FTRLProx_validate: FTRL-Proximal Linear Model Validation

In rFTRLProximal: FTRL-Proximal Algorithm

Description

An advanced interface for FTRL-Proximal online learning model validation.

Usage

FTRLProx_validate(x, y, family = c("gaussian", "binomial", "poisson"),
  params = list(alpha = 0.1, beta = 1, l1 = 1, l2 = 1), epoch = 1, val_x,
  val_y, eval, verbose = TRUE)

Arguments

x	a transposed dgCMatrix.
y	a vector containing labels.
family	link function to be used in the model. "gaussian", "binomial" and "poisson" are avaliable.
params	a list of parameters of FTRL-Proximal Algorithm. alpha alpha in the per-coordinate learning rate beta beta in the per-coordinate learning rate l1 L1 regularization parameter l2 L2 regularization parameter
epoch	The number of iterations over training data to train the model.
val_x	a transposed dgCMatrix for validation.
val_y	a vector containing labels for validation.
eval	a evaluation metrics computing function, the first argument shoule be prediction, the second argument shoule be label.
verbose	logical value. Indicating if the validation result for each epoch is displayed or not.

Value

a FTRL-Proximal linear model object

References

H. B. McMahan, G. Holt, D. Sculley, et al. "Ad click prediction: a view from the trenches". In: _The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013_. Ed. by I. S.Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman and R. Uthurusamy. ACM, 2013, pp. 1222-1230. DOI: 10.1145/2487575.2488200. <URL: http://doi.acm.org/10.1145/2487575.2488200>.

Examples

library(data.table)
library(FeatureHashing)
library(MLmetrics)
data(ipinyou)
m.train <- FTRLProx_Hashing(~ 0 + ., ipinyou.train[, -"IsClick", with = FALSE],
                            hash.size = 2^13, signed.hash = FALSE, verbose = TRUE)
m.test <- FTRLProx_Hashing(~ 0 + ., ipinyou.test[,-"IsClick", with = FALSE],
                           hash.size = 2^13, signed.hash = FALSE, verbose = TRUE)
ftrl_model_val <- FTRLProx_validate(x = m.train, y = as.numeric(ipinyou.train$IsClick),
                                    family = "binomial",
                                    params = list(alpha = 0.01, beta = 0.1, l1 = 1.0, l2 = 1.0),
                                    epoch = 20,
                                    val_x = m.test,
                                    val_y = as.numeric(ipinyou.test$IsClick),
                                    eval = AUC, verbose = TRUE)

rFTRLProximal documentation built on May 29, 2017, 9:50 p.m.