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_results=None, alpha_per_target=False, store_cv_values='deprecated', 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

Parameters:

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 strictly 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 scoring_parameter) 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_results (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.
store_cv_values (bool) – 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).

Base class for all transformers.

Methods

fit(dataset: Union[DataFrame, DataFrame]) → BaseEstimator¶: Runs universal logics for all fit implementations.

fit_transform(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'fit_transform_') → Union[DataFrame, DataFrame]¶

Method not supported for this class.

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns :returns: Transformed dataset.

get_input_cols() → List[str]¶

Input columns getter.

Returns:: Input columns.

get_label_cols() → List[str]¶

Label column getter.

Returns:: Label column(s).

get_output_cols() → List[str]¶

Output columns getter.

Returns:: Output columns.

get_params(deep: bool = True) → Dict[str, Any]¶

Get the snowflake-ml parameters for this transformer.

Parameters:: deep – If True, will return the parameters for this transformer and contained subobjects that are transformers.
Returns:: Parameter names mapped to their values.

get_passthrough_cols() → List[str]¶

Passthrough columns getter.

Returns:: Passthrough column(s).

get_sample_weight_col() → Optional[str]¶

Sample weight column getter.

Returns:: Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) → Dict[str, Any]¶

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Parameters:

default_sklearn_obj – Sklearn object used to get default parameter values. Necessary when sklearn_added_keyword_to_version_dict is provided.
sklearn_initial_keywords – Initial keywords in sklearn.
sklearn_unused_keywords – Sklearn keywords that are unused in snowml.
snowml_only_keywords – snowml only keywords not present in sklearn.
sklearn_added_keyword_to_version_dict – Added keywords mapped to the sklearn versions in which they were added.
sklearn_added_kwarg_value_to_version_dict – Added keyword argument values mapped to the sklearn versions in which they were added.
sklearn_deprecated_keyword_to_version_dict – Deprecated keywords mapped to the sklearn versions in which they were deprecated.
sklearn_removed_keyword_to_version_dict – Removed keywords mapped to the sklearn versions in which they were removed.

Returns:

Sklearn parameter names mapped to their values.

predict(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]¶

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

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
Returns:: Transformed dataset.

score(dataset: Union[DataFrame, DataFrame]) → float¶

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

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
Returns:: Score.

score_samples(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'score_samples_') → Union[DataFrame, DataFrame]¶

Method not supported for this class.

Raises:

TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
output_cols_prefix – Prefix for the response columns

Returns:

Output dataset with probability of the sample for each class in the model.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None¶

set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → RidgeCV¶

Input columns setter.

Parameters:: input_cols – A single input column or multiple input columns.
Returns:: self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Label column setter.

Parameters:: label_cols – A single label column or multiple label columns if multi task learning.
Returns:: self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Output columns setter.

Parameters:: output_cols – A single output column or multiple output columns.
Returns:: self

set_params(**params: Any) → None¶

Set the parameters of this transformer.

The method works on simple transformers as well as on sklearn compatible pipelines with nested objects, once the transformer has been fit. Nested objects have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params – Transformer parameter names mapped to their values.
Raises:: SnowflakeMLException – Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Passthrough columns setter.

Parameters:: passthrough_cols – Column(s) that should not be used or modified by the estimator/transformer. Estimator/Transformer just passthrough these columns without any modifications.
Returns:: self

set_sample_weight_col(sample_weight_col: Optional[str]) → Base¶

Sample weight column setter.

Parameters:: sample_weight_col – A single column that represents sample weight.
Returns:: self

to_sklearn() → Any¶: Get sklearn.linear_model.RidgeCV object.

Attributes

model_signatures¶

Returns model signature of current class.

Raises:: SnowflakeMLException – If estimator is not fitted, then model signature cannot be inferred
Returns:: Dict with each method and its input output signature