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acordes
Rhodochrosite | Level 12

Hi @Rick_SAS 

As we're migrating to Viya 3.5 we can finally use the IML actionset.

But I cannot manage to get it run correctly as an actionset.

Here comes what works in Proc IML. 


/* Compute cosine similarity matrices in SAS/IML */
proc iml;
/* exclude any row with a missing value */
start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;
store module=(ExtractCompleteCases CosSimCols CosSimRows);
 
data casuser.glove_red;
set public.glove(obs=50000);
run;

proc iml;
load module=(CosSimCols CosSimRows);
use casuser.glove_red(datalimit=3000m obs=100);
read all var _num_ into x;
read all var _char_ into word;
close;

idx=loc(word="percent");

res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
res=distance(x[idx,],x);
min=(x-repeat(x[idx,],nrow(x),1)) [,+];
avg=(x[,:]-x[idx,][,:]);
end;

labl = word;
cosRY = CosSimRows(X);
call heatmapcont(cosRY) xvalues=labl yvalues=labl title="Cosine Similarity between Vehicles";


call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=20;

most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

print most alike avy;

And here's what gives the following error.

ERROR: (execution) Unable to allocate sufficient memory.

Additionally I cannot call the modules if I declare them outside with Proc IML like before. 

/* Compute cosine similarity matrices in SAS/IML */
proc iml;
/* exclude any row with a missing value */
start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;
store module=(ExtractCompleteCases CosSimCols CosSimRows);
 

proc cas;
   loadactionset 'iml';    /* load the iml action set (only once per session) */
run;

data casuser.glove_red;
set public.glove(obs=50000);
run;

options casdatalimit=3000m;
proc cas;
source imly;

KeepStmt = 'KEEP=_numeric_ obs=50000';
KeepStmt2 = 'KEEP=_character_ obs=50000';
	X = MatrixCreateFromCas('casuser', 'glove_red', KeepStmt );
	word=MatrixCreateFromCas('casuser', 'glove_red', KeepStmt2);

start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;

idx=loc(word="year");

res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
res=distance(x[idx,],x);
min=(x-repeat(x[idx,],nrow(x),1)) [,+];
avg=(x[,:]-x[idx,][,:]);
end;

labl = word;
cosRY = CosSimRows(X);
call heatmapcont(cosRY) xvalues=labl yvalues=labl title="Cosine Similarity between Vehicles";


call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=20;

most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

print most alike avy;


endsource;
iml / code=imly;
run;

to create the data:

