opendp.extras.sklearn.decomposition package

Module contents

This module requires extra installs: pip install 'opendp[scikit-learn]'

For convenience, all the functions of this module are also available from opendp.prelude. We suggest importing under the conventional name dp:

>>> import opendp.prelude as dp

The methods of this module will then be accessible at dp.sklearn.decomposition.

See also our tutorial on diffentially private PCA.

class opendp.extras.sklearn.decomposition.PCA(*, epsilon, row_norm, n_samples, n_features, n_components=None, n_changes=1, whiten=False)

DP wrapper for sklearn’s PCA.

Trying to create an instance without sklearn installed will raise an ImportError.

See the tutorial on diffentially private PCA for details.

Parameters:

• whiten (bool) – Mirrors the corresponding sklearn parameter: When True (False by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances.

• epsilon (float) –

• row_norm (float) –

• n_samples (int) –

• n_features (int) –

• n_components (int | float | str | None) –

• n_changes (int) –

fit(X, y=None)

Fit the model with X.

Parameters:

• X – Training data, where n_samples is the number of samples and n_features is the number of features.

• y – Ignored

measurement()

Return a measurement that releases a fitted model.

Return type:

Measurement

n_features

Number of features

class opendp.extras.sklearn.decomposition.PCAEpsilons(eigvals, eigvecs, mean)

Bases: NamedTuple

Tuple used to describe the ε-expenditure per changed record in the input data

Parameters:

• eigvals (float) –

• eigvecs (Sequence[float]) –

• mean (float | None) –

eigvals: float

Alias for field number 0

eigvecs: Sequence[float]

Alias for field number 1

mean: float | None

Alias for field number 2

opendp.extras.sklearn.decomposition.make_private_pca(input_domain, input_metric, unit_epsilon, norm=None, num_components=None)

Construct a Measurement that returns the data mean, singular values and right singular vectors.

Parameters:

• input_domain (Domain) – instance of array2_domain(size=_, num_columns=_)

• input_metric (Metric) – instance of symmetric_distance()

• unit_epsilon (float | PCAEpsilons) – ε-expenditure per changed record in the input data

• norm (float | None) – clamp each row to this norm bound

• num_components – optional, number of eigenvectors to release. defaults to num_columns from input_domain

Returns:

a Measurement that computes a tuple of (mean, S, Vt)

Return type:

Measurement