Generalized low rank decomposition of an H2O data frame.
h2o.glrm(training_frame, cols = NULL, model_id = NULL,
validation_frame = NULL, ignore_const_cols = TRUE,
score_each_iteration = FALSE, loading_name = NULL, transform = c("NONE",
"STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE"), k = 1,
loss = c("Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic",
"Periodic"), loss_by_col = c("Quadratic", "Absolute", "Huber", "Poisson",
"Hinge", "Logistic", "Periodic", "Categorical", "Ordinal"),
loss_by_col_idx = NULL, multi_loss = c("Categorical", "Ordinal"),
period = 1, regularization_x = c("None", "Quadratic", "L2", "L1",
"NonNegative", "OneSparse", "UnitOneSparse", "Simplex"),
regularization_y = c("None", "Quadratic", "L2", "L1", "NonNegative",
"OneSparse", "UnitOneSparse", "Simplex"), gamma_x = 0, gamma_y = 0,
max_iterations = 1000, max_updates = 2000, init_step_size = 1,
min_step_size = 1e-04, seed = -1, init = c("Random", "SVD", "PlusPlus",
"User"), svd_method = c("GramSVD", "Power", "Randomized"), user_y = NULL,
user_x = NULL, expand_user_y = TRUE, impute_original = FALSE,
recover_svd = FALSE, max_runtime_secs = 0)
Id of the training data frame (Not required, to allow initial validation of model parameters).
(Optional) A vector containing the data columns on which k-means operates.
Destination id for this model; auto-generated if not specified.
Id of the validation data frame.
Logical
. Ignore constant columns. Defaults to TRUE.
Logical
. Whether to score during each iteration of model training. Defaults to FALSE.
Frame key to save resulting X
Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE.
Rank of matrix approximation Defaults to 1.
Numeric loss function Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic". Defaults to Quadratic.
Loss function by column (override) Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal".
Loss function by column index (override)
Categorical loss function Must be one of: "Categorical", "Ordinal". Defaults to Categorical.
Length of period (only used with periodic loss function) Defaults to 1.
Regularization function for X matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None.
Regularization function for Y matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None.
Regularization weight on X matrix Defaults to 0.
Regularization weight on Y matrix Defaults to 0.
Maximum number of iterations Defaults to 1000.
Maximum number of updates, defaults to 2*max_iterations Defaults to 2000.
Initial step size Defaults to 1.
Minimum step size Defaults to 0.0001.
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).
Initialization mode Must be one of: "Random", "SVD", "PlusPlus", "User". Defaults to PlusPlus.
Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to Power.
User-specified initial Y
User-specified initial X
Logical
. Expand categorical columns in user-specified initial Y Defaults to TRUE.
Logical
. Reconstruct original training data by reversing transform Defaults to FALSE.
Logical
. Recover singular values and eigenvectors of XY Defaults to FALSE.
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[http://arxiv.org/abs/1410.0342]. Unpublished manuscript, Stanford Electrical Engineering Department 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.
library(h2o)
h2o.init()
ausPath <- system.file("extdata", "australia.csv", package="h2o")
australia.hex <- h2o.uploadFile(path = ausPath)
h2o.glrm(training_frame = australia.hex, k = 5, loss = "Quadratic", regularization_x = "L1",
gamma_x = 0.5, gamma_y = 0, max_iterations = 1000)
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