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Posted to commits@systemml.apache.org by mb...@apache.org on 2016/09/15 09:13:09 UTC
incubator-systemml git commit: [HOTFIX] Fix build of scala examples
and function call error handling
Repository: incubator-systemml
Updated Branches:
refs/heads/master 085009a36 -> f9fbcb76f
[HOTFIX] Fix build of scala examples and function call error handling
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/f9fbcb76
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/f9fbcb76
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/f9fbcb76
Branch: refs/heads/master
Commit: f9fbcb76f2ad280e3a8a3f26d6d9221ccbaeaae0
Parents: 085009a
Author: Matthias Boehm <mb...@us.ibm.com>
Authored: Thu Sep 15 11:12:21 2016 +0200
Committer: Matthias Boehm <mb...@us.ibm.com>
Committed: Thu Sep 15 11:12:21 2016 +0200
----------------------------------------------------------------------
.../instructions/cp/FunctionCallCPInstruction.java | 2 +-
.../org/apache/sysml/api/ml/BaseSystemMLClassifier.scala | 10 +++++-----
.../org/apache/sysml/api/ml/BaseSystemMLRegressor.scala | 8 ++++----
3 files changed, 10 insertions(+), 10 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/f9fbcb76/src/main/java/org/apache/sysml/runtime/instructions/cp/FunctionCallCPInstruction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/runtime/instructions/cp/FunctionCallCPInstruction.java b/src/main/java/org/apache/sysml/runtime/instructions/cp/FunctionCallCPInstruction.java
index 529bd25..917a7f2 100644
--- a/src/main/java/org/apache/sysml/runtime/instructions/cp/FunctionCallCPInstruction.java
+++ b/src/main/java/org/apache/sysml/runtime/instructions/cp/FunctionCallCPInstruction.java
@@ -151,7 +151,7 @@ public class FunctionCallCPInstruction extends CPInstruction
CPOperand operand = _boundInputParamOperands.get(i);
String varname = operand.getName();
//error handling non-existing variables
- if( !operand.isLiteral() && ec.containsVariable(varname) ) {
+ if( !operand.isLiteral() && !ec.containsVariable(varname) ) {
throw new DMLRuntimeException("Input variable '"+varname+"' not existing on call of " +
DMLProgram.constructFunctionKey(_namespace, _functionName) + " (line "+getLineNum()+").");
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/f9fbcb76/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala
----------------------------------------------------------------------
diff --git a/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala b/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala
index c9c05e0..7415109 100644
--- a/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala
+++ b/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala
@@ -28,7 +28,7 @@ import org.apache.spark.ml.param.{ Params, Param, ParamMap, DoubleParam }
import org.apache.sysml.runtime.matrix.MatrixCharacteristics
import org.apache.sysml.runtime.matrix.data.MatrixBlock
import org.apache.sysml.runtime.DMLRuntimeException
-import org.apache.sysml.runtime.instructions.spark.utils.{ RDDConverterUtilsExt => RDDConverterUtils }
+import org.apache.sysml.runtime.instructions.spark.utils.{ RDDConverterUtilsExt, RDDConverterUtils }
import org.apache.sysml.api.mlcontext._
import org.apache.sysml.api.mlcontext.ScriptFactory._
import org.apache.spark.sql._
@@ -110,7 +110,7 @@ trait BaseSystemMLClassifier extends BaseSystemMLEstimator {
val isSingleNode = false
val ml = new MLContext(df.rdd.sparkContext)
val mcXin = new MatrixCharacteristics()
- val Xin = RDDConverterUtils.vectorDataFrameToBinaryBlock(sc, df.asInstanceOf[DataFrame], mcXin, false, "features")
+ val Xin = RDDConverterUtils.dataFrameToBinaryBlock(sc, df.asInstanceOf[DataFrame], mcXin, false, true)
val revLabelMapping = new java.util.HashMap[Int, String]
val yin = PredictionUtils.fillLabelMapping(df, revLabelMapping)
val ret = getTrainingScript(isSingleNode)
@@ -142,7 +142,7 @@ trait BaseSystemMLClassifierModel extends BaseSystemMLEstimatorModel {
val isSingleNode = false
val ml = new MLContext(sc)
