Fix scaladoc build errors (#13189)

* Fix scaladoc errors from missing classpath

Remove duplicate scalastyle plugin

* Fix scaladoc warnings

Also enable and fix all feature and deprecation warnings

docs/mxdoc.py

@@ -110,8 +110,13 @@ def build_scala(app):
 def build_scala_docs(app):
     """build scala doc and then move the outdir"""
     scala_path = app.builder.srcdir + '/../scala-package'
-    # scaldoc fails on some apis, so exit 0 to pass the check
-    _run_cmd('cd ' + scala_path + '; scaladoc `find . -type f -name "*.scala" | egrep \"\/core|\/infer\" | egrep -v \"Suite|javaapi\"`; exit 0')
+    scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"Suite\"'
+    scala_doc_classpath = ':'.join([
+        '`find native -name "*.jar" | grep "target/lib/" | tr "\\n" ":" `',
+        '`find macros -name "*-SNAPSHOT.jar" | tr "\\n" ":" `'
+    ])
+    _run_cmd('cd {}; scaladoc `{}` -classpath {} -feature -deprecation'
+             .format(scala_path, scala_doc_sources, scala_doc_classpath))
     dest_path = app.builder.outdir + '/api/scala/docs'
     _run_cmd('rm -rf ' + dest_path)
     _run_cmd('mkdir -p ' + dest_path)

scala-package/core/pom.xml

@@ -93,10 +93,6 @@
         <groupId>org.scalastyle</groupId>
         <artifactId>scalastyle-maven-plugin</artifactId>
       </plugin>
-      <plugin>
-        <groupId>org.scalastyle</groupId>
-        <artifactId>scalastyle-maven-plugin</artifactId>
-      </plugin>
     </plugins>
   </build>
   <dependencies>

scala-package/core/src/main/scala/org/apache/mxnet/Context.scala

@@ -17,6 +17,8 @@
 
 package org.apache.mxnet
 
+import scala.language.implicitConversions
+
 object Context {
   val devtype2str = Map(1 -> "cpu", 2 -> "gpu", 3 -> "cpu_pinned")
   val devstr2type = Map("cpu" -> 1, "gpu" -> 2, "cpu_pinned" -> 3)

scala-package/core/src/main/scala/org/apache/mxnet/Executor.scala

@@ -224,7 +224,6 @@ class Executor private[mxnet](private[mxnet] val handle: ExecutorHandle,
   /**
    * Get dictionary representation of argument arrrays.
    * @return The dictionary that maps name of arguments to NDArrays.
-   * @throws IllegalArgumentException if there are duplicated names in the arguments.
    */
   def argDict: Map[String, NDArray] = {
     if (_argDict == null) {
@@ -236,7 +235,6 @@ class Executor private[mxnet](private[mxnet] val handle: ExecutorHandle,
   /**
    * Get dictionary representation of gradient arrays.
    * @return The dictionary that maps name of arguments to gradient arrays.
-   * @throws IllegalArgumentException if there are duplicated names in the grads.
    */
   def gradDict: Map[String, NDArray] = {
     if (_gradDict == null) {
@@ -248,7 +246,6 @@ class Executor private[mxnet](private[mxnet] val handle: ExecutorHandle,
   /**
    * Get dictionary representation of auxiliary states arrays.
    * @return The dictionary that maps name of auxiliary states to NDArrays.
-   * @throws IllegalArgumentException if there are duplicated names in the auxiliary states.
    */
   def auxDict: Map[String, NDArray] = {
     if (_auxDict == null) {
@@ -265,8 +262,6 @@ class Executor private[mxnet](private[mxnet] val handle: ExecutorHandle,
    *        Whether allow extra parameters that are not needed by symbol
    *        If this is True, no error will be thrown when arg_params or aux_params
    *        contain extra parameters that is not needed by the executor.
-   * @throws IllegalArgumentException
-   *         If there is additional parameters in the dict but allow_extra_params=False
    */
   def copyParamsFrom(argParams: Map[String, NDArray],
                      auxParams: Map[String, NDArray],

scala-package/core/src/main/scala/org/apache/mxnet/IO.scala

@@ -25,6 +25,7 @@ import org.slf4j.LoggerFactory
 import scala.annotation.varargs
 import scala.collection.immutable.ListMap
 import scala.collection.mutable.ListBuffer
+import scala.language.implicitConversions
 /**
  * IO iterators for loading training & validation data
  */
@@ -340,11 +341,11 @@ abstract class DataIter extends Iterator[DataBatch] {
   def getIndex(): IndexedSeq[Long]
 
