Setting or propagating orientation (rowwise or columnwise) of a Matrix #492

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1. eriknw

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   on Aug 21, 2023
2. jim22k commented on Aug 23, 2023

   

   jim22k

   on Aug 23, 2023

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   I prefer .new(order="columnwise")
3. Transurgeon commented on Aug 23, 2023

   

   Transurgeon

   on Aug 23, 2023

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   Hey thanks for bringing this up Erik, I think it would be a great addition.
   I also would prefer .new(order="columwise") but I think all three options are equally suitable for this.

   How difficult would it be to propagate the orientation of the input matrices to the result matrix? I feel like this is how it should work (intuitively) anyways. (as in, if someone cared enough about orientation to set a matrix to col, it should probably propagate)

   Could you provide some pointers on how one could get started on adding this? I would love to give it a go.
4. eriknw commented on Aug 24, 2023

   

   eriknw

   on Aug 24, 2023

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   Great, thanks @Transurgeon!

   I think I would approach this as three separate tasks (and separate PRs):

   • Add order=None to .new() methods on matrix-like objects and also the constructors for which it makes sense
     • I would start by adding it to TransposedMatrix.new, which will also need it added to Matrix.__new__.
     • Other matrix-like classes with .new: MatrixInfixExpr, MatrixExpression, MatrixIndexExpr (and masks?)
   • Add matrix.orientation or matrix.order or whatever attribute that can also be assigned the order (or None)
     • Getting and setting it today should only work if backend == "suitesparse" by using A.ss.orientation, otherwise raise NotImplementedError (b/c we're waiting on the next version of the spec to be released)
   • Propagate the orientation of the input matrices
     • Again, I would begin small, such as Transposed.new
     • It may be a little trickier to do this elsewhere, but it may be best to determine the orientation to propagate when e.g. MatrixExpression is created.

   Working on any of these would be a great learning experience. Please let us know how we can make contributing easier or the code easier to navigate. I'm happy to assist and teach, so please ping us/me if you have any questions or blockers.

   Btw, our get_order utility function already handles all the variations of order=... that I shared.

   • {"C", "row", "rows", "rowwise"} -> "rowwise"
   • {"F", "col", "cols", "column", "columns", "colwise", "columnwise"} -> "columnwise"
5. Transurgeon commented on Aug 26, 2023

   

   Transurgeon

   on Aug 26, 2023

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   Hey @eriknw , thanks for your explanations.
   If I understand correctly, the first PR is just there to add an optional argument order to the new() constructor for various Matrix objects. It won't cover any implementation or actually setting the order.
   P.S could you give a primer on masks? In my understanding they are used to select a subset of the output (given a certain condition). How come we would need to add order to masks?
   P.S.S I noticed that I am traversing the repo quite inefficiently (when searching for classes for example), I know about grep but was wondering if you had any efficiency related tips & tricks.
6. eriknw commented on Aug 27, 2023

   

   eriknw

   on Aug 27, 2023

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   I would recommend implementing and testing anything that gets added, so maybe the first two items should be swapped or done simultaneously.

   Quick tour of classes! These all have .new

   In [1]: import graphblas as gb
   
   In [2]: A = gb.Matrix(bool)
   
   In [3]: type(A.T)
   Out[3]: graphblas.core.matrix.TransposedMatrix
   
   In [4]: type(A & A)  # pass to monoid or binary operator gives MatrixExpression (see example below)
   Out[4]: graphblas.core.infix.MatrixEwiseMultExpr
   
   In [5]: type(A | A)  # pass to monoid or binary operator gives MatrixExpression (see example below)
   Out[5]: graphblas.core.infix.MatrixEwiseAddExpr
   
   In [6]: type(A @ A)  # pass to semiring operator gives MatrixExpression (see example below)
   Out[6]: graphblas.core.infix.MatrixMatMulExpr
   
   In [7]: type(A[:, :])  # used for assign and extract
   Out[7]: graphblas.core.matrix.MatrixIndexExpr

   Masks also have .new, which results in boolean matrices. We don't have e.g. MatrixMask yet, but maybe we should?

   In [8]: type(A.S)
   Out[8]: graphblas.core.mask.StructuralMask
   
   In [9]: type(A.V)
   Out[9]: graphblas.core.mask.ValueMask
   
   In [10]: type(~A.S)
   Out[10]: graphblas.core.mask.ComplementedStructuralMask
   
   In [11]: type(~A.V)
   Out[11]: graphblas.core.mask.ComplementedValueMask

   MatrixExpression is the bread-and-butter of expressions--they are just waiting on an output to update or .new to be called:

   In [13]: type(A.ewise_mult(A, 'lor'))
   Out[13]: graphblas.core.matrix.MatrixExpression
   
   In [14]: type(gb.binary.lor(A & A))
   Out[14]: graphblas.core.matrix.MatrixExpression
   
   In [15]: type(A.ewise_add(A, 'lor'))
   Out[15]: graphblas.core.matrix.MatrixExpression
   
   In [16]: type(gb.binary.lor(A | A))
   Out[16]: graphblas.core.matrix.MatrixExpression
   
   In [17]: type(gb.semiring.any_pair(A @ A))
   Out[17]: graphblas.core.matrix.MatrixExpression

   We have a couple more objects that do not have .new methods. These are used to update a matrix in-place. Note that when I call A() below, one would typically pass a mask and/or and accumulator such as A("+") or A(B.S).

   In [18]: type(A())
   Out[18]: graphblas.core.expr.Updater
   
   In [19]: type(A[:, :]())
   Out[19]: graphblas.core.expr.Assigner
   
   In [20]: type(A()[:, :])
   Out[20]: graphblas.core.expr.Assigner

   Most implementation occurs in graphblas.core. To begin, you are likely to need to poke around in matrix, infix, and expr under graphblas.core.

   I'll share more about masks soon.