Refactor project package to org.apache.sysml

.gitignore

@@ -37,14 +37,14 @@ src/main/java/Dml.tokens
 src/main/java/DmlLexer.tokens
 src/main/java/Pydml.tokens
 src/main/java/PydmlLexer.tokens
-src/main/java/com/ibm/bi/dml/parser/antlr4/DmlBaseListener.java
-src/main/java/com/ibm/bi/dml/parser/antlr4/DmlLexer.java
-src/main/java/com/ibm/bi/dml/parser/antlr4/DmlListener.java
-src/main/java/com/ibm/bi/dml/parser/antlr4/DmlParser.java
-src/main/java/com/ibm/bi/dml/parser/python/PydmlBaseListener.java
-src/main/java/com/ibm/bi/dml/parser/python/PydmlLexer.java
-src/main/java/com/ibm/bi/dml/parser/python/PydmlListener.java
-src/main/java/com/ibm/bi/dml/parser/python/PydmlParser.java
+src/main/java/org/apache/sysml/parser/antlr4/DmlBaseListener.java
+src/main/java/org/apache/sysml/parser/antlr4/DmlLexer.java
+src/main/java/org/apache/sysml/parser/antlr4/DmlListener.java
+src/main/java/org/apache/sysml/parser/antlr4/DmlParser.java
+src/main/java/org/apache/sysml/parser/python/PydmlBaseListener.java
+src/main/java/org/apache/sysml/parser/python/PydmlLexer.java
+src/main/java/org/apache/sysml/parser/python/PydmlListener.java
+src/main/java/org/apache/sysml/parser/python/PydmlParser.java
 src/test/scripts/**/in
 src/test/scripts/**/out
 src/test/scripts/**/expected

bin/systemml

@@ -55,7 +55,7 @@ USER_DIR=$PWD
 
 BUILD_DIR=${PROJECT_ROOT_DIR}/target
 HADOOP_LIB_DIR=${BUILD_DIR}/lib
-DML_SCRIPT_CLASS=${BUILD_DIR}/classes/com/ibm/bi/dml/api/DMLScript.class
+DML_SCRIPT_CLASS=${BUILD_DIR}/classes/org/apache/sysml/api/DMLScript.class
 
 BUILD_ERR_MSG="You must build the project before running this script."
 BUILD_DIR_ERR_MSG="Could not find target directory \"${BUILD_DIR}\". ${BUILD_ERR_MSG}"
@@ -130,7 +130,7 @@ CMD="java -Xmx8g -Xms4g -Xmn1g \
      -cp $CLASSPATH \
      -Dlog4j.configuration=file:'$PROJECT_ROOT_DIR/conf/log4j.properties' \
      -Duser.dir='$USER_DIR' \
-     com.ibm.bi.dml.api.DMLScript \
+     org.apache.sysml.api.DMLScript \
      -f '$SCRIPT_FILE' \
      -exec singlenode \
      -config='$PROJECT_ROOT_DIR/conf/SystemML-config.xml' \

bin/systemml.bat

@@ -111,7 +111,7 @@ set CMD=java -Xmx4g -Xms2g -Xmn400m ^
      -cp "%CLASSPATH%" ^
      -Dlog4j.configuration=file:"%PROJECT_ROOT_DIR%\conf\log4j.properties" ^
      -Duser.dir="%USER_DIR%" ^
-     com.ibm.bi.dml.api.DMLScript ^
+     org.apache.sysml.api.DMLScript ^
      -f %SCRIPT_FILE% ^
      -exec singlenode ^
      -config="%PROJECT_ROOT_DIR%\conf\SystemML-config.xml" ^

docs/Language Reference/SystemML_Language_Reference.html

@@ -3673,7 +3673,7 @@ <h2><a name="_Toc425846332"><span style='mso-fareast-font-family:"Times New Roma
 margin-left:.5in;margin-bottom:.0001pt'><span class=GramE><span
 style='font-size:11.0pt;font-family:"Courier New";mso-bidi-font-style:italic'>implemented</span></span><span
 style='font-size:11.0pt;font-family:"Courier New";mso-bidi-font-style:italic'>
-in (<span class=SpellE>classname</span>=&quot;<span class=SpellE>com.ibm.bi.dml.packagesupport.JLapackEigenWrapper</span>&quot;)<o:p></o:p></span></p>
+in (<span class=SpellE>classname</span>=&quot;<span class=SpellE>org.apache.sysml.packagesupport.JLapackEigenWrapper</span>&quot;)<o:p></o:p></span></p>
 