   // The name and shape of data provided by this iterator
-  @deprecated
+  @deprecated("Use provideDataDesc instead", "1.3.0")
   def provideData: ListMap[String, Shape]
 
   // The name and shape of label provided by this iterator
-  @deprecated
+  @deprecated("Use provideLabelDesc instead", "1.3.0")
   def provideLabel: ListMap[String, Shape]
 
   // Provide type:DataDesc of the data
@@ -404,7 +405,7 @@ object DataDesc {
     }
   }
 
-  @deprecated
+  @deprecated("Please use DataDesc methods instead", "1.3.0")
   implicit def ListMap2Descs(shapes: ListMap[String, Shape]): IndexedSeq[DataDesc] = {
     if (shapes != null) {
       shapes.map { case (k, s) => new DataDesc(k, s) }.toIndexedSeq

scala-package/core/src/main/scala/org/apache/mxnet/KVStore.scala

@@ -286,7 +286,7 @@ class KVStore(private[mxnet] val handle: KVStoreHandle) extends NativeResource {
       case cachedStates: MXKVStoreCachedStates =>
         val bis = new BufferedInputStream (new FileInputStream (fname) )
         try {
-        val bArray = Stream.continually (bis.read).takeWhile (- 1 !=).map (_.toByte).toArray
+        val bArray = Stream.continually (bis.read).takeWhile (_ != -1).map (_.toByte).toArray
           cachedStates.deserializeState(bArray)
         } finally {
           bis.close ()

scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala

@@ -25,6 +25,7 @@ import org.slf4j.LoggerFactory
 
 import scala.collection.mutable
 import scala.collection.mutable.{ArrayBuffer, ListBuffer}
+import scala.language.implicitConversions
 import scala.ref.WeakReference
 
 /**

scala-package/core/src/main/scala/org/apache/mxnet/Optimizer.scala

@@ -144,7 +144,7 @@ abstract class Optimizer extends Serializable {
   def deserializeState(bytes: Array[Byte]): AnyRef
 
   // Set individual learning rate scale for parameters
-  @deprecated("Use setLrMult instead.")
+  @deprecated("Use setLrMult instead.", "0.10.0")
   def setLrScale(lrScale: Map[Int, Float]): Unit = {
     val argsLrScale: Map[Either[Int, String], Float] = lrScale.map { case (k, v) => Left(k) -> v }
     setLrMult(argsLrScale)

scala-package/core/src/main/scala/org/apache/mxnet/ResourceScope.scala

@@ -27,7 +27,7 @@ import scala.util.Try
 import scala.util.control.{ControlThrowable, NonFatal}
 
 /**
-  * This class manages automatically releasing of [[NativeResource]]s
+  * This class manages automatically releasing of `org.apache.mxnet.NativeResource`s
   */
 class ResourceScope extends AutoCloseable {
 
@@ -43,8 +43,8 @@ class ResourceScope extends AutoCloseable {
   ResourceScope.addToThreadLocal(this)
 
   /**
-    * Releases all the [[NativeResource]] by calling
-    * the associated [[NativeResource.close()]] method
+    * Releases all the `org.apache.mxnet.NativeResource` by calling
+    * the associated`'org.apache.mxnet.NativeResource.close()` method
     */
   override def close(): Unit = {
     ResourceScope.removeFromThreadLocal(this)

scala-package/core/src/main/scala/org/apache/mxnet/Symbol.scala

@@ -22,6 +22,7 @@ import org.apache.mxnet.DType.DType
 import org.slf4j.{Logger, LoggerFactory}
 
 import scala.collection.mutable.{ArrayBuffer, ListBuffer}
+import scala.language.implicitConversions
 
 /**
  * Symbolic configuration API of mxnet. <br />

scala-package/core/src/main/scala/org/apache/mxnet/Visualization.scala

@@ -21,6 +21,7 @@ import scala.util.parsing.json._
 import java.io.File
 import java.io.PrintWriter
 import scala.collection.mutable.ArrayBuffer
+import scala.language.postfixOps
 
 object Visualization {
 

scala-package/core/src/main/scala/org/apache/mxnet/io/MXDataIter.scala

@@ -158,11 +158,11 @@ private[mxnet] class MXDataIter(private[mxnet] val handle: DataIterHandle,
   }
 
