[SYSTEMDS-2596] Update MICE implementation to use matrix intermediates
Closes #972.
diff --git a/scripts/builtin/mice.dml b/scripts/builtin/mice.dml
index a06a357..f40ac81 100644
--- a/scripts/builtin/mice.dml
+++ b/scripts/builtin/mice.dml
@@ -19,16 +19,15 @@
#
#-------------------------------------------------------------
-# Builtin function Implements Multiple Imputation using Chained Equations (MICE) for nominal data
+# Built-in function Implements Multiple Imputation using Chained Equations (MICE)
#
# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
-# F String --- Data Frame
-# cMask Double --- A 0/1 row vector for identifying numeric (0) adn categorical features (1)
+# X String --- Data Matrix (Recoded Matrix for categorical features)
+# cMask Double --- A 0/1 row vector for identifying numeric (0) and categorical features (1)
# iter Integer 3 Number of iteration for multiple imputations
-# complete Integer 3 A complete dataset generated though a specific iteration
# ---------------------------------------------------------------------------------------------
@@ -36,249 +35,145 @@
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
-# dataset Double --- imputed dataset
-# singleSet Double --- A complete dataset generated though a specific iteration
+# output Double --- imputed dataset
-# Assumption missing value are represented with empty string i.e ",," in csv file
-# variables with suffix n are storing continuous/numeric data and variables with suffix c are storing categorical data
-s_mice= function(Frame[String] F, Matrix[Double] cMask, Integer iter = 3, Integer complete = 3, Boolean verbose = FALSE)
-return(Frame[String] dataset, Frame[String] singleSet)
+
+# Assumption missing value are represented with empty string i.e ",," in CSV file
+# variables with suffix n are storing continuos/numeric data and variables with suffix c are storing categorical data
+m_mice= function(Matrix[Double] X, Matrix[Double] cMask, Integer iter = 3, Boolean verbose = FALSE)
+ return(Matrix[Double] output)
{
+ lastIndex = ncol(X);
+ sumMax = sum(cMask);
+
+ # if all features are numeric add a categorical features
+ # if all features are categorical add a numeric features
+ if(sumMax == 0 | sumMax == ncol(cMask)) {
+ X = cbind(X, matrix(1, nrow(X), 1))
+ cMask = cbind(cMask, matrix(ifelse(sumMax==0, 1, 0), 1, 1))
+ }
- if(ncol(F) == 1)
- stop("invalid argument: can not apply mice on single column")
+ # separate categorical and continuous features
+ nX = removeEmpty(target=X, margin="cols", select=(cMask==0))
+ cX = removeEmpty(target=X, margin="cols", select= cMask)
+
+ # store the mask of numeric missing values
+ Mask_n = is.na(nX);
+ nX = replace(target=nX, pattern=NaN, replacement=0);
+ # initial mean imputation
+ X_n = nX+(Mask_n*colMeans(nX))
- if(complete > iter)
- complete = iter
-
-
- # adding a temporary feature (in-case all attributes are of same type)
- F = cbind(F, as.frame(matrix(1,nrow(F), 1)))
- cMask = cbind(cMask, matrix(1,1,1))
-
- n = nrow(F)
- row = n*complete;
- col = ncol(F)
- Result = matrix(0, rows=1, cols = col)
- Mask_Result = matrix(0, rows=1, cols=col)
- scat = seq(1, ncol(cMask))
- cat = removeEmpty(target=scat, margin="rows", select=t(cMask))
