Build an Aggregated Frame

Source: R/aggregator.R

Builds an Aggregated Frame of an H2OFrame.

h2o.aggregator(training_frame, x, model_id = NULL, ignore_const_cols = TRUE,
  target_num_exemplars = 5000, rel_tol_num_exemplars = 0.5,
  transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"),
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
  "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  save_mapping_frame = FALSE)

Arguments

training_frame	Id of the training data frame.
x	A vector containing the character names of the predictors in the model.
model_id	Destination id for this model; auto-generated if not specified.
ignore_const_cols	Logical. Ignore constant columns. Defaults to TRUE.
target_num_exemplars	Targeted number of exemplars Defaults to 5000.
rel_tol_num_exemplars	Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents) Defaults to 0.5.
transform	Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NORMALIZE.
categorical_encoding	Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
save_mapping_frame	Logical. Whether to export the mapping of the aggregated frame Defaults to FALSE.

Examples

# NOT RUN {
library(h2o)
h2o.init()
df <- h2o.createFrame(rows=100, cols=5, categorical_fraction=0.6, integer_fraction=0,
binary_fraction=0, real_range=100, integer_range=100, missing_fraction=0)
target_num_exemplars=1000
rel_tol_num_exemplars=0.5
encoding="Eigen"
agg <- h2o.aggregator(training_frame=df,
target_num_exemplars=target_num_exemplars,
rel_tol_num_exemplars=rel_tol_num_exemplars,
categorical_encoding=encoding)
# }