lale.lib.sklearn.linear_svc module

class lale.lib.sklearn.linear_svc.LinearSVC(*, penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1.0, class_weight=None, verbose=0, random_state=None, max_iter=1000)

Bases: PlannedIndividualOp

Linear Support Vector Classification from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • penalty (‘l1’ or ‘l2’, default ‘l2’) –

    Norm used in the penalization.

    See also constraint-1, constraint-2, constraint-3.

  • loss (‘hinge’ or ‘squared_hinge’, default ‘squared_hinge’) –

    Loss function.

    See also constraint-1, constraint-2, constraint-3.

  • dual (union type, default True) –

    Select the algorithm to either solve the dual or primal optimization problem.

    • boolean

      Prefer dual=False when n_samples > n_features.

    • or ‘auto’

      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.

    See also constraint-2, constraint-3.

  • tol (float, >0.0, <=0.01 for optimizer, default 0.0001) – Tolerance for stopping criteria.

  • C (float, >0.0, >=0.03125 for optimizer, <=32768 for optimizer, loguniform distribution, default 1.0) – Penalty parameter C of the error term.

  • multi_class (‘ovr’ or ‘crammer_singer’, default ‘ovr’) –

    Determines the multi-class strategy if y contains more than two classes.

    See also constraint-1, constraint-2, constraint-3.

  • fit_intercept (boolean, default True) – Whether to calculate the intercept for this model.

  • intercept_scaling (float, >0.0, <=1.0 for optimizer, not for optimizer, default 1.0) – Append a constant feature with constant value intercept_scaling to the instance vector.

  • class_weight (union type, not for optimizer, default None) –

    • None

      By default, all classes have weight 1.

    • or ‘balanced’

      Adjust weights by inverse frequency.

    • or dict, not for optimizer

      Dictionary mapping class labels to weights.

  • verbose (integer, not for optimizer, default 0) – Enable verbose output.

  • random_state (union type, not for optimizer, default None) –

    Seed of pseudo-random number generator.

    • numpy.random.RandomState

    • or None

      RandomState used by np.random

    • or integer

      Explicit seed.

  • max_iter (integer, >=1, >=10 for optimizer, <=1000 for optimizer, not for optimizer, default 1000) – The maximum number of iterations to be run.

Notes

constraint-1 : union type

The combination of penalty=`l1` and loss=`hinge` is not supported. If multi_class=’crammer_singer’, the options loss, penalty and dual will be ignored.

  • penalty : ‘l2’

  • or loss : ‘squared_hinge’

  • or multi_class : ‘crammer_singer’

constraint-2 : union type

The combination of penalty=`l2` and loss=`hinge` is not supported when dual=False. If multi_class=’crammer_singer’, the options loss, penalty and dual will be ignored.

  • penalty : ‘l1’

  • or loss : ‘squared_hinge’

  • or dual : True

  • or multi_class : ‘crammer_singer’

constraint-3 : union type

The combination of penalty=`l1` and loss=`squared_hinge` is not supported when dual=True. If multi_class=’crammer_singer’, the options loss, penalty and dual will be ignored.

  • penalty : ‘l2’

  • or loss : ‘hinge’

  • or dual : False

  • or multi_class : ‘crammer_singer’

decision_function(X)

Confidence scores for all classes.

Note: The decision_function method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array) –

The outer array is over samples aka rows.

  • items : array of items : float

    The inner array is over features aka columns.

Returns

result – Confidence scores for samples for each class in the model.

  • array of items : array of items : float

    In the multi-way case, score per (sample, class) combination.

  • or array of items : float

    In the binary case, score for self._classes[1].

Return type

union type

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array) –

    The outer array is over samples aka rows.

    • items : array of items : float

      The inner array is over features aka columns.

  • y (union type) –

    The predicted classes.

    • array of items : float

    • or array of items : string

    • or array of items : boolean

  • sample_weight (union type, optional) –

    Sample weights.

    • array of items : float

    • or None

      Samples are equally weighted.

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array, optional) –

The outer array is over samples aka rows.

  • items : array of items : float

    The inner array is over features aka columns.

Returns

result – Predict class labels for samples in X.

  • array of items : float

  • or array of items : string

  • or array of items : boolean

Return type

union type