Creates a data frame in H2O with n-th order interaction features between categorical columns, as specified by the user.
h2o.interaction(data, destination_frame, factors, pairwise, max_factors, min_occurrence)
| data | An H2OFrame object containing the categorical columns. |
|---|---|
| destination_frame | A string indicating the destination key. If empty, this will be auto-generated by H2O. |
| factors | Factor columns (either indices or column names). |
| pairwise | Whether to create pairwise interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. |
| max_factors | Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) |
| min_occurrence | Min. occurrence threshold for factor levels in pair-wise interaction terms |
Returns an H2OFrame object.
# NOT RUN { library(h2o) h2o.init() # Create some random data myframe <- h2o.createFrame(rows = 20, cols = 5, seed = -12301283, randomize = TRUE, value = 0, categorical_fraction = 0.8, factors = 10, real_range = 1, integer_fraction = 0.2, integer_range = 10, binary_fraction = 0, binary_ones_fraction = 0.5, missing_fraction = 0.2, response_factors = 1) # Turn integer column into a categorical myframe[,5] <- as.factor(myframe[,5]) head(myframe, 20) # Create pairwise interactions pairwise <- h2o.interaction(myframe, factors = list(c(1, 2), c("C2", "C3", "C4")), pairwise = TRUE, max_factors = 10, min_occurrence = 1) head(pairwise, 20) h2o.levels(pairwise, 2) # Create 5-th order interaction higherorder <- h2o.interaction(myframe, destination_frame = 'higherorder', factors = c(1,2,3,4,5), pairwise = FALSE, max_factors = 10000, min_occurrence = 1) head(higherorder, 20) # Limit the number of factors of the "categoricalized" integer column # to at most 3 factors, and only if they occur at least twice head(myframe[,5], 20) trim_integer_levels <- h2o.interaction(myframe, factors = "C5", pairwise = FALSE, max_factors = 3, min_occurrence = 2) head(trim_integer_levels, 20) # Put all together myframe <- h2o.cbind(myframe, pairwise, higherorder, trim_integer_levels) myframe head(myframe, 20) summary(myframe) # }