A Quick Introduction to HDFS

The Hadoop filesystem is called HDFS, and today I’m going to give a short introduction to how it works for a beginner.

The Hadoop File System (HDFS) sits on top of a Hadoop cluster and facilitates the distributed storage and access of files.  When a file is stored in HDFS, it is split into chunks called “blocks”. They can be of different sizes. The blocks are scattered between the nodes. These nodes have a daemon running called a datanode. There is one node called the namenode that has metadata about the blocks and their whereabouts.

To protect against network or disk failure data is replicated in three places across the cluster. This makes the data redundant. Therefore if one datanode goes down, there are other copies of the data elsewhere. When this happens a new copy of the data is created, so that there are always three.

The namenode is even more important, because it has metadata about all the files. If there is a network issue, all of the data will be unavailable. However, if the disk on the namenode fails, the data may be lost forever, because the namenode has all the information about how the pieces of the files go together. We’d still have all the chunks on the data nodes, but we’d have no idea what file they go to.

To get around this issue, one solution is to also mount the drive on a network file system (NFS). Another way to approach this (which is a better alternative) is to have an active namenode and a standby namenode. This way, there is a “backup” if something goes wrong.

Some commands:
  • To list files on HDFS:
    • $ hadoop fs -ls
  • To put files on HDFS:
    • $ hadoop fs -put filename
    • this takes a local file and puts on HDFS
  • To display the end of a file :
    • $ hadoop fs -tail filename
  • Most bash commands will work if you put a dash in front of them
    • $ hadoop fs -cat
    • $ hadoop fs -mv
    • $ hadoop fs -mkdir
    • etc…

Chopsticks Game – A Combinatorial Challenge

point-finger2trans

So I don’t know if anyone else is familiar with this game, but I just remember playing it with friends in middle school and it occurred to me the other day that it would be an interesting game to analyze combinatorially, and perhaps write a game playing algorithm for. This game can be found in more detail here: http://en.wikipedia.org/wiki/Chopsticks_(hand_game)

Players: 2+.

Rules of Play: Players each begin with two “piles” of points, and each pile has 1 point to begin with. We used fingers to represent this, one finger on each hand.

On each turn, a player can choose to do one of two things:

  1. Send points from one of the player’s pile to one of the opponents piles. So if Player 1 wanted to send 1 point to Player 2’s left pile, then Player 2 would have 2 points in the left pile and Player 1 would still have 1 point in his left pile. Player 1 does not lose points, they are simply “cloned” over to the opponents pile.
  2. If the player has an even number of points in one pile and zero points in the other pile, the player may elect to split his points evenly between the two piles. This consumes the player’s turn. Example: if Player 1 has (0     4) then he can use his turn to split his points, giving him (2     2).

If a player gets exactly 5 points in either pile, that pile loses all of its points and reverts to 0. However, if points applied goes over 5 (such as adding 2 points to a 4 point pile), then the remainder of points are added. (meaning that 4 + 2 = 1). The opponent simply gets points mod 5.

If a player gets to 0 points in both their piles, then they lose. The last person that has points remaining wins.
=========================
Okay, so let’s break this down. Here’s an example game for those of you that are more visually oriented (follow the turns by reading left to right, moves are marked with red arrows):

Okay, so let’s point out a few things about this game.

  • On turn 4, player 2 adds 3 points to player 1’s 2 points, making 5. The rules state that any pile with exactly 5 points reverts to zero.
  • On turn 7, player 2 adds 3 points to 3 points. 3+3=6 as we all know, but 6 \equiv 1 \mod 5, so player 1 now has one point in his pile.
  • On turn 13, player 2 decides to split his points, turning his one pile of 4 into two piles of 2. This consumes his turn.
  • On turn 15, player 2 adds 2 points to player 1’s 3, thus reverting his pile to zero. Player 1 now has no more points to play with, so player 2 wins.

