This article was first published on
T. Moudiki's Webpage - Python , and kindly contributed to python-bloggers. (You can report issue about the content on this page here)
Want to share your content on python-bloggers? click here.
Want to share your content on python-bloggers? click here.
This post is about LSBoost, an Explainable ‘AI’ algorithm which uses Gradient Boosted randomized networks for pattern recognition. As we’ve discussed it last week LSBoost is a cousin of GFAGBM’s LS_Boost. In LSBoost, more specifically, the so called weak learners from LS_Boost are based on randomized neural networks’ components and variants of Least Squares regression models.
I’ve already presented some promising examples of use of LSBoost based on Ridge Regression weak learners. In mlsauce’s version 0.7.1, the Lasso can also be used as an alternative ingredient to the weak learners. Here is a comparison of the regression coefficients obtained by using mlsauce’s implementation of Ridge regression and the Lasso:
R example: LSBoostRegressor with Ridge regression and the Lasso
The following example is about training set error vs testing set error, as a function of the regularization parameter, both for Ridge regression and Lasso-based weak learners.
Packages and data
# 0 - Packages and data -------------------------------------------------------
library(devtools)
devtools::install_github("thierrymoudiki/mlsauce/R-package")
library(mlsauce)
library(datasets)
print(summary(datasets::mtcars))
X <- as.matrix(datasets::mtcars[, -1])
y <- as.integer(datasets::mtcars[, 1])
n <- dim(X)[1]
p <- dim(X)[2]
set.seed(21341)
train_index <- sample(x = 1:n, size = floor(0.8*n), replace = TRUE)
test_index <- -train_index
X_train <- as.matrix(X[train_index, ])
y_train <- as.double(y[train_index])
X_test <- as.matrix(X[test_index, ])
y_test <- as.double(y[test_index])
LSBoost using Ridge regression
# 1 - Ridge -------------------------------------------------------------------
obj <- mlsauce::LSBoostRegressor() # default h is Ridge
print(obj$get_params())
n_lambdas <- 100
lambdas <- 10**seq(from=-6, to=6,
length.out = n_lambdas)
rmse_matrix <- matrix(NA, nrow = 2, ncol = n_lambdas)
rownames(rmse_matrix) <- c("training rmse", "testing rmse")
for (j in 1:n_lambdas)
{
obj$set_params(reg_lambda = lambdas[j])
obj$fit(X_train, y_train)
rmse_matrix[, j] <- c(sqrt(mean((obj$predict(X_train) - y_train)**2)),
sqrt(mean((obj$predict(X_test) - y_test)**2)))
}
LSBoost using the Lasso
# 2 - Lasso -------------------------------------------------------------------
obj <- mlsauce::LSBoostRegressor(solver = "lasso")
print(obj$get_params())
n_lambdas <- 100
lambdas <- 10**seq(from=-6, to=6,
length.out = n_lambdas)
rmse_matrix2 <- matrix(NA, nrow = 2, ncol = n_lambdas)
rownames(rmse_matrix2) <- c("training rmse", "testing rmse")
for (j in 1:n_lambdas)
{
obj$set_params(reg_lambda = lambdas[j])
obj$fit(X_train, y_train)
rmse_matrix2[, j] <- c(sqrt(mean((obj$predict(X_train) - y_train)**2)),
sqrt(mean((obj$predict(X_test) - y_test)**2)))
}
R session info
> print(session_info()) ─ Session info ───────────────────────────────────────────────────────────── setting value version R version 4.0.2 (2020-06-22) os Ubuntu 16.04.6 LTS system x86_64, linux-gnu ui RStudio language (EN) collate C.UTF-8 ctype C.UTF-8 tz Etc/UTC date 2020-07-31 ─ Packages ───────────────────────────────────────────────────────────────── package * version date lib source assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.2) backports 1.1.8 2020-06-17 [1] RSPM (R 4.0.2) callr 3.4.3 2020-03-28 [1] RSPM (R 4.0.2) cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.2) crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.2) curl 4.3 2019-12-02 [1] RSPM (R 4.0.2) desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.2) devtools * 2.3.1 2020-07-21 [1] RSPM (R 4.0.2) digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.2) ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.2) fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.2) fs 1.4.2 2020-06-30 [1] RSPM (R 4.0.2) glue 1.4.1 2020-05-13 [1] RSPM (R 4.0.2) jsonlite 1.7.0 2020-06-25 [1] RSPM (R 4.0.2) lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.2) magrittr 1.5 2014-11-22 [1] RSPM (R 4.0.2) Matrix 1.2-18 2019-11-27 [2] CRAN (R 4.0.2) memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.2) mlsauce * 0.7.1 2020-07-31 [1] Github (thierrymoudiki/mlsauce@68e391a) pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2) pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.2) prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.2) processx 3.4.3 2020-07-05 [1] RSPM (R 4.0.2) ps 1.3.3 2020-05-08 [1] RSPM (R 4.0.2) R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.2) rappdirs 0.3.1 2016-03-28 [1] RSPM (R 4.0.2) Rcpp 1.0.5 2020-07-06 [1] RSPM (R 4.0.2) remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.2) reticulate 1.16 2020-05-27 [1] RSPM (R 4.0.2) rlang 0.4.7 2020-07-09 [1] RSPM (R 4.0.2) rprojroot 1.3-2 2018-01-03 [1] RSPM (R 4.0.2) rstudioapi 0.11 2020-02-07 [1] RSPM (R 4.0.2) sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.2) testthat 2.3.2 2020-03-02 [1] RSPM (R 4.0.2) usethis * 1.6.1 2020-04-29 [1] RSPM (R 4.0.2) withr 2.2.0 2020-04-20 [1] RSPM (R 4.0.2) [1] /home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0 [2] /opt/R/4.0.2/lib/R/library
No post in August
To leave a comment for the author, please follow the link and comment on their blog: T. Moudiki's Webpage - Python .
Want to share your content on python-bloggers? click here.