cuda_ml_lasso {cuda.ml}

Train a linear model using LASSO regression.

Description

Train a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) regression.

Usage

cuda_ml_lasso(x, ...)

## Default S3 method:
cuda_ml_lasso(x, ...)

## S3 method for class 'data.frame'
cuda_ml_lasso(
  x,
  y,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

## S3 method for class 'matrix'
cuda_ml_lasso(
  x,
  y,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

## S3 method for class 'formula'
cuda_ml_lasso(
  formula,
  data,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

## S3 method for class 'recipe'
cuda_ml_lasso(
  x,
  data,
  alpha = 1,
  max_iter = 1000L,
  tol = 0.001,
  fit_intercept = TRUE,
  normalize_input = FALSE,
  selection = c("cyclic", "random"),
  ...
)

Arguments

x

Depending on the context:

* A __data frame__ of predictors. * A __matrix__ of predictors. * A __recipe__ specifying a set of preprocessing steps * created from [recipes::recipe()]. * A __formula__ specifying the predictors and the outcome.

...

Optional arguments; currently unused.

y

A numeric vector (for regression) or factor (for classification) of desired responses.

alpha

Multiplier of the L1 penalty term (i.e., the result would become and Ordinary Least Square model if alpha were set to 0). Default: 1.

max_iter

The maximum number of coordinate descent iterations. Default: 1000L.

tol

Stop the coordinate descent when the duality gap is below this threshold. Default: 1e-3.

fit_intercept

If TRUE, then the model tries to correct for the global mean of the response variable. If FALSE, then the model expects data to be centered. Default: TRUE.

normalize_input

Ignored when fit_intercept is FALSE. If TRUE, then the predictors will be normalized to have a L2 norm of 1. Default: FALSE.

selection

If "random", then instead of updating coefficients in cyclic order, a random coefficient is updated in each iteration. Default: "cyclic".

formula

A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.

data

When a __recipe__ or __formula__ is used, data is specified as a __data frame__ containing the predictors and (if applicable) the outcome.

Value

A LASSO regressor that can be used with the 'predict' S3 generic to make predictions on new data points.

Examples

library(cuda.ml)

model <- cuda_ml_lasso(formula = mpg ~ ., data = mtcars, alpha = 1e-3)
cuda_ml_predictions <- predict(model, mtcars)

# predictions will be comparable to those from a `glmnet` model with `lambda`
# set to 1e-3 and `alpha` set to 1
# (in `glmnet`, `lambda` is the weight of the penalty term, and `alpha` is
#  the elastic mixing parameter between L1 and L2 penalties.

library(glmnet)

glmnet_model <- glmnet(
  x = as.matrix(mtcars[names(mtcars) != "mpg"]), y = mtcars$mpg,
  alpha = 1, lambda = 1e-3, nlambda = 1, standardize = FALSE
)

glm_predictions <- predict(
  glmnet_model, as.matrix(mtcars[names(mtcars) != "mpg"]),
  s = 0
)

print(
  all.equal(
    as.numeric(glm_predictions),
    cuda_ml_predictions$.pred,
    tolerance = 1e-2
  )
)