We can think about this game as a combinatorial problem. What are the optimal positions to play? How would one program a computer to play this game? I plan to create an interactive web game where players can try this for themselves.

A Genetic Algorithm for Computing Ramsey Numbers: Update

Friends_strangers_graph

All the 78 possible friends-strangers graphs with 6 nodes. For each graph the red/blue nodes shows a sample triplet of mutual friends/strangers.

In my last post on this topic, I discussed how I was working on a genetic algorithm to search mathematical graphs for elusive properties called Ramsey Numbers. (For a refresher on genetic algorithms,  visit here, and for a refresher on Ramsey Numbers, visit here). I’ve been doing some work on it since then (check out the code here), and I thought I would describe some improvements and further progress I’ve made in this area.

New features:

  • colorings dumped to a file at the end of each run
  • ability to load in data sets from file, further refining of the data than starting from scratch each time

The next problem I ran up against while working through this was that even if I am able to load in previously analyzed data, I still only have one fitness function that checks a static set of edges. As I see it, there are two ways to solve this:

  • Make the current fitness function dynamic; that is, it tests a different set of edges every time. However, this is counterproductive to the purpose of the program “eliminating” certain sets of edges in each “round”. However, this would be easier to maintain than the other option, which is
  • Make a “FitnessHandler” method that takes in a value for which method to run, and uses that to determine what set of edges to test. However, this would lead to a lot of extra code and overhead. I’m thinking having a static variable at the beginning of each run with what “fitness method” to start on, so that it doesn’t have to start on round one each time.

I haven’t fully decided which of these I will go with. I feel like the second one fills my purpose of methodically “weeding out” the improbable graphs, but its going to be a lot of extra work. Oh well, nothing worthwhile ever came easy…

Leave a note here or on my github if you have suggestions!

Twitter FriendCloud

Today I’m going to talk about a personal project I’ve been working on recently. I was trying to come up with some way to make a cool project with natural language processing and I had also noticed that with the rise of social networks, there is a treasure trove of data out there waiting to be analyzed. I’m a fairly active user of Twitter, and its a fun way to get short snippets of info from people or topics you’re interested in. I know personally, I have found a lot of math, science, and tech people that have twitter accounts and post about their work or the latest news in the field. I find just on a short inspection that the people I follow tend to fall into certain “groups”:

  • math
  • computer science
  • general science
  • academia
  • authors
  • tech bloggers
  • feminism
  • various political and social activist figures (civil rights, digital privacy, etc)

This list gives a pretty good insight into the things I am most interested in and like hearing about on my twitter timeline. Now I thought to myself, what if I could automate this process and analyze any user’s timeline to find out what “groups” their friends fell into as well? I had my project.

Currently in the beginning stages, I decided to tentatively call my project “FriendCloud” and registered with the API to start messing around. I’m using Python-Twitter to interact with the twitter API, and its helping me to get practice with Python at the same time. The first thing I wanted to do was be able to pull down a list of all the people that I follow. Since I follow a little over 1000 people, this proved to be a daunting task with the rate limiting that Twitter has built in to their API. At the moment, what I had to do was get my script to pull as many user objects as possible until the rate limit ran out, then put the program to sleep for 15 minutes until the rates refreshed and I could download more.

It took a little over an hour to get the list of all my friends and I am trying to look into a way to do this quicker in the future. After that, I can go through users and pull down a selection of their tweets. After this is done, I have a corpus of text that I can analyze. I have been using NLTK (a Python NLP toolkit) to pick out some of the most common keywords and themes. There is a lot of extraneous data to deal with, but as I pare it down I’ve noticed some interesting trends just in my own tweets.

I hope in the future to be able to extend this to the people I follow on twitter and be able to place them into rough “groups” based on their most commonly tweeted keywords (similar to how a word cloud works). In this way, a user can get an at a glance look at what topics the person is most likely interested in and what sort of people they may be likely to follow in the future.

