diff --git a/scikits/learn/metrics/__init__.py b/scikits/learn/metrics/__init__.py
index f67eee7dc0bea917596ede2770d752a64f484f86..d0a4fb764f7fd5d9c2ad8999e43d9bebc9d9d04b 100644
--- a/scikits/learn/metrics/__init__.py
+++ b/scikits/learn/metrics/__init__.py
@@ -1,19 +1,3 @@
-"""
-Metrics module with score functions, performance metrics and
-pairwise metrics or distances computation
-"""
-
-from .metrics import confusion_matrix, roc_curve, auc, precision_score, \
-                recall_score, fbeta_score, f1_score, zero_one_score, \
-                precision_recall_fscore_support, classification_report, \
-                precision_recall_curve, explained_variance_score, r2_score, \
-                zero_one, mean_square_error, hinge_loss
-
-from .cluster import homogeneity_completeness_v_measure
-from .cluster import homogeneity_score
-from .cluster import completeness_score
-from .cluster import v_measure_score
-from .pairwise import euclidean_distances, pairwise_distances
 import warnings
 warnings.warn('scikits.learn is deprecated, please use sklearn')
 from sklearn.metrics import *
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 32309b93f6a6fce5904e60bcf6c002f7cabcc4e7..fcac47650bdf7199c3e29df20b85d83777ec0735 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -13,7 +13,7 @@ from ..utils.extmath import safe_sparse_dot
 
 # Utility Functions
 def check_pairwise_arrays(X, Y):
-    """ Sets X and Y appropriately and checks inputs
+    """ Set X and Y appropriately and checks inputs
 
     If Y is None, it is set as a pointer to X (i.e. not a copy).
     If Y is given, this does not happen.
@@ -131,7 +131,7 @@ def euclidian_distances(*args, **kwargs):
 
 
 def l1_distances(X, Y=None, sum_over_features=True):
-    """ Computes the L1 distances between the vectors in X and Y.
+    """ Compute the L1 distances between the vectors in X and Y.
 
     With sum_over_features equal to False it returns the componentwise
     distances.
@@ -293,16 +293,17 @@ def rbf_kernel(X, Y=None, gamma=0):
 
 
 # Helper functions - distance
-pairwise_distance_functions = {}
-pairwise_distance_functions['euclidean'] = euclidean_distances
-pairwise_distance_functions['l2'] = euclidean_distances
-pairwise_distance_functions['l1'] = l1_distances
-pairwise_distance_functions['manhattan'] = l1_distances
-pairwise_distance_functions['cityblock'] = l1_distances
+pairwise_distance_functions = {
+    'euclidean':euclidean_distances,
+    'l2':euclidean_distances,
+    'l1':l1_distances,
+    'manhattan':l1_distances,
+    'cityblock':l1_distances
+    }
 
 
 def pairwise_distances(X, Y=None, metric="euclidean", **kwds):
-    """ Calculates the distance matrix from a vector array X and optional Y.
+    """ Compute the distance matrix from a vector array X and optional Y.
 
     This method takes either a vector array or a distance matrix, and returns
     a distance matrix. If the input is a vector array, the distances are
@@ -368,15 +369,16 @@ def pairwise_distances(X, Y=None, metric="euclidean", **kwds):
 
 
 # Helper functions - distance
-pairwise_kernel_functions = {}
-pairwise_kernel_functions['rbf'] = rbf_kernel
-pairwise_kernel_functions['sigmoid'] = sigmoid_kernel
-pairwise_kernel_functions['polynomial'] = polynomial_kernel
-pairwise_kernel_functions['linear'] = linear_kernel
+pairwise_kernel_functions = {
+    'rbf':rbf_kernel,
+    'sigmoid':sigmoid_kernel,
+    'polynomial':polynomial_kernel,
+    'linear':linear_kernel
+    }
 
 
 def pairwise_kernels(X, Y=None, metric="euclidean", **kwds):
-    """ Calculates the kernel between arrays X and optional array Y.
+    """ Compute the kernel between arrays X and optional array Y.
 
     This method takes either a vector array or a kernel matrix, and returns
     a kernel matrix. If the input is a vector array, the kernels are
diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
index 438806e9f96a3b569d89d7ed02079e288d73f83c..9fdcc0db56174a2addf11269340e44a4ea01a492 100644
--- a/sklearn/metrics/tests/test_pairwise.py
+++ b/sklearn/metrics/tests/test_pairwise.py
@@ -59,6 +59,11 @@ def test_pairwise_kernels():
         K1 = pairwise_kernels(X, Y=Y, metric=metric)
         K2 = function(X, Y=Y)
         assert_equal(K1, K2)
+        # Test with sparse X and Y
+        X_sparse = csr_matrix(X)
+        Y_sparse = csr_matrix(Y)
+        K1 = pairwise_kernels(X, Y=Y, metric=metric)
+        assert_equal(K1, K2)
     # Test with a callable function, with given keywords.
     metric = callable_rbf_kernel
     kwds = {}
@@ -74,7 +79,7 @@ def callable_rbf_kernel(x, y, **kwds):
 
 
 def test_euclidean_distances():
-    """Check the pairwise Euclidean distances computation"""
+    """ Check the pairwise Euclidean distances computation"""
     X = [[0]]
     Y = [[1], [2]]
     D = euclidean_distances(X, Y)
@@ -87,7 +92,7 @@ def test_euclidean_distances():
 
 
 def test_kernel_symmetry():
-    """valid kernels should be symmetric"""
+    """ Valid kernels should be symmetric"""
     rng = np.random.RandomState(0)
     X = rng.random_sample((5, 4))
     for kernel in (linear_kernel, polynomial_kernel, rbf_kernel,