Fix deprecated and unnecessary uses of np.sum

aesara/compile/profiling.py

@@ -527,7 +527,7 @@ class ProfileStats:
         hs += ["<#apply>"]
         es += [" %4d  "]
 
-        upto_length = np.sum([len(x) for x in hs]) + len(hs)
+        upto_length = sum(len(x) for x in hs) + len(hs)
         maxlen = max(self.line_width - upto_length, 0)
         hs += ["<Class name>"]
         es += ["%s"]
@@ -624,7 +624,7 @@ class ProfileStats:
         hs += ["<#apply>"]
         es += ["  %4d  "]
 
-        upto_length = np.sum([len(x) for x in hs]) + len(hs)
+        upto_length = sum(len(x) for x in hs) + len(hs)
         maxlen = max(self.line_width - upto_length, 0)
         hs += ["<Op name>"]
         es += ["%s"]
@@ -703,7 +703,7 @@ class ProfileStats:
         if self.variable_shape:
             hs += ["<Mflops>", "<Gflops/s>"]
 
-        upto_length = np.sum([len(x) for x in hs]) + len(hs)
+        upto_length = sum(len(x) for x in hs) + len(hs)
         maxlen = max(self.line_width - upto_length, 0)
         hs += ["<Apply name>"]
         es += ["%s"]

aesara/scan/op.py

@@ -3086,7 +3086,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
         b = e
         e = e + self.n_mit_mot
         ib = ie
-        ie = ie + int(np.sum([len(x) for x in self.tap_array[: self.n_mit_mot]]))
+        ie = ie + int(sum(len(x) for x in self.tap_array[: self.n_mit_mot]))
         clean_eval_points = []
         for inp, evp in zip(inputs[b:e], eval_points[b:e]):
             if evp is not None:
@@ -3102,12 +3102,10 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
         e = e + self.n_mit_sot
         ib = ie
         ie = ie + int(
-            np.sum(
-                [
-                    len(x)
-                    for x in self.tap_array[
-                        self.n_mit_mot : self.n_mit_mot + self.n_mit_sot
-                    ]
+            sum(
+                len(x)
+                for x in self.tap_array[
+                    self.n_mit_mot : self.n_mit_mot + self.n_mit_sot
                 ]
             )
         )
@@ -3161,7 +3159,7 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
         inner_other = self_inputs[ie:] + inner_eval_points[ib:]
 
         # Outputs
-        n_mit_mot_outs = int(np.sum([len(x) for x in self.mit_mot_out_slices]))
+        n_mit_mot_outs = int(sum(len(x) for x in self.mit_mot_out_slices))
 
         b = 0
         e = n_mit_mot_outs

aesara/tensor/subtensor.py

@@ -1598,8 +1598,8 @@ class IncSubtensor(COp):
         else:
             op_is_set = 0
         fail = sub["fail"]
-        view_ndim = node.inputs[0].ndim - np.sum(
-            [not isinstance(idx, slice) for idx in self.idx_list]
+        view_ndim = node.inputs[0].ndim - sum(
+            not isinstance(idx, slice) for idx in self.idx_list
         )
 
         copy_of_x = self.copy_of_x(x)

tests/tensor/test_basic.py

@@ -729,12 +729,11 @@ class TestAlloc:
             fgrad = aesara.function([some_vector], grad_derp, mode=self.mode)
 
             topo_obj = fobj.maker.fgraph.toposort()
-            assert np.sum([isinstance(node.op, type(alloc_)) for node in topo_obj]) == 0
+            assert sum(isinstance(node.op, type(alloc_)) for node in topo_obj) == 0
 
             topo_grad = fgrad.maker.fgraph.toposort()
             assert (
-                np.sum([isinstance(node.op, type(alloc_)) for node in topo_grad])
-                == n_alloc
+                sum(isinstance(node.op, type(alloc_)) for node in topo_grad) == n_alloc
             ), (alloc_, subtensor, n_alloc, topo_grad)
             fobj(test_params)
             fgrad(test_params)
@@ -748,7 +747,7 @@ class TestAlloc:
 
