lale.lib.sklearn.svc module

class lale.lib.sklearn.svc.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None, break_ties=False)

Bases: PlannedIndividualOp

Support Vector Classification from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • C (float, >0.0, >=0.03125 for optimizer, <=32768 for optimizer, loguniform distribution, optional, not for optimizer, default 1.0) – Penalty parameter C of the error term.

  • kernel (union type, default 'rbf') –

    Specifies the kernel type to be used in the algorithm.

    • ’precomputed’, not for optimizer

    • or ‘linear’, ‘poly’, ‘rbf’, or ‘sigmoid’

    • or callable, not for optimizer

    See also constraint-1.

  • degree (integer, >=0, >=2 for optimizer, <=5 for optimizer, default 3) – Degree of the polynomial kernel function (‘poly’).

  • gamma (union type, default 'scale') –

    Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

    • ’scale’ or ‘auto’

    • or float, >0.0, >=3.0517578125e-05 for optimizer, <=8 for optimizer, loguniform distribution

  • coef0 (float, >=-1 for optimizer, <=1 for optimizer, optional, not for optimizer, default 0.0) – Independent term in kernel function.

  • shrinking (boolean, default True) – Whether to use the shrinking heuristic.

  • probability (boolean, optional, default False) – Whether to enable probability estimates.

  • tol (float, >0.0, <=0.01 for optimizer, default 0.001) – Tolerance for stopping criteria.

  • cache_size (integer, >=0, <=1000 for optimizer, not for optimizer, default 200) – Specify the size of the kernel cache (in MB).

  • class_weight (union type, optional, 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 (boolean, optional, not for optimizer, default False) – Enable verbose output.

  • max_iter (integer, >=1 for optimizer, <=1000 for optimizer, not for optimizer, default -1) – Hard limit on iterations within solver, or -1 for no limit.

  • decision_function_shape (‘ovo’ or ‘ovr’, not for optimizer, default ‘ovr’) – Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2).

  • random_state (union type, optional, 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.

  • break_ties (boolean, optional, not for optimizer, default False) – If true, decision_function_shape=’ovr’, and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned.

Notes

constraint-1 : union type

Sparse precomputed kernels are not supported.

  • negated type of ‘X/isSparse’

  • or kernel : negated type of ‘precomputed’

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 – The predicted classes.

  • array of items : float

  • or array of items : string

  • or array of items : boolean

Return type

union type

predict_proba(X)

Probability estimates for all classes.

Note: The predict_proba 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 – The outer array is over samples aka rows.

  • items : array of items : float

    The inner array has items corresponding to each class.

Return type

array