Progress bar added to learn and populate functions

build.sbt

@@ -66,7 +66,8 @@ lazy val commonSettings = Seq(
     "com.gurobi" % "gurobi" % "6.0",
     "org.apache.commons" % "commons-math3" % "3.0",
     "org.scalatest" % "scalatest_2.11" % "2.2.4",
-    "ch.qos.logback" % "logback-classic" % "1.1.7"
+    "ch.qos.logback" % "logback-classic" % "1.1.7",
+    "me.tongfei" % "progressbar" % "0.5.5"
   ),
   fork := true,
   connectInput in run := true,

saul-core/src/main/scala/edu/illinois/cs/cogcomp/saul/classifier/Learnable.scala

@@ -24,6 +24,7 @@ import edu.illinois.cs.cogcomp.saul.lbjrelated.LBJLearnerEquivalent
 import edu.illinois.cs.cogcomp.saul.parser.{ IterableToLBJavaParser, LBJavaParserToIterable }
 import edu.illinois.cs.cogcomp.saul.test.TestReal
 import edu.illinois.cs.cogcomp.saul.util.Logging
+import me.tongfei.progressbar.ProgressBar
 
 import scala.reflect.ClassTag
 
@@ -226,24 +227,28 @@ abstract class Learnable[T <: AnyRef](val node: Node[T], val parameters: Paramet
     logger.debug(classifier.getExtractor.getCompositeChildren.toString)
     logger.debug(classifier.getLabeler.toString)
     logger.info(s"Learnable: Learn with data of size ${data.size}")
-    logger.info(s"Training: $iteration iterations remain.")
 
     isTraining = true
+    val pb = new ProgressBar("Training", iteration)
+    pb.start()
 
     (iteration to 1 by -1).foreach(remainingIteration => {
-      if (remainingIteration % 10 == 0)
-        logger.info(s"Training: $remainingIteration iterations remain.")
 
       node.clearPropertyCache()
       data.foreach(classifier.learn)
+      pb.step()
     })
 
+    pb.stop()
     classifier.doneLearning()
     isTraining = false
   }
 
   def learnWithDerivedInstances(numIterations: Int, featureVectors: Iterable[FeatureVector]): Unit = {
     isTraining = true
+    val pb = new ProgressBar("Training", numIterations)
+    pb.start()
+
     val propertyNameSet = feature.map(_.name).toSet
     (0 until numIterations).foreach { _ =>
       featureVectors.foreach {
@@ -262,7 +267,9 @@ abstract class Learnable[T <: AnyRef](val node: Node[T], val parameters: Paramet
           }
           classifier.learn(featureVector)
       }
+      pb.step()
     }
+    pb.stop()
     classifier.doneLearning()
     isTraining = false
   }

saul-core/src/main/scala/edu/illinois/cs/cogcomp/saul/datamodel/node/Node.scala

@@ -12,9 +12,10 @@ import edu.illinois.cs.cogcomp.saul.datamodel.edge.Edge
 import edu.illinois.cs.cogcomp.saul.datamodel.property.Property
 import edu.illinois.cs.cogcomp.saul.datamodel.property.features.discrete.DiscreteProperty
 import edu.illinois.cs.cogcomp.saul.util.Logging
-
 import java.util.concurrent.atomic.AtomicInteger
 
+import me.tongfei.progressbar.ProgressBar
+
 import scala.collection.mutable
 import scala.collection.mutable.{ ArrayBuffer, ListBuffer, HashMap => MutableHashMap, LinkedHashSet => MutableSet, Map => MutableMap }
 import scala.reflect.ClassTag
@@ -156,7 +157,13 @@ class Node[T <: AnyRef](val keyFunc: T => Any = (x: T) => x, val tag: ClassTag[T
     populateEdge: Boolean = true,
     populateJoinNodes: Boolean = true
   ): Unit = {
-    ts.foreach(addInstance(_, train, populateEdge, populateJoinNodes))
+    val pb = new ProgressBar("Population", ts.size)
+    pb.start()
+    ts.foreach(i => {
+      addInstance(i, train, populateEdge, populateJoinNodes)
+      pb.step()
+    })
+    pb.stop()
   }
 
   /** Relational operators */