secure_learning.models.secure_model

Abstract class for secure-learning models.

Attributes

DEFAULT_L1_PENALTY
DEFAULT_L2_PENALTY

Classes

SolverTypes	The possible solver types associated to models.
PenaltyTypes	The possible penalty types associated to models.
Model	Abstract secure-learn model class.

Module Contents

secure_learning.models.secure_model.DEFAULT_L1_PENALTY = 1

secure_learning.models.secure_model.DEFAULT_L2_PENALTY = 1

class secure_learning.models.secure_model.SolverTypes(*args, **kwds)

The possible solver types associated to models.

GD

class secure_learning.models.secure_model.PenaltyTypes(*args, **kwds)

The possible penalty types associated to models.

NONE

L1

L2

ELASTICNET

class secure_learning.models.secure_model.Model(solver_type: SolverTypes = SolverTypes.GD, penalty: PenaltyTypes = PenaltyTypes.NONE, **penalty_args: float)

Abstract secure-learn model class.

name = ''

property solver: tno.mpc.mpyc.secure_learning.solvers.solver.Solver

Return solver used by current model.

Raises:

SecureLearnUninitializedSolverError – raised when solver is not yet initiated

Returns:

Solver used by current model.

initialize_solver(solver_type: SolverTypes, penalty: PenaltyTypes, **penalty_args: float) → None

Initialize solver.

Parameters:

• solver_type – Type of the requested solver.

• penalty – Type of penalties

• penalty_args – Coefficient(s) of the given penalty

gradient_function(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], weights: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], grad_per_sample: False) → tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint]

gradient_function(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], weights: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], grad_per_sample: True) → List[tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint]]

gradient_function(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], weights: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], grad_per_sample: bool) → tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint] | List[tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint]]

Evaluate the gradient function.

Parameters:

• X – Independent data.

• y – Dependent data.

• weights – Weight vector.

• grad_per_sample – Return gradient per sample if True, return aggregated gradient of all data if False.

Returns:

Value(s) of gradient evaluated with the provided parameters.

abstractmethod score(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], weights: tno.mpc.mpyc.secure_learning.utils.Vector[float] | tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint]) → mpyc.sectypes.SecureFixedPoint

Compute the model score.

Parameters:

• X – Test data.

• y – True value for $X$.

• weights – Weight vector.

Returns:

Score of the model prediction.

async compute_weights_mpc(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], tolerance: float = 0.01, minibatch_size: int | None = None, weights_init: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint] | None = None, nr_maxiters: int = 100, print_progress: bool = False, secure_permutations: bool = False) → tno.mpc.mpyc.secure_learning.utils.Vector[float]

Train the model, compute and return the model weights.

Parameters:

• X – Training data

• y – Target vector

• tolerance – Threshold for convergence

• minibatch_size – The size of the minibatch

• weights_init – Initial weight vector to use

• nr_maxiters – Threshold for the number of iterations

• print_progress – Set to True to print progress every few iterations

• secure_permutations – Set to True to perform matrix permutation securely

Raises:

SecureLearnTypeError – if the training or target data does not consist of secure numbers

Returns:

Weight vector

async cross_validate(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], y: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], tolerance: float = 0.01, minibatch_size: int | None = None, weights_init: tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint] | None = None, nr_maxiters: int = 100, print_progress: bool = False, secure_permutations: bool = False, folds: int | List[Tuple[List[int], List[int]]] = 5) → tno.mpc.mpyc.secure_learning.utils.Vector[float]

Evaluate metrics over the model prediction using CV.

Parameters:

• X – Train data.

• y – Target variable for X

• tolerance – Threshold for convergence

• minibatch_size – The size of the minibatch

• weights_init – Initial weight vector to use

• nr_maxiters – Threshold for the number of iterations

• print_progress – Set to True to print progress

• secure_permutations – Set to True to perform matrix permutation securely

• folds – Folding sets. If set to $k$ (integer) then a KFold (from sklearn.model_selection) is used. If it is not set then KFold is called with $k=5$. It also possible to pass custom folds as a list of tuples of train and test indexes: e.g. $[([2, 3], [0, 1, 4]), ([0, 1, 3], [2, 4]), ([0, 1, 2], [3, 4])]$ is a 3-fold of an array of five elements $([2, 3] , [0, 1, 4] )$ -> 1st fold, elements with indexes $[2, 3]$ are used in the train set, while elements with indexes [0, 1, 4] are used in the test set $([0, 1, 3] , [2, 4] )$ -> 2nd fold, elements with indexes $[0, 1, 3]$ are used in the train set, while elements with indexes [2, 4] are used in the test set $([0, 1, 2] , [3, 4] )$ -> 3rd fold, elements with indexes $[0, 1, 2]$ are used in the train set, while elements with indexes $[3, 4]$ are used in the test set

Returns:

List of scores of the model prediction.

static predict(X: tno.mpc.mpyc.secure_learning.utils.Matrix[mpyc.sectypes.SecureFixedPoint], weights: tno.mpc.mpyc.secure_learning.utils.Vector[float] | tno.mpc.mpyc.secure_learning.utils.Vector[mpyc.sectypes.SecureFixedPoint], **kwargs: Any) → List[mpyc.sectypes.SecureFixedPoint]

Abstractmethod:

Predicts target values for input data.

Parameters:

• X – Input data with all features

• weights – Weight vector of the model

• kwargs – Additional keyword arguments that are needed to predict

Returns:

Target values