Merge pull request #6817 from TomDLT/logistic_class_weight

[MRG] use class_weight through sample_weight in LogisticRegression with liblinear

doc/whats_new.rst

@@ -203,6 +203,11 @@ Bug fixes
     - Fix bug where expected and adjusted mutual information were incorrect if
       cluster contingency cells exceeded ``2**16``. By `Joel Nothman`_.
 
+    - Fix bug in :class:`linear_model.LogisticRegressionCV` where
+      ``solver='liblinear'`` did not accept ``class_weights='balanced``.
+      (`#6817 <https://github.com/scikit-learn/scikit-learn/pull/6817>`_).
+      By `Tom Dupre la Tour`_.
+
 
 API changes summary
 -------------------

sklearn/linear_model/logistic.py

@@ -618,23 +618,9 @@ def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
     # are assigned to the original labels. If it is "balanced", then
     # the class_weights are assigned after masking the labels with a OvR.
     le = LabelEncoder()
-
     if isinstance(class_weight, dict) or multi_class == 'multinomial':
-        if solver == "liblinear":
-            if classes.size == 2:
-                # Reconstruct the weights with keys 1 and -1
-                temp = {1: class_weight[pos_class],
-                        -1: class_weight[classes[0]]}
-                class_weight = temp.copy()
-            else:
-                raise ValueError("In LogisticRegressionCV the liblinear "
-                                 "solver cannot handle multiclass with "
-                                 "class_weight of type dict. Use the lbfgs, "
-                                 "newton-cg or sag solvers or set "
-                                 "class_weight='balanced'")
-        else:
-            class_weight_ = compute_class_weight(class_weight, classes, y)
-            sample_weight *= class_weight_[le.fit_transform(y)]
+        class_weight_ = compute_class_weight(class_weight, classes, y)
+        sample_weight *= class_weight_[le.fit_transform(y)]
 
     # For doing a ovr, we need to mask the labels first. for the
     # multinomial case this is not necessary.
@@ -740,7 +726,7 @@ def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True,
                                      maxiter=max_iter, tol=tol)
         elif solver == 'liblinear':
             coef_, intercept_, n_iter_i, = _fit_liblinear(
-                X, target, C, fit_intercept, intercept_scaling, class_weight,
+                X, target, C, fit_intercept, intercept_scaling, None,
                 penalty, dual, verbose, max_iter, tol, random_state,
                 sample_weight=sample_weight)
             if fit_intercept:

sklearn/linear_model/tests/test_logistic.py

@@ -547,35 +547,35 @@ def test_logistic_regression_solvers_multiclass():
 
 
 def test_logistic_regressioncv_class_weights():
-    X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
-                               n_classes=3, random_state=0)
-
-    msg = ("In LogisticRegressionCV the liblinear solver cannot handle "
-           "multiclass with class_weight of type dict. Use the lbfgs, "
-           "newton-cg or sag solvers or set class_weight='balanced'")
-    clf_lib = LogisticRegressionCV(class_weight={0: 0.1, 1: 0.2},
-                                   solver='liblinear')
-    assert_raise_message(ValueError, msg, clf_lib.fit, X, y)
-    y_ = y.copy()
-    y_[y == 2] = 1
-    clf_lib.fit(X, y_)
-    assert_array_equal(clf_lib.classes_, [0, 1])
-
-    # Test for class_weight=balanced
-    X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
-                               random_state=0)
-    clf_lbf = LogisticRegressionCV(solver='lbfgs', fit_intercept=False,
-                                   class_weight='balanced')
-    clf_lbf.fit(X, y)
-    clf_lib = LogisticRegressionCV(solver='liblinear', fit_intercept=False,
-                                   class_weight='balanced')
-    clf_lib.fit(X, y)
-    clf_sag = LogisticRegressionCV(solver='sag', fit_intercept=False,
-                                   class_weight='balanced', max_iter=2000)
-    clf_sag.fit(X, y)
-    assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=4)
-    assert_array_almost_equal(clf_sag.coef_, clf_lbf.coef_, decimal=4)
-    assert_array_almost_equal(clf_lib.coef_, clf_sag.coef_, decimal=4)
+    for weight in [{0: 0.1, 1: 0.2}, {0: 0.1, 1: 0.2, 2: 0.5}]:
+        n_classes = len(weight)
+        for class_weight in (weight, 'balanced'):
+            X, y = make_classification(n_samples=30, n_features=3,
+                                       n_repeated=0,
+                                       n_informative=3, n_redundant=0,
+                                       n_classes=n_classes, random_state=0)
+
+            clf_lbf = LogisticRegressionCV(solver='lbfgs', Cs=1,
+                                           fit_intercept=False,
+                                           class_weight=class_weight)
+            clf_ncg = LogisticRegressionCV(solver='newton-cg', Cs=1,
+                                           fit_intercept=False,
+                                           class_weight=class_weight)
+            clf_lib = LogisticRegressionCV(solver='liblinear', Cs=1,
+                                           fit_intercept=False,
+                                           class_weight=class_weight)
+            clf_sag = LogisticRegressionCV(solver='sag', Cs=1,
+                                           fit_intercept=False,
+                                           class_weight=class_weight,
+                                           tol=1e-5, max_iter=10000,
+                                           random_state=0)
+            clf_lbf.fit(X, y)
+            clf_ncg.fit(X, y)
+            clf_lib.fit(X, y)
+            clf_sag.fit(X, y)
+            assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=4)
+            assert_array_almost_equal(clf_ncg.coef_, clf_lbf.coef_, decimal=4)
+            assert_array_almost_equal(clf_sag.coef_, clf_lbf.coef_, decimal=4)
 
 
 def test_logistic_regression_sample_weights():
@@ -926,7 +926,6 @@ def test_n_iter():
         assert_equal(clf.n_iter_.shape, (1, n_cv_fold, n_Cs))
 
 
-@ignore_warnings
 def test_warm_start():
     # A 1-iteration second fit on same data should give almost same result
     # with warm starting, and quite different result without warm starting.
@@ -947,11 +946,11 @@ def test_warm_start():
                                              solver=solver,
                                              random_state=42, max_iter=100,
                                              fit_intercept=fit_intercept)
-                    clf.fit(X, y)
-                    coef_1 = clf.coef_
+                    with ignore_warnings(category=ConvergenceWarning):
+                        clf.fit(X, y)
+                        coef_1 = clf.coef_
 
-                    clf.max_iter = 1
-                    with ignore_warnings():
+                        clf.max_iter = 1
                         clf.fit(X, y)
                     cum_diff = np.sum(np.abs(coef_1 - clf.coef_))
                     msg = ("Warm starting issue with %s solver in %s mode "

sklearn/utils/optimize.py

@@ -17,6 +17,8 @@
 import warnings
 from scipy.optimize.linesearch import line_search_wolfe2, line_search_wolfe1
 
+from ..exceptions import ConvergenceWarning
+
 
 class _LineSearchError(RuntimeError):
     pass
@@ -198,5 +200,5 @@ def newton_cg(grad_hess, func, grad, x0, args=(), tol=1e-4,
 
     if warn and k >= maxiter:
         warnings.warn("newton-cg failed to converge. Increase the "
-                      "number of iterations.")
+                      "number of iterations.", ConvergenceWarning)
     return xk, k