# 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:** **alpha** : Regularization strength - must be a positive float. Larger values specify stronger regularization. **fit_intercept** : If True, Ridge tries to correct for the global mean of y. If False, the model expects that you have centered the data. **solver** : 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. **tol** : The tolerance used by the `lsmr` solver. Has no impact on other solvers. **max_iter** : 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** : 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](../../cuml/index.html.md#output-data-type-configuration) for more info. **verbose** : Sets logging level. It must be one of `cuml.common.logger.level_*`. See [Verbosity Levels](../../cuml/index.html.md#verbosity-levels) for more info. * **Attributes:** **coef_** : The estimated coefficients for the linear regression model. **intercept_** : 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_** : The solver that was used at fit time. **n_iter_** : The number of iterations the solver ran per-target if the `'lsmr'` solver was used, or `None` for other solvers. ### Methods | [`fit`](#cuml.linear_model.Ridge.fit)(self, X, y[, sample_weight]) | Fit the model with X and y. | |-------------------------------------------------------------------------------------|-------------------------------| ### Notes For additional docs, see [Scikit-learn’s Ridge Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html). ### Examples ```pycon >>> import cupy as cp >>> import cudf >>> from cuml import Ridge ``` ```pycon >>> 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)) ``` ```pycon >>> 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... ``` #### as_sklearn()[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/interop.py#L206) 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(self, X, y, sample_weight=None) → 'Ridge'[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/linear_model/ridge.pyx#L362) Fit the model with X and y. * **Parameters:** **X** : 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. **y** : 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_weight** : 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)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/interop.py#L222) Create a cuml estimator from a scikit-learn estimator. * **Parameters:** **model** : 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)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L191) 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)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/linear_model/base.py#L14) Predicts `y` values for `X`. * **Parameters:** **X** : 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:** **preds** : Predicted values
For more information on how to configure cuML’s output type, refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration). #### score(X, y, sample_weight=None, \*\*kwargs)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/mixins.py#L66) Scoring function for regression estimators Returns the coefficient of determination R^2 of the prediction. * **Parameters:** **X** : 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. **y** : 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_weight** : 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:** **score** : R^2 of self.predict(X) wrt. y. #### set_params(\*\*params)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L200) 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