BF/ENH: fixed 'ValueError: buffer ValueError: buffer' when dict_fact_…

…fast.pyx functions are called with mmapped X
arthurmensch · Jan 13, 2017 · c69fdf2 · c69fdf2
1 parent d993dbb
commit c69fdf2
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Showing 2 changed files with 20 additions and 11 deletions.
diff --git a/modl/dict_fact_fast.pyx b/modl/dict_fact_fast.pyx
@@ -31,7 +31,11 @@ ctypedef void (*AXPY)(int* N, floating* alpha, floating* X, int* incX,
 ctypedef floating (*ASUM)(int* N, floating* X, int* incX) nogil
 
 def _enet_regression_multi_gram(floating[:, :, ::1] G, floating[:, ::1] Dx,
-                                floating[:, ::1] X,
+
+                                # on mmapped input, floating[:, ::1] X ==> ValueError: buffer
+				# source array is read-only
+                                np.ndarray[floating, ndim=2] X,
+
                                 floating[: , ::1] code,
                                 long[:] indices,
                                 floating l1_ratio, floating alpha,
@@ -65,7 +69,7 @@ def _enet_regression_multi_gram(floating[:, :, ::1] G, floating[:, ::1] Dx,
 
     cdef floating[:] this_code
     cdef floating[::1] this_Dx
-    cdef floating[:] this_X
+    cdef np.ndarray[floating, ndim=1] this_X
     cdef floating[:, ::1] this_G
 
     if floating is float:
@@ -113,7 +117,11 @@ def _batch_weight(long count, long batch_size,
 
 
 def _enet_regression_single_gram(floating[:, ::1] G, floating[:, ::1] Dx,
-                                floating[:, ::1] X,
+
+                                # on mmapped input, floating[:, ::1] X ==> ValueError: buffer
+				# source array is read-only
+                                np.ndarray[floating, ndim=2] X,
+
                                 floating[:, ::1] code,
                                 long[:] indices,
                                 floating l1_ratio, floating alpha,
@@ -147,9 +155,8 @@ def _enet_regression_single_gram(floating[:, ::1] G, floating[:, ::1] Dx,
     cdef str format
     cdef floating[:] this_code
     cdef floating[::1] this_Dx
-    cdef floating[:] this_X
+    cdef np.ndarray[floating, ndim=1] this_X
     cdef floating[:, ::1] G_copy
-    cdef floating[:, ::1] code_copy
 
     if floating is float:
         posv = sposv
@@ -168,9 +175,6 @@ def _enet_regression_single_gram(floating[:, ::1] G, floating[:, ::1] Dx,
         for j in range(n_components):
             G[j, j] += alpha
 
-        code_copy = view.array((batch_size, n_components),
-                                      sizeof(floating),
-                                      format=format, mode='c')
         posv(&UP, &n_components, &batch_size,
         G_ptr,
         &n_components,
@@ -251,7 +255,11 @@ cdef floating max(int n, floating* a) nogil:
 cdef void enet_coordinate_descent_gram(floating[:] w, floating alpha, floating beta,
                                  floating[:, ::1] Q,
                                  floating[::1] q,
-                                 floating[:] y,
+
+                                 # on mmapped input, floating[:, ::1] y ==> ValueError: buffer
+				 # source array is read-only
+                                 np.ndarray[floating, ndim=1] y,
+
                                  int max_iter, floating tol, bint positive):
     """Cython version of the coordinate descent algorithm
         for Elastic-Net regression

diff --git a/modl/fmri.py b/modl/fmri.py
@@ -322,8 +322,9 @@ def fit(self, imgs, y=None, confounds=None, raw=False):
 
         self.masker_._shelving = False
 
-        # components = self.dict_fact_.components_
-        # self.components_ = _normalize_and_flip(components)
+        # normalize learned components
+        components = self.dict_fact_.components_
+        self.components_ = _normalize_and_flip(components)
 
         return self