data PUBLIC.GLOVE_RED;
  infile datalines dsd truncover;
  input VAR1:$841. VAR2:32. VAR3:32. VAR4:32. VAR5:32. VAR6:32. VAR7:32. VAR8:32. VAR9:32. VAR10:32. VAR11:32. VAR12:32. VAR13:32. VAR14:32. VAR15:32. VAR16:32. VAR17:32. VAR18:32. VAR19:32. VAR20:32. VAR21:32. VAR22:32. VAR23:32. VAR24:32. VAR25:32. VAR26:32. VAR27:32. VAR28:32. VAR29:32. VAR30:32. VAR31:32. VAR32:32. VAR33:32. VAR34:32. VAR35:32. VAR36:32. VAR37:32. VAR38:32. VAR39:32. VAR40:32. VAR41:32. VAR42:32. VAR43:32. VAR44:32. VAR45:32. VAR46:32. VAR47:32. VAR48:32. VAR49:32. VAR50:32. VAR51:32. VAR52:32. VAR53:32. VAR54:32. VAR55:32. VAR56:32. VAR57:32. VAR58:32. VAR59:32. VAR60:32. VAR61:32. VAR62:32. VAR63:32. VAR64:32. VAR65:32. VAR66:32. VAR67:32. VAR68:32. VAR69:32. VAR70:32. VAR71:32. VAR72:32. VAR73:32. VAR74:32. VAR75:32. VAR76:32. VAR77:32. VAR78:32. VAR79:32. VAR80:32. VAR81:32. VAR82:32. VAR83:32. VAR84:32. VAR85:32. VAR86:32. VAR87:32. VAR88:32. VAR89:32. VAR90:32. VAR91:32. VAR92:32. VAR93:32. VAR94:32. VAR95:32. VAR96:32. VAR97:32. VAR98:32. VAR99:32. VAR100:32. VAR101:32.;
datalines4;
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season,-0.11922,-0.22701,0.72857,-1.2353,-0.61431,-0.21162,1.1514,0.54402,-0.90197,-0.41669,0.44351,-0.9858,0.5835,-0.56379,-0.22593,0.0039534,-0.0030483,0.42783,0.15913,0.86645,-0.39026,0.28904,-0.39147,0.78224,0.92115,-0.24897,0.037316,-0.62325,0.055504,-0.61437,-0.93197,0.53306,0.11501,-0.49384,-0.63212,0.29129,-0.78414,0.4628,-1.1368,0.2162,0.57121,-0.29181,0.48042,-0.012972,0.61193,0.43858,0.82714,-1.4074,0.72607,-1.1348,-0.33508,-0.55288,-0.30892,0.90494,0.15793,-2.9313,-0.34886,0.30364,0.64373,1.2306,-0.95707,1.0438,-0.51568,-0.24783,0.76059,-0.062309,-0.14341,-0.55713,-0.37103,0.072251,-0.01049,-0.15103,-0.49545,0.21362,-0.14376,0.49824,-0.90065,0.23313,-0.56199,-0.10691,0.10622,0.40275,-1.2343,0.11715,-0.83084,-0.62424,-0.40395,-0.19902,0.2356,0.587,-0.96245,0.18076,-0.40111,0.44392,-0.73176,-0.31401,-0.34014,1.1755,0.59137,-0.15081
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public,0.27528,0.22559,-0.29646,-0.19067,0.18936,0.61855,-0.89264,0.28183,0.40389,-0.065414,-0.50938,-0.08886,-0.20152,-0.13439,-0.021077,-0.24169,0.54612,0.65442,-0.48053,0.39893,-0.28369,0.13064,0.28003,-0.58927,-0.35813,-0.58996,0.20764,0.030577,-0.24032,0.17751,0.38048,0.5518,-0.043817,0.59522,-0.83406,0.62643,-0.3808,0.0078803,-0.3535,0.4471,-0.30419,-0.55965,-0.28996,0.19426,0.20166,-0.27511,0.49588,-0.42664,0.10917,-0.87574,0.23565,-0.68206,0.22442,0.74806,-0.19348,-2.6988,0.71671,-0.45328,2.2088,0.02737,-0.099598,0.16888,0.14004,0.13678,0.78804,0.045185,0.42819,-0.036869,1.0838,0.062532,-0.040702,-0.27305,0.29982,-0.58137,-0.20059,0.29416,0.35478,0.28035,-1.3558,-0.53859,1.0382,0.010252,0.44126,0.46009,-1.5,-0.34445,-0.65755,0.6388,0.18468,-0.70406,0.25236,-0.50973,-0.17074,0.42255,-0.44975,0.63835,0.28452,-0.4795,0.59833,0.2318
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;;;;
run;

 

1 ACCEPTED SOLUTION

Accepted Solutions
Rick_SAS
SAS Super FREQ

Maybe you already solved the problem, but I looked at your program today and have a few comments:

  1. If you want to STORE modules that you can later LOAD in the iml action, you need to store them in the action, not in PROC IML (which runs on the SAS client).
  2. You probably don't need to specify the caslib in the MatrixCreateFromCAS function. Instead of hard-coding 
    X = MatrixCreateFromCas('casuser', 'glove_red', KeepStmt );​
    you can write 
    X = MatrixCreateFromCas(' ', 'glove_red', KeepStmt );​
    which means "use the current active caslib.  (Also, make sure you understand the difference between a CAS-engine libref, which is a SAS-side libref, and a caslib, which exists on the CAS server.)
  3. As mentioned previously, the graphical functions (and some other functions and statements) 
    are supported ONLY in PROC IML, which runs on the SAS client. They are not available in the action, which runs on the CAS server.
  4. The following program runs correctly on Viya 4, but I assume it also works on Viya 3.5.
cas;                       /* start/connect to CAS session */
proc casutil;
   load data=GLOVE_RED casout="glove_red" replace;
   list tables;
quit;
/* equivalent to 
libname myLib cas; 
data myLib.glove_red;   *on CAS server ;
set GLOVE_RED;          *on SAS client ;
run;
*/

options casdatalimit=3000m;
proc cas;
   loadactionset 'iml';    /* load the iml action set (only once per session) */
run;

