h2o4gpu.elastic_net_classifier {h2o4gpu}

Elastic Net Classifier

Description

Elastic Net Classifier

Usage

h2o4gpu.elastic_net_classifier(alpha = 1, l1_ratio = 0.5,
  fit_intercept = TRUE, normalize = FALSE, precompute = FALSE,
  max_iter = 5000L, copy_X = TRUE, tol = 0.01, warm_start = FALSE,
  positive = FALSE, random_state = NULL, selection = "cyclic",
  n_gpus = -1L, lambda_stop_early = TRUE, glm_stop_early = TRUE,
  glm_stop_early_error_fraction = 1, verbose = FALSE, n_threads = NULL,
  gpu_id = 0L, lambda_min_ratio = 1e-07, n_lambdas = 100L, n_folds = 5L,
  tol_seek_factor = 0.1, store_full_path = 0L, lambda_max = NULL,
  lambdas = NULL, double_precision = NULL, order = NULL,
  backend = "h2o4gpu")

Arguments

alpha

Constant that multiplies the penalty terms. Defaults to 1.0. See the notes for the exact mathematical meaning of this parameter.alpha = 0 is equivalent to an ordinary least square, solved by the :class:LinearRegressionSklearn object. For numerical reasons, using alpha = 0 with the LassoSklearn object is not advised. Given this, you should use the :class:LinearRegressionSklearn object.

l1_ratio

The ElasticNetSklearn mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

fit_intercept

Whether the intercept should be estimated or not. If FALSE, the data is assumed to be already centered.

normalize

This parameter is ignored when fit_intercept is set to FALSE. If TRUE, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:h2o4gpu.preprocessing.StandardScaler before calling fit on an estimator with normalize=FALSE.

precompute

Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always TRUE to preserve sparsity.

max_iter

The maximum number of iterations

copy_X

If TRUE, X will be copied; else, it may be overwritten.

tol

The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

warm_start

When set to TRUE, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.

positive

When set to TRUE, forces the coefficients to be positive.

random_state

The seed of the pseudo random number generator that selects a random feature to update. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If NULL, the random number generator is the RandomState instance used by np.random. Used when selection == 'random'.

selection

If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e-4.

n_gpus

Number of gpu's to use in GLM solver.

lambda_stop_early

Stop early when there is no more relative improvement on train or validation.

glm_stop_early

Stop early when there is no more relative improvement in the primary and dual residuals for ADMM.

glm_stop_early_error_fraction

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).

verbose

Print verbose information to the console if set to > 0.

n_threads

Number of threads to use in the gpu. Each thread is an independent model builder.

gpu_id

ID of the GPU on which the algorithm should run.

lambda_min_ratio

Minimum lambda ratio to maximum lambda, used in lambda search.

n_lambdas

Number of lambdas to be used in a search.

n_folds

Number of cross validation folds.

tol_seek_factor

Factor of tolerance to seek once below null model accuracy. Default is 1E-1, so seeks tolerance of 1E-3 once below null model accuracy for tol=1E-2.

store_full_path

Whether to store full solution for all alphas and lambdas. If 1, then during predict will compute best and full predictions.

lambda_max

Maximum Lambda value to use. Default is NULL, and then internally compute standard maximum

lambdas

overrides n_lambdas, lambda_max, and lambda_min_ratio.

double_precision

Internally set unless using _ptr methods. Value can either be 0 (float32) or 1(float64)

order

Order of data. Default is NULL, and internally determined (unless using _ptr methods) whether row 'r' or column 'c' major order.

backend

Which backend to use. Options are 'auto', 'sklearn', 'h2o4gpu'. Saves as attribute for actual backend used.