 <p class=MsoNormal>A UDF invocation specifies the function identifier, variable
 identifiers for calling parameters, and the variables to be populated by the
@@ -9479,7 +9479,7 @@ <h1><a name="_Toc425846348"></a><span class=SpellE><span style='mso-bookmark:
 <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><span class=SpellE><span
 class=GramE><span style='font-size:11.0pt;font-family:"Courier New";mso-bidi-font-style:
 italic'>scala</span></span></span><span style='font-size:11.0pt;font-family:
-"Courier New";mso-bidi-font-style:italic'>&gt; import <span class=SpellE>com.ibm.bi.dml.api.MLContext</span><o:p></o:p></span></p>
+"Courier New";mso-bidi-font-style:italic'>&gt; import <span class=SpellE>org.apache.sysml.api.MLContext</span><o:p></o:p></span></p>
 
 <p class=MsoNormal style='margin:0in;margin-bottom:.0001pt'><o:p>&nbsp;</o:p></p>
 

docs/dml-language-reference.md

@@ -407,7 +407,7 @@ userParam=value | User-defined parameter to invoke the package. | Yes | Any non-
     # example of an external UDF
     eigen = externalFunction(matrix[double] A) 
     return (matrix[double] evec, matrix[double] eval) 
-    implemented in (classname="com.ibm.bi.dml.packagesupport.JLapackEigenWrapper")
+    implemented in (classname="org.apache.sysml.packagesupport.JLapackEigenWrapper")
 
 A UDF invocation specifies the function identifier, variable identifiers for calling parameters, and the variables to be populated by the returned values from the function. The syntax for function calls is as follows.
 
@@ -1186,7 +1186,7 @@ The MLContext API allows users to pass RDDs as input/output to SystemML through
 
 Typical usage for MLContext using Spark's Scala Shell is as follows:
 
-    scala> import com.ibm.bi.dml.api.MLContext
+    scala> import org.apache.sysml.api.MLContext
 
 Create input DataFrame from CSV file and potentially perform some feature transformation
 

docs/mlcontext-programming-guide.md

@@ -44,17 +44,17 @@ An `MLContext` object can be created by passing its constructor a reference to t
 
 <div data-lang="Spark Shell" markdown="1">
 {% highlight scala %}
-scala>import com.ibm.bi.dml.api.MLContext
-import com.ibm.bi.dml.api.MLContext
+scala>import org.apache.sysml.api.MLContext
+import org.apache.sysml.api.MLContext
 
 scala> val ml = new MLContext(sc)
-ml: com.ibm.bi.dml.api.MLContext = com.ibm.bi.dml.api.MLContext@33e38c6b
+ml: org.apache.sysml.api.MLContext = org.apache.sysml.api.MLContext@33e38c6b
 {% endhighlight %}
 </div>
 
 <div data-lang="Statements" markdown="1">
 {% highlight scala %}
-import com.ibm.bi.dml.api.MLContext
+import org.apache.sysml.api.MLContext
 val ml = new MLContext(sc)
 {% endhighlight %}
 </div>
@@ -125,27 +125,27 @@ an `MLOutput` object. The `getScalar()` method extracts a scalar value from a `D
 
 <div data-lang="Spark Shell" markdown="1">
 {% highlight scala %}
-scala> import com.ibm.bi.dml.api.MLOutput
-import com.ibm.bi.dml.api.MLOutput
+scala> import org.apache.sysml.api.MLOutput
+import org.apache.sysml.api.MLOutput
 
 scala> def getScalar(outputs: MLOutput, symbol: String): Any =
      | outputs.getDF(sqlContext, symbol).first()(1)
-getScalar: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Any
+getScalar: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Any
 
 scala> def getScalarDouble(outputs: MLOutput, symbol: String): Double =
      | getScalar(outputs, symbol).asInstanceOf[Double]
-getScalarDouble: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Double
+getScalarDouble: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Double
 
 scala> def getScalarInt(outputs: MLOutput, symbol: String): Int =
      | getScalarDouble(outputs, symbol).toInt
-getScalarInt: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Int
+getScalarInt: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Int
 
 {% endhighlight %}
 </div>
 
 <div data-lang="Statements" markdown="1">
 {% highlight scala %}
-import com.ibm.bi.dml.api.MLOutput
+import org.apache.sysml.api.MLOutput
 def getScalar(outputs: MLOutput, symbol: String): Any =
 outputs.getDF(sqlContext, symbol).first()(1)
 def getScalarDouble(outputs: MLOutput, symbol: String): Double =
@@ -176,11 +176,11 @@ to convert the `DataFrame df` to a SystemML binary-block matrix, which is repres
 
 <div data-lang="Spark Shell" markdown="1">
 {% highlight scala %}
-scala> import com.ibm.bi.dml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
-import com.ibm.bi.dml.runtime.instructions.spark.utils.{RDDConverterUtilsExt=>RDDConverterUtils}
+scala> import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
+import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt=>RDDConverterUtils}
 
