tests: move reset seed to fixture [1/2] (#2702)

* move reset seed to fixture
* updating test outputs
* Update src/conftest.py

src/conftest.py

@@ -0,0 +1,40 @@
+from pathlib import Path
+from typing import Optional
+
+from lightning_utilities.core.imports import package_available
+
+if package_available("pytest") and package_available("doctest"):
+    import doctest
+
+    import pytest
+
+    MANUAL_SEED = doctest.register_optionflag("MANUAL_SEED")
+
+    @pytest.fixture(autouse=True)
+    def reset_random_seed(seed: int = 42) -> None:  # noqa: PT004
+        """Reset the random seed before running each doctest."""
+        import random
+
+        import numpy as np
+        import torch
+
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+    class DoctestModule(pytest.Module):
+        """A custom module class that augments collected doctests with the reset_random_seed fixture."""
+
+        def collect(self) -> GeneratorExit:
+            """Augment collected doctests with the reset_random_seed fixture."""
+            for item in super().collect():
+                if isinstance(item, pytest.DoctestItem):
+                    item.add_marker(pytest.mark.usefixtures("reset_random_seed"))
+                yield item
+
+    def pytest_collect_file(parent: Path, path: Path) -> Optional[DoctestModule]:
+        """Collect doctests and add the reset_random_seed fixture."""
+        if path.ext == ".py":
+            return DoctestModule.from_parent(parent, fspath=path)
+        return None

src/torchmetrics/audio/_deprecated.py

@@ -13,7 +13,6 @@ class _PermutationInvariantTraining(PermutationInvariantTraining):
 
     >>> import torch
     >>> from torchmetrics.functional import scale_invariant_signal_noise_ratio
-    >>> _ = torch.manual_seed(42)
     >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
     >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
     >>> pit = _PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
@@ -79,20 +78,19 @@ class _SignalDistortionRatio(SignalDistortionRatio):
     """Wrapper for deprecated import.
 
     >>> import torch
-    >>> g = torch.manual_seed(1)
     >>> preds = torch.randn(8000)
     >>> target = torch.randn(8000)
     >>> sdr = _SignalDistortionRatio()
     >>> sdr(preds, target)
-    tensor(-12.0589)
+    tensor(-11.9930)
     >>> # use with pit
     >>> from torchmetrics.functional import signal_distortion_ratio
     >>> preds = torch.randn(4, 2, 8000)  # [batch, spk, time]
     >>> target = torch.randn(4, 2, 8000)
     >>> pit = _PermutationInvariantTraining(signal_distortion_ratio,
     ...     mode="speaker-wise", eval_func="max")
     >>> pit(preds, target)
-    tensor(-11.6051)
+    tensor(-11.7277)
 
     """
 

src/torchmetrics/audio/dnsmos.py

@@ -76,11 +76,10 @@ class DeepNoiseSuppressionMeanOpinionScore(Metric):
     Example:
         >>> from torch import randn
         >>> from torchmetrics.audio import DeepNoiseSuppressionMeanOpinionScore
-        >>> g = torch.manual_seed(1)
         >>> preds = randn(8000)
         >>> dnsmos = DeepNoiseSuppressionMeanOpinionScore(8000, False)
         >>> dnsmos(preds)
-        tensor([2.2285, 2.1132, 1.3972, 1.3652], dtype=torch.float64)
+        tensor([2.2687, 2.0766, 1.1375, 1.2722], dtype=torch.float64)
 
     """
 

src/torchmetrics/audio/pesq.py

@@ -71,17 +71,16 @@ class PerceptualEvaluationSpeechQuality(Metric):
             If ``mode`` is not either ``"wb"`` or ``"nb"``
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import PerceptualEvaluationSpeechQuality
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn(8000)
-        >>> target = torch.randn(8000)
+        >>> preds = randn(8000)
+        >>> target = randn(8000)
         >>> pesq = PerceptualEvaluationSpeechQuality(8000, 'nb')
         >>> pesq(preds, target)
-        tensor(2.2076)
+        tensor(2.2885)
         >>> wb_pesq = PerceptualEvaluationSpeechQuality(16000, 'wb')
         >>> wb_pesq(preds, target)
-        tensor(1.7359)
+        tensor(1.6805)
 
