rFTRLProximal (version 1.0.0)

FTRLProx_validate: FTRL-Proximal Linear Model Validation

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. 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)