithms with demonstrations based on an extensive machine learning framework. Definitely one to read and come back to for reference.