Dim Red Glow

A blog about data mining, games, stocks and adventures.

suffix arrays maybe

I've been hard at work in my free time working on this cancer image processing. i got my image load and processing down so it takes 7 minutes to load the images (this includes the white-black color balancing that makes "black" consistent on all images) The problem i'm having now is finding the best way to match a 32x32 block from the 512x512 images against all the other images to find similar entities.

I tried a brute force approach that measured differences between blocks. that wasn't gonna work. the runtime would have been centuries and the results were meh at best. So just to speed it up (before i improve the meh results) I then tried making an index out of the data by some averages and what not from each cell, storing them in 4 bytes as an index. I associated all rows that tied to that index in a hashmap for that index. the problem with this is, there are to many similar cells. That is to say the averaging functions weren't doing enough to distinguish some very similar looking cells. So the runtime went to like 7 years... with still meh results.

I tried short circuiting the results and just give me "something" after a few tries but even this was proving to take a loooong time to run. why is this taking so long? well 250,000 base images and i divide each one in to 32x32 segments and then i do a few versions of the grid where i offset by 16. i end up with  16*16+15*16+16*15+15*15 cells for each of the 250,000 images i want to process. since i really dont want to process every single possible cell because of runtime concerns. Or do I?

So sitting here thinking about it. I learned about suffix arrays years ago for some bioinformatics class i took (a story for another time). I've implemented one on 3 separate occasions but its been a while. What are they? they are a clever way of using pointers in to your original array. then sorting the those pointers by what they point at. if you had the string "ABA" your initial pointers would be 0,1,2 then after they are sorted they would be 0,2,1 (assuming you sort "end  of line" after "B") why is this useful? well you can quickly see where similar things the string occur by scooting over one to the right or one to the left.

I could then put all the images grid segments in to a giant array of bytes and sort them (not a fast process, but really not that slow either). I want to keep the little images as they represent my features i'll feed in to the database, i could make smaller images but 961 features seems more than enough for now. the first real problem here is the array of images is about 250,000,000 long and each of those has 1k of bytes in it. so you need a lot of memory (i'm okay there, the indexing i was doing before had the same problem) the 2nd thing you need to think about is the image segment. its really not rendered the way that is useful.

when i worked on the indexing i thought about that some and tried making averages for the whole grid. this kinda worked but really it dances around the issue cause i was only making 4 bytes of averages from a 1k image. I think to do this right, you need to translate the images in to something more... jpeg-y. you want 1 byte that represents some average details then subsequent bytes to refine the average so that as you move from the left to the right of byte array you get more and more detailed information. this will put similiar images next to each other once sorted.  You can certain translate an image in to another form that does something like that. i'll have to.

Once i've done that, and once i have the data loaded in to a suffix array its a simple matter of looking at the nearest cells in the suffix array and seeing if they are in a cancer patient or not (skipping cells that reference the original image for that grid, and cells in test data). once you've done all that you  can get an average value for cancer/nocancer and save it for that feature.

This method leaves the original images on stretched or touched in anyway (other than the color balance) which means a small person may not line up with a large person. and also there are times when the image is zoomed in to much in the cat scan so there is clipping etc... these problems can be resolved if there are enough images (so you can find someone similar to yourself) a bit like sampling enough voices will eventually give you a person who has the same accent as yourself. the question is, is there enough data?

hopefully i can get all this done by monday and have a crack at actually making some predictions with the results using a normal gbm.

oh image process... how difficult you are

I've decided to compete in https://www.kaggle.com/c/data-science-bowl-2017 which is a contest where you process patient cat scan photos and try to identify stage 1 cancer people. there are something like 1500 patients and and each has 100+ cat scan slices. the number of  slices is not consistent and the order of the slices is seemingly random (they have guid's as names).

Until now i haven't done image processing contests. in fact in most of my contests i dont bother with feature creation at all if i can avoid it. That's a different thing than i enjoy working on. That can't be avoided with image processing. It is a whole thing unto itself. So why try one now? I had a close friends die from lung cancer. It is unlikely this technology would have helped him since by the time he went in to get his cough checked on, it was clear he had cancer from cat scans. But it still seems like a good pursuit and hits close to home. The prize money (which is huge) is also nice but so few get that, that it cant be a real draw.