   // The name and shape of data provided by this iterator
-  @deprecated
+  @deprecated("Please use provideDataDesc instead", "1.3.0")
   override def provideData: ListMap[String, Shape] = _provideData
 
   // The name and shape of label provided by this iterator
-  @deprecated
+  @deprecated("Please use provideLabelDesc instead", "1.3.0")
   override def provideLabel: ListMap[String, Shape] = _provideLabel
 
   // Provide type:DataDesc of the data

scala-package/core/src/main/scala/org/apache/mxnet/io/NDArrayIter.scala

@@ -237,11 +237,11 @@ class NDArrayIter(data: IndexedSeq[(DataDesc, NDArray)],
 
 
   // The name and shape of data provided by this iterator
-  @deprecated
+  @deprecated("Please use provideDataDesc instead", "1.3.0")
   override def provideData: ListMap[String, Shape] = _provideData
 
   // The name and shape of label provided by this iterator
-  @deprecated
+  @deprecated("Please use provideLabelDesc instead", "1.3.0")
   override def provideLabel: ListMap[String, Shape] = _provideLabel
 
   // Provide type:DataDesc of the data

scala-package/core/src/main/scala/org/apache/mxnet/io/PrefetchingIter.scala

@@ -178,11 +178,11 @@ class PrefetchingIter(
   override def getPad(): Int = this.currentBatch.pad
 
   // The name and shape of label provided by this iterator
-  @deprecated
+  @deprecated("Please use provideDataDesc instead", "1.3.0")
   override def provideLabel: ListMap[String, Shape] = this._provideLabel
 
   // The name and shape of data provided by this iterator
-  @deprecated
+  @deprecated("Please use provideLabelDesc instead", "1.3.0")
   override def provideData: ListMap[String, Shape] = this._provideData
 
   // Provide type:DataDesc of the data

scala-package/core/src/main/scala/org/apache/mxnet/io/ResizeIter.scala

@@ -134,13 +134,13 @@ class ResizeIter(
   }
 
   // The name and shape of data provided by this iterator
-  @deprecated
+  @deprecated("Please use provideDataDesc instead", "1.3.0")
   override def provideData: ListMap[String, Shape] = {
     dataIter.provideData
   }
 
   // The name and shape of label provided by this iterator
-  @deprecated
+  @deprecated("Please use provideLabelDesc instead", "1.3.0")
   override def provideLabel: ListMap[String, Shape] = {
     dataIter.provideLabel
   }

scala-package/core/src/main/scala/org/apache/mxnet/javaapi/Context.scala

@@ -17,6 +17,7 @@
 package org.apache.mxnet.javaapi
 
 import collection.JavaConverters._
+import scala.language.implicitConversions
 
 class Context(val context: org.apache.mxnet.Context) {
 

scala-package/core/src/main/scala/org/apache/mxnet/javaapi/IO.scala

@@ -17,6 +17,8 @@
 
 package org.apache.mxnet.javaapi
 
+import scala.language.implicitConversions
+
 class DataDesc(val dataDesc: org.apache.mxnet.DataDesc) {
 
