dkerschbaumer commented on a change in pull request #1334:
URL: https://github.com/apache/systemds/pull/1334#discussion_r671124041
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File path: scripts/builtin/xgboostPredict.dml
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@@ -0,0 +1,147 @@
+# INPUT PARAMETERS:
+# ----------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ----------------------------------------------------------------------------
+# X Matrix --- Matrix of feature
vectors we want to predict (X_test)
+# M Matrix --- The model created at
xgboost
+# sml_type Integer 1 Supervised machine
learning type: 1 = Regression(default), 2 = Classification
+# learning_rate Double 0.3 the learning rate used
in the model
+
+# RETURN VALUES
+# ----------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ----------------------------------------------------------------------------
+# P Matrix --- The predictions of the samples using the given
xgboost model. (y_prediction)
+# ----------------------------------------------------------------------------
+
+m_xgboostPredict = function(Matrix[Double] X, Matrix[Double] M, Integer
sml_type = 1, Double learning_rate = 0.3
+) return (Matrix[Double] P) {
+
+ nr_trees = max(M[2,])
+ P = matrix(0, rows=nrow(X), cols=1)
+ initial_prediction = M[6,1]
+ trees_M_offset = calculateTreesOffset(M)
+ if(sml_type == 1) # Regression
+ {
+ for(entry in 1:nrow(X)) # go though each entry in X and calculate the new
prediction
+ {
+ output_values = matrix(0, rows=1, cols=0)
+
+ for(i in 1:nr_trees) # go through all trees
+ {
+ begin_cur_tree = as.scalar(trees_M_offset[i,])
+ if(i == nr_trees)
+ end_cur_tree = ncol(M)
+ else
+ end_cur_tree = as.scalar(trees_M_offset[i+1,]) - 1
+ output_value = getOutputValueForEntryPredict(X[entry,], M[,
begin_cur_tree:end_cur_tree])
+ output_values = cbind(output_values, as.matrix(output_value))
+ }
+ P[entry,] = initial_prediction + learning_rate * sum(output_values)
+ }
+ }
+ else # Classification
+ {
+ assert(sml_type == 2)
+ for(entry in 1:nrow(X)) # go though each entry in X and calculate the new
prediction
Review comment:
done in
https://github.com/apache/systemds/pull/1334/commits/c750df72571574b0c59d2cec7a732d5d6a558ef2
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