In my last post, I wrote about the productivity achievable with Python, telling the story of creating the SPSSINC TURF extension command and dialog box. Well, when the cat’s away, the mice will play. This post is about scalability and optimizing the TURF algorithm,
The TURF algorithm is computationally explosive. It has to compute a number […]
In my last post, I wrote about the productivity achievable with Python, telling the story of creating the SPSSINC TURF extension command and dialog box. Well, when the cat’s away, the mice will play. This post is about scalability and optimizing the TURF algorithm,
The TURF algorithm is computationally explosive. It has to compute a number of set unions that grows very rapidly as the problem size grows and harvest the best. Apart from the number of cases, which affects the time required to compute a set union and the amount of memory required, the size is determined mainly by the number of variables, N, and the maximum number of variables that can be combined. e.g., best three variables, depth.
If we hold the depth constant at 10, i.e., find the best combination of up to 10 variables, the number of unions required grows like this as we increase the number of variables.
N unions
3 4
6 57
12 4070
24 4,540,361
48 8,682,997,422
Looking in another dimension, fixing the number of variables at 24 and varying the depth, the union count grows like this.
depth unions
3 1330
6 190,026
12 9,740,661
24 16,777,191
48 variables and a depth of 24 would require 156,861,290,196,829 unions!
This clearly can get out of hand with what seem to be reasonable problems! I added code to precalculate and report the number of unions required and syntax to set a limit on problem size to make the user better informed, but that is not enough.
In calculating the set unions, I was careful to make the code pretty efficient. That works well. But I found that as the number of sets got into the millions, the algorithm stalled and eventually exhausted the machine memory and failed.
Some experimentation showed that the set calculations completed in a reasonable amount of time, but finding the best combinations was very slow. A little optimization of that part of the Python code sped it up by a factor of 4. But I could see that what was killing the code was my strategy of first accumulating the reach count for all the sets and then picking out the best ones. Even though each reach statistic saved added only a small object to the list, the number of such objects was coming to dominate memory and time usage.
Handling the result list had initially seemed to me to be a trivial part of the process, but it clearly is not as the size grows. So I needed to change the code to only keep reach counts for combinations that have a chance at making the final list. Each new count needs to be checked against the counts already computed. Then the code should discard that count if it was dominated by the others, and it should replace a count if it is better than the worst already in the list.
Doing this efficiently requires an entirely different data structure for keeping the list of counts. A heap is a data structure that has two useful properties for this problem. First, the smallest element is always at the head of the list, and, second, elements can be added or deleted quickly while maintaining the heap property.
Python provides a heap data structure in the heapq module in the standard library. For this problem, I actually needed to keep a list of heaps, one for each different number of combinations, but I could use the heapq functions for each one. One other problem is that the heap items are a single number, and I needed to keep something a little more complicated (the count and a list of the variables that produced it). Because Python does not use strong types, I could easily create an object that acted like an integer for comparison purposes but held all the information I needed. A Python motto is, “if it walks like a duck and talks like a duck, it’s a duck”.
With these changes, the result management parts of the algorithm now run in a constant and small amount of memory and the harvesting of the best combinations is very fast. A test problem that previously died after consuming 1.5GB of memory now runs in 30MB – and finishes. Of course, constructing and counting all those set unions can still take a long time, but that’s the nature of the problem. I am not going to try that problem requiring 156 trillion sets.
There are several points to this story.
- You may find a need to optimize in places you didn’t expect: testing and measuring is important.
- Producing an initial version and putting it out for real world use can quickly flush out the places that need work. Being able to respond quickly and outside annual product release cycles as we can on Developer Central is a great help. It can change how one builds software.
- Python provides a rich set of technology that can be tapped without the need to invent or reinvent the basics.
You can download the SPSSINC TURF module from the Downloads section of SPSS Developer Central, www.spss.com/devcentral. It requires at least SPSS Statistics Version 17 and the Python plug-in. And it’s free.
