[SPARK-32815][ML][3.0] Fix LibSVM data source loading error on file paths with glob metacharacters

mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala

@@ -100,7 +100,7 @@ private[libsvm] class LibSVMFileFormat
         "though the input. If you know the number in advance, please specify it via " +
         "'numFeatures' option to avoid the extra scan.")
 
-      val paths = files.map(_.getPath.toUri.toString)
+      val paths = files.map(_.getPath.toString)
       val parsed = MLUtils.parseLibSVMFile(sparkSession, paths)
       MLUtils.computeNumFeatures(parsed)
     }

mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala

@@ -110,7 +110,8 @@ object MLUtils extends Logging {
       DataSource.apply(
         sparkSession,
         paths = paths,
-        className = classOf[TextFileFormat].getName
+        className = classOf[TextFileFormat].getName,
+        options = Map(DataSource.GLOB_PATHS_KEY -> "false")
       ).resolveRelation(checkFilesExist = false))
       .select("value")
 

mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala

@@ -191,4 +191,24 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
       spark.sql("DROP TABLE IF EXISTS libsvmTable")
     }
   }
+
+  test("SPARK-32815: Test LibSVM data source on file paths with glob metacharacters") {
+    withTempDir { dir =>
+      val basePath = dir.getCanonicalPath
+      // test libsvm writer / reader without specifying schema
+      val svmFileName = "[abc]"
+      val escapedSvmFileName = "\\[abc\\]"
+      val rawData = new java.util.ArrayList[Row]()
+      rawData.add(Row(1.0, Vectors.sparse(2, Seq((0, 2.0), (1, 3.0)))))
+      val struct = new StructType()
+        .add("labelFoo", DoubleType, false)
+        .add("featuresBar", VectorType, false)
+      val df = spark.createDataFrame(rawData, struct)
+      df.write.format("libsvm").save(s"$basePath/$svmFileName")
+      val df2 = spark.read.format("libsvm").load(s"$basePath/$escapedSvmFileName")
+      val row1 = df2.first()
+      val v = row1.getAs[SparseVector](1)
+      assert(v == Vectors.sparse(2, Seq((0, 2.0), (1, 3.0))))
+    }
+  }
 }