h2o.svd: Singular value decomposition of an H2O data frame using the...

Description Usage Arguments Value References Examples

Description

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

Usage

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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)

Arguments

training_frame

Id of the training data frame (Not required, to allow initial validation of model parameters).

x

A vector containing the character names of the predictors in the model.

destination_key

(Optional) The unique hex 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: Power and Randomized are 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.

Value

Returns 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[http://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.

Examples

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library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.svd(training_frame = australia.hex, nv = 8)


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