updating lightgbm to 2.3.170

microsoft · Apr 7, 2020 · 714a2d3 · 714a2d3
1 parent 4d99879
commit 714a2d3
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Showing 5 changed files with 16 additions and 13 deletions.
diff --git a/build.sbt b/build.sbt
@@ -24,7 +24,7 @@ libraryDependencies ++= Seq(
   "com.jcraft" % "jsch" % "0.1.54",
   "com.microsoft.cognitiveservices.speech" % "client-sdk" % "1.10.0",
   "org.apache.httpcomponents" % "httpclient" % "4.5.6",
-  "com.microsoft.ml.lightgbm" % "lightgbmlib" % "2.3.150",
+  "com.microsoft.ml.lightgbm" % "lightgbmlib" % "2.3.170",
   "com.github.vowpalwabbit" % "vw-jni" % "8.7.0.3",
   "com.linkedin.isolation-forest" %% "isolation-forest_2.4.3" % "0.3.2",
   "org.apache.spark" %% "spark-avro" % sparkVersion

diff --git a/src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMDataset.scala b/src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMDataset.scala
@@ -10,9 +10,6 @@ import com.microsoft.ml.lightgbm._
   * @param dataset The native representation of the dataset.
   */
 class LightGBMDataset(val dataset: SWIGTYPE_p_void) extends AutoCloseable {
-  var featureNames: Option[SWIGTYPE_p_p_char] = None
-  var featureNamesOpt2: Option[Array[String]] = None
-
   def validateDataset(): Unit = {
     // Validate num rows
     val numDataPtr = lightgbmlib.new_intp()

diff --git a/src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMUtils.scala b/src/main/scala/com/microsoft/ml/spark/lightgbm/LightGBMUtils.scala
@@ -33,6 +33,13 @@ object LightGBMUtils {
     }
   }
 
+  def validateArray(result: SWIGTYPE_p_void, component: String): Unit = {
+    if (result == null) {
+      throw new Exception(component + " call failed in LightGBM with error: "
+        + lightgbmlib.LGBM_GetLastError())
+    }
+  }
+
   /** Loads the native shared object binaries lib_lightgbm.so and lib_lightgbm_swig.so
     */
   def initializeNativeLibrary(): Unit = {

diff --git a/src/main/scala/com/microsoft/ml/spark/lightgbm/TrainUtils.scala b/src/main/scala/com/microsoft/ml/spark/lightgbm/TrainUtils.scala
@@ -180,13 +180,12 @@ private object TrainUtils extends Serializable {
 
   def getEvalNames(boosterPtr: Option[SWIGTYPE_p_void]): Array[String] = {
     // Need to keep track of best scores for each metric, see callback.py in lightgbm for reference
-    val evalCountsPtr = lightgbmlib.new_intp()
-    val resultCounts = lightgbmlib.LGBM_BoosterGetEvalCounts(boosterPtr.get, evalCountsPtr)
-    LightGBMUtils.validate(resultCounts, "Booster Get Eval Counts")
-    val evalCounts = lightgbmlib.intp_value(evalCountsPtr)
-    // For debugging, can get metric names:
-    val evalNamesPtr = lightgbmlib.LGBM_BoosterGetEvalNamesSWIG(boosterPtr.get, evalCounts)
-    (0 until evalCounts).map(lightgbmlib.stringArray_getitem(evalNamesPtr, _)).toArray
+    // For debugging, can get metric names
+    val stringArrayHandle = lightgbmlib.LGBM_BoosterGetEvalNamesSWIG(boosterPtr.get)
+    LightGBMUtils.validateArray(stringArrayHandle, "Booster Get Eval Names")
+    val evalNames = lightgbmlib.StringArrayHandle_get_strings(stringArrayHandle)
+    lightgbmlib.StringArrayHandle_free(stringArrayHandle)
+    evalNames
   }
 
   def trainCore(trainParams: TrainParams, boosterPtr: Option[SWIGTYPE_p_void],

diff --git a/src/test/scala/com/microsoft/ml/spark/lightgbm/split1/VerifyLightGBMClassifier.scala b/src/test/scala/com/microsoft/ml/spark/lightgbm/split1/VerifyLightGBMClassifier.scala
@@ -309,9 +309,9 @@ class VerifyLightGBMClassifier extends Benchmarks with EstimatorFuzzing[LightGBM
     // If the max delta step is specified, assert AUC differs (assert parameter works)
     // Note: the final max output of leaves is learning_rate * max_delta_step, so param should reduce the effect
     val Array(train, test) = taskDF.randomSplit(Array(0.8, 0.2), seed)
-    val baseModelWithLR = baseModel.setLearningRate(0.9).setNumIterations(100)
+    val baseModelWithLR = baseModel.setLearningRate(0.9).setNumIterations(200)
     val scoredDF1 = baseModelWithLR.fit(train).transform(test)
-    val scoredDF2 = baseModelWithLR.setMaxDeltaStep(0.1).fit(train).transform(test)
+    val scoredDF2 = baseModelWithLR.setMaxDeltaStep(0.5).fit(train).transform(test)
     assertBinaryImprovement(scoredDF1, scoredDF2)
   }