H2o encoders starter

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This document contains a startup script for H2O in R. It is a silly example (why would anybody want to train a deep encoder on the iris dataset) but it helps people get started. This setup is meant for local use, not for cluster setup.

Just copy the code in!



h2o.init(ip = 'localhost', port = 54321, nthreads= -1, max_mem_size = '4g')


h2o_df <- as.h2o(iris)

mod_nn <- h2o.deeplearning(
  x = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'),
  training_frame = h2o_df,
  hidden = c(2),
  epochs = 100,
  activation = 'Tanh',
  autoencoder = TRUE

features <- h2o.deepfeatures(mod_nn, h2o_df, layer=1)
pltr_nn <- features %>% 
  as.data.frame %>% 
  mutate(species = iris$Species)

colnames(pltr_nn) <- c("x1", "x2", "species")

ggplot() + 
  geom_point(data=pltr_nn, aes(x1, x2, colour = species))


mod_pca <- h2o.prcomp(
  x = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'),
  training_frame = h2o_df, k = 2, transform = 'STANDARDIZE'

pltr_pca <- h2o.predict(mod_pca, h2o_df, num_pc=2) %>% 
  as.data.frame %>% 
  mutate(species = iris$Species)

colnames(pltr_pca) <- c("x1", "x2", "species")

ggplot() + 
  geom_point(data=pltr_pca, aes(x1, x2, colour = species))

The syntax is very lovely R-like. One just has to remember that h2o assumes a slightly different internal datastructure to keep things performant (similarly to SparkR). It does offer some machine learning possiblilities not available to SparkR users. Features!

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