val mcXin = new MatrixCharacteristics()
- val Xin = RDDConverterUtils.vectorDataFrameToBinaryBlock(df.rdd.sparkContext, df.asInstanceOf[DataFrame], mcXin, false, "features")
+ val Xin = RDDConverterUtils.dataFrameToBinaryBlock(df.rdd.sparkContext, df.asInstanceOf[DataFrame], mcXin, false, true)
val script = getPredictionScript(mloutput, isSingleNode)
val Xin_bin = new BinaryBlockMatrix(Xin, mcXin)
val modelPredict = ml.execute(script._1.in(script._2, Xin_bin))
@@ -150,11 +150,11 @@ trait BaseSystemMLClassifierModel extends BaseSystemMLEstimatorModel {
val predictedDF = PredictionUtils.updateLabels(isSingleNode, predLabelOut.getDataFrame("Prediction"), null, "C1", labelMapping).select("__INDEX", "prediction")
if(outputProb) {
val prob = modelPredict.getDataFrame(probVar, true).withColumnRenamed("C1", "probability").select("__INDEX", "probability")
- val dataset = RDDConverterUtils.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
+ val dataset = RDDConverterUtilsExt.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
return PredictionUtils.joinUsingID(dataset, PredictionUtils.joinUsingID(prob, predictedDF))
}
else {
- val dataset = RDDConverterUtils.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
+ val dataset = RDDConverterUtilsExt.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
return PredictionUtils.joinUsingID(dataset, predictedDF)
}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/f9fbcb76/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLRegressor.scala
----------------------------------------------------------------------
diff --git a/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLRegressor.scala b/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLRegressor.scala
index 73bf9be..ed0fabb 100644
--- a/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLRegressor.scala
+++ b/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLRegressor.scala
@@ -29,7 +29,7 @@ import org.apache.spark.ml.param.{ Params, Param, ParamMap, DoubleParam }
import org.apache.sysml.runtime.matrix.MatrixCharacteristics
import org.apache.sysml.runtime.matrix.data.MatrixBlock
import org.apache.sysml.runtime.DMLRuntimeException
-import org.apache.sysml.runtime.instructions.spark.utils.{ RDDConverterUtilsExt => RDDConverterUtils }
+import org.apache.sysml.runtime.instructions.spark.utils.{ RDDConverterUtilsExt, RDDConverterUtils }
import org.apache.sysml.api.mlcontext._
import org.apache.sysml.api.mlcontext.ScriptFactory._
@@ -47,7 +47,7 @@ trait BaseSystemMLRegressor extends BaseSystemMLEstimator {
val isSingleNode = false
val ml = new MLContext(df.rdd.sparkContext)
val mcXin = new MatrixCharacteristics()
- val Xin = RDDConverterUtils.vectorDataFrameToBinaryBlock(sc, df.asInstanceOf[DataFrame], mcXin, false, "features")
+ val Xin = RDDConverterUtils.dataFrameToBinaryBlock(sc, df.asInstanceOf[DataFrame], mcXin, false, true)
val yin = df.select("label")
val ret = getTrainingScript(isSingleNode)
val Xbin = new BinaryBlockMatrix(Xin, mcXin)
@@ -75,12 +75,12 @@ trait BaseSystemMLRegressorModel extends BaseSystemMLEstimatorModel {
val isSingleNode = false
val ml = new MLContext(sc)
val mcXin = new MatrixCharacteristics()
- val Xin = RDDConverterUtils.vectorDataFrameToBinaryBlock(df.rdd.sparkContext, df.asInstanceOf[DataFrame], mcXin, false, "features")
+ val Xin = RDDConverterUtils.dataFrameToBinaryBlock(df.rdd.sparkContext, df.asInstanceOf[DataFrame], mcXin, false, true)
val script = getPredictionScript(mloutput, isSingleNode)
val Xin_bin = new BinaryBlockMatrix(Xin, mcXin)
val modelPredict = ml.execute(script._1.in(script._2, Xin_bin))
val predictedDF = modelPredict.getDataFrame(predictionVar).select("__INDEX", "C1").withColumnRenamed("C1", "prediction")
- val dataset = RDDConverterUtils.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
+ val dataset = RDDConverterUtilsExt.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, "__INDEX")
return PredictionUtils.joinUsingID(dataset, predictedDF)
}
}
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