-
- if(nrow(cat) == ncol(F))
- cMask[1,ncol(cMask)] = 0
+ # store the mask of categorical missing values
+ Mask_c = is.na(cX);
+ cX = replace(target=cX, pattern=NaN, replacement=0);
+ colMode = colMode(cX)
+ # initial mode imputation
+ X_c = cX+(Mask_c*colMode)
- s=""
- for(i in 1: nrow(cat), check =0)
- s = s+as.integer(as.scalar(cat[i, 1]))+",";
+ # reconstruct original matrix using sparse matrices p and q
+ p = table(seq(1, ncol(nX)), removeEmpty(target=seq(1, ncol(cMask)), margin="rows", select=t(cMask==0)), ncol(nX), ncol(X))
+ q = table(seq(1, ncol(cX)), removeEmpty(target=seq(1, ncol(cMask)), margin="rows", select=t(cMask)), ncol(cX), ncol(X))
+ X1 = (X_n %*% p) + (X_c %*% q)
+ Mask1 = is.na(X)
+ X = replace(target=X, pattern=NaN, replacement=0);
+ d = ncol(X1)
+ n = nrow(X1)
- # encoding categorical columns using recode transformation
- jspecR = "{ids:true, recode:["+s+"]}";
- [X, M] = transformencode(target=F, spec=jspecR);
-
- XO = replace(target=X, pattern=NaN, replacement=0);
+ # compute index of categorical features
+ encodeIndex = removeEmpty(target=t(seq(1, ncol(X1))), margin="cols", select=cMask)
- # remove categorical features and impute continuous features with mean
- eX_n = removeEmpty(target=X, margin="cols", select=(cMask==0))
- col_n = ncol(eX_n);
- # storing the mask/address of missing values
- Mask_n = is.na(eX_n);
- inverseMask_n = 1 - Mask_n;
- # replacing the empty cells in encoded data with 0
- eX_n = replace(target=eX_n, pattern=NaN, replacement=0);
- # filling the missing data with their means
- X2_n = eX_n+(Mask_n*colMeans(eX_n))
- # matrices for computing actul data
- p_n = table(seq(1, ncol(eX_n)), removeEmpty(target=scat, margin="rows", select=t(cMask==0)))
- if(ncol(p_n) < ncol(cMask))
- p_n = cbind(p_n, matrix(0, nrow(p_n), ncol(cMask)-ncol(p_n)))
- q = XO * cMask
-
- # Taking out the categorical features for initial imputation by mode
- eX_c = removeEmpty(target = q, margin = "cols")
- col_c = ncol(eX_c);
- eX_c2 = removeEmpty(target = eX_c, margin = "rows", select = (rowSums(eX_c != 0)==col_c))
- colMod = matrix(0, 1, ncol(eX_c))
- # compute columnwise mode
- parfor(i in 1: col_c) {
- f = eX_c2[, i] # adding one in data for dealing with zero category
- cat_counts = table(f, 1, n, 1); # counts for each category
- mode = as.scalar(rowIndexMax(t(cat_counts)));
- colMod[1,i] = mode
- }
-
- # find the mask of missing values
- tmpMask_c = (eX_c==0) * colMod # fill missing values with mode
-
- # Generate a matrix of actual length
- p_c = table(seq(1, ncol(tmpMask_c)), removeEmpty(target=scat, margin ="rows", select=t(cMask)), ncol(tmpMask_c), ncol(cMask))
+ s = "";
+ for(i in 1:ncol(encodeIndex))
+ s = s + as.integer(as.scalar(encodeIndex[1, i])) + ",";
- Mask_c = tmpMask_c %*% p_c
- inverseMask_c = Mask_c == 0
- r = X2_n %*% p_n
- qr = q + r
- X2_c = qr + Mask_c
- Mask_c = Mask_c != 0
-
-
- # one-hot encoding of categorical features
+ # specifications for one-hot encoding of categorical features
jspecDC = "{ids:true, dummycode:["+s+"]}";
- [dX, dM] = transformencode(target=as.frame(X2_c), spec=jspecDC);
- # recoding of metadata of OHE features to get the number of distinct elements