-scala> import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics;
-import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics
+scala> import org.apache.sysml.runtime.matrix.MatrixCharacteristics;
+import org.apache.sysml.runtime.matrix.MatrixCharacteristics
 
 scala> val numRowsPerBlock = 1000
 numRowsPerBlock: Int = 1000
@@ -189,18 +189,18 @@ scala> val numColsPerBlock = 1000
 numColsPerBlock: Int = 1000
 
 scala> val mc = new MatrixCharacteristics(numRows, numCols, numRowsPerBlock, numColsPerBlock)
-mc: com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics = [100000 x 1000, nnz=-1, blocks (1000 x 1000)]
+mc: org.apache.sysml.runtime.matrix.MatrixCharacteristics = [100000 x 1000, nnz=-1, blocks (1000 x 1000)]
 
 scala> val sysMlMatrix = RDDConverterUtils.dataFrameToBinaryBlock(sc, df, mc, false)
-sysMlMatrix: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@2bce3248
+sysMlMatrix: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@2bce3248
 
 {% endhighlight %}
 </div>
 
 <div data-lang="Statements" markdown="1">
 {% highlight scala %}
-import com.ibm.bi.dml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
-import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics;
+import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
+import org.apache.sysml.runtime.matrix.MatrixCharacteristics;
 val numRowsPerBlock = 1000
 val numColsPerBlock = 1000
 val mc = new MatrixCharacteristics(numRows, numCols, numRowsPerBlock, numColsPerBlock)
@@ -268,7 +268,7 @@ nargs: scala.collection.immutable.Map[String,String] = Map(Xin -> " ", Mout -> "
 scala> val outputs = ml.execute("shape.dml", nargs)
 15/10/12 16:29:15 WARN : Your hostname, derons-mbp.usca.ibm.com resolves to a loopback/non-reachable address: 127.0.0.1, but we couldn't find any external IP address!
 15/10/12 16:29:15 WARN OptimizerUtils: Auto-disable multi-threaded text read for 'text' and 'csv' due to thread contention on JRE < 1.8 (java.version=1.7.0_80).
-outputs: com.ibm.bi.dml.api.MLOutput = com.ibm.bi.dml.api.MLOutput@4d424743
+outputs: org.apache.sysml.api.MLOutput = org.apache.sysml.api.MLOutput@4d424743
 
 scala> val m = getScalarInt(outputs, "m")
 m: Int = 100000
@@ -362,11 +362,11 @@ mean value of the matrix.
 
 <div data-lang="Spark Shell" markdown="1">
 {% highlight scala %}
-scala> import com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes
-import com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes
+scala> import org.apache.sysml.runtime.matrix.data.MatrixIndexes
+import org.apache.sysml.runtime.matrix.data.MatrixIndexes
 
-scala> import com.ibm.bi.dml.runtime.matrix.data.MatrixBlock
-import com.ibm.bi.dml.runtime.matrix.data.MatrixBlock
+scala> import org.apache.sysml.runtime.matrix.data.MatrixBlock
+import org.apache.sysml.runtime.matrix.data.MatrixBlock
 
 scala> import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.api.java.JavaPairRDD
@@ -383,15 +383,15 @@ scala> def minMaxMean(mat: JavaPairRDD[MatrixIndexes, MatrixBlock], rows: Int, c
      | val meanOut = getScalarDouble(outputs, "meanOut")
      | (minOut, maxOut, meanOut)
      | }
-minMaxMean: (mat: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock], rows: Int, cols: Int, ml: com.ibm.bi.dml.api.MLContext)(Double, Double, Double)
+minMaxMean: (mat: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock], rows: Int, cols: Int, ml: org.apache.sysml.api.MLContext)(Double, Double, Double)
 
 {% endhighlight %}
 </div>
 
 <div data-lang="Statements" markdown="1">
 {% highlight scala %}
-import com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes
-import com.ibm.bi.dml.runtime.matrix.data.MatrixBlock
+import org.apache.sysml.runtime.matrix.data.MatrixIndexes
+import org.apache.sysml.runtime.matrix.data.MatrixBlock
 import org.apache.spark.api.java.JavaPairRDD
 def minMaxMean(mat: JavaPairRDD[MatrixIndexes, MatrixBlock], rows: Int, cols: Int, ml: MLContext): (Double, Double, Double) = {
 ml.reset()
@@ -452,7 +452,7 @@ to standard output.
 
