Apply pyupgrade to tests.tensor

tests/tensor/nnet/test_conv.py

@@ -97,7 +97,7 @@ class TestConv2D(utt.InferShapeTester):
             return rval
 
         output = sym_conv2d(input, filters)
-        output.name = "conv2d(%s,%s)" % (input.name, filters.name)
+        output.name = "conv2d({},{})".format(input.name, filters.name)
         theano_conv = theano.function([input, filters], output, mode=self.mode)
 
         # initialize input and compute result

tests/tensor/nnet/test_corr.py

 import numpy as np
 import pytest
-from six import integer_types
 
 import theano
 import theano.tensor as tt
@@ -75,7 +74,7 @@ class TestCorr2D(utt.InferShapeTester):
             return rval
 
         output = sym_CorrMM(input, filters)
-        output.name = "CorrMM()(%s,%s)" % (input.name, filters.name)
+        output.name = "CorrMM()({},{})".format(input.name, filters.name)
         theano_corr = theano.function([input, filters], output, mode=self.mode)
 
         # initialize input and compute result
@@ -110,7 +109,7 @@ class TestCorr2D(utt.InferShapeTester):
             padHW = np.floor(dil_fil_shape2d / 2).astype("int32")
         elif isinstance(border_mode, tuple):
             padHW = np.array(border_mode)
-        elif isinstance(border_mode, integer_types):
+        elif isinstance(border_mode, int):
             padHW = np.array([border_mode, border_mode])
         else:
             raise NotImplementedError("Unsupported border_mode {}".format(border_mode))

tests/tensor/nnet/test_corr3d.py

 import numpy as np
 import pytest
-from six import integer_types
 
 import theano
 import theano.tensor as tt
@@ -73,7 +72,7 @@ class TestCorr3D(utt.InferShapeTester):
             return rval
 
         output = sym_Corr3dMM(input, filters)
-        output.name = "Corr3dMM()(%s,%s)" % (input.name, filters.name)
+        output.name = "Corr3dMM()({},{})".format(input.name, filters.name)
         theano_corr = theano.function([input, filters], output, mode=self.mode)
 
         # initialize input and compute result
@@ -112,7 +111,7 @@ class TestCorr3D(utt.InferShapeTester):
             padHWD = np.floor(dil_fil_shape3d / 2).astype("int32")
         elif isinstance(border_mode, tuple):
             padHWD = np.array(border_mode)
-        elif isinstance(border_mode, integer_types):
+        elif isinstance(border_mode, int):
             padHWD = np.array([border_mode, border_mode, border_mode])
         else:
             raise NotImplementedError("Unsupported border_mode {}".format(border_mode))

tests/tensor/signal/test_pool.py

@@ -465,7 +465,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
             )
             assert (
                 numpy_output_val.shape == outputshp
-            ), "outshape is %s, calculated shape is %s" % (
+            ), "outshape is {}, calculated shape is {}".format(
                 outputshp,
                 numpy_output_val.shape,
             )
@@ -516,7 +516,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 )
                 assert (
                     numpy_output_val.shape == outputshp
-                ), "outshape is %s, calculated shape is %s" % (
+                ), "outshape is {}, calculated shape is {}".format(
                     outputshp,
                     numpy_output_val.shape,
                 )

tests/tensor/test_basic.py

@@ -2546,8 +2546,8 @@ COMPLEX_DTYPES = ALL_DTYPES[-2:]
 
 def multi_dtype_checks(shape1, shape2, dtypes=ALL_DTYPES, nameprefix=""):
     for dtype1, dtype2 in itertools.combinations(dtypes, 2):
-        name1 = "%s_%s_%s" % (nameprefix, dtype1, dtype2)
-        name2 = "%s_%s_%s" % (nameprefix, dtype2, dtype1)
+        name1 = "{}_{}_{}".format(nameprefix, dtype1, dtype2)
+        name2 = "{}_{}_{}".format(nameprefix, dtype2, dtype1)
         obj1 = rand_of_dtype(shape1, dtype1)
         obj2 = rand_of_dtype(shape2, dtype2)
         yield (name1, (obj1, obj2))
@@ -2556,8 +2556,8 @@ def multi_dtype_checks(shape1, shape2, dtypes=ALL_DTYPES, nameprefix=""):
 
