MLfeature

Feature engineering toolkit for Spark MLlib:

• Data preprocessing:
  • Handle imbalanced dataset: DataBalancer
  • Handle missing values: (Implemented in Spark 2.2, SPARK-13568)
    • Impute continuous missing values with mean: MissingValueMeanImputor
• Feature selection:
  • VarianceSelector: remove fetures with low variance
  • UnivariateSelector: feature selection with univariate metrics
  • ByModelSelector: feature selection with a model
• Feature transformers:
  • Enhanced Bucketizer: MyBucketizer (Waiting to be merged, SPARK-19781)
  • Enhanced StringIndexer: MyStringIndexer (Merged with Spark 2.2, SPARK-17233)

Handle imbalcned dataset

• DataBalancer: Make an balanced dataset with multiple strategies:
  • Re-sampling:
    • over-sampling
    • under-sampling
    • middle-sampling
  • SMOTE: TODO

Example:

val data = Array("a", "a", "b", "c")
val dataFrame = data.toSeq.toDF("feature")
val balancer = new DataBalancer()
  .setStrategy("oversampling")
  .setInputCol("feature")
val result = balacner.transform(dataFrame)
result.show(100)

val data: Seq[String] = Seq("a", "a", "a", "a", "b", "b","b", "c")
val dataFrame = data.toDF("feature")
val balancer = new DataBalancer()
  .setStrategy("undersampling")
  .setInputCol("feature")
val result = balancer.transform(dataFrame)
result.show(100)

val data: Seq[String] = Seq("a", "a", "a", "a", "b", "b","b", "c")
val dataFrame = data.toDF("feature")
val balancer = new DataBalancer()
  .setStrategy("middlesampling")
  .setInputCol("feature")
val result = balancer.transform(dataFrame)
result.show(100)

Handle missing values

• MissingValueMeanImputer: Impute continuous missing values with mean

Feature Selection

VarianceSelector

VarianceSelector is a simple baseline approach to feature selection. It removes all features whose variance doesn’t meet some threshold. By default, it removes all zero-variance features, i.e. features that have the same value in all samples.

val data = Array(
  Vectors.dense(0, 1.0, 0),
  Vectors.dense(0, 3.0, 0),
  Vectors.dense(0, 4.0, 0),
  Vectors.dense(0, 5.0, 0),
  Vectors.dense(1, 6.0, 0)
)
val expected = Array(
  Vectors.dense(1.0),
  Vectors.dense(3.0),
  Vectors.dense(4.0),
  Vectors.dense(5.0),
  Vectors.dense(6.0)
)
val df = data.zip(expected).toSeq.toDF("features", "expected")
val selector = new VarianceSelector()
  .setInputCol("features")
  .setOutputCol("selected")
  .setThreshold(3)
val result = selector.transform(df)
result.select("expected", "selected").collect()
  .foreach { case Row(vector1: Vector, vector2: Vector) =>
    assert(vector1.equals(vector2), "Transformed vector is different with expected.")
  }

UnivariateSelector

TODO

ByModelSelector

TODO

Feature transform

MyBucketizer: Enhanced Bucketizer

Put NULLs and values out of bounds into a special bucket as well as NaN.

Example:

val splits = Array(-0.5, 0.0, 0.5)
val validData = Array(-0.5, -0.3, 0.0, 0.2)
val expectedBuckets = Array(0.0, 0.0, 1.0, 1.0)
val dataFrame: DataFrame = validData.zip(expectedBuckets).toSeq.toDF("feature", "expected")
val bucketizer: MyBucketizer = new MyBucketizer()
  .setInputCol("feature")
  .setOutputCol("result")
  .setSplits(splits)
val transformed = bucketizer.transform(dataFrame)

MyStringIndxer: Enhanced StringIndexer

Give NULLs and unseen lables a special index.

Example:

val data = Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c"))
val df = data.toDF("id", "label")
val indexer = new MyStringIndexer()
  .setInputCol("label")
  .setOutputCol("labelIndex")
  .fit(df)
val transformed = indexer.transform(df)