- [metaTransform, metaTransformMeta] = transformencode(target=dM, spec=jspecR);
- metaTransform = replace(target=metaTransform, pattern=NaN, replacement=0)
- # counting distinct elements in each categorical feature
- dcDistincts = colMaxs(metaTransform)
- dist = dcDistincts + (1-cMask)
-
- # creating a mask matrix of OHE features
- dXMask = matrix(0, 1, ncol(dX))
- index = 1
- for(k in 1:col) {
- nDistk = as.scalar(dcDistincts[1,k]);
- if(nDistk != 0) {
- dXMask[1,index:(index+nDistk-1)] = matrix(1,1,nDistk)
- index += nDistk;
- }
- else
- index += 1
- }
-
- #multiple imputations
- for(k in 1:iter)
+ for(k in 1:iter) # start iterative imputation
{
- Mask_Filled_n = Mask_n;
- Mask_Filled_c = Mask_c
- in_n = 1; in_c = 1; i=1; j=1; # variables for index selection
- while(i <= ncol(dX))
+ Mask_Filled = Mask1
+ inverseMask = Mask1 == 0
+ # OHE of categorical features
+ [dX, dM] = transformencode(target=as.frame(X1), spec=jspecDC);
+ dist = (colMaxs(X1) * cMask) + (cMask == 0) # number of distinct items in categorical features
+ i=1; j=1; in_c=1;
+
+ while(i < ncol(dX))
{
- if(as.scalar(dXMask[1,i]) == 0)
+ j = (i + as.scalar(dist[1,in_c])) - 1 # index value for iterating OHE columns
+ if(sum(Mask1[, in_c]) > 0 & as.scalar(cMask[, in_c]) == 0) # impute numeric features
{
# construct column selector
- sel = cbind(matrix(1,1,i-1), as.matrix(0), matrix(1,1,ncol(dX)-i));
+ selX = matrix(1,1,ncol(dX))
+ selX[1,i:j] = matrix(0,1,as.scalar(dist[1,in_c]))
+ selY = cbind(matrix(1,1,in_c-1), as.matrix(0), matrix(1,1,d-in_c));
# prepare train data set X and Y
- slice1 = removeEmpty(target = dX, margin = "rows", select = inverseMask_n[,in_n])
- train_X = removeEmpty(target = slice1, margin = "cols", select = sel);
- train_Y = slice1[,i]
+ slice1 = removeEmpty(target = dX, margin = "rows", select = inverseMask[,in_c])
+ slice1a = removeEmpty(target = X1, margin = "rows", select = inverseMask[,in_c])
+ train_X = removeEmpty(target = slice1, margin = "cols", select = selX);
+ train_Y = slice1a[,in_c]
+
# prepare score data set X and Y for imputing Y
- slice2 = removeEmpty(target = dX, margin = "rows", select = Mask_n[,in_n])
- test_X = removeEmpty(target = slice2, margin = "cols", select = sel);
- test_Y = slice2[,i]
- # learning a regression line
+ slice2 = removeEmpty(target = dX, margin = "rows", select = Mask1[,in_c])
+ slice2a = removeEmpty(target = X1, margin = "rows", select = Mask1[,in_c])
+ test_X = removeEmpty(target = slice2, margin = "cols", select = selX);
+ test_Y = slice2a[,in_c]
+
+ # learn a regression line
beta = lm(X=train_X, y=train_Y, verbose=FALSE, icpt=1, reg = 1e-7, tol = 1e-7);
# predicting missing values
pred = lmpredict(X=test_X, w=beta, icpt=1)
# imputing missing column values (assumes Mask_Filled being 0/1-matrix)
- R = removeEmpty(target=Mask_Filled_n[,in_n] * seq(1,n), margin="rows");
- #TODO modify removeEmpty to return zero row and n columns
+ R = removeEmpty(target=Mask_Filled[, in_c] * seq(1,nrow(X1)), margin="rows");
+ # TODO modify removeEmpty to return zero row and n columns
if(!(nrow(R) == 1 & as.scalar(R[1,1] == 0)))
- Mask_Filled_n[,in_n] = table(R, 1, pred, n, 1);
- in_n = in_n + 1;
+ Mask_Filled[,in_c] = table(R, 1, pred, nrow(X1), 1);
