h2o.aggregator: Build an Aggregated Frame

Description

Builds an Aggregated Frame of an H2OFrame.

Usage

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,
  num_iteration_without_new_exemplar = 500,
  export_checkpoints_dir = NULL
)

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.
num_iteration_without_new_exemplar: The number of iterations to run before aggregator exits if the number of exemplars collected didn't change Defaults to 500.
export_checkpoints_dir: Automatically export generated models to this directory.

Examples

Run this code

if (FALSE) {
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)
}

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