scikit-learn
diff --git a/‎sklearn/utils/validation.py
Lines changed: 20 additions & 53 deletions b/‎sklearn/utils/validation.py
Lines changed: 20 additions & 53 deletions
@@ -135,7 +135,7 @@ def _num_samples(x):
 
 
 def check_consistent_length(*arrays):
-    n_samples = [_num_samples(X) for X in arrays]
+    n_samples = [_num_samples(X) for X in arrays if X is not None]
     uniques = np.unique(n_samples)
     if len(uniques) > 1:
         raise ValueError("Found arrays with inconsistent numbers of samples: %s"
@@ -192,7 +192,8 @@ def _ensure_sparse_format(array, allowed_sparse, convert_sparse_to, dtype,
 
 
 def check_array(array, allowed_sparse=None, dtype=None, order=None, copy=False,
-                force_all_finite=True, convert_sparse_to=None, make_2d=True):
+                force_all_finite=True, convert_sparse_to=None, make_2d=True,
+                allow_nd=False):
     """Check everything about an array"""
     if isinstance(allowed_sparse, str):
         allowed_sparse = [allowed_sparse]
@@ -207,6 +208,9 @@ def check_array(array, allowed_sparse=None, dtype=None, order=None, copy=False,
         if make_2d:
             array = np.atleast_2d(array)
         array = np.array(array, dtype=dtype, order=order, copy=copy)
+        if not allow_nd and array.ndim >= 3:
+            raise ValueError("Found array with dim %d. Expected <= 2" %
+                             array.ndim)
         if force_all_finite:
             _assert_all_finite(array)
 
@@ -275,60 +279,23 @@ def check_arrays(*arrays, **options):
 
     if len(arrays) == 0:
         return None
-
-    n_samples = _num_samples(arrays[0])
+    check_consistent_length(*arrays)
+
+    order = 'C' if check_ccontiguous else None
+    force_finite = not allow_nans
+    if sparse_format == 'dense':
+        allow_sparse = None
+    elif sparse_format is None:
+        allow_sparse = ['csr', 'csc']
+    else:
+        allow_sparse = sparse_format
 
     checked_arrays = []
     for array in arrays:
-        array_orig = array
-        if array is None:
-            # special case: ignore optional y=None kwarg pattern
-            checked_arrays.append(array)
-            continue
-        size = _num_samples(array)
-
-        if size != n_samples:
-            raise ValueError("Found array with dim %d. Expected %d"
-                             % (size, n_samples))
-
-        if (force_arrays or hasattr(array, "__array__")
-                or hasattr(array, "shape")):
-            if sp.issparse(array):
-                if sparse_format == 'csr':
-                    array = <
10000
span class=pl-s1>array.tocsr()
-                elif sparse_format == 'csc':
-                    array = array.tocsc()
-                elif sparse_format == 'dense':
-                    raise TypeError('A sparse matrix was passed, but dense '
-                                    'data is required. Use X.toarray() to '
-                                    'convert to a dense numpy array.')
-                if check_ccontiguous:
-                    array.data = np.ascontiguousarray(array.data, dtype=dtype)
-                elif hasattr(array, 'data'):
-                    array.data = np.asarray(array.data, dtype=dtype)
-                elif array.dtype != dtype:
-                    # Cast on the required dtype
-                    array = array.astype(dtype)
-                if not allow_nans:
-                    if hasattr(array, 'data'):
-                        _assert_all_finite(array.data)
-                    else:
-                        # DOK sparse matrices
-                        _assert_all_finite(array.values())
-            else:
-                if check_ccontiguous:
-                    array = np.ascontiguousarray(array, dtype=dtype)
-                elif dtype is not None or force_arrays:
-                    array = np.asarray(array, dtype=dtype)
-                if not allow_nans:
-                    _assert_all_finite(array)
-
-            if force_arrays and not allow_nd and array.ndim >= 3:
-                raise ValueError("Found array with dim %d. Expected <= 2" %
-                                 array.ndim)
-
-        if copy and array is array_orig:
-            array = array.copy()
+        if (force_arrays or hasattr(array, "__array__") or hasattr(array, "shape")):
+            array = check_array(array, allow_sparse, dtype, order, copy=copy,
+                                make_2d=False, allow_nd=allow_nd,
+                                force_all_finite=force_finite)
         checked_arrays.append(array)
 
     return checked_arrays