 
 {% highlight java %}
-package com.ibm.bi.dml;
+package org.apache.sysml;
 
 import java.util.HashMap;
 
@@ -462,8 +462,8 @@ import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
-import com.ibm.bi.dml.api.MLContext;
-import com.ibm.bi.dml.api.MLOutput;
+import org.apache.sysml.api.MLContext;
+import org.apache.sysml.api.MLOutput;
 
 public class MLContextExample {
 
@@ -835,7 +835,7 @@ This cell contains helper methods to return `Double` and `Int` values from outpu
 **Cell:**
 {% highlight scala %}
 // Helper functions
-import com.ibm.bi.dml.api.MLOutput
+import org.apache.sysml.api.MLOutput
 
 def getScalar(outputs: MLOutput, symbol: String): Any =
     outputs.getDF(sqlContext, symbol).first()(1)
@@ -849,10 +849,10 @@ def getScalarInt(outputs: MLOutput, symbol: String): Int =
 
 **Output:**
 {% highlight scala %}
-import com.ibm.bi.dml.api.MLOutput
-getScalar: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Any
-getScalarDouble: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Double
-getScalarInt: (outputs: com.ibm.bi.dml.api.MLOutput, symbol: String)Int
+import org.apache.sysml.api.MLOutput
+getScalar: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Any
+getScalarDouble: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Double
+getScalarInt: (outputs: org.apache.sysml.api.MLOutput, symbol: String)Int
 {% endhighlight %}
 
 
@@ -867,9 +867,9 @@ and single-column `label` matrix, both represented by the
 **Cell:**
 {% highlight scala %}
 // Imports
-import com.ibm.bi.dml.api.MLContext
-import com.ibm.bi.dml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
-import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics;
+import org.apache.sysml.api.MLContext
+import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt => RDDConverterUtils}
+import org.apache.sysml.runtime.matrix.MatrixCharacteristics;
 
 // Create SystemML context
 val ml = new MLContext(sc)
@@ -890,16 +890,16 @@ val cnt2 = y2.count()
 
 **Output:**
 {% highlight scala %}
-import com.ibm.bi.dml.api.MLContext
-import com.ibm.bi.dml.runtime.instructions.spark.utils.{RDDConverterUtilsExt=>RDDConverterUtils}
-import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics
-ml: com.ibm.bi.dml.api.MLContext = com.ibm.bi.dml.api.MLContext@38d59245
-mcX: com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics = [10000 x 1000, nnz=-1, blocks (1000 x 1000)]
-mcY: com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics = [10000 x 1, nnz=-1, blocks (1000 x 1000)]
-X: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@b5a86e3
-y: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@56377665
-X2: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@650f29d2
-y2: org.apache.spark.api.java.JavaPairRDD[com.ibm.bi.dml.runtime.matrix.data.MatrixIndexes,com.ibm.bi.dml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@334857a8
+import org.apache.sysml.api.MLContext
+import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt=>RDDConverterUtils}
+import org.apache.sysml.runtime.matrix.MatrixCharacteristics
+ml: org.apache.sysml.api.MLContext = org.apache.sysml.api.MLContext@38d59245
+mcX: org.apache.sysml.runtime.matrix.MatrixCharacteristics = [10000 x 1000, nnz=-1, blocks (1000 x 1000)]
+mcY: org.apache.sysml.runtime.matrix.MatrixCharacteristics = [10000 x 1, nnz=-1, blocks (1000 x 1000)]
+X: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@b5a86e3
+y: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@56377665
+X2: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@650f29d2
+y2: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] = org.apache.spark.api.java.JavaPairRDD@334857a8
 cnt1: Long = 10
 cnt2: Long = 10
 {% endhighlight %}
@@ -936,7 +936,7 @@ val trainingTimePerIter = trainingTime / iters
 **Output:**
 {% highlight scala %}
 start: Long = 1444672090620
-outputs: com.ibm.bi.dml.api.MLOutput = com.ibm.bi.dml.api.MLOutput@5d2c22d0
+outputs: org.apache.sysml.api.MLOutput = org.apache.sysml.api.MLOutput@5d2c22d0
 trainingTime: Double = 1.176
 B: org.apache.spark.sql.DataFrame = [C1: double]
 r2: Double = 0.9677079547216473

docs/quick-start-guide.md

@@ -357,7 +357,7 @@ If you encounter a `"java.lang.OutOfMemoryError"` you can edit the invocation
 script (`runStandaloneSystemML.sh` or `runStandaloneSystemML.bat`) to increase
 the memory available to the JVM, i.e: 
 
-    java -Xmx16g -Xms4g -Xmn1g -cp ${CLASSPATH} com.ibm.bi.dml.api.DMLScript \
+    java -Xmx16g -Xms4g -Xmn1g -cp ${CLASSPATH} org.apache.sysml.api.DMLScript \
          -f ${SCRIPT_FILE} -exec singlenode -config=SystemML-config.xml \
          $@