Ridge#

class cuml.linear_model.Ridge(
alpha=1.0,
*,
fit_intercept=True,
solver='auto',
tol=0.0001,
max_iter=None,
copy_X=True,
output_type=None,
verbose=False,
)#

Linear least squares with L2 regularization.

Ridge extends LinearRegression by providing L2 regularization on the coefficients when predicting response y with a linear combination of the predictors in X. It can reduce the variance of the predictors, and improves the conditioning of the problem.

Parameters:
alphafloat or array of shape (n_targets,), default=1.0

Regularization strength - must be a positive float. Larger values specify stronger regularization.

fit_interceptbool, default=True

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

solver{‘auto’, ‘eig’, ‘svd’, ‘lsmr’}, default=’auto’

The solver to use when fitting:

  • ‘auto’: will select ‘eig’ if supported, falling back to ‘lsmr’ if X is sparse, and ‘svd’ otherwise.

  • ‘eig’: uses an eigendecomposition of the covariance matrix. It is faster than SVD, but potentially unstable. It doesn’t support multi-target y or sparse X.

  • ‘svd’: uses an SVD decomposition. It’s slower, but stable. It doesn’t support sparse X.

  • ‘lsmr’: uses cupyx.scipy.sparse.linalg.lsmr, an iterative algorithm. It is typically the fastest, and supports all options.

tolfloat, default=1e-4

The tolerance used by the lsmr solver. Has no impact on other solvers.

max_iterint, default=None

Maximum number of iterations for the lsmr solver. Defaults to None for no limit. Has no impact on other solvers.

copy_X: bool, default=True

If True, X will never be mutated. Setting to False may reduce memory usage, at the cost of potentially mutating X.

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.

intercept_float or array, shape (n_targets,)

The independent term. If fit_intercept is False, will be 0. Will be an array when fit on multi-target y, otherwise will be a float.

solver_str

The solver that was used at fit time.

n_iter_numpy.ndarray or None, shape (n_targets,)

The number of iterations the solver ran per-target if the 'lsmr' solver was used, or None for other solvers.

Methods

fit(self, X, y[, sample_weight, convert_dtype])

Fit the model with X and y.

Notes

For additional docs, see Scikit-learn’s Ridge Regression.

Examples

>>> import cupy as cp
>>> import cudf
>>> from cuml import Ridge

>>> X = cudf.DataFrame()
>>> X['col1'] = cp.array([1,1,2,2], dtype = cp.float32)
>>> X['col2'] = cp.array([1,2,2,3], dtype = cp.float32)
>>> y = cudf.Series(cp.array([6.0, 8.0, 9.0, 11.0], dtype=cp.float32))

>>> ridge = Ridge(alpha=1e-5).fit(X, y)
>>> print(ridge.coef_)
0 1.000...
1 1.999...
>>> print(ridge.intercept_)
3.0...
>>> 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 = ridge.predict(X_new)
>>> print(preds)
0 15.999...
1 14.999...
fit(
self,
X,
y,
sample_weight=None,
*,
convert_dtype='deprecated',
) → 'Ridge'[source]#

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.

convert_dtypebool, optional (default = ‘deprecated’)

Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.

On this page