Baunsgaard commented on code in PR #1925:
URL: https://github.com/apache/systemds/pull/1925#discussion_r1363757838
##########
scripts/builtin/imputeByKNN.dml:
##########
@@ -66,103 +64,76 @@ m_imputeByKNN = function(Matrix[Double] X, String
method="dist", Int seed=-1, Do
distance_matrix = dist(filled_matrix)
#Change 0 value so rowIndexMin will ignore that diagonal value
- distance_matrix = replace(target = distance_matrix, pattern = 0,
replacement = 999)
+ distance_matrix = replace(target=distance_matrix, pattern=0,
replacement=999)
#Get the minimum distance row-wise computation
minimum_index = rowIndexMin(distance_matrix)
#Create aligned matrix from minimum index
- aligned = table(minimum_index, seq(1, nrow(X)), odim1 = nrow(X), odim2 =
nrow(X))
+ aligned = table(minimum_index, seq(1, nrow(X)), odim1=nrow(X),
odim2=nrow(X))
#Get the X records that need to be imputed
imputedValue = t(filled_matrix) %*% aligned
-
- #Update the mask value
- masked = t(imputedValue) * masked
+ imputedValue = t(imputedValue)
}
else if(method == "dist_missing") {
#assuming small missing values
- #Split the matrix into containing NaN values (missing records) and not
containing NaN values (M2 records)
- I = (rowSums(is.nan(X))!=0)
- missing = removeEmpty(target=filled_matrix, margin="rows", select=I)
-
- Y = (rowSums(is.nan(X))==0)
- M2 = removeEmpty(target=filled_matrix, margin = "rows", select = Y)
-
- #Calculate the euclidean distance between fully records and missing
records, and then find the min value row wise
- dotM2 = rowSums(M2 * M2) %*% matrix(1, rows = 1, cols = nrow(missing))
- dotMissing = t(rowSums(missing * missing) %*% matrix(1, rows = 1, cols =
nrow(M2)))
- D = sqrt(dotM2 + dotMissing - 2 * (M2 %*% t(missing)))
- minD = rowIndexMin(t(D))
-
- #Get the index location of the missing value
- pos = rowMaxs(is.nan(X))
- missing_indices = seq(1, nrow(pos)) * pos
-
- #Put the replacement value in the missing indices
- I2 = removeEmpty(target=missing_indices, margin="rows")
- R = table(I2,1,minD,odim1 = nrow(X), odim2=1)
-
- #Replace the 0 to avoid error in table()
- R = replace(target = R, pattern = 0, replacement = nrow(X)+1)
-
- #Create aligned matrix from minimum index
- aligned = table(R, seq(1, nrow(X)), odim1 = nrow(X), odim2 = nrow(X))
-
- #Reshape the subset
- reshaped = rbind(M2, matrix(0, rows = nrow(X) - nrow(M2), cols = ncol(X)))
-
- #Get the M2 records that need to be imputed
- imputedValue = t(reshaped) %*% aligned
-
- #Update the mask value
- masked = t(imputedValue) * masked
+ imputedValue = compute_missing_values(X, filled_matrix, seed, 1.0)
}
else if(method == "dist_sample"){
#assuming large missing values
+ imputedValue = compute_missing_values(X, filled_matrix, seed, sample_frac)
+ }
+ else {
+ print("Method is unknown or not yet implemented")
Review Comment:
change this to stop instead of print
##########
scripts/builtin/imputeByKNN.dml:
##########
@@ -66,103 +64,76 @@ m_imputeByKNN = function(Matrix[Double] X, String
method="dist", Int seed=-1, Do
distance_matrix = dist(filled_matrix)
#Change 0 value so rowIndexMin will ignore that diagonal value
- distance_matrix = replace(target = distance_matrix, pattern = 0,
replacement = 999)
+ distance_matrix = replace(target=distance_matrix, pattern=0,
replacement=999)
#Get the minimum distance row-wise computation
minimum_index = rowIndexMin(distance_matrix)
#Create aligned matrix from minimum index
- aligned = table(minimum_index, seq(1, nrow(X)), odim1 = nrow(X), odim2 =
nrow(X))
+ aligned = table(minimum_index, seq(1, nrow(X)), odim1=nrow(X),
odim2=nrow(X))
#Get the X records that need to be imputed
imputedValue = t(filled_matrix) %*% aligned
-
- #Update the mask value
- masked = t(imputedValue) * masked
+ imputedValue = t(imputedValue)
}
else if(method == "dist_missing") {
#assuming small missing values
- #Split the matrix into containing NaN values (missing records) and not
containing NaN values (M2 records)
- I = (rowSums(is.nan(X))!=0)
- missing = removeEmpty(target=filled_matrix, margin="rows", select=I)
-
- Y = (rowSums(is.nan(X))==0)
- M2 = removeEmpty(target=filled_matrix, margin = "rows", select = Y)
-
- #Calculate the euclidean distance between fully records and missing
records, and then find the min value row wise
- dotM2 = rowSums(M2 * M2) %*% matrix(1, rows = 1, cols = nrow(missing))
- dotMissing = t(rowSums(missing * missing) %*% matrix(1, rows = 1, cols =
nrow(M2)))
- D = sqrt(dotM2 + dotMissing - 2 * (M2 %*% t(missing)))
- minD = rowIndexMin(t(D))
-
- #Get the index location of the missing value
- pos = rowMaxs(is.nan(X))
- missing_indices = seq(1, nrow(pos)) * pos
-
- #Put the replacement value in the missing indices
- I2 = removeEmpty(target=missing_indices, margin="rows")
- R = table(I2,1,minD,odim1 = nrow(X), odim2=1)
-
- #Replace the 0 to avoid error in table()
- R = replace(target = R, pattern = 0, replacement = nrow(X)+1)
-
- #Create aligned matrix from minimum index
- aligned = table(R, seq(1, nrow(X)), odim1 = nrow(X), odim2 = nrow(X))
-
- #Reshape the subset
- reshaped = rbind(M2, matrix(0, rows = nrow(X) - nrow(M2), cols = ncol(X)))
-
- #Get the M2 records that need to be imputed
- imputedValue = t(reshaped) %*% aligned
-
- #Update the mask value
- masked = t(imputedValue) * masked
+ imputedValue = compute_missing_values(X, filled_matrix, seed, 1.0)
}
else if(method == "dist_sample"){
#assuming large missing values
+ imputedValue = compute_missing_values(X, filled_matrix, seed, sample_frac)
+ }
+ else {
+ print("Method is unknown or not yet implemented")
+ }
+
+ #Impute the value
+ result = replace(target=X, pattern=NaN, replacement=0)
+ result = result + (imputedValue * is.nan(X))
+}
+
+compute_missing_values = function (Matrix[Double] X, Matrix[Double]
filled_matrix, Int seed, Double sample_frac)
+return (Matrix[Double] imputedValue)
Review Comment:
indent with 4 spaces on the return or break lines in the function
definitions.
(just to keep it consistent with other builtins.)
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