For modeling with SVM in R, I have used kernlab package (ksvm method)with Windows Xp operating system and 2 GB RAM. But having more data rows as 201497, I can'nt able to provide more memory for processing of data modeling (getting issue : can not allocate vector size greater than 2.7 GB).
Therefore, I have used Amazon micro and large instance for SCM modeling. But, it have same issue as local machine (can not allocate vector size greater than 2.7 GB).
Can any one suggest me the solution of this problem with BIG DATA modeling or Is there something wrong with this.
Without a reproducible example it is hard to say if the dataset is just too big, or if some parts of your script are suboptimal. A few general pointers:
Take a look at the High Performance Computing Taskview, this lists the main R packages relevant for working with BigData.
You use your entire dataset for training your model. You could try to take a subset (say 10%) and fit your model on that. Repeating this procedure a few times will yield insight into if the model fit is sensitive to which subset of the data you use.
Some analysis techniques, e.g. PCA analysis, can be done by processing the data iteratively, i.e. in chunks. This makes analyses possible on very big datasets possible (>> 100 gb). I'm not sure if this is possible with kernlab.
Check if the R version you are using is 64 bit.
This earlier question might be of interest.
Related
I am trying to perform Random Forest classification on genomic data with ~200k predictors and ~20 rows. Predictors have been already pruned for autocorrelation. I tried to use the 'ranger' R package, but it complains it cannot allocate 164Gb vector (I do have 32Gb RAM).
Is there any RF implementation that can manage the analysis given
the available RAM (I would like to avoid increasing the swap)?
Should I maybe use a different algorithm (for what I read, RF should
deal alright with p>>n)?
If it's genomic data, are there a lot of zeroes? If so, you might be able to convert into a sparse matrix, using the Matrix package. I believe ranger has been able to work with sparse matrices for a while, and this can help a lot with memory issues.
As far as I know, ranger is the best R random forest package available for datasets where p >> n.
I have written R program that generates a random vector of length 1 million. I need to simulate it 1 million times. Out of the 1 million simulations, I will be using 50K observed vectors (chosen in some random manner) as samples. So, 50K cross 1M is the sample size. Is there way to deal it in R?
There are few problems and some not so good solutions.
First R cannot store such huge matrix in my machine. It exceeds RAM memory. I looked into packages like bigmemory, ffbase etc that uses hard disk space. But such a huge data can have size in TB. I have 200GB hard disk available in my machine.
Even if storing is possible, there is a problem of running time. The code may take more than 100Hrs of running time!
Can anyone please suggest a way out! Thanks
This answer really stands as something in between a comment and an answer. The easy way out of your dilemma is to not work with such massive data sets. You can most likely take a reasonably-sized representative subset of that data (say requiring no more than a few hundred MB) and train your model this way.
If you have to use the model in production on actual data sets with millions of observations, then the problem would no longer be related to R.
If possible use sparse matrix techniques
If possible try leveraging storage memory and chunking the object into parts
If possible try to use Big Data tools such as H2O
Leverage multicore and HPC computing with pbdR, parallel, etc
Consider using a spot instance of a Big Data / HPC cloud VPS instance on AWS, Azure, DigitalOcean, etc. Most offer distributions with R preinstalled and with a high RAM multicore instance you can "spin up" (start) and down (stop) quickly and cheaply
Use sampling and statistical solutions when possible
Consider doing some of your simulations or pre-simulation steps in a relational database, or something like Spark + Scala; some have R integration nowadays, actually
There exist a very large own-collected dataset of size [2000000 12672] where the rows shows the number of instances and the columns, the number of features. This dataset occupies ~60 Gigabyte on the local hard disk. I want to train a linear SVM on this dataset. The problem is that I have only 8 Gigabyte of RAM! so I cannot load all data once. Is there any solution to train the SVM on this large dataset? Generating the dataset is on my own desire, and currently are is HDF5 format.
Thanks
Welcome to machine learning! One of the hard things about working in this space is the compute requirements. There are two main kinds of algorithms, on-line and off-line.
Online: supports feeding in examples one at a time, each one improving the model slightly
Offline: supports feeding in the entire dataset at once, achieving higher accuracy than an On-line model
Many typical algorithms have both on-line, and off-line implementations, but an SVM is not one of them. To the best of my knowledge, SVMs are traditionally an off-line only algorithm. The reason for this is a lot of the fine details around "shattering" the dataset. I won't go too far into the math here, but if you read into it it should become apparent.