Beeminder and Self-Tracking: Five Months In

So back in January, I started using a little service called Beeminder to track my goals and encourage me to do more things I’ve been putting off. I also experimented with several other Quantified Self tools, and learned a lot about  how simply tracking what we do day to day can open our eyes to things we need to improve at and things we perhaps do too much of (for me, its instant messaging with my friends). It’s been five months since I started this journey of learning about myself, and I thought I’d give an update about how its been going.

My findings: I couldn’t be happier with the results.

I know its easy to get started on a new diet or workout program and feel all motivated and enthusiastic at first. That’s how New Year’s resolutions start, after all. We get all excited about changing our lives and then a few days or weeks later, its back to the same old same old. When I started tracking myself and using Beeminder for my goals, I was afraid a similar thing would happen. It’s just a fad, I thought. My newest obsession. It will subside in a few weeks. I’m happy to say that five months later, I am still going strong and I have made  a lot of progress.

Wait, what is all this Beeminder/Quantified Self stuff? What are you talking about?

Glad you asked. I did a post on all this stuff a few months ago called Quantifying the Self and discussed the nuts and bolts of all this, but here’s a quick review:

Beeminder is a web service that allows you to track your goals by plotting progress points on a graph each day. You try to stay on “the yellow brick road” (that is, the goal you set for yourself — say, doing 20 pushups a day) and if you fall off the road, in order to recommit and get back on the road you pledge money (you’re basically “betting” that you will be able to keep up with your goal). If you fall off the road again, you pay up. Here’s a picture:

readmore

As you can see, the data points are staying above the yellow line, meaning I am on track for my goal. This screenshot was taken back in February, after I had been tracking my reading habits for only a month. Let’s take a look at my graph now:

readmoremay2013

There are a lot more data points, and I’ve racked up over 2000 minutes read (that’s 9 books) since January! There are a few flat parts (in March I did a lot of traveling and didn’t find a lot of time to read), and because of this I actually derailed once. But I pledged my $5 and so far I’ve been doing well once again. I really enjoy seeing the graph and the data points grow, and the Android App has been absolutely essential to my success. It doesn’t get much easier than entering in a few numbers into the app every day.

Of course, this is just my most successful graph. I have others that I have utterly failed at, like flossing my teeth:

flossfail

This one…has a lot more flatlining. A product of good old akrasia. As you can see, I’ve derailed on this one and have been for quite some time…I think I’m about ready to pony up the cash and try again though (for real this time!) Some people have told me that paying the service money when you fail your goals seems cruel or manipulative on their part, but I don’t see it that way at all. This is something want to do, and if I don’t follow through with it, then there has to be some sort of pain associated with that to deter me from failing again. And besides, this company has done so much for me and I use the service so much I am happy to give them a few bucks here and there. The staff is great and responsive and the website is always getting updates. They deserve it.

In addition, I’ve found from my pedometer app on my phone that I’ve walked over 600,000 steps this year! That’s over 300 miles. The day to day walking may not seem like much, but adding it all up like that certainly has a big impact on me and motivates me to keep going and do more.

My most recent project involves taming the beast that is my email inbox. I’ll admit it: I’m an email hoarder. While other people keep their inboxes neat, tidy, and organized, I have a giant deluge of thousands of emails just sitting there, taking up space and making it impossible to find anything. Usually, if I don’t find an email interesting (such as an advertisement or a newsletter) I won’t even click on it. Thus, there are hundreds of unread emails as well. This is a product of my laziness over time, and it just keeps getting worse. I’ve decided its finally time to do something about it. I set up a Beeminder goal to track my inbox size, and I’m going to either file away or delete 500 emails per week until my inbox reaches the fabled Inbox Zero. Once I reach that milestone, the challenge will be to keep it there, and keep my email inbox manageable and not overflowing like it was before. This leads to less stress, easier location of important emails, and if something is in the inbox, it means I need to deal with it right away. I feel like this will give great gains in productivity and overall happiness.

