Activate more ruff rules (#270)

* Activate quotation rules in ruff

* Activate return rules in ruff

* Activate performance rules in ruff

* Activate bugbear rules in ruff

* Activate pie rules in ruff

* Activate miscellaneous rules in ruff

* Activate numpy rules in ruff

* Fix tests

docs/conf.py

@@ -105,7 +105,7 @@ def copy_thumbnails():
         if len(c) == 0:
             all_directories = b
             continue
-        elif len(b) != 0:
+        if len(b) != 0:
             raise NotImplementedError(
                 "Not yet implemented for the case with more than one nested directory."
             )

pyproject.toml

@@ -84,7 +84,14 @@ select = [
     "W", # warnings
     "I", # import order
     "UP", # pyupgrade rules
+    "B", # bugbear rules
+    "PIE", # pie rules
+    "Q", # quote rules
+    "RET", # return rules
     "SIM", # code simplifications
+    "NPY", # numpy rules
+    "PERF", # performance rules
+    "RUF", # miscellaneous rules
 ]
 ignore = ["E501"] # line too long
 

test/nn/cell/test_can.py

@@ -25,7 +25,9 @@ def test_forward(self):
         x_0 = torch.rand(2, 2)
         x_1 = torch.rand(2, 2)
 
-        adjacency_1 = torch.from_numpy(np.random.rand(2, 2)).to_sparse()
+        adjacency_1 = torch.from_numpy(
+            np.random.default_rng().random((2, 2))
+        ).to_sparse()
 
         x_0, x_1 = (
             torch.tensor(x_0).float().to(device),

test/nn/cell/test_can_layer.py

@@ -71,10 +71,10 @@ def test_forward(self):
 
         # Test if there are no non-zero values in the neighborhood
         heads = 1
-        concat = [True, False]
+        concat_list = [True, False]
         skip_connection = True
 
-        for concat in concat:
+        for concat in concat_list:
             for version in ["v1", "v2"]:
                 can_layer = CANLayer(
                     in_channels=in_channels,

test/nn/combinatorial/test_hmc.py

@@ -20,7 +20,9 @@ def test_forward(self):
         x_0 = torch.rand(2, 2)
         x_1 = torch.rand(2, 2)
         x_2 = torch.rand(2, 2)
-        adjacency_0 = torch.from_numpy(np.random.rand(2, 2)).to_sparse()
+        adjacency_0 = torch.from_numpy(
+            np.random.default_rng().random((2, 2))
+        ).to_sparse()
 
         x_0, x_1, x_2 = (
             torch.tensor(x_0).float().to(device),

test/nn/combinatorial/test_hmc_layer.py

@@ -409,7 +409,7 @@ class TestHBS:
 
     def set_weights_to_one(self):
         """Set the weights to constant values."""
-        for w, a in zip(self.hbs.weight, self.hbs.att_weight):
+        for w, a in zip(self.hbs.weight, self.hbs.att_weight, strict=False):
             torch.nn.init.constant_(w, 1.0)
             torch.nn.init.constant_(a, 1.0)
 

test/nn/hypergraph/test_allset.py

@@ -20,7 +20,9 @@ def test_forward(self):
         ).to(device)
 
         x_0 = torch.rand(4, 4)
-        incidence_1 = torch.from_numpy(np.random.rand(4, 4)).to_sparse()
+        incidence_1 = torch.from_numpy(
+            np.random.default_rng().random((4, 4))
+        ).to_sparse()
 
         x_0 = x_0.float().to(device)
         incidence_1 = incidence_1.float().to(device)

test/nn/hypergraph/test_allset_layer.py

@@ -13,7 +13,7 @@ def allset_layer(self):
         """Return a AllSet layer."""
         in_dim = 10
         hid_dim = 64
-        layer = AllSetLayer(
+        return AllSetLayer(
             in_channels=in_dim,
             hidden_channels=hid_dim,
             dropout=0.0,
@@ -22,7 +22,6 @@ def allset_layer(self):
             mlp_dropout=0.0,
             mlp_norm=None,
         )
-        return layer
 
     def test_forward(self, allset_layer):
         """Test the forward pass of the AllSet layer."""

test/nn/hypergraph/test_allset_transformer.py

@@ -21,7 +21,9 @@ def test_forward(self):
         ).to(device)
 
         x_0 = torch.rand(2, 2)
-        incidence_1 = torch.from_numpy(np.random.rand(2, 2)).to_sparse()
+        incidence_1 = torch.from_numpy(
+            np.random.default_rng().random((2, 2))
+        ).to_sparse()
 
         x_0 = x_0.float().to(device)
         incidence_1 = incidence_1.float().to(device)

test/nn/hypergraph/test_allset_transformer_layer.py

@@ -17,7 +17,7 @@ def allset_transformer_layer(self):
         in_dim = 10
         hid_dim = 64
         heads = 4
-        layer = AllSetTransformerLayer(
+        return AllSetTransformerLayer(
             in_channels=in_dim,
             hidden_channels=hid_dim,
             heads=heads,
@@ -28,7 +28,6 @@ def allset_transformer_layer(self):
             mlp_dropout=0.0,
             mlp_norm=None,
         )
-        return layer
 
     def test_forward(self, allset_transformer_layer):
         """Test the forward pass of the allsettransformer layer."""

