cleaning

tests/metrics/classification/inputs.py

@@ -1,16 +1,12 @@
-import os
-import pytest
-import numpy as np
+from collections import namedtuple
+
 import torch
 
-from collections import namedtuple
 from tests.metrics.utils import (
     NUM_BATCHES,
-    NUM_PROCESSES,
     BATCH_SIZE,
     NUM_CLASSES,
-    EXTRA_DIM,
-    THRESHOLD
+    EXTRA_DIM
 )
 
 Input = namedtuple('Input', ["preds", "target"])

tests/metrics/classification/test_accuracy.py

@@ -1,16 +1,9 @@
-import os
+import numpy as np
 import pytest
 import torch
-import numpy as np
-from collections import namedtuple
-from functools import partial
-
-from pytorch_lightning.metrics.classification.accuracy import Accuracy
 from sklearn.metrics import accuracy_score
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import THRESHOLD
-
+from pytorch_lightning.metrics.classification.accuracy import Accuracy
 from tests.metrics.classification.inputs import (
     _testing_binary_prob_inputs,
     _testing_binary_inputs,
@@ -21,6 +14,8 @@
     _testing_multidim_multiclass_prob_inputs,
     _testing_multidim_multiclass_inputs,
 )
+from tests.metrics.utils import THRESHOLD
+from tests.metrics.utils import compute_batch
 
 torch.manual_seed(42)
 
@@ -32,7 +27,7 @@ def _sk_accuracy_binary_prob(preds, target):
     return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
 
-def _sk_accuracy_binary_sk_metric(preds, target):
+def _sk_accuracy(preds, target):
     sk_preds = preds.view(-1).numpy()
     sk_target = target.view(-1).numpy()
 
@@ -46,41 +41,20 @@ def _sk_accuracy_multilabel_prob(preds, target):
     return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
 
-def _sk_accuracy_multilabel(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
-
-
 def _sk_accuracy_multiclass_prob(preds, target):
     sk_preds = torch.argmax(preds, dim=len(preds.shape) - 1).view(-1).numpy()
     sk_target = target.view(-1).numpy()
 
     return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
 
-def _sk_accuracy_multiclass(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
-
-
 def _sk_accuracy_multidim_multiclass_prob(preds, target):
     sk_preds = torch.argmax(preds, dim=len(preds.shape) - 2).view(-1).numpy()
     sk_target = target.view(-1).numpy()
 
     return accuracy_score(y_true=sk_target, y_pred=sk_preds)
 
 
-def _sk_accuracy_multidim_multiclass(preds, target):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-
-    return accuracy_score(y_true=sk_target, y_pred=sk_preds)
-
-
 def test_accuracy_invalid_shape():
     with pytest.raises(ValueError):
         acc = Accuracy()
@@ -91,11 +65,11 @@ def test_accuracy_invalid_shape():
 @pytest.mark.parametrize("dist_sync_on_step", [True, False])
 @pytest.mark.parametrize("preds, target, sk_metric", [
     (_testing_binary_prob_inputs.preds, _testing_binary_prob_inputs.target, _sk_accuracy_binary_prob),
-    (_testing_binary_inputs.preds, _testing_binary_inputs.target, _sk_accuracy_binary_sk_metric),
+    (_testing_binary_inputs.preds, _testing_binary_inputs.target, _sk_accuracy),
     (_testing_multilabel_prob_inputs.preds, _testing_multilabel_prob_inputs.target, _sk_accuracy_multilabel_prob),
-    (_testing_multilabel_inputs.preds, _testing_multilabel_inputs.target, _sk_accuracy_multilabel),
+    (_testing_multilabel_inputs.preds, _testing_multilabel_inputs.target, _sk_accuracy),
     (_testing_multiclass_prob_inputs.preds, _testing_multiclass_prob_inputs.target, _sk_accuracy_multiclass_prob),
-    (_testing_multiclass_inputs.preds, _testing_multiclass_inputs.target, _sk_accuracy_multiclass),
+    (_testing_multiclass_inputs.preds, _testing_multiclass_inputs.target, _sk_accuracy),
     (
             _testing_multidim_multiclass_prob_inputs.preds,
             _testing_multidim_multiclass_prob_inputs.target,
@@ -104,7 +78,7 @@ def test_accuracy_invalid_shape():
     (
             _testing_multidim_multiclass_inputs.preds,
             _testing_multidim_multiclass_inputs.target,
-            _sk_accuracy_multidim_multiclass
+            _sk_accuracy
     )
 ])
 def test_accuracy(ddp, dist_sync_on_step, preds, target, sk_metric):

tests/metrics/classification/test_f_beta.py

@@ -1,17 +1,12 @@
-import os
 import pytest
-import torch
-import numpy as np
-from collections import namedtuple
-
 from functools import partial
 
