I have data-set of epinions website and want to implement the recommendation system
At the first step I should change the structure of data-set an it should be like 120000*780000 rows and columns
Its really big matrix and because of lack of memory it's not possible to do it
In my work every user should have M-dimensional vector , And M is total number of items that is 780000
I cant use sparse matrix because I need indexes and its too slow
What can I do now? How can I have this big data-set in matlab ?
You can try different things to reduce the amount of data:
Take a random subset of your observations: 120.000 observations is quite a lot, try randomly splitting it in several smaller subsets and check which is the performance of the system.
Use PCA to reduce the dimensionality of your data: 780.000 dimensions is A LOT. You will probably get a drastic reduction of the number of dimensions with PCA.
If your data is mostly zero or constant, you can actually use sparse matrices. Sparse matrices keep track of the indexes of your non-zero data, so don't worry about that.
Related
I have a sparse binary matrices whose properties I want to analyze over the binary field. The application is to analyze some sparse, binary error-correcting codes. The matrices themselves are too big to handle as full dense matrices, with sizes on the order of 10,000 x 30,000 and bigger, even though only a small percentange of entries are going to be filled. I want to be able to do binary linear algebra while exploiting the matrices' sparsity.
The two main things I will need to do are:
-finding a basis of intersection of its row space with the row space of another sparse matrix
-finding its rank
I've seen that there some packages to find subspace intersection (e.g. this MuPAD function) and to find the rank of a matrix over different fields (like gfrank), but they take prohibitively long time for the matrices I'm working with.
Is there anything like this available? Or any tricks that can be used to do this? If this is possible in another programming language that would also be helpful.
I have a nparray vector with 0s and 1s with 37k rows and 6k columns.
When I try to run Kmeans Clustering in Pyspark, it takes almost forever to load and I cannot get the output. Is there any way to reduce the processing time or any other tricks to solve this issue?
I think that you may have too many columns, you could have faced the dimensionality course. Wikipedia link
[...] The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse. This sparsity is problematic for any method that requires statistical significance. In order to obtain a statistically sound and reliable result, the amount of data needed to support the result often grows exponentially with the dimensionality. [...]
In order to solve this problem, did you consider reducing your columns, using only relevant ones? Check again this Wikipedia link
[...] Feature projection transforms the data in the high-dimensional space to a space of fewer dimensions. The data transformation may be linear, as in principal component analysis (PCA), but many nonlinear dimensionality reduction techniques also exist. [...]
I am try to do clustering from a large dataset dim:
rows: 1.4 million
cols:900
expected number of clusters: 10,000 (10k)
Problem is : size of my dataset 10Gb, and I have RAM of 16Gb. I am trying to implement in Matlab. It will be big help for me if someone could response to it.
P.S. So far i have tried with hierarchical clustering. in one paper, tehy have suggested to go for "fixed radius incremental pre-clustering". But I didnt understand the procedure.
Thanks in advance.
Use some algorithm that does not require a distance matrix. Instead, choose one that can be index accelerated.
Anuthing with a distance matrix will exceed your memory. But even when not requiring this (e.g., SLINK uses only O(n) memory) it still may take too long. Indexes could reduce the runtime to O(n log n) although on your data, indexes may have problems.
Index accelerated algorithms are for example: OPTICS, DBSCAN.
Just don't use the really bad Matlab scripts for these algorithms.
I have two very sparse distributed matrixes of dimension 1,000,000,000 x 1,000,000,000 and I want to compute the matrix multiplication efficiently.
I tried to create a BlockMatrix from a CoordinateMatrix but it's a lot of memory (where in reality the non zero data are around ~500'000'000) and the time of computation is enormous.
So there is another way to create a sparse matrix and compute a multiplication efficiently in a distributed way in Spark? Or i have to compute it manually?
You must obviously use a storage format for sparse matrices that makes use of their sparsity.
Now, without knowing anything about how you handle matrices and which libraries you use, there's no helping you but to ask you to look at the linear algebra libraries of your choice and look for sparse storage formats; the "good old" Fortran-based libraries that underly a lot of modern math libs support them, and so chances are that you really have to do but a little googling with yourlibraryname + "sparse matrix".
second thoughts:
Sparse matrixes really don't lend themselves to distribution very well; think about the operations you'd have to do to coordinate distribution compared to the actual multiplications/additions.
Also, ~5e8 non-zero elements in a 1e18 element matrix are definitely a lot of memory, and since you don't specify how much you consider a lot to be, it's very possible there's nothing wrong with it. Assuming you're using the default double precision, that's 5e8 * 8B = 4GB of pure numbers, not counting the coordinates needed for sparse storage. So, if you've got ~10GB of memory, I wouldn't be surprised at all.
As there is no build-in method in Spark to perform a matrix multiplication with sparse matrixes. I resolved by reduce at best the sparsity of the matrices before perform the matrice multiplication with BlockMatrix (that not support sparse matrix).
Last edit: Even with the sparsity optimization I had a lot of problems with large dataset. Finally, I decided to implement it myself. Now is running very fast. I hope that a matrix implementation with sparse matrix will be implemented in Spark as I think there are a lot of application that can make use of this.
I have a very large and sparse matrix of size 180GB(text , 30k * 3M) containing only the entries and no additional data. I have to do matrix multiplication , inversion and some similar linear algebra operations over it. I tried octave and simple single-threaded C code for the multiplication but my system RAM of 40GB gets used up very fast and then I can find the program starts thrashing. Is there any other options available to me. I am not familiar with MathLab or any other matrix operational library that can help me in doing so.
When I run a simple matrix multiplication of two matrices with 10 rows and 3 M cols, and its transpose, it gives the following error :
memory exhausted or requested size too large for range of Octave's index type
I am not sure whether the same would work on Matlab or not. For sparse matrix representation and matrix multiplication, is there another library or code.
if there are few enough nonzero entries, I suggest creating a sparse matrix S with appropriate dimensions and max nonzero entries; see matlab create sparse matrix. Then as #oleg komarov described, load the matrix in blocks and assign the nonzero entries from each block into the correct address in the sparse matrix S. I feel that if your matrix is sparse enough, then loading it is really the only difficulty you face. I had similar issues with large transfer operators.
Have you considered performing your processing in blocks? Transposition and multiplications work very well with block matrix processing (see https://en.wikipedia.org/wiki/Block_matrix) and that will get you around any limitations about the indices.
This wouldn't help you with matrix inversion though unless you can decompose your matrix in blocks when blocks that aren't on the diagonal are completely empty, which isn't stated in your assumptions.
Octave has a limit in both the memory resources of about 2GB and the maximum number of indices a matrix can hold of about 2^32 (for 32 bits Octave). MatLab doesn't have such a memory limit, since it will use all of your memory resources, swapping file included. Thus you could try with MatLab by setting a huge swapfile, you may then compute your operations (but it will anyway take quite along time...).
If you are interested by other approaches, you may take a look into out-of-core computing which aims to promote new methods to process huge datasets that cannot reside all in memory, but rather store it on disk and load efficiently the bits that are necessary.
For a practical approach, you may take a look into Blaze for Python (notice: still in development!).