Use more natural class_weight="auto" heuristic

scikit-learn · Mar 5, 2015 · 518ca64 · 518ca64
1 parent 8dbe3f8
commit 518ca64
Show file tree

Hide file tree

Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py
@@ -47,8 +47,9 @@ def compute_class_weight(class_weight, classes, y):
             raise ValueError("classes should have valid labels that are in y")
 
         # inversely proportional to the number of samples in the class
-        recip_freq = 1. / bincount(y_ind)
-        weight = recip_freq[le.transform(classes)] / np.mean(recip_freq)
+        recip_freq = len(y) / (len(le.classes_) *
+                               bincount(y_ind).astype(np.float64))
+        weight = recip_freq[le.transform(classes)]
     else:
         # user-defined dictionary
         weight = np.ones(classes.shape[0], dtype=np.float64, order='C')

diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -905,10 +905,9 @@ def check_class_weight_auto_linear_classifier(name, Classifier):
     coef_auto = classifier.fit(X, y).coef_.copy()
 
     # Count each label occurrence to reweight manually
-    mean_weight = (1. / 3 + 1. / 2) / 2
     class_weight = {
-        1: 1. / 3 / mean_weight,
-        -1: 1. / 2 / mean_weight,
+        1: 5. / (2 * 3),
+        -1: 5. / (2 * 2)
     }
     classifier.set_params(class_weight=class_weight)
     coef_manual = classifier.fit(X, y).coef_.copy()