Merge pull request #22 from Leona-LYT/main

delete _make_fair_classification function and modified related test file

Author: statmlben

rehline/__init__.py

@@ -5,13 +5,15 @@
 from ._data import make_fair_classification
 from ._internal import rehline_internal, rehline_result
 from ._path_sol import plqERM_Ridge_path_sol
+from ._sklearn_mixin import plq_Ridge_Classifier, plq_Ridge_Regressor
 
 __all__ = ("ReHLine_solver",
            "_BaseReHLine",
            "ReHLine",
            "plqERM_Ridge",
            "CQR_Ridge",
            "plqERM_Ridge_path_sol",
+           "plq_Ridge_Classifier",
+           "plq_Ridge_Regressor",
            "_make_loss_rehline_param",
-           "_make_constraint_rehline_param"
-           "make_fair_classification")
+           "_make_constraint_rehline_param")

rehline/_sklearn_mixin.py

@@ -410,7 +410,6 @@ def fit(self, X, y, sample_weight=None):
             col = np.full((X.shape[0], 1), self.intercept_scaling, dtype=X.dtype)
             X_aug = np.hstack([X, col])
 
-        # Delegate to base solver (it will build loss/constraints from the X we pass)
         super().fit(X_aug, y, sample_weight=sample_weight)
 
         # Split intercept from coefficients to match sklearn's linear model API

tests/_test_fairsvm.py

@@ -1,19 +1,25 @@
 ## Test SVM on simulated dataset
 import numpy as np
-
-from rehline import make_fair_classification, plqERM_Ridge
+from sklearn.datasets import make_classification
+from sklearn.preprocessing import StandardScaler
+from rehline import plqERM_Ridge
 
 np.random.seed(1024)
 # simulate classification dataset
-X, y, X_sen = make_fair_classification()
-n, d = X.shape
-C = 0.5
+n, d, C = 100, 5, 0.5
+X, y = make_classification(n, d)
+y = 2*y - 1
+
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+sen_idx = [0]
 
 ## solution provided by ReHLine
 # build-in hinge loss for svm
 clf = plqERM_Ridge(loss={'name': 'svm'}, C=C)
 
 # specific the param of FairSVM
+X_sen = X[:,sen_idx]
 A = np.repeat([X_sen @ X], repeats=[2], axis=0) / n
 A[1] = -A[1]
 # suppose the fair tolerance is 0.01