h2o.aggregator

0th

Percentile

Build an Aggregated Frame

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.

Aliases
  • h2o.aggregator
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)
# }
Documentation reproduced from package h2o, version 3.22.1.1, License: Apache License (== 2.0)

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