snowflake.ml.modeling.linear_model.RidgeClassifier

class snowflake.ml.modeling.linear_model.RidgeClassifier(*, alpha=1.0, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, class_weight=None, 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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

Classifier using Ridge regression For more details on this class, see sklearn.linear_model.RidgeClassifier

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.

alpha: float, default=1.0

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.

fit_intercept: bool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already 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. The default value is determined by scipy.sparse.linalg.

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.

class_weight: dict or ‘balanced’, default=None

Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one.

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).

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 unbiased and more flexible version named SAGA. Both methods 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.

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.

Methods

decision_function(dataset[, output_cols_prefix])

Predict confidence scores for samples For more details on this function, see sklearn.linear_model.RidgeClassifier.decision_function

fit(dataset)

Fit Ridge classifier model For more details on this function, see sklearn.linear_model.RidgeClassifier.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 class labels for samples in X For more details on this function, see sklearn.linear_model.RidgeClassifier.predict

score(dataset)

Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.linear_model.RidgeClassifier.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.RidgeClassifier object.

Attributes

model_signatures

Returns model signature of current class.
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