h2o.svd: Singular value decomposition of an H2O data frame using the...
In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

h2o.svd

R Documentation

Singular value decomposition of an H2O data frame using the power method

Description

Singular value decomposition of an H2O data frame using the power method

Usage

h2o.svd(
  training_frame,
  x,
  destination_key,
  model_id = NULL,
  validation_frame = NULL,
  ignore_const_cols = TRUE,
  score_each_iteration = FALSE,
  transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"),
  svd_method = c("GramSVD", "Power", "Randomized"),
  nv = 1,
  max_iterations = 1000,
  seed = -1,
  keep_u = TRUE,
  u_name = NULL,
  use_all_factor_levels = TRUE,
  max_runtime_secs = 0,
  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.
`destination_key`	(Optional) The unique key assigned to the resulting model. Automatically generated if none is provided.
`model_id`	Destination id for this model; auto-generated if not specified.
`validation_frame`	Id of the validation data frame.
`ignore_const_cols`	`Logical`. Ignore constant columns. Defaults to TRUE.
`score_each_iteration`	`Logical`. Whether to score during each iteration of model training. Defaults to FALSE.
`transform`	Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE.
`svd_method`	Method for computing SVD (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to GramSVD.
`nv`	Number of right singular vectors Defaults to 1.
`max_iterations`	Maximum iterations Defaults to 1000.
`seed`	Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to -1 (time-based random number).
`keep_u`	`Logical`. Save left singular vectors? Defaults to TRUE.
`u_name`	Frame key to save left singular vectors
`use_all_factor_levels`	`Logical`. Whether first factor level is included in each categorical expansion Defaults to TRUE.
`max_runtime_secs`	Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
`export_checkpoints_dir`	Automatically export generated models to this directory.

Value

an object of class H2ODimReductionModel.

References

N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.

Examples

## Not run: 
library(h2o)
h2o.init()
australia_path <- system.file("extdata", "australia.csv", package = "h2o")
australia <- h2o.uploadFile(path = australia_path)
h2o.svd(training_frame = australia, nv = 8)

## End(Not run)

h2o documentation built on May 29, 2024, 4:26 a.m.