/* define cosine similarity modules in iml action */
source DefineModules;
/* exclude any row with a missing value */
start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;
store module=(ExtractCompleteCases CosSimCols CosSimRows);
endsource;
iml / code=DefineModules;
run;

source imly;
load module=(ExtractCompleteCases CosSimCols CosSimRows);
KeepStmt = 'KEEP=_numeric_ obs=50000';
KeepStmt2 = 'KEEP=_character_ obs=50000';
X = MatrixCreateFromCas(' ', 'glove_red', KeepStmt );
word=MatrixCreateFromCas(' ', 'glove_red', KeepStmt2);

idx=loc(word="year");
res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
	res=distance(x[idx,],x);
	min=(x-repeat(x[idx,],nrow(x),1)) [,+];
	avg=(x[,:]-x[idx,][,:]);
end;

labl = word;
cosRY = CosSimRows(X);
call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=20;
most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

print most alike avy;

/* if you want to visualize the data by using a heatmap, write data 
   and visualize on the client. 
*/
call MatrixWriteToCAS(word, "Word");
call MatrixWriteToCAS(cosRy, "", "Similar", "x1":cats("x",char(ncol(cosRy))));
    
endsource;
iml / code=imly;
run;

proc casutil;
   list tables;  /* verify that Word and Similar were created */
quit;

View solution in original post

11 REPLIES 11
Rick_SAS
SAS Super FREQ

PROC IML executes on the client. The client can read and write SAS data sets, create graphics, and LOAD modules that were stored on the client by a previous run of PROC IML.

 

The iml action runs on the CAS server. It can read and write CAS tables, it cannot display graphics, and it can LOAD modules that were stored on the server by a previous run of the action.

 

 

Rick_SAS
SAS Super FREQ

And, with respect, I suggest you start with a simpler example that will be easier to debug and discuss. A fundamental paradigm of computer programming is to start with a simple program that works. Then modify and expand that program until it does what you want.

acordes
Rhodochrosite | Level 12

It works as Proc Iml so my expectation goes that it should work in the actionset setting... 

And I had tried the hello world example before 😉

So I'll try again after eliminating the graph related lines of the code.  

Rick_SAS
SAS Super FREQ

Maybe you already solved the problem, but I looked at your program today and have a few comments:

  1. If you want to STORE modules that you can later LOAD in the iml action, you need to store them in the action, not in PROC IML (which runs on the SAS client).
  2. You probably don't need to specify the caslib in the MatrixCreateFromCAS function. Instead of hard-coding 
    X = MatrixCreateFromCas('casuser', 'glove_red', KeepStmt );​
    you can write 
    X = MatrixCreateFromCas(' ', 'glove_red', KeepStmt );​
    which means "use the current active caslib.  (Also, make sure you understand the difference between a CAS-engine libref, which is a SAS-side libref, and a caslib, which exists on the CAS server.)
  3. As mentioned previously, the graphical functions (and some other functions and statements) 
    are supported ONLY in PROC IML, which runs on the SAS client. They are not available in the action, which runs on the CAS server.
  4. The following program runs correctly on Viya 4, but I assume it also works on Viya 3.5.
cas;                       /* start/connect to CAS session */
proc casutil;
   load data=GLOVE_RED casout="glove_red" replace;
   list tables;
quit;
/* equivalent to 
libname myLib cas; 
data myLib.glove_red;   *on CAS server ;
set GLOVE_RED;          *on SAS client ;
run;
*/

options casdatalimit=3000m;
proc cas;
   loadactionset 'iml';    /* load the iml action set (only once per session) */
run;

/* define cosine similarity modules in iml action */
source DefineModules;
/* exclude any row with a missing value */
start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;
store module=(ExtractCompleteCases CosSimCols CosSimRows);
endsource;
iml / code=DefineModules;
run;

source imly;
load module=(ExtractCompleteCases CosSimCols CosSimRows);
KeepStmt = 'KEEP=_numeric_ obs=50000';
KeepStmt2 = 'KEEP=_character_ obs=50000';
X = MatrixCreateFromCas(' ', 'glove_red', KeepStmt );
word=MatrixCreateFromCas(' ', 'glove_red', KeepStmt2);

idx=loc(word="year");
res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
	res=distance(x[idx,],x);
	min=(x-repeat(x[idx,],nrow(x),1)) [,+];
	avg=(x[,:]-x[idx,][,:]);
end;

labl = word;
cosRY = CosSimRows(X);
call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=20;
most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

print most alike avy;

