Add TypedOneHotEncoder

ml/src/main/scala/frameless/ml/feature/TypedOneHotEncoder.scala

@@ -0,0 +1,47 @@
+package frameless
+package ml
+package feature
+
+import frameless.ml.feature.TypedOneHotEncoder.HandleInvalid
+import frameless.ml.internals.UnaryInputsChecker
+import org.apache.spark.ml.Estimator
+import org.apache.spark.ml.feature.{OneHotEncoderEstimator, OneHotEncoderModel}
+import org.apache.spark.ml.linalg.Vector
+
+/**
+ * A one-hot encoder that maps a column of category indices to a column of binary vectors, with
+ * at most a single one-value per row that indicates the input category index.
+ *
+ *  @see `TypedStringIndexer` for converting categorical values into category indices
+ */
+class TypedOneHotEncoder[Inputs] private[ml](oneHotEncoder: OneHotEncoderEstimator, inputCol: String)
+  extends TypedEstimator[Inputs, TypedOneHotEncoder.Outputs, OneHotEncoderModel] {
+
+  override val estimator: Estimator[OneHotEncoderModel] = oneHotEncoder
+    .setInputCols(Array(inputCol))
+    .setOutputCols(Array(AppendTransformer.tempColumnName))
+
+  def setHandleInvalid(value: HandleInvalid): TypedOneHotEncoder[Inputs] =
+    copy(oneHotEncoder.setHandleInvalid(value.sparkValue))
+
+  def setDropLast(value: Boolean): TypedOneHotEncoder[Inputs] =
+    copy(oneHotEncoder.setDropLast(value))
+
+  private def copy(newOneHotEncoder: OneHotEncoderEstimator): TypedOneHotEncoder[Inputs] =
+    new TypedOneHotEncoder[Inputs](newOneHotEncoder, inputCol)
+}
+
+object TypedOneHotEncoder {
+
+  case class Outputs(output: Vector)
+
+  sealed abstract class HandleInvalid(val sparkValue: String)
+  object HandleInvalid {
+    case object Error extends HandleInvalid("error")
+    case object Keep extends HandleInvalid("keep")
+  }
+
+  def apply[Inputs](implicit inputsChecker: UnaryInputsChecker[Inputs, Int]): TypedOneHotEncoder[Inputs] = {
+    new TypedOneHotEncoder[Inputs](new OneHotEncoderEstimator(), inputsChecker.inputCol)
+  }
+}

ml/src/test/scala/frameless/ml/feature/TypedOneHotEncoderTests.scala

@@ -0,0 +1,57 @@
+package frameless
+package ml
+package feature
+
+import frameless.ml.feature.TypedOneHotEncoder.HandleInvalid
+import org.apache.spark.ml.linalg._
+import org.scalacheck.{Arbitrary, Gen}
+import org.scalacheck.Prop._
+import shapeless.test.illTyped
+
+class TypedOneHotEncoderTests extends FramelessMlSuite {
+
+  test(".fit() returns a correct TypedTransformer") {
+    implicit val arbInt = Arbitrary(Gen.choose(0, 99))
+    def prop[A: TypedEncoder : Arbitrary] = forAll { (x2: X2[Int, A], dropLast: Boolean) =>
+      val encoder = TypedOneHotEncoder[X1[Int]].setDropLast(dropLast)
+      val inputs = 0.to(x2.a).map(i => X2(i, x2.b))
+      val ds = TypedDataset.create(inputs)
+      val model = encoder.fit(ds).run()
+      val resultDs = model.transform(TypedDataset.create(Seq(x2))).as[X3[Int, A, Vector]]
+      val result = resultDs.collect.run()
+      if (dropLast) {
+        result == Seq (X3(x2.a, x2.b,
+          Vectors.sparse(x2.a, Array.emptyIntArray, Array.emptyDoubleArray)))
+      } else {
+        result == Seq (X3(x2.a, x2.b,
+          Vectors.sparse(x2.a + 1, Array(x2.a), Array(1.0))))
+      }
+    }
+
+    check(prop[Double])
+    check(prop[String])
+  }
+
+  test("param setting is retained") {
+    implicit val arbHandleInvalid: Arbitrary[HandleInvalid] = Arbitrary {
+      Gen.oneOf(HandleInvalid.Keep, HandleInvalid.Error)
+    }
+
+    val prop = forAll { handleInvalid: HandleInvalid =>
+      val encoder = TypedOneHotEncoder[X1[Int]]
+        .setHandleInvalid(handleInvalid)
+      val ds = TypedDataset.create(Seq(X1(1)))
+      val model = encoder.fit(ds).run()
+
+      model.transformer.getHandleInvalid == handleInvalid.sparkValue
+    }
+
+    check(prop)
+  }
+
+  test("create() compiles only with correct inputs") {
+    illTyped("TypedOneHotEncoder.create[Double]()")
+    illTyped("TypedOneHotEncoder.create[X1[Double]]()")
+    illTyped("TypedOneHotEncoder.create[X2[String, Long]]()")
+  }
+}