scripts/utils/project.dml

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# Utility script to project columns from input matrix.
#
# Parameters:
#    X       : (input)  filename of data matrix
#    P       : (input)  filename of 1-column projection matrix containing columnIDs
#    o       : (output) filename of output matrix with projected columns
#    exclude : (default FALSE) TRUE means P contains columnIds to be projected
#                              FALSE means P contains columnsIDS to be excluded
#    ofmt    : (default binary) format of output matrix
#
# Example:
#   hadoop jar SystemML.jar -f algorithms/utils/project.dml -nvargs X="/tmp/M.mtx" P="/tmp/P.mtx" o="/tmp/PX.mtx" 
#
# Assumptions:
# The order of colIDs in P is preserved. Order of columns in result is same as order of columns in P.
#      i.e. projecting columns 4 and 2 of X results in a matrix with columns 4 and 2.
# If P specifies the exclude list, then projected columns are order preserved.

exclude = ifdef ($exclude, FALSE);
ofmt = ifdef ($ofmt, "binary");

X = read ($X)
P = read ($P)

# create projection matrix using projection list and sequence matrix, and pad with 0s. The size of
# PP is nbrOfColsInX x nbrOfColsToKeep

if (exclude==FALSE)
{
   # create projection matrix using projection list and sequence matrix, and pad with 0s. The size
   # of PP is nbrOfColsInX x nbrOfColsToKeep
   PP = table(P, seq(1, nrow(P), 1), ncol(X), nrow(P)) 

 } else {
   # create new vector P with list of columns to keep using original vector P containing exclude
   # columns. These are all small vector operations.
   C = table(P, seq(1, nrow(P), 1))
   E = rowSums(C);
      
   # Row pad w/ 0s
   EE = matrix (0, rows=ncol(X), cols=1)
   EE[1:nrow(E),1] = E

   # Convert exclude column list to include column list, and create column indices
   EE = (EE == 0)
   EE = EE * seq(1, ncol(X), 1)
   P = removeEmpty(target=EE, margin="rows")

   PP = table(P, seq(1, nrow(P), 1), ncol(X), nrow(P))

}

# Perform projection using permutation matrix
PX = X %*% PP

# Write output
write (PX, $o, format=ofmt)