}
-
- if( (as.scalar(dXMask[1,i]) == 1) & (sum(Mask_c[, in_c]) != 0) )
+ else if (sum(Mask1[, in_c]) > 0 & as.scalar(cMask[, in_c]) != 0) # impute categorical features
{
- j = (i + as.scalar(dist[1,in_c])) - 1
-
# construct column selector
selX = matrix(1,1,ncol(dX))
selX[1,i:j] = matrix(0,1,as.scalar(dist[1,in_c]))
- selY = cbind(matrix(1,1,in_c-1), as.matrix(0), matrix(1,1,col-in_c));
+ selY = cbind(matrix(1,1,in_c-1), as.matrix(0), matrix(1,1,d-in_c));
# prepare train data set X and Y
- slice1 = removeEmpty(target = dX, margin = "rows", select = inverseMask_c[,in_c])
- slice1a = removeEmpty(target = X2_c, margin = "rows", select = inverseMask_c[,in_c])
+ slice1 = removeEmpty(target = dX, margin = "rows", select = inverseMask[,in_c])
+ slice1a = removeEmpty(target = X1, margin = "rows", select = inverseMask[,in_c])
train_X = removeEmpty(target = slice1, margin = "cols", select = selX);
train_Y = slice1a[,in_c]
-
+
# prepare score data set X and Y for imputing Y
- slice2 = removeEmpty(target = dX, margin = "rows", select = Mask_c[,in_c])
- slice2a = removeEmpty(target = X2_c, margin = "rows", select = Mask_c[,in_c])
+ slice2 = removeEmpty(target = dX, margin = "rows", select = Mask1[,in_c])
+ slice2a = removeEmpty(target = X1, margin = "rows", select = Mask1[,in_c])
test_X = removeEmpty(target = slice2, margin = "cols", select = selX);
test_Y = slice2a[,in_c]
-
+
# train classification model
beta = multiLogReg(X=train_X, Y=train_Y, icpt = 1, tol = 0.00000001, reg = 0.001, maxi = 100, maxii=0, verbose=FALSE)
# predicting missing values
[prob,pred,acc] = multiLogRegPredict(X=test_X, B=beta, Y = test_Y)
# imputing missing column values (assumes Mask_Filled being 0/1-matrix)
- R = removeEmpty(target=Mask_Filled_c[,in_c] * seq(1,n), margin="rows");
+ R = removeEmpty(target=Mask_Filled[,in_c] * seq(1,n), margin="rows");
#TODO modify removeEmpty to return zero row and n columns
if(!(nrow(R) == 1 & as.scalar(R[1,1] == 0)))
- Mask_Filled_c[,in_c] = table(R, 1, pred, n, 1);
- i = as.integer(j)
+ Mask_Filled[,in_c] = table(R, 1, pred, n, 1);
}
- if(in_c < col)
- in_c = in_c + 1
- i = i+1;
+ i = as.integer(j)+1
+ in_c = in_c + 1
}
-
- nM = ((Mask_Filled_n) %*% p_n) + Mask_Filled_c
- Result = rbind(Result, nM+XO)
- Mask_Result = rbind(Mask_Result, nM)
- [dX, dM] = transformencode(target=as.frame(nM+XO), spec=jspecDC);
+ X1 = X + Mask_Filled
}
-
- # compute output indices
- Result = Result[2: n*iter+1, ]
- Mask_Result = Mask_Result[2: n*iter+1, ]
- index = (((complete*n)-n)+1)
- # voting for aggregation of categorical imputations
- agg = cAggregate(Mask_Result*cMask, iter, n)
-
- # aggregating the results
- Agg_Matrix = matrix(0,n, col)
- for(d in 1:iter)
- Agg_Matrix = Agg_Matrix + Mask_Result[(((d-1)*n)+1):(n*d),]
- Agg_Matrix = (Agg_Matrix/iter)
-
- Agg_Matrix = Agg_Matrix * (cMask == 0)
- Agg_Matrix = Agg_Matrix + agg
+ output = X1[,1:lastIndex]
+}
- dataset = XO + Agg_Matrix
- singleSet = Result[index:row, ]
-
- # decoding nominal columns
- dataset = transformdecode(target=dataset, spec=jspecR, meta=M);
- singleSet = transformdecode(target=singleSet, spec=jspecR, meta=M);
-
- # removing extra categorical column