/* if you want to visualize the data by using a heatmap, write data 
   and visualize on the client. 
*/
call MatrixWriteToCAS(word, "Word");
call MatrixWriteToCAS(cosRy, "", "Similar", "x1":cats("x",char(ncol(cosRy))));
    
endsource;
iml / code=imly;
run;

proc casutil;
   list tables;  /* verify that Word and Similar were created */
quit;
acordes
Rhodochrosite | Level 12

If I run your code with the reduced dummy data set of 200 obs then it works fine. 

But if I apply it to the 400k rows table it gives an error. (I checked for the presence of the word to compare against). 

ERROR: (execution) Unable to allocate sufficient memory.

I'll open a ticket for the remote admin of our sas viya installation. 

 

Rick_SAS
SAS Super FREQ

Another comment: You should add a _ROWID_ variable to your PUBLIC.GLOVE_RED data by inserting the statement
_ROWID_ = _N_;

in the DATA step.

 

The reason is explained in the section of the documentation that discusses reading and writing CAS tables. It is important to remember that reading a CAS table is a multithreaded operation. That means that the observations in the CAS table might be read in an order that is different from the SAS-side data table that you originally created. (In practice, the "different order" issue only happens for large data sets because small data sets are typically read on one thread. But it is best to write robust code that can handle the general case.) In your example, it is important that the order of the rows match the order of the columns. If you add a _ROWID_ variable to the data, then the MatrixCreateFromCAS calls will ensure that the rows of the matrices are the same as the rows of the SAS-side data set.

 

acordes
Rhodochrosite | Level 12

Thank you Rick.

In order not to fall into the thread-trap I have created the following _ROWID_.

 

_rowid_=put(_threadid_,8.) || '_' || Put(_n_,8.);

It's character but it should work as well, shouldn't it?

And it's not clear to me if the MatrixCreateFromCAS automatically recognizes the id variable. 

 

Rick_SAS
SAS Super FREQ

1. In your example, you are creating the data in a single thread. I suggest you create a NUMERICAL _ROWID_ variable with values 1:N.  For more information about the _ROWID_ variable, see

https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/casactiml/casactiml_iml_details05.htm

and

https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/casactiml/casactiml_langref_sect054.htm

 

2. Regarding the size of the matrix, if you read in 400k rows, won't that require a cosine similarity matrix that is 400k x 400k? That is a huge matrix. Please read the article, "How much RAM do I need to store that matrix?" Each copy of a 400k x 400k matrix requires 1192 GB of RAM. That's more than a terabyte for each copy.

 

What are you going to do with a matrix that size, even if you can load it? You can't produce a heatmap for it. At 38 pixels per centimeter, the smallest heatmap would be 10.5 meters wide!  You can't perform any reasonable matrix computations on it. 

 

Your program uses the DISTANCE function, which is quadratic in time as a function of the number of rows.  On my desktop machine, it takes 11 seconds to compute the distance matrix for a 15000 x 100 matrix. If you extrapolate out to 400k rows, the predicted time JUST FOR the DISTANCE computation is about 2.5 hours. I came up with this number by running the following program. Your results might differ.

 

proc iml;
call randseed(123);
p = 100;
N = {1000, 5000, 10000, 15000};
time = 0*N;
do i = 1 to nrow(N);
   X = j(N[i], p);
   call randgen(X, "Uniform");   /* simulate a p x N matrix */
   t0 = time();        /* time the DISTANCE function */
   D = distance(X);
   time[i] = time() - t0;
end;

print N time;
title "How Long to Compute NxN Distance Matrix?";
call series(N, time) grid={x y} option="markers";

create Test var {N 'Time'};
append;
close;
QUIT;

data Score;
N = 400000; Time = .;
run;

data All;
set Test Score;
run;

proc glm data=All plots=none noprint;
model Time = N N*N;
output out=GLMOut P=Pred;
run;

proc print data=GLMOut label; 
label Pred="Predicted Time (s)";
run;

 

 

 

Because CAS keeps 

acordes
Rhodochrosite | Level 12

In proc IML it runs but it takes time using the complete data set of 400k rows x 100 columns. 

