h2o.aggregator function

Build an Aggregated Frame

Build an Aggregated Frame

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, 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

## 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) ## End(Not run)
  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11