Spark ML throws exception for Decision Tree classification: Column features must be of type numeric but was actually of type struct - scala

Question

I am trying to create a Spark ML model with the Decision Tree Classifier to perform classification , but I am getting an error saying the features in my training set should be of type numeric instead of type struct.
Here is the minimal reproducible example that I tried:
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.linalg.VectorUDT
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml._
import org.apache.spark.ml.classification.DecisionTreeClassificationModel
import org.apache.spark.ml.classification.DecisionTreeClassifier
val df8 = Seq(
("2022-08-22 10:00:00",417.7,419.97,419.97,417.31,"nothing"),
("2022-08-22 11:30:00",417.35,417.33,417.46,416.77,"buy"),
("2022-08-22 13:00:00",417.55,417.68,418.04,417.48,"sell"),
("2022-08-22 14:00:00",417.22,417.8,421.13,416.83,"sell")
)
val df77 = spark.createDataset(df8).toDF("30mins_date","30mins_close","30mins_open","30mins_high","30mins_low", "signal")
val assembler_features = new VectorAssembler()
.setInputCols(Array("30mins_close","30mins_open","30mins_high","30mins_low"))
.setOutputCol("features")
val output2 = assembler_features.transform(df77)
val indexer = new StringIndexer()
.setInputCol("signal")
.setOutputCol("signalIndex")
val indexed = indexer.fit(output2).transform(output2)
val assembler_label = new VectorAssembler()
.setInputCols(Array("signalIndex"))
.setOutputCol("signalIndexV")
val output = assembler_label.transform(indexed)
val dt = new DecisionTreeClassifier()
.setLabelCol("features")
.setFeaturesCol("signalIndexV")
val Array(trainingData, testData) = output.select("features", "signalIndexV").randomSplit(Array(0.7, 0.3))
val model = dt.fit(trainingData)
Output error:
java.lang.IllegalArgumentException: requirement failed: Column features must be of type numeric but was actually of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>>.
at scala.Predef$.require(Predef.scala:281)
at org.apache.spark.ml.util.SchemaUtils$.checkNumericType(SchemaUtils.scala:78)
at org.apache.spark.ml.PredictorParams.validateAndTransformSchema(Predictor.scala:54)
at org.apache.spark.ml.PredictorParams.validateAndTransformSchema$(Predictor.scala:47)
at org.apache.spark.ml.classification.Classifier.org$apache$spark$ml$classification$ClassifierParams$$super$validateAndTransformSchema(Classifier.scala:73)
at org.apache.spark.ml.classification.ClassifierParams.validateAndTransformSchema(Classifier.scala:43)
at org.apache.spark.ml.classification.ClassifierParams.validateAndTransformSchema$(Classifier.scala:39)
at org.apache.spark.ml.classification.ProbabilisticClassifier.org$apache$spark$ml$classification$ProbabilisticClassifierParams$$super$validateAndTransformSchema(ProbabilisticClassifier.scala:51)
at org.apache.spark.ml.classification.ProbabilisticClassifierParams.validateAndTransformSchema(ProbabilisticClassifier.scala:38)
at org.apache.spark.ml.classification.ProbabilisticClassifierParams.validateAndTransformSchema$(ProbabilisticClassifier.scala:34)
at org.apache.spark.ml.classification.DecisionTreeClassifier.org$apache$spark$ml$tree$DecisionTreeClassifierParams$$super$validateAndTransformSchema(DecisionTreeClassifier.scala:46)
at org.apache.spark.ml.tree.DecisionTreeClassifierParams.validateAndTransformSchema(treeParams.scala:245)
at org.apache.spark.ml.tree.DecisionTreeClassifierParams.validateAndTransformSchema$(treeParams.scala:241)
at org.apache.spark.ml.classification.DecisionTreeClassifier.validateAndTransformSchema(DecisionTreeClassifier.scala:46)
at org.apache.spark.ml.Predictor.transformSchema(Predictor.scala:177)
at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:71)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:133)
... 61 elided
I tried above code in spark-shell environment:
spark v 3.3.1
scala v 2.12.15
Here is what trainingData looks like
+-----------------------------+------------+
|features |signalIndexV|
+-----------------------------+------------+
|[417.7,419.97,419.97,417.31] |[2.0] |
|[417.35,417.33,417.46,416.77]|[1.0] |
|[417.55,417.68,418.04,417.48]|[0.0] |
|[417.22,417.8,421.13,416.83] |[0.0] |
+-----------------------------+------------+
So what did I do wrong ? How can I convert column features into numeric type ?