Train a SVM model. — cuda_ml

Usage

cuda_ml_svm(x, ...)

# Default S3 method
cuda_ml_svm(x, ...)

# S3 method for class 'data.frame'
cuda_ml_svm(
  x,
  y,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

# S3 method for class 'matrix'
cuda_ml_svm(
  x,
  y,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

# S3 method for class 'formula'
cuda_ml_svm(
  formula,
  data,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

# S3 method for class 'recipe'
cuda_ml_svm(
  x,
  data,
  cost = 1,
  kernel = c("rbf", "tanh", "polynomial", "linear"),
  gamma = NULL,
  coef0 = 0,
  degree = 3L,
  tol = 0.001,
  max_iter = NULL,
  nochange_steps = 1000L,
  cache_size = 1024,
  epsilon = 0.1,
  sample_weights = NULL,
  ...
)

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.

cost

A positive number for the cost of predicting a sample within or on the wrong side of the margin. Default: 1.

kernel

Type of the SVM kernel function (must be one of "rbf", "tanh", "polynomial", or "linear"). Default: "rbf".

gamma

The gamma coefficient (only relevant to polynomial, RBF, and tanh kernel functions, see explanations below). Default: 1 / (num features).

The following kernels are implemented: - RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) - TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) - POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree - LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.

coef0

The 0th coefficient (only applicable to polynomial and tanh kernel functions, see explanations below). Default: 0.

The following kernels are implemented: - RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) - TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) - POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree - LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.

degree

Degree of the polynomial kernel function (note: not applicable to other kernel types, see explanations below). Default: 3.

The following kernels are implemented: - RBF K(x_1, x_2) = exp(-gamma |x_1-x_2|^2) - TANH K(x_1, x_2) = tanh(gamma <x_1,x_2> + coef0) - POLYNOMIAL K(x_1, x_2) = (gamma <x_1,x_2> + coef0)^degree - LINEAR K(x_1,x_2) = <x_1,x_2>, where < , > denotes the dot product.

tol

Tolerance to stop fitting. Default: 1e-3.

max_iter

Maximum number of outer iterations in SmoSolver. Default: 100 * (num samples).

nochange_steps

Number of steps with no change w.r.t convergence. Default: 1000.

cache_size

Size of kernel cache (MiB) in device memory. Default: 1024.

epsilon

Epsilon parameter of the epsilon-SVR model. There is no penalty for points that are predicted within the epsilon-tube around the target values. Please note this parameter is only relevant for regression tasks. Default: 0.1.

sample_weights

Optional weight assigned to each input data point.

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 SVM classifier / regressor object that can be used with the 'predict' S3 generic to make predictions on new data points.

Examples


library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  # Classification

  model <- cuda_ml_svm(
    formula = Species ~ .,
    data = iris,
    kernel = "rbf"
  )

  predictions <- predict(model, iris[names(iris) != "Species"])

  # Regression

  model <- cuda_ml_svm(
    formula = mpg ~ .,
    data = mtcars,
    kernel = "rbf"
  )

  predictions <- predict(model, mtcars)
}