So what will i be doing / what have i do so far? I've loaded the images using some trial software and a simple c# program (they are dicom medical images). It took a long while to realize that was the way to go. I tried using some open source packages and stuff but the images kept coming out a little off and i didn't know why.

I've normalized the images for clarity by removing gray backgrounds and re-balancing the image brightness with that in mind This really makes the details clear and gives all the images the same light levels to compare with each other. I did not try maximizing the light levels, presumably they already are but i might need to implement that just in case.

I tried removing non lung artifacts. things like the clothing and the table they are lying on, but the results were sketchy. I didn't want to lose anything important in the image by accident. So after many attempts i undid the work and decided to come back to it later.

I setup 2 dataminig databases for my data. 1 to load image results in and 1 to produce the actual final prediction. the image results will go in to a normal datamining database. In that each image slice will be a row with a predicted value of cancer or no-cancer (based on the person who it was taken from, not on if that particular image had cancer or not). The images will be split in to a grid of small cells and a 2nd grid that is offset by half a cell so corner regions are not ignored. I probably should do 2 other grids as well (i have not yet) that are offset by half a width or half a height respectfully (not just a 2nd with both). these tiles will be used to compare with all other people's image's to see/find the closest match.

Mapping the data from the image database to the real/result database is a little bit of a mystery. I have a solution to do it but i'm not sure it's the best solution. Normalizing the feature count can be done a number of different ways. We'll just see have to see what works best.

That last part.. compare with all other people's image's to see/find the closest match. is the hard part. that little statement right there is what humans do so well and computers do not. That is where i hope to really add and stand out in this competition. till i get everything working i'm just going to do a simple difference measure in light levels and take the closest matching tile as the winner... but later once i get everything working weill, i've got plans to really improve the matching algorithm... everything from doing a 2-d DTW (which has about the most abhorrent run time ever.. to building a data miner for the tiles... to just doing fuzzy matching of images .. to looking at the best match for each pixel in the entire square... to ???

clearly i've got lots of ideas/things i want to try. but the first step is just to get the whole jalopy running.


The legacy Grand Prix in louisvie, ky

I took a deck I've been working on to the legacy grand prix this weekend. It was definitely tier 2 still. I had a lot of near misses (loss in 3 games, 1 match win in 4 rounds) and ended up dropping the main event and testing it a lot in side events this weekend.. I called it aether vise.  I'm not done with the deck. I've gotten a lot of great information to improve it. The deck is as follows (copy-paste from http://tappedout.net )

So my notes go like this:

a little more tweaking and i think the deck will sing. right now it feels like an 8 cylinder car with 1 dead plug. i think the amount of control is right where I want it. It was the win cons that gave me problems.(with an occasional exception of too much or not enough mana. I'm chalking that up to normal game play).

So many near misses. no blow outs in either direction (except me vs elves... poor elves guy. 1 game i lost, i misplayed winter orb over sphere of resistance turn 2. The games weren't much of a challenge. Oh! and me vs dead guy ale.. that deck is seems just too slow or he had absolutely rotten luck both games).

I feel like my deck tries a little to hard to do certain things and not hard enough at other things. I need it to be a smooth tool box deck. almost everything needs to be a 1 of.

So changes: i think every win con should all be 1 of  (Except ghripr aether grid that should be a two of since its value is huge and it shuts off winter orb). which means i have to cut a black vice. I'm going to put Ajani Vegenant in place. I was also pleased with adding a chandra main. that is i ran it with a chandra in it in side events (instead of 1 of the 3 sun droplets). I think she is ideal as a 1 of. sun droplet as a 2 of is fine... 3 was good to but 1 would leave me looking for it too often for spiteful visions or as another mechanism to slow the opponent's win. 

