snowflake.ml.modeling.linear_model.Ridge¶
- class snowflake.ml.modeling.linear_model.Ridge(*, alpha=1.0, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, solver='auto', positive=False, random_state=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶
Bases:
BaseTransformerLinear least squares with l2 regularization For more details on this class, see sklearn.linear_model.Ridge
- alpha: {float, ndarray of shape (n_targets,)}, default=1.0
Constant that multiplies the L2 term, controlling regularization strength. alpha must be a non-negative float i.e. in [0, inf).
When alpha = 0, the objective is equivalent to ordinary least squares, solved by the
LinearRegressionobject. For numerical reasons, using alpha = 0 with the Ridge object is not advised. Instead, you should use theLinearRegressionobject.If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.
- fit_intercept: bool, default=True
Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e.
Xandyare expected to be centered).- copy_X: bool, default=True
If True, X will be copied; else, it may be overwritten.
- max_iter: int, default=None
Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000. For ‘lbfgs’ solver, the default value is 15000.
- tol: float, default=1e-4
The precision of the solution (coef_) is determined by tol which specifies a different convergence criterion for each solver:
‘svd’: tol has no impact.
‘cholesky’: tol has no impact.
‘sparse_cg’: norm of residuals smaller than tol.
‘lsqr’: tol is set as atol and btol of scipy.sparse.linalg.lsqr, which control the norm of the residual vector in terms of the norms of matrix and coefficients.
‘sag’ and ‘saga’: relative change of coef smaller than tol.
‘lbfgs’: maximum of the absolute (projected) gradient=max|residuals| smaller than tol.
- solver: {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’
Solver to use in the computational routines:
‘auto’ chooses the solver automatically based on the type of data.
‘svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. It is the most stable solver, in particular more stable for singular matrices than ‘cholesky’ at the cost of being slower.
‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closed-form solution.
‘sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for large-scale data (possibility to set tol and max_iter).
‘lsqr’ uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure.
‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its improved, unbiased version named SAGA. Both methods also use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
‘lbfgs’ uses L-BFGS-B algorithm implemented in scipy.optimize.minimize. It can be used only when positive is True.
All solvers except ‘svd’ support both dense and sparse data. However, only ‘lsqr’, ‘sag’, ‘sparse_cg’, and ‘lbfgs’ support sparse input when fit_intercept is True.
- positive: bool, default=False
When set to
True, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case.- random_state: int, RandomState instance, default=None
Used when
solver== ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.- input_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.
- label_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
- output_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
- drop_input_cols: Optional[bool], default=False
If set, the response of predict(), transform() methods will not contain input columns.
Methods
fit(dataset)Fit Ridge regression model For more details on this function, see sklearn.linear_model.Ridge.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.Ridge.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.Ridge.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.Ridge object.
Attributes
model_signaturesReturns model signature of current class.