AdaOpt classifier

AdaOpt(
  n_iterations = 50L,
  learning_rate = 0.3,
  reg_lambda = 0.1,
  reg_alpha = 0.5,
  eta = 0.01,
  gamma = 0.01,
  k = 3L,
  tolerance = 0,
  n_clusters = 0,
  batch_size = 100L,
  row_sample = 1,
  type_dist = "euclidean-f",
  cache = TRUE,
  n_clusters_input = 0,
  clustering_method = "kmeans",
  cluster_scaling = "standard",
  seed = 123L
)

Arguments

n_iterations: number of iterations of the optimizer at training time
learning_rate: controls the speed of the optimizer at training time
reg_lambda: L2 regularization parameter for successive errors in the optimizer (at training time)
reg_alpha: L1 regularization parameter for successive errors in the optimizer (at training time)
eta: controls the slope in gradient descent (at training time)
gamma: controls the step size in gradient descent (at training time)
k: number of nearest neighbors selected at test time for classification
tolerance: controls early stopping in gradient descent (at training time)
n_clusters: number of clusters, if MiniBatch k-means is used at test time (for faster prediction)
batch_size: size of the batch, if MiniBatch k-means is used at test time (for faster prediction)
row_sample: percentage of rows chosen from training set (by stratified subsampling, for faster prediction)
type_dist: distance used for finding the nearest neighbors; currently euclidean-f (euclidean distances calculated as whole), euclidean (euclidean distances calculated row by row), cosine (cosine distance)
cache: if the nearest neighbors are cached or not, for faster retrieval in subsequent calls
n_clusters_input: number of clusters a priori on inpu data
clustering_method: either "kmeans" or "gmm" (Gaussian mixture)
cluster_scaling: either 'standard', 'minmax', 'robust'
seed: reproducibility seed for initial weak learner and clustering

Value

An object of class AdaOpt

Examples


if (FALSE) {
library(datasets)

X <- as.matrix(iris[, 1:4])
y <- as.integer(iris[, 5]) - 1L

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(iris[train_index, 1:4])
y_train <- as.integer(iris[train_index, 5]) - 1L
X_test <- as.matrix(iris[test_index, 1:4])
y_test <- as.integer(iris[test_index, 5]) - 1L


obj <- mlsauce::AdaOpt()

print(obj$get_params())

obj$fit(X_train, y_train)

print(obj$score(X_test, y_test))}