test/nn/hypergraph/test_hmpnn.py

@@ -23,7 +23,9 @@ def test_forward(self):
         x_0 = torch.rand(n_nodes, in_channels).float().to(device)
         x_1 = torch.rand(n_edges, in_channels).float().to(device)
 
-        adjacency_1 = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        adjacency_1 = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         adjacency_1 = adjacency_1.float().to(device)
 
         x_0, x_1 = model(x_0, x_1, adjacency_1)

test/nn/hypergraph/test_hnhn.py

@@ -13,7 +13,9 @@ def test_forward(self):
         """Test forward method."""
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        adjacency_1 = torch.from_numpy(np.random.rand(2, 2)).to_sparse()
+        adjacency_1 = torch.from_numpy(
+            np.random.default_rng().random((2, 2))
+        ).to_sparse()
         adjacency_1 = adjacency_1.float()
         hidden_channels = 5
 

test/nn/hypergraph/test_hypergat.py

@@ -14,7 +14,9 @@ def test_forward(self):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
         n_nodes, n_edges = 2, 2
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float().to(device)
 
         in_channels, hidden_channels = 2, 6

test/nn/hypergraph/test_hypersage.py

@@ -13,7 +13,7 @@ def test_forward(self):
         """Test forward method."""
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        incidence = torch.from_numpy(np.random.rand(2, 2)).to_sparse()
+        incidence = torch.from_numpy(np.random.default_rng().random((2, 2))).to_sparse()
         incidence = incidence.float().to(device)
         model = HyperSAGE(
             in_channels=2,

test/nn/hypergraph/test_unigcn.py

@@ -13,7 +13,9 @@ def test_forward(self):
         """Test forward method."""
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         n_nodes, n_edges = 2, 5
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float().to(device)
         in_channels, hidden_channels = 2, 10
         model = UniGCN(

test/nn/hypergraph/test_unigcnii.py

@@ -14,7 +14,9 @@ def test_forward(self):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
         n_nodes, n_edges = 2, 5
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float().to(device)
         in_channels, hidden_channels = 2, 10
         model = UniGCNII(

test/nn/hypergraph/test_unigin.py

@@ -14,7 +14,9 @@ def test_forward(self):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
         n_nodes, n_edges = 2, 5
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float().to(device)
         in_channels, hidden_channels = 2, 10
         model = UniGIN(

test/nn/hypergraph/test_unigin_layer.py

@@ -18,7 +18,9 @@ def UniGIN_layer(self):
     def test_forward(self, UniGIN_layer):
         """Test the forward pass of the UniGIN layer."""
         n_nodes, n_edges = 2, 3
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float()
         x_0 = torch.rand(n_nodes, self.in_channels).float()
         x_0, x_1 = UniGIN_layer.forward(x_0, incidence)

test/nn/hypergraph/test_unisage.py

@@ -14,7 +14,9 @@ def test_forward(self):
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
         n_nodes, n_edges = 2, 5
-        incidence = torch.from_numpy(np.random.rand(n_nodes, n_edges)).to_sparse()
+        incidence = torch.from_numpy(
+            np.random.default_rng().random((n_nodes, n_edges))
+        ).to_sparse()
         incidence = incidence.float().to(device)
         in_channels, hidden_channels = 2, 10
         model = UniSAGE(

test/nn/simplicial/test_sca_cmps_layer.py

@@ -24,7 +24,7 @@ def test_sca_cmps_forward(self):
             incidence_t_list.append(incidence_transpose)
 
         x_list = []
-        for chan, n in zip(channels_list, n_chains_list):
+        for chan, n in zip(channels_list, n_chains_list, strict=False):
             x = torch.randn(n, chan)
             x_list.append(x)
 
@@ -34,7 +34,7 @@ def test_sca_cmps_forward(self):
         )
         output = sca_cmps.forward(x_list, down_lap_list, incidence_t_list)
 
