SVEKER.ExplainingSVR

class SVEKER.ExplainingSVR(*, kernel_type: Literal['rbf', 'tanimoto', 'poly', 'simgoid'] = 'rbf', degree: int = 3, gamma: float = 1.0, coef0: float = 0, tol: float = 0.001, C: float = 1, epsilon: float = 0.1, shrinking: bool = True, cache_size: float = 200, verbose: bool = False, max_iter: int = -1, no_player_value=0.0)

Class for SVR with exact Shapley values. Inherits from ExplainingSVM and from sklearn.SVR.

For documentation of the sklearn methods, please take a look at the documentation there. Here, only the newly implemented methods will be discussed.

__init__(*, kernel_type: Literal['rbf', 'tanimoto', 'poly', 'simgoid'] = 'rbf', degree: int = 3, gamma: float = 1.0, coef0: float = 0, tol: float = 0.001, C: float = 1, epsilon: float = 0.1, shrinking: bool = True, cache_size: float = 200, verbose: bool = False, max_iter: int = -1, no_player_value=0.0) → None

Initializes SVC with exact Shapley values.

Parameters:

• C (float) – Regularization parameter

• kernel_type (str) – Kernel to be used, must be in ['tanimoto', 'rbf', 'poly', 'sigmoid']

• gamma (float) – Parameter \(\gamma\), only for Polynomial, RBF, Sigmoid kernel.

• degree (float) – Parameter \(d\), only for Polynomial kernel, must be non-negative.

• coef0 (float) – Parameter \(r\), only for Polynomial, Sigmoid kernel.

• epsilon (float)

• shrinking (bool) – Wheter to use shrinking heuristic

• cache_size (float) – Kernel chache size

• class_weight (dict or 'balanced') – Set the parameter \(C\) of class :mahtL`i` to class_weight[i]*C for SVC, if not given, all classes will have the weight of unity.

• verbose (bool) – Enables verbose output

• max_iter (int) – Maximum number of iterations, if -1 no limit

• no_player_value (float) – Defines the no player value

property expected_value

Expected value

Returns:

Expected value

Return type:

float

fit(X, y, sample_weight=None)

Fits the classifier and saves the explicit support vectors.

Parameters:

• X (array_like) – Training vectors

• y (array_like) – Target values

• sample_weights (array_like or None) – Per-sample weight

set_params(**params)

Sets the parameters of the class. Necessary in order to work with sklearn Pipelines and grid searches

Parameters:

params – parameter to change with corresponding value

Returns:

Returns Class with changed parameters.

shapley_values(x: ndarray)

Calculate the Shapley values for multiple query vectors.

Parameters:

vector (np.ndarray) – Vector(s) to be explained using Shapley values

Returns:

Shapley values for each feature in vector(s)

Return type:

np.ndarray