Reconstruct Training Data via H2O GLRM Model

Reconstruct the training data and impute missing values from the H2O GLRM model by computing the matrix product of X and Y, and transforming back to the original feature space by minimizing each column's loss function.

h2o.reconstruct(object, data, reverse_transform = FALSE)

Arguments

object	An H2ODimReductionModel object that represents the model to be used for reconstruction.
data	An H2OFrame object representing the training data for the H2O GLRM model. Used to set the domain of each column in the reconstructed frame.
reverse_transform	(Optional) A logical value indicating whether to reverse the transformation from model-building by re-scaling columns and adding back the offset to each column of the reconstructed frame.

Value

Returns an H2OFrame object containing the approximate reconstruction of the training data;

See also

h2o.glrm for making an H2ODimReductionModel.

Examples

# NOT RUN {
library(h2o)
h2o.init()
irisPath <- system.file("extdata", "iris_wheader.csv", package="h2o")
iris.hex <- h2o.uploadFile(path = irisPath)
iris.glrm <- h2o.glrm(training_frame = iris.hex, k = 4, transform = "STANDARDIZE",
                      loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000)
iris.rec <- h2o.reconstruct(iris.glrm, iris.hex, reverse_transform = TRUE)
head(iris.rec)
# }