skwdro.linear_models.LogisticRegression

class skwdro.linear_models.LogisticRegression(rho: float = 0.01, l2_reg: float = 0.0, fit_intercept: bool = True, cost: str = 't-NLC-2-2', solver='entropic_torch', solver_reg: float | None = None, sampler_reg: float | None = None, n_zeta_samples: int = 10, random_state: int = 0, opt_cond: ~skwdro.solvers.optim_cond.OptCondTorch | None = <skwdro.solvers.optim_cond.OptCondTorch object>)

A Wasserstein Distributionally Robust logistic regression classifier.

The cost function is XXX

Uncertainty is XXX

Parameters:

rho: float, default=1e-2

Robustness radius

l2_reg: float, default=None

l2 regularization

fit_intercept: boolean, default=True

Determines if an intercept is fit or not

cost: str, default=”n-NC-1-2”

Tiret-separated code to define the transport cost: “<engine>-<cost id>-<k-norm type>-<power>” for

solver: str, default=’entropic_torch’

Solver to be used: ‘entropic’, ‘entropic_torch’ (_pre or _post) or ‘dedicated’

solver_reg: float, default=1e-2

regularization value for the entropic solver

n_zeta_samples: int, default=10

number of adversarial samples to draw

opt_cond: Optional[OptCondTorch]

optimality condition, see OptCondTorch

Attributes:

coef_array, shape (n_features,)

parameter vector ( in the cost function formula)

intercept_float

constant term in decision function.

Examples

>>> import numpy as np
>>> from skwdro.linear_models import LogisticRegression
>>> from sklearn.datasets import make_blobs
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=0)
>>> y = np.sign(y-0.5)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
>>> estimator = LogisticRegression()
>>> estimator.fit(X_train,y_train)
LogisticRegression()
>>> estimator.predict(X_test)
array([-1., -1., -1.,  1., -1.,  1.,  1., -1., -1.,  1.,  1.,  1., -1.,
        1.,  1.,  1.,  1.,  1., -1., -1., -1.,  1.,  1., -1., -1.,  1.,
       -1.,  1.,  1.,  1.,  1.,  1., -1.])
>>> estimator.score(X_test,y_test)
0.9393939393939394