SpSum Op: pep8'ified, perform rolled back to basic (clearer/safer)…

theano/sparse/sandbox/sp.py

@@ -57,41 +57,34 @@ class SpSum(Op):
         assert isinstance(x.type, theano.sparse.SparseType)
         b = ()
         if self.axis is not None:
-            b=(False,)
+            b = (False,)
         z = tensor.tensor(broadcastable=b, dtype=x.dtype)
         return gof.Apply(self, [x], [z])
 
     def infer_shape(self, node, shapes):
-        r=None
+        r = None
         if self.axis is None:
-            r=[()]
+            r = [()]
         elif self.axis == 0:
-            r=[(shapes[0][1],)]
+            r = [(shapes[0][1],)]
         else:
-            r=[(shapes[0][0],)]
+            r = [(shapes[0][0],)]
         return r
 
     def perform(self,node, (x,), (z,)):
         if self.axis is None:
             z[0] = numpy.asarray(x.sum())
         else:
-            s = set(xrange(len(x.shape))) - set([self.axis])
-            myreshape = map((lambda i: x.shape[i]), s)
-            if x.format not in ('csc', 'csr'):
-                x = x.asformat(x.format)
-            z[0] = numpy.asarray(x.sum(axis = self.axis)).reshape(myreshape)
-
-            #case by case code for reference
-            #if self.axis == 0:
-            #    if x.format == 'csc':
-            #        z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape((x.shape[1],))
-            #    else:
-            #        z[0] = numpy.asarray(x.asformat(x.format).sum(axis=self.axis)).reshape((x.shape[1],))
-            #if self.axis == 1:
-            #    if x.format == 'csr':
-            #        z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape((x.shape[0],))
-            #    else:
-            #        z[0] = numpy.asarray(x.asformat(x.format).sum(axis=self.axis)).reshape((x.shape[0],))
+            if self.axis == 0:
+                if x.format == 'csc':
+                   z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape((x.shape[1],))
+               else:
+                   z[0] = numpy.asarray(x.asformat(x.format).sum(axis=self.axis)).reshape((x.shape[1],))
+            elif self.axis == 1:
+               if x.format == 'csr':
+                   z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape((x.shape[0],))
+               else:
+                   z[0] = numpy.asarray(x.asformat(x.format).sum(axis=self.axis)).reshape((x.shape[0],))
 
     def grad(self,(x,), (gz,)):
         if self.axis is None:

theano/sparse/sandbox/test_sp.py

@@ -361,11 +361,6 @@ class TestSP(unittest.TestCase):
                     utt.verify_grad(d, [kvals])
 
     def test_sp_sum(self):
-
-        print '\n\n*************************************************'
-        print '           TEST SUM'
-        print '*************************************************'
-
         from theano.sparse.sandbox.sp import SpSum
 
         # TODO: test both grad.
@@ -375,13 +370,13 @@ class TestSP(unittest.TestCase):
 
         for format, cast in cases:
 
-            print 'format: %(format)s'%locals()
+            #print 'format: %(format)s' % locals()
             x = theano.sparse.SparseType(format=format,
                                          dtype=theano.config.floatX)()
             x_data = numpy.arange(20).reshape(5,4).astype(theano.config.floatX)
 
             # Sum on all axis
-            print 'sum on all axis...'
+            #print 'sum on all axis...'
             z = theano.sparse.sandbox.sp.sp_sum(x)
             assert z.type.broadcastable == ()
             f = theano.function([x], z)
@@ -391,7 +386,7 @@ class TestSP(unittest.TestCase):
             assert out == expected
 
             # Sum on axis 0
-            print 'sum on axis 0...'
+            #print 'sum on axis 0...'
             z = theano.sparse.sandbox.sp.sp_sum(x, axis=0)
             assert z.type.broadcastable == (False,)
             f = theano.function([x], z)
@@ -401,7 +396,7 @@ class TestSP(unittest.TestCase):
             assert (out == expected).all()
 
             # Sum on axis 1
-            print 'sum on axis 1...'
+            #print 'sum on axis 1...'
             z = theano.sparse.sandbox.sp.sp_sum(x, axis=1)
             assert z.type.broadcastable == (False,)
             f = theano.function([x], z)