 def multi_dtype_cast_checks(shape, dtypes=ALL_DTYPES, nameprefix=""):
     for dtype1, dtype2 in itertools.combinations(dtypes, 2):
-        name1 = "%s_%s_%s" % (nameprefix, dtype1, dtype2)
-        name2 = "%s_%s_%s" % (nameprefix, dtype2, dtype1)
+        name1 = "{}_{}_{}".format(nameprefix, dtype1, dtype2)
+        name2 = "{}_{}_{}".format(nameprefix, dtype2, dtype1)
         obj1 = rand_of_dtype(shape, dtype1)
         obj2 = rand_of_dtype(shape, dtype2)
         yield (name1, (obj1, dtype2))
@@ -3176,7 +3176,7 @@ class TestCast:
             # Casts from foo to foo
             [
                 (
-                    "%s_%s" % (rand_of_dtype((2,), dtype), dtype),
+                    "{}_{}".format(rand_of_dtype((2,), dtype), dtype),
                     (rand_of_dtype((2,), dtype), dtype),
                 )
                 for dtype in ALL_DTYPES
@@ -3499,7 +3499,7 @@ class TestShape:
         assert (eval_outputs([s]) == [5, 3]).all()
 
     def test_basic1(self):
-        s = shape(np.ones((2)))
+        s = shape(np.ones(2))
         assert (eval_outputs([s]) == [2]).all()
 
     def test_basic2(self):
@@ -5393,7 +5393,7 @@ class TestMatinv:
         # Here, as_tensor_variable actually uses the data allocated by np.
         diff = ab - as_tensor_variable(np.ones((dim, dim), dtype=config.floatX))
         # Sum of squared errors
-        ssdiff = sum((diff ** 2.0))
+        ssdiff = sum(diff ** 2.0)
 
         g_b = grad(ssdiff, b)
 
@@ -6144,7 +6144,7 @@ def test_is_flat():
     # given outdim
 
     # Constant variable
-    assert tt.is_flat(tt.as_tensor_variable(np.zeros((10))))
+    assert tt.is_flat(tt.as_tensor_variable(np.zeros(10)))
     assert tt.is_flat(tt.as_tensor_variable(np.zeros((10, 10, 10))), ndim=3)
     assert not tt.is_flat(tt.as_tensor_variable(np.zeros((10, 10, 10))))
 

tests/tensor/test_blas.py

@@ -378,7 +378,7 @@ class TestGemm:
         )
 
 
-class TestGemmNoFlags(object):
+class TestGemmNoFlags:
     gemm = gemm_no_inplace
     M = 4
     N = 5
@@ -1467,7 +1467,7 @@ def matrixmultiply(a, b):
     return c
 
 
-class BaseGemv(object):
+class BaseGemv:
     mode = mode_blas_opt  # can be overridden with self.mode
     shared = staticmethod(theano.shared)
 

tests/tensor/test_blas_c.py

@@ -321,7 +321,7 @@ class TestCGemvFloat64(BaseGemv, OptimizationTestMixin):
         skip_if_blas_ldflags_empty()
 
 
-class TestCGemvNoFlags(object):
+class TestCGemvNoFlags:
     mode = mode_blas_opt
     gemv = CGemv(inplace=False)
     M = 4

tests/tensor/test_elemwise.py

@@ -514,7 +514,7 @@ class TestCAReduce(unittest_tools.InferShapeTester):
             f = theano.function([x], e.shape, mode=mode)
             if not (
                 scalar_op in [scalar.maximum, scalar.minimum]
-                and ((xsh == () or np.prod(xsh) == 0))
+                and (xsh == () or np.prod(xsh) == 0)
             ):
                 try:
                     assert all(f(xv) == zv.shape)

tests/tensor/test_fourier.py

@@ -12,7 +12,7 @@ class TestFourier(utt.InferShapeTester):
     rng = np.random.RandomState(43)
 