And just in the interest of transparency, my goals are all publicly viewable on my beeminder page, and that makes me not accountable to just one person, but the internet at large when you display your successes and failures in this way. There’s something strangely motivational about that.

What have I learned?

As for some of the other services, they didn’t stick quite as well. But the trial period of testing out these new things definitely taught me a lot about how my mind works and how we can battle this beast of distraction and bad judgement that rears its ugly head daily. Humans aren’t necessarily rational creatures by nature, but we can learn what mistakes we make and how our minds try to trick us. Then we can trick it right back. I just bought the book Thinking, Fast and Slow by Daniel Kahneman, and it discusses at length these “cognitive biases” and ways to get around them. The result? A happier, healthier, more productive life.

Final Thoughts

If you’re curious about what self-tracking can do for you, I urge you to try tracking a simple thing in your life, something you already do, for just a week. It becomes like a game to try to “beat your best score” and its really fulfilling to see your progress over time. I really recommend trying this to anyone that is interested in achieving their goals and improving their life. It’s surely changed mine.

Introduction to Genetic Algorithms

In response to my previous article about genetic algorithms for Ramsey theory, a few readers asked me to give a bit more of an introduction about genetic algorithms. Here you will find a beginner’s look at what a genetic algorithm is, what it is useful for, and how you can use one in your own work.

What is a genetic algorithm (GA)?

To begin with, let’s talk about what a genetic algorithm is, on its most basic level. The word genetic suggests something to do with biology. If you can reach way back to your high school science classes you might remember the basic genetic process: plants and animals are born, mate with one another, and create new generations. Over time, these generations tend to emphasize certain traits essential to survival while downplaying the weaker (recessive) traits. A genetic algorithm works much the same way. We come up with a population of possible solutions to a problem, “mate” them together, and look at our new solutions to see if they are any better. Over time, we can create solutions that converge to better and better values. This is useful when a problem is too complex to search all possibilities. Below you will see an image describing how a simple GA works.

GeneticAlgorithms (1)

Crossovers, fitness, mutation, oh my!

Before moving on, it would be useful to define some of the terms used above.

  • chromosome:  a proposed “solution” to the problem at hand. This is usually represented by a bitstring, that is to say, a list of 0’s and 1’s. It can also hold any other information that is crucial to our problem.
  • population: a collection of these “chromosomes” that we use to combine together and make new generations. The population represents the set of all the ideas that we have about this problem at the moment
  • fitness: the fitness of a chromosome is a number representing how good it is. That is, the fitness represents how good this solution is at solving our problem. An example of this is if you were trying to find the shortest route to get somewhere. Fitness for a problem like this would be a number representing the distance it takes for each path you could choose. In the end, you want to find the path with the shortest distance. Ultimately, the fitness function is defined by the programmer, and it can measure whatever you want.
  • mutation: mutation is the random entering of new data into the gene pool. Just like in biology, sometimes mutations occur and create things that were never intended. However, sometimes this inadvertent change can be to our advantage if we’re getting stuck. A common example of a mutation is to just change a small part of the chromosome, and move on.
  • crossover: the “crossover function” represents the operations that we do in order to “mate” two (or sometimes more) of our chromosomes. There are many different types of crossover techniques, some better for certain situations than others, but at the heart of it a crossover just represents a way to combine the “traits” of two or more chromosomes into a new “baby” chromosome to be inserted into our population. This is the bulk of a genetic algorithms’ work. As the population evolves and new generations of solutions are created, the goal is to keep around the solutions with “good” fitness, and get rid of the chromosomes that aren’t doing so well. The crossover is also user-defined, and tweaking it to optimize results is common.
  • termination condition: all good things must come to an end. While we in theory could just leave our algorithm running forever, that would not be very helpful because as I will discuss later in this post, solutions can’t keep getting better forever. In addition, sometimes you won’t actually get to the “best solution” and instead will converge on what’s called a “local minima/maxima”. When this happens, it means you’ve found an “okay” solution, but the population got flooded with many similar chromosomes and couldn’t improve itself after that point. Think of it this way: if everyone in the world were exactly the same, would you expect any different from their children? The termination condition you choose depends on the problem, but some common terminating conditions are:
    • a) finding the best solution (ideal)
    • b) running a preset number of generations and using that as a cutoff point
    • c) quitting after every member of the population falls within a certain similarity range (this means that no new/better solutions are likely to be produced)

But why would I want to do all this?

