Builds a 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, representation_name = NULL, 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, export_checkpoints_dir = NULL )
training_frame | Id of the training data frame. |
---|---|
cols | (Optional) A vector containing the data columns on which k-means operates. |
model_id | Destination id for this model; auto-generated if not specified. |
validation_frame | Id of the validation data frame. |
ignore_const_cols |
|
score_each_iteration |
|
representation_name | Frame key to save resulting X |
loading_name | [Deprecated] Use representation_name instead. Frame key to save resulting X. |
transform | Transformation of training data Must be one of: "NONE", "STANDARDIZE", "NORMALIZE", "DEMEAN", "DESCALE". Defaults to NONE. |
k | Rank of matrix approximation Defaults to 1. |
loss | Numeric loss function Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic". Defaults to Quadratic. |
loss_by_col | Loss function by column (override) Must be one of: "Quadratic", "Absolute", "Huber", "Poisson", "Hinge", "Logistic", "Periodic", "Categorical", "Ordinal". |
loss_by_col_idx | Loss function by column index (override) |
multi_loss | Categorical loss function Must be one of: "Categorical", "Ordinal". Defaults to Categorical. |
period | Length of period (only used with periodic loss function) Defaults to 1. |
regularization_x | Regularization function for X matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. |
regularization_y | Regularization function for Y matrix Must be one of: "None", "Quadratic", "L2", "L1", "NonNegative", "OneSparse", "UnitOneSparse", "Simplex". Defaults to None. |
gamma_x | Regularization weight on X matrix Defaults to 0. |
gamma_y | Regularization weight on Y matrix Defaults to 0. |
max_iterations | Maximum number of iterations Defaults to 1000. |
max_updates | Maximum number of updates, defaults to 2*max_iterations Defaults to 2000. |
init_step_size | Initial step size Defaults to 1. |
min_step_size | Minimum step size Defaults to 0.0001. |
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). |
init | Initialization mode Must be one of: "Random", "SVD", "PlusPlus", "User". Defaults to PlusPlus. |
svd_method | Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) Must be one of: "GramSVD", "Power", "Randomized". Defaults to Randomized. |
user_y | User-specified initial Y |
user_x | User-specified initial X |
expand_user_y |
|
impute_original |
|
recover_svd |
|
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. |
an object of class H2ODimReductionModel.
M. Udell, C. Horn, R. Zadeh, S. Boyd (2014). Generalized Low Rank Models[https://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[https://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.
# NOT RUN { library(h2o) h2o.init() australia_path <- system.file("extdata", "australia.csv", package = "h2o") australia <- h2o.uploadFile(path = australia_path) h2o.glrm(training_frame = australia, k = 5, loss = "Quadratic", regularization_x = "L1", gamma_x = 0.5, gamma_y = 0, max_iterations = 1000) # }