h2o.prcomp: Principal components analysis of an H2O data frame using the power method

Description

Principal components analysis of an H2O data frame using the power method to calculate the singular value decomposition of the Gram matrix.

Usage

h2o.prcomp(training_frame, x, model_id = NULL, validation_frame = NULL,
  ignore_const_cols = TRUE, score_each_iteration = FALSE,
  transform = c("NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"),
  pca_method = c("GramSVD", "Power", "Randomized", "GLRM"), k = 1,
  max_iterations = 1000, use_all_factor_levels = FALSE,
  compute_metrics = TRUE, impute_missing = FALSE, seed = -1,
  max_runtime_secs = 0)

Arguments

training_frame

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

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

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.

pca_method

Method for computing PCA (Caution: Power and GLRM are currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized", "GLRM". Defaults to GramSVD.

Rank of matrix approximation Defaults to 1.

max_iterations

Maximum training iterations Defaults to 1000.

use_all_factor_levels

Logical. Whether first factor level is included in each categorical expansion Defaults to FALSE.

compute_metrics

Logical. Whether to compute metrics on the training data Defaults to TRUE.

impute_missing

Logical. Whether to impute missing entries with the column mean Defaults to FALSE.

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).

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.

Value

Returns an object of class .

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

Run this code

library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.prcomp(training_frame = australia.hex, k = 8, transform = "STANDARDIZE")