DOC be more flexible for 32 bits arch

Author: glemaitre

imblearn/ensemble/_forest.py

@@ -234,11 +234,8 @@ class labels (multi-output problem).
     >>> clf = BalancedRandomForestClassifier(max_depth=2, random_state=0)
     >>> clf.fit(X, y)  # doctest: +ELLIPSIS
     BalancedRandomForestClassifier(...)
-    >>> print(clf.feature_importances_)
-    [ 0.21506735  0.0104961   0.00706549  0.17414694  0.00556422  0.00704686
-      0.19779549  0.01865445  0.00608294  0.00490484  0.00866699  0.00251414
-      0.00339721  0.01174379  0.09380596  0.05049964  0.0033278   0.01008566
-      0.15534173  0.01379241]
+    >>> print(clf.feature_importances_)  # doctest: +ELLIPSIS
+    [...]
     >>> print(clf.predict([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     ...                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
     [1]

imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py

@@ -100,8 +100,8 @@ class InstanceHardnessThreshold(BaseUnderSampler):
     Original dataset shape Counter({{1: 900, 0: 100}})
     >>> iht = InstanceHardnessThreshold(random_state=42)
     >>> X_res, y_res = iht.fit_resample(X, y)
-    >>> print('Resampled dataset shape %s' % Counter(y_res))
-    Resampled dataset shape Counter({{1: 574, 0: 100}})
+    >>> print('Resampled dataset shape %s' % Counter(y_res))  # doctest: +ELLIPSIS
+    Resampled dataset shape Counter({{1: 5..., 0: 100}})
 
     """