rFTRLProximal (version 1.0.0)

FTRLProx_cv: FTRL-Proximal Linear Model Cross Validation

Description

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

Usage

FTRLProx_cv(x, y, family = c("gaussian", "binomial", "poisson"), params = list(alpha = 0.1, beta = 1, l1 = 1, l2 = 1), epoch = 1, folds, eval)

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.

folds

list provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).

eval

a evaluation metrics computing function, the first argument shoule be prediction, the second argument shoule be label.

Value

a list with the following elements is returned:

• dt a data.table with each mean and standard deviation stat for training set and test set
• pred a numerical vector with predictions for each CV-fold for the model having been trained on the data in all other folds.

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(FeatureHashing)
library(data.table)
library(rBayesianOptimization)
library(MLmetrics)
data(ipinyou)
m.train <- FTRLProx_Hashing(~ 0 + ., ipinyou.train[,-"IsClick", with = FALSE],
                            hash.size = 2^13, signed.hash = FALSE, verbose = TRUE)
ftrl_model_cv <- FTRLProx_cv(x = m.train, y = as.numeric(ipinyou.train$IsClick),
                             family = "binomial",
                             params = list(alpha = 0.01, beta = 0.1, l1 = 1.0, l2 = 1.0),
                             epoch = 10,
                             folds = KFold(as.numeric(ipinyou.train$IsClick), nfolds = 5),
                             eval = AUC)