snowflake.ml.modeling.svm.LinearSVC¶

class snowflake.ml.modeling.svm.LinearSVC(*, penalty='l2', loss='squared_hinge', dual='warn', tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000, 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: BaseTransformer

Linear Support Vector Classification For more details on this class, see sklearn.svm.LinearSVC

penalty: {‘l1’, ‘l2’}, default=’l2’: Specifies the norm used in the penalization. The ‘l2’ penalty is the standard used in SVC. The ‘l1’ leads to coef_ vectors that are sparse.
loss: {‘hinge’, ‘squared_hinge’}, default=’squared_hinge’: Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. The combination of penalty='l1' and loss='hinge' is not supported.
dual: “auto” or bool, default=True: Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. dual=”auto” will choose the value of the parameter automatically, based on the values of n_samples, n_features, loss, multi_class and penalty. If n_samples < n_features and optmizer supports chosen loss, multi_class and penalty, then dual will be set to True, otherwise it will be set to False.
tol: float, default=1e-4: Tolerance for stopping criteria.
C: float, default=1.0: Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
multi_class: {‘ovr’, ‘crammer_singer’}, default=’ovr’: Determines the multi-class strategy if y contains more than two classes. "ovr" trains n_classes one-vs-rest classifiers, while "crammer_singer" optimizes a joint objective over all classes. While crammer_singer is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If "crammer_singer" is chosen, the options loss, penalty and dual will be ignored.
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 already centered).
intercept_scaling: float, default=1.0: When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
class_weight: dict or ‘balanced’, default=None: Set the parameter C of class i to class_weight[i]*C for SVC. 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)).
verbose: int, default=0: Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.
random_state: int, RandomState instance or None, default=None: Controls the pseudo random number generation for shuffling the data for the dual coordinate descent (if dual=True). When dual=False the underlying implementation of LinearSVC is not random and random_state has no effect on the results. Pass an int for reproducible output across multiple function calls. See Glossary.
max_iter: int, default=1000: The maximum number of iterations to be run.
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

`decision_function`(dataset[, output_cols_prefix])	Predict confidence scores for samples For more details on this function, see sklearn.svm.LinearSVC.decision_function
`fit`(dataset)	Fit the model according to the given training data For more details on this function, see sklearn.svm.LinearSVC.fit
`predict`(dataset)	Predict class labels for samples in X For more details on this function, see sklearn.svm.LinearSVC.predict
`score`(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.svm.LinearSVC.score
`set_input_cols`(input_cols)	Input columns setter.
`to_sklearn`()	Get sklearn.svm.LinearSVC object.

Attributes

model_signatures

Returns model signature of current class.