create inputs.py

tests/unittests/clustering/inputs.py

@@ -0,0 +1,38 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections import namedtuple
+
+import torch
+
+from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests.helpers import seed_all
+
+seed_all(42)
+
+
+Input = namedtuple("Input", ["preds", "target"])
+NUM_CLASSES = 10
+
+
+_single_target_inputs1 = Input(
+    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
+    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
+)
+
+_single_target_inputs2 = Input(
+    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
+    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
+)
+
+_float_inputs = Input(preds=torch.rand((NUM_BATCHES, BATCH_SIZE)), target=torch.rand((NUM_BATCHES, BATCH_SIZE)))

tests/unittests/clustering/test_mutual_info_score.py

@@ -11,38 +11,19 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections import namedtuple
-
 import pytest
 import torch
 from sklearn.metrics import mutual_info_score as sklearn_mutual_info_score
 from torchmetrics.clustering.mutual_info_score import MutualInfoScore
 from torchmetrics.functional.clustering.mutual_info_score import mutual_info_score
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_CLASSES
+from unittests.clustering.inputs import _float_inputs, _single_target_inputs1, _single_target_inputs2
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
-NUM_CLASSES = 10
-
-_single_target_inputs1 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_single_target_inputs2 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_float_inputs = Input(
-    preds=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-    target=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-)
-
 
 @pytest.mark.parametrize(
     "preds, target",

tests/unittests/clustering/test_normalized_mutual_info_score.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections import namedtuple
 from functools import partial
 
 import pytest
@@ -20,30 +19,13 @@
 from torchmetrics.clustering import NormalizedMutualInfoScore
 from torchmetrics.functional.clustering import normalized_mutual_info_score
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests import BATCH_SIZE, NUM_CLASSES
+from unittests.clustering.inputs import _float_inputs, _single_target_inputs1, _single_target_inputs2
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
-NUM_CLASSES = 10
-
-_single_target_inputs1 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_single_target_inputs2 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_float_inputs = Input(
-    preds=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-    target=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-)
-
 
 @pytest.mark.parametrize(
     "preds, target",

tests/unittests/clustering/test_rand_score.py

@@ -11,38 +11,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections import namedtuple
-
 import pytest
 import torch
 from sklearn.metrics import rand_score as sklearn_rand_score
 from torchmetrics.clustering.rand_score import RandScore
 from torchmetrics.functional.clustering.rand_score import rand_score
 
-from unittests import BATCH_SIZE, NUM_BATCHES
+from unittests.clustering.inputs import _float_inputs, _single_target_inputs1, _single_target_inputs2
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-Input = namedtuple("Input", ["preds", "target"])
-NUM_CLASSES = 10
-
-_single_target_inputs1 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_single_target_inputs2 = Input(
-    preds=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-    target=torch.randint(high=NUM_CLASSES, size=(NUM_BATCHES, BATCH_SIZE)),
-)
-
-_float_inputs = Input(
-    preds=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-    target=torch.rand((NUM_BATCHES, BATCH_SIZE)),
-)
-
 
 @pytest.mark.parametrize(
     "preds, target",