It's also worth noting that the complexity of an SVM is somewhere between n^2 and n^3, meaning that even if you could load everything into memory it would take ages to actually train the model. It's very typical to test with a much smaller portion of your dataset before moving to the full dataset.
When moving to the full dataset you would have to run this on a much larger machine than your own, but AWS should have something large enough for you, though at your size of data I highly advise using something other than an SVM. At large data sizes, neural net approaches really shine, and can be trained in a more realistic amount of time.
As alluded to in the comments, there's also the concept of an out-of-core algorithm that can operate directly on objects stored on disk. The only group I know with a good offering of out-of-core algorithms is dato. It's a commercial product, but might be your best solution here.
A stochastic gradient descent approach to SVM could help, as it scales well and avoids the n^2 problem. An implementation available in R is RSofia, which was created by a team at Google and is discussed in Large Scale Learning to Rank. In the paper, they show that compared to a traditional SVM, the SGD approach significantly decreases the training time (this is due to 1, the pairwise learning method and 2, only a subset of the observations end up being used to train the model).
Note that RSofia is a little more bare bones than some of the other SVM packages available in R; for example, you need to do your own centering and scaling of features.
As to your memory problem, it'd be a little surprising if you needed the entire dataset - I would expect that you'd be fine reading in a sample of your data and then training your model on that. To confirm this, you could train multiple models on different samples and then estimate performance on the same holdout set - the performance should be similar across the different models.
You don't say why you want Linear SVM, but if you can consider another model that often gives superior results then check out the hpelm python package. It can read an HDF5 file directly. You can find it here https://pypi.python.org/pypi/hpelm It trains on segmented data, that can even be pre-loaded (called async) to speed up reading from slow hard disks.
I am using the library e1071. In particular, I'm using the svm function. My dataset has 270 fields and 800,000 rows. I've been running this program for 24+ hours now, and I have no idea if it's hung or still running properly. The command I issued was:
svmmodel <- svm(V260 ~ ., data=traindata);
I'm using windows, and using the task manager, the status of Rgui.exe is "Not Responding". Did R crash already? Are there any other tips / tricks to better gauge to see what's happening inside R or the SVM learning process?
If it helps, here are some additional things I noticed using resource monitor (in windows):
CPU usage is at 13% (stable)
Number of threads is at 3 (stable)
Memory usage is at 10,505.9 MB +/- 1 MB (fluctuates)
As I'm writing this thread, I also see "similar questions" and am clicking on them. It seems that SVM training is quadratic or cubic. But still, after 24+ hours, if it's reasonable to wait, I will wait, but if not, I will have to eliminate SVM as a viable predictive model.
As mentioned in the answer to this question, "SVM training can be arbitrary long" depending on the parameters selected.
If I remember correctly from my ML class, running time is roughly proportional to the square of the number training examples, so for 800k examples you probably do not want to wait.
Also, as an anecdote, I once ran e1071 in R for more than two days on a smaller data set than yours. It eventually completed, but the training took too long for my needs.
Keep in mind that most ML algorithms, including SVM, will usually not achieve the desired result out of the box. Therefore, when you are thinking about how fast you need it to run, keep in mind that you will have to pay the running time every time you tweak a tuning parameter.
Of course you can reduce this running time by sampling down to a smaller training set, with the understanding that you will be learning from less data.
By default the function "svm" from e1071 uses radial basis kernel which makes svm induction computationally expensive. You might want to consider using a linear kernel (argument kernel="linear") or use a specialized library like LiblineaR built for large datasets. But your dataset is really large and if linear kernel does not do the trick then as suggested by others you can use a subset of your data to generate the model.
I need to do run a simple two-stage least squares regression on large data matrices. This just requires some crossprod() and solve() commands, but the matrices have dimensions 100,000 by 5000 matrix. My understanding is that holding a matrix like this in memory would take up a bit less than 4GB of memory. Unfortunately, my 64-bit Win7 machine only has 8GB of RAM. When I try to manipulate the matrices in question, I get the usual 'can't allocate vector of size' message.
I have considered a number of options such as the ff and bigmemory packages. However, the base R functions for the matrix operations I need only support the usual matrix object type, not the bigmatrix type.
It seems like it may be possible to extend the code from biglm(), but I'm on a tight schedule for this project, so I wanted to check-in with you all to see if there existed a ready-made solution for problems like this. Apologies if this was addressed before (I couldn't find it) or if the question is too generic.
Yes, a ready-made solution exist in biglm, the package you already identified. Linear regression can work with an updating scheme; that basic property is implemented in the package.
Dump your data to disk, say to SQLite and study the package documentation and proceed in, say, 10 chunks on 10,000 each.