ElasticNet

class cuml.linear_model.ElasticNet(

alpha=1.0,

*,

l1_ratio=0.5,

fit_intercept=True,

max_iter=1000,

tol=0.001,

solver='auto',

selection='cyclic',

output_type=None,

verbose=False,

)

Linear regression with combined L1 and L2 priors as regularizer.

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_ratiofloat, 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.

solver{‘auto’, ‘cd’, ‘qn’}, default=’auto’

The solver to use.

• ‘auto’: uses ‘cd’ for dense inputs, and ‘qn’ for sparse inputs

• ‘cd’: uses coordinate descent. Only supports dense inputs.

• ‘qn’: uses quasi-newton methods. Supports sparse and dense inputs.

You may find the alternative ‘qn’ algorithm is faster when the number of features is sufficiently large but the sample size is small.

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

How selections are made when solver="cd". 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{None, ‘input’, ‘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.

verboseint or boolean, default=False

Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.

Attributes:

coef_array, shape (n_features)

The estimated coefficients for the linear regression model.

sparse_coef_sparse matrix, shape (n_targets, n_features)

Sparse representation of the fitted coef_.

intercept_float

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

n_iter_int

The number of iterations taken by the solver.

Methods

fit(X, y[, sample_weight])

Fit the model with X and y.

Notes

For additional docs, see scikitlearn’s ElasticNet.

Examples

>>> import cupy as cp
>>> import cudf
>>> from cuml.linear_model import ElasticNet
>>> enet = ElasticNet(alpha = 0.1, l1_ratio=0.5, solver='qn')
>>> X = cudf.DataFrame()
>>> X['col1'] = cp.array([0, 1, 2], dtype = cp.float32)
>>> X['col2'] = cp.array([0, 1, 2], dtype = cp.float32)
>>> y = cudf.Series(cp.array([0.0, 1.0, 2.0], dtype = cp.float32) )
>>> result_enet = enet.fit(X, y)
>>> print(result_enet.coef_)
0    0.445...
1    0.445...
dtype: float32
>>> print(result_enet.intercept_)
0.108433...
>>> X_new = cudf.DataFrame()
>>> X_new['col1'] = cp.array([3,2], dtype = cp.float32)
>>> X_new['col2'] = cp.array([5,5], dtype = cp.float32)
>>> preds = result_enet.predict(X_new)
>>> print(preds)
0    3.674...
1    3.228...
dtype: float32

as_sklearn()

Convert this estimator into an equivalent scikit-learn (or scikit-learn extension) estimator.

Returns:

sklearn.base.BaseEstimator

A scikit-learn compatible estimator instance that mirrors the trained state of the current estimator.

fit(

X,

y,

sample_weight=None,

) → ElasticNet

Fit the model with X and y.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

yarray-like (device or host) shape = (n_samples, 1)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

sample_weightarray-like (device or host) shape = (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

classmethod from_sklearn(model)

Create a cuml estimator from a scikit-learn estimator.

Parameters:

modelsklearn.base.BaseEstimator

A compatible scikit-learn (or scikit-learn extension) estimator.

Returns:

cls

A new instance of this cuml estimator class that mirrors the state of the input estimator.

Notes

output_type of the estimator is set to “numpy” by default, as these cannot be inferred from training arguments. If something different is required, then please use cuml’s output_type configuration utilities.

get_params(deep=True)

Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

predict(X)

Predicts y values for X.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

predscuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_samples, 1)

Predicted values

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

score(X, y, sample_weight=None, **kwargs)

Scoring function for regression estimators

Returns the coefficient of determination R^2 of the prediction.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

yarray-like (device or host) shape = (n_samples, 1)

Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

sample_weightarray-like (device or host) shape = (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

scorefloat

R^2 of self.predict(X) wrt. y.

set_params(**params)

Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

property sparse_coef_

Sparse representation of the fitted coef_.