- dataset = dataset[,1:col-1]
- singleSet = singleSet[,1:col-1]
- }
-
-
-cAggregate = function(Matrix[Double] Mask_Result, Integer iter, Integer n)
-return (Matrix[Double] agg)
-{
- conflict = matrix(0, n, ncol(Mask_Result))
- uCount = 0
- vCount = 0
- for(d in seq(1,(iter-1), 1))
- {
- u = Mask_Result[(((d-1)*n)+1):(n*d),]
- v = Mask_Result[(((d)*n)+1):(n*(d+1)),]
- if(sum(u != v) > 0) {
- conflict = u != v
- u1 = conflict * u;
- v1 = conflict * v;
- for(i in 1: iter)
- {
- s = Mask_Result[(((i-1)*n)+1):(n*i),]
- s = s * conflict
- if(sum(u1 != s ) == 0)
- uCount = uCount + 1
- if(sum(v1 != s) == 0)
- vCount = vCount + 1
- }
- # copy the results of u in v
- if(uCount > vCount)
- Mask_Result[(((d)*n)+1):(n*(d+1)),] = Mask_Result[(((d-1)*n)+1):(n*d),]
- # copy the results of v in u
- else
- Mask_Result[(((d-1)*n)+1):(n*d),] = Mask_Result[(((d)*n)+1):(n*(d+1)),]
- d = 1
- }
+colMode = function (Matrix[Double] X) return (Matrix[Double] colMode) {
+ d = ncol(X)
+ n = nrow(X)
+ colMode = matrix(0, 1, ncol(X))
+ # compute column wise mode
+ parfor(i in 1: d) {
+ X_c = removeEmpty(target=X, margin = "rows", select=(rowSums(X != 0)==d))
+ cat_counts = table(X_c[, i], 1, n, 1); # counts for each category
+ colMode[1,i] = as.scalar(rowIndexMax(t(cat_counts))) # mode
}
- agg = Mask_Result[1:n,]
}
diff --git a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinMiceTest.java b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinMiceTest.java
index 5c3ad22..725859b 100644
--- a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinMiceTest.java
+++ b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinMiceTest.java
@@ -36,9 +36,8 @@
private static final String TEST_CLASS_DIR = TEST_DIR + BuiltinMiceTest.class.getSimpleName() + "/";
private final static String DATASET = SCRIPT_DIR +"functions/transform/input/ChickWeight.csv";
- private final static double eps = 0.2;
+ private final static double eps = 0.16;
private final static int iter = 3;
- private final static int com = 2;
@Override
public void setUp() {
@@ -50,21 +49,35 @@
runMiceNominalTest(mask, 1, LopProperties.ExecType.CP);
}
+// @Test
+// public void testMiceMixSpark() {
+// double[][] mask = {{ 0.0, 0.0, 1.0, 1.0, 0.0}};
+// runMiceNominalTest(mask, 1, LopProperties.ExecType.SPARK);
+// }
+
@Test
public void testMiceNumberCP() {
double[][] mask = {{ 0.0, 0.0, 0.0, 0.0, 0.0}};
runMiceNominalTest(mask, 2, LopProperties.ExecType.CP);
}
+// @Test
+// public void testMiceNumberSpark() {
+// double[][] mask = {{ 0.0, 0.0, 0.0, 0.0, 0.0}};
+// runMiceNominalTest(mask, 2, LopProperties.ExecType.SPARK);
+// }
+
@Test
public void testMiceCategoricalCP() {
double[][] mask = {{ 1.0, 1.0, 1.0, 1.0, 1.0}};
runMiceNominalTest(mask, 3, LopProperties.ExecType.CP);
}
- // @Test
- // public void testMiceSpark() {
- // runMiceNominalTest( LopProperties.ExecType.SPARK);
- // }
+
+// @Test
+// public void testMiceCategoricalSpark() {
+// double[][] mask = {{ 1.0, 1.0, 1.0, 1.0, 1.0}};
+// runMiceNominalTest(mask, 3, LopProperties.ExecType.SPARK);
+// }
private void runMiceNominalTest(double[][] mask, int testType, LopProperties.ExecType instType) {
Types.ExecMode platformOld = setExecMode(instType);
@@ -72,18 +85,17 @@