     def setup_method(self):
-        super(TestFourier, self).setup_method()
+        super().setup_method()
         self.op_class = Fourier
         self.op = fft
 

tests/tensor/test_keepdims.py

 import numpy as np
 import pytest
-from six import integer_types
 
 import theano
 from theano import function, tensor
@@ -12,7 +11,7 @@ class TestKeepDims:
     def makeKeepDims_local(self, x, y, axis):
         if axis is None:
             newaxis = list(range(x.ndim))
-        elif isinstance(axis, integer_types):
+        elif isinstance(axis, int):
             if axis < 0:
                 newaxis = [axis + x.type.ndim]
             else:

tests/tensor/test_misc.py

@@ -61,10 +61,10 @@ def test_bug_2009_07_17_borrowed_output():
     z = g(a, b, c)
     z_backup = copy.copy(z)
     id_z = id(z)
-    print(("Output z after first call: %s" % (z,)))
+    print("Output z after first call: {}".format(z))
     a[0, 0] = 1
     id_other = id(g(a, b, c))
-    print(("Output z after second call: %s" % (z,)))
+    print("Output z after second call: {}".format(z))
     # Ensure that calling the function again returns a pointer towards a new
     # array.
     assert id_z != id_other

tests/tensor/test_mlp.py

@@ -63,7 +63,7 @@ def gen_data():
     return rval
 
 
-class LogisticRegression(object):
+class LogisticRegression:
     """Multi-class Logistic Regression Class
 
     The logistic regression is fully described by a weight matrix :math:`W`
@@ -131,7 +131,7 @@ class LogisticRegression(object):
         return tt.log(self.p_y_given_x[tt.arange(y.shape[0]), y])
 
 
-class HiddenLayer(object):
+class HiddenLayer:
     def __init__(self, rng, input, n_in, n_out, activation=tt.tanh, name_prefix=""):
         """
         Typical hidden layer of a MLP: units are fully-connected and have
@@ -179,7 +179,7 @@ class HiddenLayer(object):
         self.params = [self.W]
 
 
-class MLP(object):
+class MLP:
     """Multi-Layer Perceptron Class
 
     A multilayer perceptron is a feedforward artificial neural network model

tests/tensor/test_mpi.py

@@ -54,7 +54,7 @@ def test_mpi_roundtrip():
     env = os.environ.copy()
     flags = env.get("THEANO_FLAGS", "")
     keep_flags = ",".join(
-        (f for f in flags.split(",") if not f.startswith("init_gpu_device"))
+        f for f in flags.split(",") if not f.startswith("init_gpu_device")
     )
     env["THEANO_FLAGS"] = keep_flags
     p = subprocess.Popen(

tests/tensor/test_sharedvar.py

@@ -693,10 +693,10 @@ def makeSharedTester(
     internal_type_=np.ndarray,
     check_internal_type_=lambda a: isinstance(a, np.ndarray),
     theano_fct_=lambda a: a * 2,
-    ref_fct_=lambda a: np.asarray((a * 2)),
+    ref_fct_=lambda a: np.asarray(a * 2),
     cast_value_=np.asarray,
 )
-class TestSharedOptions(object):
+class TestSharedOptions:
     pass
 
 

tests/tensor/test_subtensor.py

@@ -222,7 +222,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
 
     @change_flags(compute_test_value="off")
     def test_err_bounds1(self):
-        n = self.shared((np.ones((2, 3), dtype=self.dtype) * 5))
+        n = self.shared(np.ones((2, 3), dtype=self.dtype) * 5)
         t = n[4:5, 3]
         assert isinstance(t.owner.op, Subtensor)
         old_stderr = sys.stderr
@@ -511,7 +511,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
             with pytest.raises(TypeError):
                 test_array.__getitem__((True, False))
             with pytest.raises(TypeError):
-                test_array.__getitem__(([True, False]))
+                test_array.__getitem__([True, False])
             with pytest.raises(TypeError):
                 test_array.__getitem__(([0, 1], [0, False]))
             with pytest.raises(TypeError):