[SPARK-59173][ML] Use isEmpty/nonEmpty instead of size comparisons in MLlib

### What changes were proposed in this pull request?

This replaces size-vs-zero comparisons with the direct emptiness / non-emptiness predicates in the `mllib` module:
- **Emptiness** (`x.size == 0` -> `x.isEmpty`): `TreeEnsembleModel.totalImportances` (an `OpenHashMap`, which `extends Iterable`) and two `Seq[ShuffleDependency]` checks in `ALSSuite`.
- **Non-emptiness** (`x.size > 0` -> `x.nonEmpty`): `DecisionTreeMetadata.featureArity` and `Strategy.categoricalFeaturesInfo`, both Scala `Map[Int, Int]`.

There are no Java-collection cases in this module.

Deliberately left unchanged, because they are **not** collection-emptiness checks -- they are `mllib.linalg.Vector` dimensions or numeric sizes/thresholds, and `Vector` has no `isEmpty`/`nonEmpty`:
- `ChiSqTest` (`expected` / `observed` are `Vector` dimensions, compared with `!= 0` and `== 0.0`);
- `BinaryClassificationPMMLModelExport` and `GeneralizedLinearPMMLModelExport` (`model.weights`, a `Vector`);
- `MultivariateOnlineSummarizer` (`instance`, a `Vector` -- the message reads "Vector should have dimension larger than zero");
- `BlockMatrix` (a matrix row `Vector`);
- `ALS` (`RatingBlockBuilder.size`, a custom builder with no `nonEmpty`);
- `SpearmanCorrelation` (`cachedUids.size >= 10000000`, a size threshold, not an emptiness check).

### Why are the changes needed?

`isEmpty` / `nonEmpty` state the intent directly. Behavior is unchanged: every converted receiver (`OpenHashMap`, `Seq`, Scala `Map`) defines the predicate as exactly equivalent to the original comparison.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Existing tests. This is a behavior-preserving refactor; the `mllib` module compiles cleanly. Counterpart of SPARK-59149 (SQL) and SPARK-59172 (core).

### Was this patch authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Opus 4.8)

Closes #58471 from uros-b/mllib-isempty.

Authored-by: Uros <221401595+uros-b@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

Author: uros-b

mllib/src/main/scala/org/apache/spark/ml/tree/impl/DecisionTreeMetadata.scala

@@ -66,7 +66,7 @@ private[spark] class DecisionTreeMetadata(
 
   def isMulticlass: Boolean = numClasses > 2
 
-  def isMulticlassWithCategoricalFeatures: Boolean = isMulticlass && (featureArity.size > 0)
+  def isMulticlassWithCategoricalFeatures: Boolean = isMulticlass && (featureArity.nonEmpty)
 
   def isCategorical(featureIndex: Int): Boolean = featureArity.contains(featureIndex)
 

mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala

@@ -229,7 +229,7 @@ private[ml] object TreeEnsembleModel {
       maxFeatureIndex + 1
     }
     if (d == 0) {
-      assert(totalImportances.size == 0, s"Unknown error in computing feature" +
+      assert(totalImportances.isEmpty, s"Unknown error in computing feature" +
         s" importance: No splits found, but some non-zero importances.")
     }
     val (indices, values) = totalImportances.iterator.toSeq.sortBy(_._1).unzip

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala

@@ -98,7 +98,7 @@ class Strategy @Since("1.3.0") (
    */
   @Since("1.2.0")
   def isMulticlassWithCategoricalFeatures: Boolean = {
-    isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
+    isMulticlassClassification && (categoricalFeaturesInfo.nonEmpty)
   }
 
   // scalastyle:off argcount

mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala

@@ -995,8 +995,8 @@ class ALSSuite extends MLTest with DefaultReadWriteTest with Logging {
     val shuffledItemFactors = getShuffledDependencies(itemFactors.rdd).filter { dep =>
       dep.rdd.name != null && dep.rdd.name.contains("itemFactors")
     }
-    assert(shuffledUserFactors.size == 0)
-    assert(shuffledItemFactors.size == 0)
+    assert(shuffledUserFactors.isEmpty)
+    assert(shuffledItemFactors.isEmpty)
   }
 
   private def checkRecommendations(