loadTestConfiguration(getTestConfiguration(TEST_NAME));
String HOME = SCRIPT_DIR + TEST_DIR;
fullDMLScriptName = HOME + TEST_NAME + ".dml";
- programArgs = new String[]{"-nvargs", "X=" + DATASET, "Mask="+input("M"), "iteration=" + iter, "com=" + com, "dataN=" + output("N"), "dataC=" + output("C")};
+ programArgs = new String[]{"-nvargs", "X=" + DATASET, "Mask="+input("M"), "iteration=" + iter, "dataN=" + output("N"), "dataC=" + output("C")};
writeInputMatrixWithMTD("M", mask, true);
fullRScriptName = HOME + TEST_NAME + ".R";
- rCmd = "Rscript" + " " + fullRScriptName + " " +DATASET+ " " +inputDir() + " " + expectedDir();
+ rCmd = getRCmd(DATASET, inputDir(), expectedDir());
+ setOutputBuffering(false);
runTest(true, false, null, -1);
runRScript(true);
-
- switch (testType)
- {
+ switch (testType) {
case 1:
testCategoricalOutput();
testNumericOutput();
@@ -128,6 +140,6 @@
if(countTrue / (double)dmlfileC.size() > 0.98)
Assert.assertTrue(true);
else
- Assert.fail();
+ Assert.fail("categorical test fails, the true value count is less than 98%");
}
}
\ No newline at end of file
diff --git a/src/test/scripts/functions/builtin/mice.R b/src/test/scripts/functions/builtin/mice.R
index 2237d7c..63527ca 100644
--- a/src/test/scripts/functions/builtin/mice.R
+++ b/src/test/scripts/functions/builtin/mice.R
@@ -26,34 +26,23 @@
d <- read.csv(args[1], header=FALSE )
mass <- as.matrix(readMM(paste(args[2], "M.mtx", sep="")));
-
if(sum(mass) == ncol(d))
{
-d = d[,3:4]
-mass = mass[1,3:4]
-meth=""
- for(i in 1: 2) {
- d[[names(d)[i]]] = as.factor(d[[names(d)[i]]]);
- meth = c(meth, "polyreg")
- }
-
- meth=meth[-1]
-
+ d = d[,3:4]
+ d[] <- lapply(d, factor)
+ d
+ mass = mass[1,3:4]
+ meth = meth= rep("polyreg", ncol(d))
#impute
imputeD <- mice(d,where = is.na(d), method = meth, m=3)
- R = data.frame(complete(imputeD,3))
- c = select_if(R, is.factor)
-
- # convert factor into numeric before casting to matrix
- c = sapply(c, function(x) as.numeric(as.character(x)))
- writeMM(as(as.matrix(c), "CsparseMatrix"), paste(args[3], "C", sep=""));
-} else if (sum(mass) == 0)
+ imputeD
+ R = as.matrix(complete(imputeD,3))
+ writeMM(as(R, "CsparseMatrix"), paste(args[3], "C", sep=""));
+ } else if (sum(mass) == 0)
{
- print("Generating R witout cat")
imputeD <- mice(d,where = is.na(d), method = "norm.predict", m=3)
- R = data.frame(complete(imputeD,3))
- n = select_if(R, is.numeric)
- writeMM(as(as.matrix(n), "CsparseMatrix"), paste(args[3], "N", sep=""));
+ R = as.matrix(complete(imputeD,3))
+ writeMM(as(as.matrix(R), "CsparseMatrix"), paste(args[3], "N", sep=""));
} else {
meth=""
for(i in 1: ncol(mass)) {
@@ -80,8 +69,8 @@
pred[names(d)[3], names(d)[4]] = 1
pred[names(d)[4], names(d)[3]] = 1
-
-#impute
+
+ #impute
imputeD <- mice(d,where = is.na(d), method = meth, m=3, pred = pred)
R = data.frame(complete(imputeD,3))
c = select_if(R, is.factor)
diff --git a/src/test/scripts/functions/builtin/mice.dml b/src/test/scripts/functions/builtin/mice.dml
index 7736f56..393ce3f 100644
--- a/src/test/scripts/functions/builtin/mice.dml
+++ b/src/test/scripts/functions/builtin/mice.dml
@@ -19,27 +19,59 @@
#
#-------------------------------------------------------------