I know this seems like a pretty complicated process. Why not just use a computer to figure out the real solution instead of dancing around it in this complicated manner? Well, it turns out that’s not always possible…

Enter the Traveling Salesman Problem (TSP). While there are many problems that are still very hard for us to solve with computers, this is one of the best known and most studied. It turns out that using a genetic algorithm is actually a pretty good approach and is much quicker than running an exhaustive search of all paths possible. Remember when I used the example of finding the shortest distance to go somewhere? That’s pretty much what this is. I’ve done a blog post on this before (see here). In the traveling salesman problem, there is a man that needs to visit a list of different cities, but he wants to get there and back as quickly as possible. Therefore, you need to find the shortest route to hit all the cities and return home. This isn’t always as easy as it sounds, and as the number of cities grows, so too does the time it takes to find the right one. Very quickly it becomes implausible to check every possible path, so we use a genetic algorithm to help us weed out the bad ones.

Another example of how we would use a genetic algorithm is for graph theory problems that also have a huge number of possible solutions. You can see how I applied a genetic algorithm to the problem of Ramsey numbers here.

Okay, so how do I use this in my project?

It’s pretty simple. I know it looks complicated, but once you get everything apportioned out correctly it’s not that bad. Things to think about when applying a GA to a hard problem (we will use the Traveling Salesman Problem as an example here):

  • First decide what your “chromosomes” will look like. These are the meat of your population, and these are what will be mutated and crossovered in order to create better solutions. For the TSP, we will use a a sequence of numbers denoting what cities to visit and in what order. (For example, “1 5 4 2 3 1” describes a way you could make a circuit through 5 cities).
  • Decide on how fitness will be evaluated. This is important because members with better fitness will be the ones that stick around in your population and (hopefully) make your solutions better. In our problem, fitness refers to how far our chosen path takes us. The lower the fitness score, the shorter our travel is. We want to minimize this number with our algorithm.
  • Next we need to figure out what our crossover will be. This is very important to consider, because we want something that will take parts of both of our “parents” and combine them together in some way to make a “baby”. For traveling salesman, we can’t just grab some from one parent and some from the other, because we run the risk of duplicates. (we don’t want a path to look like “1 2 2 3 4 1”, city 2 is visited twice and we never get to city 5). Therefore, we have to use more sophisticated methods. I won’t go into them here but if you’re interested check out this wikipedia page for more info on crossover techniques.

What are the downsides?

“Every rose has its thorn,” as they say, and genetic algorithms are no different. What seems like a cure-all has its hindrances as well. As I mentioned before, GAs have a problem of converging too early on a value that’s not quite ideal. Often this takes many repetitions of running the program and fine tuning things such as population size and mutation rate. Sometimes happening upon the “best” solution is a product of randomness. But in a GA, we use this randomness to our advantage as much as we can. Another inherent problem is the fact that we have to program this framework around it. Using a GA is only viable if its an extremely complex problem that cannot be solved more efficiently. For example, using a GA to solve a 5 city TSP would be foolish. We can run through all those possibilities very quickly. On the other hand, bumping that up to 50 or 500 cities proves a much harder challenge.

In addition, using a genetic algorithm means you have to find a type of chromosome, fitness function, population size, and crossover that works for you. Pick the wrong values for these, and the program will behave less than optimally. Experimentation and continually tweaking the parameters of your model is necessary. In some genetic algorithms, the fitness function for even one chromosome can take quite a while to compute. This makes some genetic algorithms very slow to apply.

