Implement fixes suggested by @jnothman

doc/whats_new.rst

@@ -52,6 +52,9 @@ Enhancements
      (`#7506` <https://github.com/scikit-learn/scikit-learn/pull/7506>_) by
      `Narine Kokhlikyan`_.
 
+   - Added ``norm_order`` parameter to :class:`feature_selection.SelectFromModel`
+     to enable selection of the norm order when ``coef_`` is more than 1D
+
 Bug fixes
 .........
 

sklearn/feature_selection/from_model.py

@@ -175,7 +175,7 @@ class SelectFromModel(BaseEstimator, SelectorMixin):
 
     norm_order : non-zero int, inf, -inf, default 1
         Order of the norm used to filter the vectors of coefficients below
-        ``threshold`` in the case where the ``coeff_`` attribute of the
+        ``threshold`` in the case where the ``coef_`` attribute of the
         estimator is of dimension 2.
 
     Attributes

sklearn/feature_selection/tests/test_from_model.py

@@ -113,7 +113,9 @@ def test_feature_importances_2d_coef():
     for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
         for order in [1, 2, np.inf]:
             # Fit SelectFromModel a multi-class problem
-            transformer = SelectFromModel(estimator=LogisticRegression(), threshold=threshold, norm_order=order)
+            transformer = SelectFromModel(estimator=LogisticRegression(),
+                                          threshold=threshold,
+                                          norm_order=order)
             transformer.fit(X, y)
             assert_true(hasattr(transformer.estimator_, 'coef_'))
             X_new = transformer.transform(X)

sklearn/utils/fixes.py

@@ -424,13 +424,20 @@ def __getstate__(self):
 
     def norm(X, ord=None, axis=None):
         """
-        Handles the axis parameter for the norm function in old versions of numpy.
+        Handles the axis parameter for the norm function
+        in old versions of numpy.
         """
 
         if axis is None or X.ndim == 1:
             result = np.linalg.norm(X, ord=ord)
             return result
 
+        if axis not in (0, 1):
+            raise NotImplementedError("""
+            The fix that adds axis parameter to the old numpy
+            norm only works for 1D or 2D arrays.
+            """)
+
         if axis == 0:
             X = X.T
 

sklearn/utils/tests/test_fixes.py

@@ -69,6 +69,7 @@ def test_masked_array_obj_dtype_pickleable():
         assert_array_equal(marr.data, marr_pickled.data)
         assert_array_equal(marr.mask, marr_pickled.mask)
 
+
 def test_norm():
     X = np.array([[-2, 4, 5],
                   [1, 3, -4],