-X = read($X, data_type="frame", format="csv");
-M = read($Mask)
-[dataset, singleSet]= mice(F=X, cMask=M, iter=$iteration, complete=$com, verbose = FALSE)
+# read data frame
+F = read($X, data_type="frame", format="csv");
+# the mask for identifying categorical columns
+Mask = read($Mask)
-if(sum(M) == ncol(X))
+# Test cases
+# case 1: if all columns are categorical
+if(sum(Mask) == ncol(F))
{
- c = as.matrix(singleSet[,3:4]) # comparing only selected columns with R results because dataset is continuos and
- write(c, $dataC) # for categorical imputation R polyreg only support upto 50 distinct items (50 categories/feature)
+ scat = seq(1, ncol(Mask))
+ s = "1";
+ for(i in 2:ncol(F))
+ s = s + "," + i;
+ # encoding categorical columns using recode transformation
+ jspecR = "{ids:true, recode:["+s+"]}";
+ [X, M] = transformencode(target=F, spec=jspecR);
+ # call mice
+ dataset = mice(X=X,cMask=Mask, iter=$iteration, verbose = FALSE )
+ # decode data back to original format
+ output = as.matrix(transformdecode(target=dataset, spec=jspecR, meta=M));
+ # cherry picking columns to compare with R results
+ output = output[, 3:4]
+ write(output, $dataC)
}
-else if (sum(M) == 0)
-{
- n = as.matrix(dataset) * (1-M)
- n = removeEmpty(target=n, margin = "cols")
- write(n, $dataN)
+# case 2: if all data is numeric
+else if(sum(Mask) == 0){
+ # no transformation is required, cast the frame into matrix and call mice
+ # as.matrix() will convert the null values into zeros, so explicitly replace zeros with NaN
+ X = replace(target = as.matrix(F), pattern = 0, replacement = NaN)
+ output = mice(X=X, cMask=Mask, iter=$iteration, verbose = FALSE )
+ write(output, $dataN)
}
+# case 3: if the data is combination of numeric and categorical columns
else
{
- c = as.matrix(dataset) * (M)
+ scat = seq(1, ncol(Mask))
+ cat = removeEmpty(target=scat, margin="rows", select=t(Mask))
+ s = "" + as.integer(as.scalar(cat[1, 1]))
+ for(i in 2:nrow(cat))
+ s = s + "," + as.integer(as.scalar(cat[i, 1]));
+
+ # encoding categorical columns using recode transformation
+ jspecR = "{ids:true, recode:["+s+"]}";
+ [X, M] = transformencode(target=F, spec=jspecR);
+ # call mice
+ dataset = mice(X=X,cMask=Mask, iter=$iteration, verbose = FALSE )
+ # decode data into original format
+ output = as.matrix(transformdecode(target=dataset, spec=jspecR, meta=M));
+ # below lines are only for testing purpose
+ c = output * (Mask)
c = removeEmpty(target=c, margin = "cols")
- n = as.matrix(dataset) * (1-M)
+ n = output * (1-Mask)
n = removeEmpty(target=n, margin = "cols")
write(n, $dataN)
write(c, $dataC)
-}
\ No newline at end of file
+}
diff --git a/src/test/scripts/functions/caching/BufferpoolLeak.dml b/src/test/scripts/functions/caching/BufferpoolLeak.dml
index 6a50ea5..d476cb6 100644
--- a/src/test/scripts/functions/caching/BufferpoolLeak.dml
+++ b/src/test/scripts/functions/caching/BufferpoolLeak.dml
@@ -21,8 +21,8 @@
X = rand(rows=$1, cols=$2, min=1, max=10);
for(i in 1:500) {
- #print("executed iteration "+i)
- [m1,m2] = mice(as.frame(X), matrix(0,1,ncol(X)),3,3, FALSE)
+ # print("executed iteration "+i)
+ m1 = mice(X, matrix(0,1,ncol(X)), 3, FALSE)
}
if( ncol(X) > $2 )
print(toString(m1));
