Merge pull request #278 from nouiz/scipy_test

theano/sandbox/linalg/ops.py

@@ -15,8 +15,10 @@ from theano.gof.opt import Optimizer
 
 try:
     import scipy.linalg
+    imported_scipy = True
 except ImportError:
-    pass # some ops (e.g. Cholesky) won't work
+    # some ops (e.g. Cholesky, Solve, A_Xinv_b) won't work
+    imported_scipy = False
 
 class Hint(Op):
     """
@@ -195,6 +197,8 @@ def is_positive(v):
 @register_stabilize
 @local_optimizer([])
 def inv_as_solve(node):
+    if not imported_scipy:
+        return False
     if node.op == dot:
         l,r = node.inputs
         if l.owner and l.owner.op == matrix_inverse:
@@ -342,6 +346,8 @@ class Cholesky(Op):
         return 'Cholesky{%s,%s}' % (lu, destr)
 
     def make_node(self, x):
+        assert imported_scipy, (
+            "Scipy not available. Scipy is needed for the Cholesky op")
         x = as_tensor_variable(x)
         return Apply(self, [x], [x.type()])
 
@@ -544,6 +550,8 @@ class Solve(Op):
     def __repr__(self):
         return 'Solve{%s}'%str(self.props())
     def make_node(self, A, b):
+        assert imported_scipy, (
+            "Scipy not available. Scipy is needed for the Solve op")
         A = as_tensor_variable(A)
         b = as_tensor_variable(b)
         otype = tensor.tensor(
@@ -731,6 +739,8 @@ def spectral_radius_bound(X, log2_exponent):
 class A_Xinv_b(Op):
     """Product of form a inv(X) b"""
     def make_node(self, a, X, b):
+        assert imported_scipy, (
+            "Scipy not available. Scipy is needed for the A_Xinv_b op")
         a = as_tensor_variable(a)
         b = as_tensor_variable(b)
         X = as_tensor_variable(X)

theano/sandbox/linalg/tests/test_linalg.py

@@ -24,7 +24,8 @@ from theano.sandbox.linalg.ops import (cholesky,
                                        #PSD_hint,
                                        trace,
                                        matrix_dot,
-                                       spectral_radius_bound
+                                       spectral_radius_bound,
+                                       imported_scipy,
                                        )
 
 from nose.plugins.skip import SkipTest
@@ -47,6 +48,9 @@ def check_upper_triangular(pd, ch_f):
 
 
 def test_cholesky():
+    if not imported_scipy:
+        raise SkipTest("Scipy needed for the Cholesky op.")
+
     rng = numpy.random.RandomState(utt.fetch_seed())
     r = rng.randn(5, 5).astype(config.floatX)
     pd = numpy.dot(r, r.T)
@@ -66,6 +70,8 @@ def test_cholesky():
 
 
 def test_cholesky_grad():
+    if not imported_scipy:
+        raise SkipTest("Scipy needed for the Cholesky op.")
     rng = numpy.random.RandomState(utt.fetch_seed())
     r = rng.randn(5, 5).astype(config.floatX)
     pd = numpy.dot(r, r.T)
@@ -78,6 +84,9 @@ def test_cholesky_grad():
 
 
 def test_cholesky_and_cholesky_grad_shape():
+    if not imported_scipy:
+        raise SkipTest("Scipy needed for the Cholesky op.")
+
     rng = numpy.random.RandomState(utt.fetch_seed())
     x = tensor.matrix()
     for l in (cholesky(x), Cholesky(lower=True)(x), Cholesky(lower=False)(x)):

theano/tensor/tests/test_blas.py

@@ -9,7 +9,7 @@ import numpy
 import theano
 from numpy import (arange, array, common_type, complex64, complex128, float32,
                   float64, newaxis, shape, transpose, zeros)
-from numpy.testing import assert_, assert_array_almost_equal
+from numpy.testing import assert_array_almost_equal
 #from numpy.testing import dec
 #from numpy.testing.noseclasses import KnownFailureTest
 
@@ -1003,7 +1003,7 @@ def matrixmultiply(a, b):
         b = b[:,newaxis]
     else:
         b_is_vector = False
-    assert_(a.shape[1] == b.shape[0])
+    assert a.shape[1] == b.shape[0]
     c = zeros((a.shape[0], b.shape[1]), common_type(a, b))
     for i in xrange(a.shape[0]):
         for j in xrange(b.shape[1]):