Previously I tried to hard to make all cards tutor-able. But that's not the best thinking. There is no reason i cant run a few win cons that aren't tutor-able ajani and chandra are both good enough to just "show up". I considered koth as well but he doesn't do enough unless you are playing a blood moon heavy deck.

the ensnaring bridges should probably be a 2 of main instead of 3 ghostly 1 ensnaring. it should be 2 and 2. Ever since i migrated away from the 4 howling mines main (down to 1 at this point) and moved away from the black vise heavy build ensnaring bridge has gotten stronger and stronger.

i also think the wastelands can go to a 1 of from 3 ... leave a spot of blink moth well and Academy Ruins (only usable with a mox).  Why? its rare that destroying a land sets me up for later. unlike blood moon wins, wasteland almost always has to combo off of the crucible to be all that good or just gives me a little early game time walk. Since i'm not running creatures to abuse the advantage its not all that valuable most of the time.  Also, as for the combo 1 ghost quarter does the same, which is in there too. All in all, sphere has the same net effect and is way better over all.

As for blink moth well, having a non counter-able way to tap winter orb (miracles) is good and that gives me 4 ways to tap the 4 orbs so hopefully that runs a little smoother. (1 relic barrier, 2 grid and 1 well). I considered a man land too. but, I don't think a man land is necessary with the addition of both planeswalkers, since miracles has a tough time with 4 drops.

The academy ruins, this would be an experiment. more than once i wanted a way to get back an artifact and it makes my expedition map better. it also gives me an out vs slow mill.

Side board thoughts: The porphyry nodes sideboard is great, I might want another or different creature hate card in place of the bridge I'm moving to main deck. the nevermore is great i just to get a lot better at picking things to name (wear/tare is a good one!). I went to 1 from 2 earlier, I can see arguments to going back to 2.  The lay lines of sanctity are great at 3. The same is true for chalice is great at 3. 2 wear and tares was good. It seems like the right amount when i want them, though there might be better choices out there. The 2nd rest in peace was less impact-full than i expected. i need to play more to see if it stays. The extra pithing needle in the side was fine, same with blood moon. I have no idea if sun droplet needs to stay in the sideboard as a 1 of. Two main seems enough except against burn and them 3 might not be a enough anyway.

the current version of the build can be found here (i renamed it)




Minecraft and data mining (no relation) and stocks (some releation)

It's the 2nd day of 2017 and soon to be 2018 or so it always seems when you look back at years. I've dawdled for to long on some old work I wanted to do and the years turning over has put that in sharp relief.

First I started a you tube channel https://www.youtube.com/channel/UCoTP8WbdsCW_6FSLz0pUSsA (Hardcore in a Hurry) for my video game exploits. I don't play a lot of video games these days, but that being said I like playing games on the hardest setting possible. I'm not a fan cheat modes or easy walk through settings (or AIs that cheat for that matter, I mean seriously? couldn't you write a better AI?). That being said, minecraft is the only thing on there now. I don't have plans to add any other types of videos right now, but things change.

Next Let me say I've spent some more time on my layered Gradient boosting stuff. I learned a little bit about what t-sne can do for me. So feeding t-sne in initially can help if the groups do self organize out of the original data but normally, I think it is something you want to do after a few iterations have passed. it seems the further down the gradient you are the better the effect. The effects arent remarkable, but they aren't terrible either. The unfortunate takeway is that it is very time intensive. running t-sne processing on the current state of the gradient descent and the source data just takes ... well a while. maybe i can cherry pick features to use and speed it up, but as it stands right now unless the data set is pretty small its not a good option for eking out more performance. 

For a couple years now i've wanted to implement a stock analysis program, so i can do personal investment. I did this once along time ago with a friend using far less sophisticated methods. That's a story for another time. I've never been happy enough with my software for datamining to start the code/project for investing. I think i'm happy enough now. The really work will be in getting the data in to a form that is easily updated daily and makes measurable testable predictions. My recent work on https://www.kaggle.com/c/santander-product-recommendation got me to rewrite parts of my code to handle time series in a different way. which is key for the training and evaluation. (a contest i never got anywhere with. I've never implemented the map@<x> evaluation so it makes it hard to train for such a thing. )