RandomForestClassifier was given input with invalid label column error in Apache Spark

Question

I am trying to find Accuracy using 5-fold cross validation using Random Forest Classifier Model in SCALA. But i am getting the following error while running:

Answer 1

RandomForestClassifier, same as many other ML algorithms, require specific metadata to be set on the label column and labels values to be integral values from [0, 1, 2 ..., #classes) represented as doubles. Typically this is handled by an upstream Transformers like StringIndexer. Since you convert labels manually metadata fields are not set and classifier cannot confirm that these requirements are satisfied.

val df = Seq(
  (0.0, Vectors.dense(1, 0, 0, 0)),
  (1.0, Vectors.dense(0, 1, 0, 0)),
  (2.0, Vectors.dense(0, 0, 1, 0)),
  (2.0, Vectors.dense(0, 0, 0, 1))
).toDF("label", "features")

val rf = new RandomForestClassifier()
  .setFeaturesCol("features")
  .setNumTrees(5)

rf.setLabelCol("label").fit(df)
// java.lang.IllegalArgumentException: RandomForestClassifier was given input ...

You can either re-encode label column using StringIndexer:

import org.apache.spark.ml.feature.StringIndexer

val indexer = new StringIndexer()
  .setInputCol("label")
  .setOutputCol("label_idx")
  .fit(df)

rf.setLabelCol("label_idx").fit(indexer.transform(df))

or set required metadata manually:

val meta = NominalAttribute
  .defaultAttr
  .withName("label")
  .withValues("0.0", "1.0", "2.0")
  .toMetadata

rf.setLabelCol("label_meta").fit(
  df.withColumn("label_meta", $"label".as("", meta))
)

Note:

Labels created using StringIndexer depend on the frequency not value:

indexer.labels
// Array[String] = Array(2.0, 0.0, 1.0)

PySpark:

In Python metadata fields can be set directly on the schema:

from pyspark.sql.types import StructField, DoubleType

StructField(
    "label", DoubleType(), False,
    {"ml_attr": {
        "name": "label",
        "type": "nominal", 
        "vals": ["0.0", "1.0", "2.0"]
    }}
)