   def this(name: String, shape: Shape, dType: DType.DType, layout: String) =

scala-package/core/src/main/scala/org/apache/mxnet/javaapi/Shape.scala

@@ -18,6 +18,7 @@
 package org.apache.mxnet.javaapi
 
 import collection.JavaConverters._
+import scala.language.implicitConversions
 
 /**
   * Shape of [[NDArray]] or other data

scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala

@@ -210,7 +210,7 @@ abstract class BaseModule {
    * @param reset Default is `True`, indicating whether we should reset the data iter before start
    *              doing prediction.
    * @return The return value will be a nested list like
-   *         `[[out1_batch1, out2_batch1, ...], [out1_batch2, out2_batch2, ...]]`
+   *         `[ [out1_batch1, out2_batch1, ...], [out1_batch2, out2_batch2, ...] ]`
    *         This mode is useful because in some cases (e.g. bucketing),
    *         the module does not necessarily produce the same number of outputs.
    */
@@ -501,7 +501,7 @@ abstract class BaseModule {
    * Get outputs of the previous forward computation.
    * @return In the case when data-parallelism is used,
    *         the outputs will be collected from multiple devices.
-   *         The results will look like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`,
+   *         The results will look like `[ [out1_dev1, out1_dev2], [out2_dev1, out2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getOutputs(): IndexedSeq[IndexedSeq[NDArray]]
@@ -519,7 +519,7 @@ abstract class BaseModule {
    * Get the gradients to the inputs, computed in the previous backward computation.
    * @return In the case when data-parallelism is used,
    *         the grads will be collected from multiple devices.
-   *         The results will look like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`,
+   *         The results will look like `[ [grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]]

scala-package/core/src/main/scala/org/apache/mxnet/module/BucketingModule.scala

@@ -339,7 +339,7 @@ class BucketingModule(symGen: AnyRef => (Symbol, IndexedSeq[String], IndexedSeq[
    * Get outputs of the previous forward computation.
    * @return In the case when data-parallelism is used,
    *         the outputs will be collected from multiple devices.
-   *         The results will look like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`,
+   *         The results will look like `[ [out1_dev1, out1_dev2], [out2_dev1, out2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   override def getOutputs(): IndexedSeq[IndexedSeq[NDArray]] = {
@@ -363,7 +363,7 @@ class BucketingModule(symGen: AnyRef => (Symbol, IndexedSeq[String], IndexedSeq[
    * Get the gradients to the inputs, computed in the previous backward computation.
    * @return In the case when data-parallelism is used,
    *         the grads will be collected from multiple devices.
-   *         The results will look like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`,
+   *         The results will look like `[ [grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   override def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]] = {

scala-package/core/src/main/scala/org/apache/mxnet/module/DataParallelExecutorGroup.scala

@@ -517,7 +517,7 @@ class DataParallelExecutorGroup private[module](
    * Get outputs of the previous forward computation.
    * @return In the case when data-parallelism is used,
    *         the outputs will be collected from multiple devices.
-   *         The results will look like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`,
+   *         The results will look like `[ [out1_dev1, out1_dev2], [out2_dev1, out2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getOutputs(): IndexedSeq[IndexedSeq[NDArray]] = {
@@ -539,7 +539,7 @@ class DataParallelExecutorGroup private[module](
    * Get the gradients to the inputs, computed in the previous backward computation.
    * @return In the case when data-parallelism is used,
    *         the grads will be collected from multiple devices.
-   *         The results will look like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`,
+   *         The results will look like `[ [grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]] = {

scala-package/core/src/main/scala/org/apache/mxnet/module/Module.scala

@@ -486,7 +486,7 @@ class Module(symbolVar: Symbol,
    * Get outputs of the previous forward computation.
    * @return In the case when data-parallelism is used,
    *         the outputs will be collected from multiple devices.
-   *         The results will look like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`,
+   *         The results will look like `[ [out1_dev1, out1_dev2], [out2_dev1, out2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getOutputs(): IndexedSeq[IndexedSeq[NDArray]] = {
@@ -510,7 +510,7 @@ class Module(symbolVar: Symbol,
    * Get the gradients to the inputs, computed in the previous backward computation.
    * @return In the case when data-parallelism is used,
    *         the grads will be collected from multiple devices.
-   *         The results will look like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`,
+   *         The results will look like `[ [grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]] = {

scala-package/core/src/main/scala/org/apache/mxnet/module/SequentialModule.scala

@@ -346,7 +346,7 @@ class SequentialModule extends BaseModule {
    * Get outputs of the previous forward computation.
    * @return In the case when data-parallelism is used,
    *         the outputs will be collected from multiple devices.
-   *         The results will look like `[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]`,
+   *         The results will look like `[ [out1_dev1, out1_dev2], [out2_dev1, out2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getOutputs(): IndexedSeq[IndexedSeq[NDArray]] = {
@@ -370,7 +370,7 @@ class SequentialModule extends BaseModule {
    * Get the gradients to the inputs, computed in the previous backward computation.
    * @return In the case when data-parallelism is used,
    *         the grads will be collected from multiple devices.
-   *         The results will look like `[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]`,
+   *         The results will look like `[ [grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2] ]`,
    *         those `NDArray` might live on different devices.
    */
   def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]] = {