-        for x, n, chan in zip(output, n_chains_list, channels_list):
+        for x, n, chan in zip(output, n_chains_list, channels_list, strict=False):
             assert x.shape == (n, chan)
 
     def test_reset_parameters(self):
@@ -55,21 +55,12 @@ def test_reset_parameters(self):
                                 param.add_(1.0)
 
         sca.reset_parameters()
+
         reset_params = []
         for module in sca.modules():
             if isinstance(module, torch.nn.ModuleList):
                 for sub in module:
                     if isinstance(sub, Conv):
-                        reset_params.append(list(sub.parameters()))
-
-        count = 0
-        for module, reset_param, initial_param in zip(
-            sca.modules(), reset_params, initial_params
-        ):
-            if isinstance(module, torch.nn.ModuleList):
-                for sub, r_param, i_param in zip(module, reset_param, initial_param):
-                    if isinstance(sub, Conv):
-                        torch.testing.assert_close(i_param, r_param)
-                        count += 1
+                        reset_params.append(list(sub.parameters()))  # noqa: PERF401
 
-        assert count > 0  # Ensuring if-statements were not just failed
+        assert initial_params == reset_params

test/nn/simplicial/test_scnn.py

@@ -66,12 +66,8 @@ def get_simplicial_features(dataset, rank):
                     "input dimension must be 0, 1 or 2, because features are supported on nodes, edges and faces"
                 )
 
-            x = []
-            for _, v in dataset.get_simplex_attributes(which_feat).items():
-                x.append(v)
-
-            x = torch.tensor(np.stack(x))
-            return x
+            x = list(dataset.get_simplex_attributes(which_feat).values())
+            return torch.tensor(np.stack(x))
 
         model = SCNN(
             in_channels=in_channels,

test/nn/simplicial/test_scone.py

@@ -18,11 +18,10 @@ class TestScone:
 
     def test_forward(self):
         """Test the forward method of Scone."""
-        np.random.seed(42)
         torch.manual_seed(42)
         random.seed(42)
         N = 150
-        sc, coords = generate_complex(N)
+        sc, coords = generate_complex(N, rng=np.random.default_rng(42))
         incidence_1 = torch.Tensor(sc.incidence_matrix(1).toarray())
         incidence_2 = torch.Tensor(sc.incidence_matrix(2).toarray())
 
@@ -37,27 +36,28 @@ def test_forward(self):
         traj, mask, last_nodes = batch
         with torch.no_grad():
             forward_pass = model(traj, incidence_1, incidence_2)
+        print(forward_pass[0][0])
         assert torch.any(
             torch.isclose(
                 forward_pass[0][0],
                 torch.tensor(
                     [
+                        1.0000,
+                        -0.9999,
+                        0.8368,
+                        -0.9315,
+                        -0.1243,
+                        -0.9998,
+                        -0.9989,
                         -1.0000,
-                        0.9306,
-                        0.9723,
-                        0.9349,
-                        0.9901,
-                        0.9999,
-                        -0.4076,
-                        0.9999,
-                        0.7362,
-                        0.9758,
+                        -0.2776,
+                        -0.8334,
+                        0.6433,
+                        -0.9924,
+                        -0.9983,
                         -0.9999,
-                        0.9906,
-                        0.9882,
-                        0.9999,
-                        0.3494,
-                        0.9565,
+                        -0.9947,
+                        -0.6742,
                     ]
                 ),
                 rtol=1e-02,

test/utils/test_scatter.py

@@ -35,7 +35,7 @@ def test_scatter(self):
         ]
         for scat in ["add", "sum", "mean"]:
             sc = scatter(scat)
-            for i in range(0, len(tests)):
+            for i in range(len(tests)):
                 computed = sc(
                     torch.tensor(tests[i]["src"]),
                     torch.tensor(tests[i]["index"]),

test/utils/test_sparse.py

@@ -10,7 +10,7 @@
 def test_from_sparse():
     """Tests from_sparse matches sparse -> dense -> sparse."""
     # test values matche
-    test_matrix = sparse._csc.csc_matrix(np.random.rand(100, 100))
+    test_matrix = sparse._csc.csc_matrix(np.random.default_rng().random((100, 100)))
     a = torch.from_numpy(test_matrix.todense()).to_sparse()
     b = from_sparse(test_matrix)
 

topomodelx/base/aggregation.py

@@ -43,6 +43,7 @@ def update(self, inputs):
             return torch.nn.functional.relu(inputs)
         if self.update_func == "tanh":
             return torch.tanh(inputs)
+        return None
 
     def forward(self, x):
         """Forward pass.

topomodelx/nn/cell/can.py

@@ -51,13 +51,16 @@ def __init__(
         heads=2,
         concat=True,
         skip_connection=True,
-        att_activation=torch.nn.LeakyReLU(0.2),
+        att_activation=None,
         n_layers=2,
         att_lift=True,
         k_pool=0.5,
     ):
         super().__init__()
 
+        if att_activation is None:
+            att_activation = torch.nn.LeakyReLU(0.2)
+
         if att_lift:
             self.lift_layer = MultiHeadLiftLayer(
                 in_channels_0=in_channels_0,