ElasticNet

class cuml.dask.linear_model.ElasticNet(*, client=None, **kwargs)

ElasticNet extends LinearRegression with combined L1 and L2 regularizations on the coefficients when predicting response y with a linear combination of the predictors in X. It can reduce the variance of the predictors, force some coefficients to be small, and improves the conditioning of the problem.

cuML’s ElasticNet an array-like object or cuDF DataFrame, uses coordinate descent to fit a linear model.

Parameters:

alphafloat (default = 1.0)

Constant that multiplies the L1 term. alpha = 0 is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using alpha = 0 with the Lasso object is not advised. Given this, you should use the LinearRegression object.

l1_ratio: float (default = 0.5)

The ElasticNet 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_interceptboolean (default = True)

If True, Lasso tries to correct for the global mean of y. If False, the model expects that you have centered the data.

max_iterint (default = 1000)

The maximum number of iterations

tolfloat (default = 1e-3)

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.

selection{‘cyclic’, ‘random’} (default=’cyclic’)

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.

output_type{‘input’, ‘array’, ‘dataframe’, ‘series’, ‘df_obj’, ‘numba’, ‘cupy’, ‘numpy’, ‘cudf’, ‘pandas’}, default=None

Return results and set estimator attributes to the indicated output type. If None, the output type set at the module level (cuml.global_settings.output_type) will be used. See Output Data Type Configuration for more info.

Attributes:

coef_array, shape (n_features)

The estimated coefficients for the linear regression model.

intercept_array

The independent term. If fit_intercept is False, will be 0.

n_iter_int

The number of iterations taken by the solver.

For additional docs, see `scikitlearn’s ElasticNet

<https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`_.

Methods

fit(X, y)	Fit the model with X and y.
predict(X[, delayed])	Predicts the y for X.

fit(X, y)

Fit the model with X and y.

Parameters:

XDask cuDF DataFrame or CuPy backed Dask Array

Dense matrix (floats or doubles) of shape (n_samples, n_features).

yDask cuDF DataFrame or CuPy backed Dask Array

Dense matrix (floats or doubles) of shape (n_samples, n_features).

predict(X, delayed=True)

Predicts the y for X.

Parameters:

XDask cuDF DataFrame or CuPy backed Dask Array

Dense matrix (floats or doubles) of shape (n_samples, n_features).

delayedbool (default = True)

Whether to do a lazy prediction (and return Delayed objects) or an eagerly executed one.

Returns:

yDask cuDF DataFrame or CuPy backed Dask Array

Dense matrix (floats or doubles) of shape (n_samples, n_features).