-from pytorch_lightning.metrics import Fbeta
+import numpy as np
+import pytest
+import torch
 from sklearn.metrics import fbeta_score
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE, NUM_CLASSES, THRESHOLD
-
+from pytorch_lightning.metrics import Fbeta
 from tests.metrics.classification.inputs import (
     _testing_binary_prob_inputs,
     _testing_binary_inputs,
@@ -22,6 +17,8 @@
     _testing_multidim_multiclass_prob_inputs,
     _testing_multidim_multiclass_inputs,
 )
+from tests.metrics.utils import NUM_CLASSES, THRESHOLD
+from tests.metrics.utils import compute_batch
 
 torch.manual_seed(42)
 
@@ -75,13 +72,6 @@ def _sk_fbeta_multidim_multiclass_prob(preds, target, average='micro', beta=1.):
     return fbeta_score(y_true=sk_target, y_pred=sk_preds, average=average, beta=beta)
 
 
-def _sk_fbeta_multidim_multiclass(preds, target, average='micro', beta=1.):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-
-    return fbeta_score(y_true=sk_target, y_pred=sk_preds, average=average, beta=beta)
-
-
 @pytest.mark.parametrize("ddp", [True, False])
 @pytest.mark.parametrize("dist_sync_on_step", [True, False])
 @pytest.mark.parametrize("average", ['micro', 'macro'])
@@ -102,7 +92,7 @@ def _sk_fbeta_multidim_multiclass(preds, target, average='micro', beta=1.):
     (
             _testing_multidim_multiclass_inputs.preds,
             _testing_multidim_multiclass_inputs.target,
-            _sk_fbeta_multidim_multiclass,
+            _sk_fbeta_multiclass,
             NUM_CLASSES,
             False
     )

tests/metrics/classification/test_precision_recall.py

@@ -1,17 +1,12 @@
-import os
 import pytest
-import torch
-import numpy as np
-from collections import namedtuple
-
 from functools import partial
 
-from pytorch_lightning.metrics import Precision, Recall
+import numpy as np
+import pytest
+import torch
 from sklearn.metrics import precision_score, recall_score
 
-from tests.metrics.utils import compute_batch, setup_ddp
-from tests.metrics.utils import NUM_BATCHES, NUM_PROCESSES, BATCH_SIZE, NUM_CLASSES, THRESHOLD
-
+from pytorch_lightning.metrics import Precision, Recall
 from tests.metrics.classification.inputs import (
     _testing_binary_prob_inputs,
     _testing_binary_inputs,
@@ -22,6 +17,8 @@
     _testing_multidim_multiclass_prob_inputs,
     _testing_multidim_multiclass_inputs,
 )
+from tests.metrics.utils import NUM_CLASSES, THRESHOLD
+from tests.metrics.utils import compute_batch
 
 torch.manual_seed(42)
 
@@ -75,13 +72,6 @@ def _sk_prec_recall_multidim_multiclass_prob(preds, target, sk_fn=precision_scor
     return sk_fn(y_true=sk_target, y_pred=sk_preds, average=average)
 
 
-def _sk_prec_recall_multidim_multiclass(preds, target, sk_fn=precision_score, average='micro'):
-    sk_preds = preds.view(-1).numpy()
-    sk_target = target.view(-1).numpy()
-
-    return sk_fn(y_true=sk_target, y_pred=sk_preds, average=average)
-
-
 @pytest.mark.parametrize("ddp", [True, False])
 @pytest.mark.parametrize("dist_sync_on_step", [True, False])
 @pytest.mark.parametrize("average", ['micro', 'macro'])
@@ -102,7 +92,7 @@ def _sk_prec_recall_multidim_multiclass(preds, target, sk_fn=precision_score, av
     (
             _testing_multidim_multiclass_inputs.preds,
             _testing_multidim_multiclass_inputs.target,
-            _sk_prec_recall_multidim_multiclass,
+            _sk_prec_recall_multiclass,
             NUM_CLASSES,
             False
     )