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snowflake.ml.modeling.svm.LinearSVC

class snowflake.ml.modeling.svm.LinearSVC(*, penalty='l2', loss='squared_hinge', dual='auto', 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, passthrough_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

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.

  • 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="auto") – 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 optimizer 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. For an intuitive visualization of the effects of scaling the regularization parameter C, see sphx_glr_auto_examples_svm_plot_svm_scale_c.py.

  • 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 or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: [x_1, …, x_n, 1], where 1 corresponds to the intercept. 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 fit_intercept is True, the instance vector x becomes [x_1, ..., x_n, intercept_scaling], i.e. a “synthetic” feature with a constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the intercept_scaling parameter can be set to a value greater than 1; the higher the value of intercept_scaling, the lower the impact of regularization on it. Then, the weights become [w_x_1, …, w_x_n, w_intercept*intercept_scaling], where w_x_1, …, w_x_n represent the feature weights and the intercept weight is scaled by intercept_scaling. This scaling allows the intercept term to have a different regularization behavior compared to the other features.

  • 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.

Base class for all transformers.

Methods

decision_function(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'decision_function_') → Union[DataFrame, DataFrame]

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

Raises:

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

Parameters:

  • dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

  • output_cols_prefix – str Prefix for the response columns

Returns:

Output dataset with results of the decision function for the samples in input dataset.

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 class labels for samples in X For more details on this function, see sklearn.svm.LinearSVC.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 mean accuracy on the given test data and labels For more details on this function, see sklearn.svm.LinearSVC.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]]]) → LinearSVC

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.svm.LinearSVC 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

Language: English