Principal component analysis of an H2O data frame

Source: R/pca.R

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

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"),
  pca_impl = c("MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX",
  "MTJ_SVD_DENSEMATRIX", "JAMA"), k = 1, max_iterations = 1000,
  use_all_factor_levels = FALSE, compute_metrics = TRUE,
  impute_missing = FALSE, seed = -1, 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.
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

Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental) Must be one of: "GramSVD", "Power", "Randomized", "GLRM". Defaults to GramSVD.
pca_impl

Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/ Must be one of: "MTJ_EVD_DENSEMATRIX", "MTJ_EVD_SYMMMATRIX", "MTJ_SVD_DENSEMATRIX", "JAMA".
k

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.
export_checkpoints_dir

Automatically export generated models to this directory.

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.

See also

h2o.svd, h2o.glrm

Examples

# NOT RUN {
library(h2o)
h2o.init()
australia_path <- system.file("extdata", "australia.csv", package = "h2o")
australia <- h2o.uploadFile(path = australia_path)
h2o.prcomp(training_frame = australia, k = 8, transform = "STANDARDIZE")
# }