Transforming a Leap Frame

    1. // Create our one hot encoder
    2. val oneHotEncoder = OneHotEncoder(shape = NodeShape.vector(1, 2, 
    3.                      inputCol = "a_string_index",
    4.                      outputCol = "a_string_oh"),
    5.   model = OneHotEncoderModel(2, dropLast = false))
    7. // Assemble some features together for use
    8. // By our linear regression
    9. val featureAssembler = VectorAssembler(
    10.      shape = NodeShape().withInput("input0", "a_string_oh").
    11.      model = VectorAssemblerModel(Seq(TensorShape(2), ScalarShape())))
    13. // Create our linear regression
    14. // It has two coefficients, as the one hot encoder
    15. // Outputs vectors of size 2
    16. val linearRegression = LinearRegression(shape = NodeShape.regression(3),
    17.   model = LinearRegressionModel(Vectors.dense(2.0, 3.0, 6.0), 23.5))
    19. // Create a pipeline from all of our transformers
    20. val pipeline = Pipeline(
    23. // Transform our leap frame using the pipeline
    24. val predictions = (for(lf <- pipeline.transform(leapFrame);
    25.                        lf2 <- lf.select("prediction")) yield {
    26.   lf2.dataset.map(_.getDouble(0))
    27. }).get.toSeq
    29. // Print our predictions
    30. //   > 365.70000000000005
    32. println(predictions.mkString("\n"))