Where can I learn more?

I hope this has covered the basics of genetic algorithms and interested you in learning more. If you would like to see a project I have done involving GAs you can read A Genetic Algorithm Approach to Ramsey Theory, and for a broader range of discussion about the theory and applications of GAs check out the book An Introduction To Genetic Algorithms by Melanie Mitchell.

Hope you enjoyed this introduction to the wide world of genetic programming; if you have questions or suggestions please leave them in the comments below!

Kinect Face Tracking — Results: Thesis Update #4

thesis pic

 

For background, see my three previous posts in this series:

  1. Facial Expression Analysis With Microsoft Kinect : Thesis Update #1
  2. Some Faces : Thesis Update #2
  3. Animation Units for Facial Expression Tracking : Thesis Update #3

My thesis has been successfully completed and defended now, and I am currently on break for the summer. I thought I would make a post to wrap up some loose ends and talk about some things I didn’t have a chance to talk about before. In my last post, I discussed the significance of animation units to my facial tracking algorithm. Now the way I use these units is pretty simple, but can lead to some complex classifications. The first thing to do is to consider what the desired face would look like. Picture it in the mind’s eye. Say for example we wanted to recognized a surprised face. How would this look? Chances are, when thinking of a surprised face, the mind visualizes raised eyebrows, wide open eyes, and a gaping mouth. The next task (and the bulk of my algorithm) asks: How do we translate these facial movements into the points tracked on our facial mesh?

We use animation units as “wrappers” for sets of points on the face plane. Instead of having to track and check multiple different places on the eyebrows or mouth for example, the animation units allow us to track the movements of those features as a whole. Since the animation unit values lie between -1 and 1, we must assign “bounds” for each expression to where if the user’s features fall within that range, we can assume the user is creating that expression. These values at present are determined by extensive testing and seeing what values are frequently displayed when creating a certain expression. It would not be difficult to build a classifier for these expression bounds, and use it to train the program over multiple different faces and expressions in order to get the best and most accurate data for each type of face.

In my application, we track six different types of expressions.

surprised

surprised

sad

sad

kissing face

kissing

happy

smiling

In addition, we look for two different types of angry faces: angry with a closed mouth (glaring at someone) or angry with an open mouth (as in yelling).

angry-closed mouth

angry – closed mouth

angry-open mouth

angry – open mouth

To see a simple flowchart detailing some preliminary bounds for each expression (not exhaustive), check out the chart here (click for larger view).

ThesisLogic (1)

There is a bit of a “lag” in my application on recognizing these expressions, because the live video stream captures many frames each second, and there is a tiny bit of delay in figuring out what expression that frame’s data fits into. As such, the output of my program is a bit inaccurate still. Because it prints off what the expression every frame, there can be a bit of a buildup and after a while it will start showing expressions at a bit of a delay. For example, if the user acts surprised sometimes the program will not actually print “surprised” for a fraction of a second afterwards, because its busy trying to run through the frames as they come in. A simple remedy to this would be to create a “buffer” of tracked points and use the average of the data over a few seconds in order to determine the facial expression. Because the camera is very sensitive, we are prone to having the data change at the slightest movement of the face. Indeed, even trying to sit as still as possible still results in some small changes in the data. Another thing I noticed that creating a buffer of data could help solve is when the camera loses track of the face for only a moment, it begins to spit out garbage data as it attempts to relocate the face.

Overall we can see a good proof of concept of the capabilities of the Kinect Face Tracking API and there is a lot of room for improvement in the future. Possible future additions/enhancements include:

  • tracking a wider range of expressions
  • wider range of accessibility (glasses/hats, children, elderly people)
  • more specific bounds for facial expressions, use neural networks or something
  • more interactive application
  • use facial expression recognition to interface with other environments (i.e., call a command by smiling)