H20: how to use gradient boosting on textual data?

Question

I am trying to implement a very simple ML learning problem, where I use text to predict some outcome. In R, some basic example would be:

import some fake but funny text data

library(caret)
library(dplyr)
library(text2vec)

dataframe <- data_frame(id = c(1,2,3,4),
                        text = c("this is a this", "this is 
                        another",'hello','what???'),
                        value = c(200,400,120,300),
                        output = c('win', 'lose','win','lose'))

> dataframe
# A tibble: 4 x 4
     id            text value output
  <dbl>           <chr> <dbl>  <chr>
1     1  this is a this   200    win
2     2 this is another   400   lose
3     3           hello   120    win
4     4         what???   300   lose

Use text2vec to get a sparse matrix representation of my text (see also https://github.com/dselivanov/text2vec/blob/master/vignettes/text-vectorization.Rmd)

#these are text2vec functions to tokenize and lowercase the text
prep_fun = tolower
tok_fun = word_tokenizer 

#create the tokens
train_tokens = dataframe$text %>% 
  prep_fun %>% 
  tok_fun

it_train = itoken(train_tokens)     
vocab = create_vocabulary(it_train)
vectorizer = vocab_vectorizer(vocab)
dtm_train = create_dtm(it_train, vectorizer)

> dtm_train
4 x 6 sparse Matrix of class "dgCMatrix"
  what hello another a is this
1    .     .       . 1  1    2
2    .     .       1 .  1    1
3    .     1       . .  .    .
4    1     .       . .  .    .

Finally, train the algo (for instance, using caret) to predict output using my sparse matrix.

mymodel <- train(x=dtm_train, y =dataframe$output, method="xgbTree")

> confusionMatrix(mymodel)
Bootstrapped (25 reps) Confusion Matrix 

(entries are percentual average cell counts across resamples)

          Reference
Prediction lose  win
      lose 17.6 44.1
      win  29.4  8.8

 Accuracy (average) : 0.264

My problem is:

I see how to import data into h20 using spark_read_csv, rsparkling and as_h2o_frame. However, for points 2. and 3. above I am completely lost.

Can someone please give me some hints or tell me if this approach is even possible with h2o?

Many thanks!!

Answer 1

You can solve this one of two ways -- 1. in R first and then move to H2O for modeling or 2. Entirely in H2O using H2O's word2vec implementation.

Use R data.frames and text2vec, then convert the sparse matrix to an H2O frame and do the modeling in H2O.

 # Use same code as above to get to this point, then:

 # Convert dgCMatrix to H2OFrame, cbind the response col
 train <- as.h2o(dtm_train)
 train$y <- as.h2o(dataframe$output)

 # Train any H2O model (e.g GBM)
 mymodel <- h2o.gbm(y = "y", training_frame = train,
                   distribution = "bernoulli", seed = 1)

Or you can train a word2vec embedding in H2O, apply it to your text to get the equivalent of a sparse matrix. Then train a H2O machine learning model (GBM). I will try edit this answer later with a working example using your data, but in the meantime, here is an example demonstrating the use of H2O's word2vec functionality in R.