snowflake.ml.modeling.linear_model.RidgeCV

class snowflake.ml.modeling.linear_model.RidgeCV(*, alphas=(0.1, 1.0, 10.0), fit_intercept=True, scoring=None, cv=None, gcv_mode=None, store_cv_values=False, alpha_per_target=False, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

Ridge regression with built-in cross-validation For more details on this class, see sklearn.linear_model.RidgeCV

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

alphas: array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)

Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If using Leave-One-Out cross-validation, alphas must be positive.

fit_intercept: bool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).

scoring: str, callable, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the negative mean squared error if cv is ‘auto’ or None (i.e. when using leave-one-out cross-validation), and r2 score otherwise.

cv: int, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the efficient Leave-One-Out cross-validation

  • integer, to specify the number of folds.

  • CV splitter,

  • An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if y is binary or multiclass, StratifiedKFold is used, else, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

gcv_mode: {‘auto’, ‘svd’, ‘eigen’}, default=’auto’

Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are:

'auto': use 'svd' if n_samples > n_features, otherwise use 'eigen'
'svd': force use of singular value decomposition of X when X is
    dense, eigenvalue decomposition of X^T.X when X is sparse.
'eigen': force computation via eigendecomposition of X.X^T
Copy

The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending on the shape of the training data.

store_cv_values: bool, default=False

Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation).

alpha_per_target: bool, default=False

Flag indicating whether to optimize the alpha value (picked from the alphas parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to True, after fitting, the alpha_ attribute will contain a value for each target. When set to False, a single alpha is used for all targets.

Methods

fit(dataset)

Fit Ridge regression model with cv For more details on this function, see sklearn.linear_model.RidgeCV.fit

get_input_cols()

Input columns getter.

get_label_cols()

Label column getter.

get_output_cols()

Output columns getter.

get_params([deep])

Get parameters for this transformer.

get_passthrough_cols()

Passthrough columns getter.

get_sample_weight_col()

Sample weight column getter.

get_sklearn_args([default_sklearn_obj, ...])

Get sklearn keyword arguments.

predict(dataset)

Predict using the linear model For more details on this function, see sklearn.linear_model.RidgeCV.predict

score(dataset)

Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.RidgeCV.score

set_drop_input_cols([drop_input_cols])

set_input_cols(input_cols)

Input columns setter.

set_label_cols(label_cols)

Label column setter.

set_output_cols(output_cols)

Output columns setter.

set_params(**params)

Set the parameters of this transformer.

set_passthrough_cols(passthrough_cols)

Passthrough columns setter.

set_sample_weight_col(sample_weight_col)

Sample weight column setter.

to_sklearn()

Get sklearn.linear_model.RidgeCV object.

Attributes

model_signatures

Returns model signature of current class.
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