I only calculate the distance for 1 row (the word to search for) against ALL other rows. 

I was playing around with te word vector "glove" and came up with this family entertainment :).

Write down the 10 most similar / related words to the term "happiness". Who gets more words right wins the contest.  

The heatmap accidentally remained in the code. It does not contribute. It's the culprit for the huge matrix. I apologize for the confusion. Nevertheless it helped me to understand how to load modules in the IML action and to think about carefully about the resulting matrix and performance issues. 

 

top.png

 

proc iml;
load module=(CosSimCols CosSimRows);
use casuser.glove(datalimit=3000m );
read all var _num_ into x;
read all var _char_ into word;
close;

idx=loc(word="happiness");

res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
res=distance(x[idx,],x);
min=(x-repeat(x[idx,],nrow(x),1)) [,+];
avg=(x[,:]-x[idx,][,:]);
end;

labl = word;
cosRY = CosSimRows(X);
call heatmapcont(cosRY) xvalues=labl yvalues=labl title="Cosine Similarity between Vehicles";


call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=10;

most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

print most alike avy;

 

 

Rick_SAS
SAS Super FREQ

Excellent. Thanks for clarifying. If you are computing only the 400k distances to one observation, you should not encounter the "Out of Memory" error.

acordes
Rhodochrosite | Level 12

For the sake of completeness, now it runs smoothly as iml action:

 

options casdatalimit=all;
proc cas;
   loadactionset 'iml';    /* load the iml action set (only once per session) */
run;

/* define cosine similarity modules in iml action */
source DefineModules;
/* exclude any row with a missing value */
start ExtractCompleteCases(X);
   if all(X ^= .) then return X;
   idx = loc(countmiss(X, "row")=0);
   if ncol(idx)>0 then return( X[idx, ] );
   else                return( {} ); 
finish;
 
/* compute the cosine similarity of columns (variables) */
start CosSimCols(X, checkForMissing=1);
   if checkForMissing then do;    /* by default, check for missing and exclude */
      Z = ExtractCompleteCases(X);
      Y = Z / sqrt(Z[##,]);       /* stdize each column */
   end;
      else Y = X / sqrt(X[##,]);  /* skip the check if you know all values are valid */
   cosY = Y` * Y;                 /* pairwise inner products */
   /* because of finite precision, elements could be 1+eps or -1-eps */
   idx = loc(cosY> 1); if ncol(idx)>0 then cosY[idx]= 1; 
   idx = loc(cosY<-1); if ncol(idx)>0 then cosY[idx]=-1; 
   return cosY;
finish;
 
/* compute the cosine similarity of rows (observations) */
start CosSimRows(X);
   Z = ExtractCompleteCases(X);  /* check for missing and exclude */
   return T(CosSimCols(Z`, 0));  /* transpose and call CosSimCols */
finish;
store module=(ExtractCompleteCases CosSimCols CosSimRows);
endsource;
iml / code=DefineModules;
run;

source imly;
/* load module=(ExtractCompleteCases CosSimCols CosSimRows); not needed this time */
KeepStmt = 'KEEP=_numeric_ obs=500000';
KeepStmt2 = 'KEEP=_character_ obs=500000';
X = MatrixCreateFromCas('public', 'glove_red', KeepStmt );
word=MatrixCreateFromCas('public', 'glove_red', KeepStmt2);

idx=loc(word="happiness");
res=j(nrow(x),1,.);
min=res;
avg=res;

if ncol(idx) then do;
	res=distance(x[idx,],x);
	min=(x-repeat(x[idx,],nrow(x),1)) [,+];
	avg=(x[,:]-x[idx,][,:]);
end;


call sortndx(id, res`, 1);
call sortndx(id_min, abs(min),1);
call sortndx(id_avg, avg,1);

last=50;
most=word[id[1:last],];
alike=word[id_min[1:last],];
avy=word[id_avg[1:last],];

/* if you want to visualize the data by using a heatmap, write data 
   and visualize on the client. 
*/
call MatrixWriteToCAS(word, "Word");
call MatrixWriteToCAS(most, "", "Similar", "x1":cats("x",char(ncol(most))));
    
endsource;
iml / code=imly;
run;

proc casutil;
   list tables;  /* verify that Word and Similar were created */
quit;

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