a9a

http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a9a

Data preparation

$ wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a
$ wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t
scala> :paste
val rawTrainDf = spark.read.format("libsvm").load("a9a")

val (max, min) = rawTrainDf.select(max($"label"), min($"label")).collect.map {
  case Row(max: Double, min: Double) => (max, min)
}

val trainDf = rawTrainDf.select(
    // `label` must be [0.0, 1.0]
    rescale($"label", lit(min), lit(max)).as("label"),
    $"features"
  )

scala> trainDf.printSchema
root
 |-- label: float (nullable = true)
 |-- features: vector (nullable = true)

scala> :paste
val testDf = spark.read.format("libsvm").load("a9a.t")
  .select(rowid(), rescale($"label", lit(min), lit(max)).as("label"), $"features")
  .explode_vector($"features")
  .select($"rowid", $"label".as("target"), $"feature", $"weight".as("value"))
  .cache

scala> testDf.printSchema
root
 |-- rowid: string (nullable = true)
 |-- target: float (nullable = true)
 |-- feature: string (nullable = true)
 |-- value: double (nullable = true)

Tutorials

[Logistic Regression]

Training

scala> :paste
val modelDf = trainDf
  .train_logregr(append_bias($"features"), $"label")
  .groupBy("feature").avg("weight")
  .toDF("feature", "weight")
  .cache

Test

scala> :paste
val predictDf = testDf
  .join(modelDf, testDf("feature") === modelDf("feature"), "LEFT_OUTER")
  .select($"rowid", ($"weight" * $"value").as("value"))
  .groupBy("rowid").sum("value")
  .select(
    $"rowid",
    when(sigmoid($"sum(value)") > 0.5, 1.0).otherwise(0.0).as("predicted")
  )

Evaluation

scala> val df = predictDf.join(testDf, predictDf("rowid").as("id") === testDf("rowid"), "INNER")

scala> (df.where($"target" === $"predicted").count + 0.0) / df.count
Double = 0.8327921286841418

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