             f = aesara.function([], out, mode=self.mode)
             topo = f.maker.fgraph.toposort()
-            assert np.sum([isinstance(node.op, type(alloc_)) for node in topo]) == 1
+            assert sum(isinstance(node.op, type(alloc_)) for node in topo) == 1
             assert not isinstance(topo[0].op, DeepCopyOp)
 
     def test_ones(self):
@@ -3610,7 +3609,7 @@ class TestDiag:
         f = aesara.function([x], g.shape)
         topo = f.maker.fgraph.toposort()
         if config.mode != "FAST_COMPILE":
-            assert np.sum([isinstance(node.op, AllocDiag) for node in topo]) == 0
+            assert sum(isinstance(node.op, AllocDiag) for node in topo) == 0
         for shp in [5, 0, 1]:
             m = rng.random(shp).astype(self.floatX)
             assert (f(m) == np.diag(m).shape).all()
@@ -3620,7 +3619,7 @@ class TestDiag:
         f = aesara.function([x], g.shape)
         topo = f.maker.fgraph.toposort()
         if config.mode != "FAST_COMPILE":
-            assert np.sum([isinstance(node.op, ExtractDiag) for node in topo]) == 0
+            assert sum(isinstance(node.op, ExtractDiag) for node in topo) == 0
         for shp in [(5, 3), (3, 5), (5, 1), (1, 5), (5, 0), (0, 5), (1, 0), (0, 1)]:
             m = rng.random(shp).astype(self.floatX)
             assert (f(m) == np.diag(m).shape).all()

tests/tensor/test_basic_opt.py

@@ -1027,8 +1027,8 @@ class TestFusion:
                 # check that the number of input to the Composite
                 # Elemwise is ok
                 if len(set(g.owner.inputs)) == len(g.owner.inputs):
-                    expected_len_sym_inputs = np.sum(
-                        [not isinstance(x, Constant) for x in topo_[0].inputs]
+                    expected_len_sym_inputs = sum(
+                        not isinstance(x, Constant) for x in topo_[0].inputs
                     )
                     assert expected_len_sym_inputs == len(sym_inputs)
 

tests/tensor/test_blas.py

@@ -862,7 +862,7 @@ def test_upcasting_scalar_nogemm():
 
     f = function([w, v, t, alpha], rval)
     t = f.maker.fgraph.toposort()
-    assert np.sum([isinstance(n.op, Gemm) for n in t]) == 0
+    assert sum(isinstance(n.op, Gemm) for n in t) == 0
     # aesara.printing.debugprint(f, print_type=True)
 
     v = fmatrix("v")
@@ -875,7 +875,7 @@ def test_upcasting_scalar_nogemm():
         f = function([w, v, t, alpha], rval)
 
     t = f.maker.fgraph.toposort()
-    assert np.sum([isinstance(n.op, Gemm) for n in t]) == 0
+    assert sum(isinstance(n.op, Gemm) for n in t) == 0
     # aesara.printing.debugprint(f, print_type=True)
 
 

tests/tensor/test_math_opt.py

@@ -1771,8 +1771,8 @@ class TestFusion:
                     # check that the number of input to the Composite
                     # Elemwise is ok
                     if len(set(g.owner.inputs)) == len(g.owner.inputs):
-                        expected_len_sym_inputs = np.sum(
-                            [not isinstance(x, Constant) for x in topo_[0].inputs]
+                        expected_len_sym_inputs = sum(
+                            not isinstance(x, Constant) for x in topo_[0].inputs
                         )
                         assert expected_len_sym_inputs == len(sym_inputs)
 

tests/tensor/test_subtensor.py

@@ -788,8 +788,8 @@ class TestSubtensor(utt.OptimizationTestMixin):
         topo_ = [node for node in topo if not isinstance(node.op, DeepCopyOp)]
         if not self.fast_compile:
             assert len(topo_) == 6
-        assert np.sum([isinstance(node.op, IncSubtensor) for node in topo_]) == 1
-        assert np.sum([isinstance(node.op, Subtensor) for node in topo_]) == 1
+        assert sum(isinstance(node.op, IncSubtensor) for node in topo_) == 1
+        assert sum(isinstance(node.op, Subtensor) for node in topo_) == 1
 
         gval = f()
         good = np.zeros_like(data)