     """
 

src/torchmetrics/audio/pit.py

@@ -61,12 +61,11 @@ class PermutationInvariantTraining(Metric):
             see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import PermutationInvariantTraining
         >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
-        >>> _ = torch.manual_seed(42)
-        >>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
-        >>> target = torch.randn(3, 2, 5) # [batch, spk, time]
+        >>> preds = randn(3, 2, 5) # [batch, spk, time]
+        >>> target = randn(3, 2, 5) # [batch, spk, time]
         >>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio,
         ...     mode="speaker-wise", eval_func="max")
         >>> pit(preds, target)

src/torchmetrics/audio/sdr.py

@@ -70,23 +70,22 @@ class SignalDistortionRatio(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import SignalDistortionRatio
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn(8000)
-        >>> target = torch.randn(8000)
+        >>> preds = randn(8000)
+        >>> target = randn(8000)
         >>> sdr = SignalDistortionRatio()
         >>> sdr(preds, target)
-        tensor(-12.0589)
+        tensor(-11.9930)
         >>> # use with pit
         >>> from torchmetrics.audio import PermutationInvariantTraining
         >>> from torchmetrics.functional.audio import signal_distortion_ratio
-        >>> preds = torch.randn(4, 2, 8000)  # [batch, spk, time]
-        >>> target = torch.randn(4, 2, 8000)
+        >>> preds = randn(4, 2, 8000)  # [batch, spk, time]
+        >>> target = randn(4, 2, 8000)
         >>> pit = PermutationInvariantTraining(signal_distortion_ratio,
         ...     mode="speaker-wise", eval_func="max")
         >>> pit(preds, target)
-        tensor(-11.6051)
+        tensor(-11.7277)
 
     """
 
@@ -302,23 +301,22 @@ class SourceAggregatedSignalDistortionRatio(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn(2, 8000) # [..., spk, time]
-        >>> target = torch.randn(2, 8000)
+        >>> preds = randn(2, 8000) # [..., spk, time]
+        >>> target = randn(2, 8000)
         >>> sasdr = SourceAggregatedSignalDistortionRatio()
         >>> sasdr(preds, target)
-        tensor(-41.6579)
+        tensor(-50.8171)
         >>> # use with pit
         >>> from torchmetrics.audio import PermutationInvariantTraining
         >>> from torchmetrics.functional.audio import source_aggregated_signal_distortion_ratio
-        >>> preds = torch.randn(4, 2, 8000)  # [batch, spk, time]
-        >>> target = torch.randn(4, 2, 8000)
+        >>> preds = randn(4, 2, 8000)  # [batch, spk, time]
+        >>> target = randn(4, 2, 8000)
         >>> pit = PermutationInvariantTraining(source_aggregated_signal_distortion_ratio,
         ...     mode="permutation-wise", eval_func="max")
         >>> pit(preds, target)
-        tensor(-41.2790)
+        tensor(-43.9780)
 
     """
 

src/torchmetrics/audio/snr.py

@@ -268,15 +268,13 @@ class ComplexScaleInvariantSignalNoiseRatio(Metric):
             If ``preds`` and ``target`` does not have the same shape.
 
     Example:
-        >>> import torch
-        >>> from torch import tensor
+        >>> from torch import randn
         >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn((1,257,100,2))
-        >>> target = torch.randn((1,257,100,2))
+        >>> preds = randn((1,257,100,2))
+        >>> target = randn((1,257,100,2))
         >>> c_si_snr = ComplexScaleInvariantSignalNoiseRatio()
         >>> c_si_snr(preds, target)
-        tensor(-63.4849)
+        tensor(-38.8832)
 
     """
 

src/torchmetrics/audio/srmr.py

@@ -76,13 +76,12 @@ class SpeechReverberationModulationEnergyRatio(Metric):
             If ``gammatone`` or ``torchaudio`` package is not installed
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn(8000)
+        >>> preds = randn(8000)
         >>> srmr = SpeechReverberationModulationEnergyRatio(8000)
         >>> srmr(preds)
-        tensor(0.3354)
+        tensor(0.3191)
 
     """
 

src/torchmetrics/audio/stoi.py

@@ -63,14 +63,13 @@ class ShortTimeObjectiveIntelligibility(Metric):
             If ``pystoi`` package is not installed
 
     Example:
-        >>> import torch
+        >>> from torch import randn
         >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
-        >>> g = torch.manual_seed(1)
-        >>> preds = torch.randn(8000)
-        >>> target = torch.randn(8000)
+        >>> preds = randn(8000)
+        >>> target = randn(8000)
         >>> stoi = ShortTimeObjectiveIntelligibility(8000, False)
         >>> stoi(preds, target)
-        tensor(-0.0100)
+        tensor(-0.0842)
 
     """
 
@@ -132,11 +131,10 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             :scale: 75
 
             >>> # Example plotting a single value
-            >>> import torch
+            >>> from torch import randn
             >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
-            >>> g = torch.manual_seed(1)
-            >>> preds = torch.randn(8000)
-            >>> target = torch.randn(8000)
+            >>> preds = randn(8000)
+            >>> target = randn(8000)
             >>> metric = ShortTimeObjectiveIntelligibility(8000, False)
             >>> metric.update(preds, target)
             >>> fig_, ax_ = metric.plot()
@@ -145,12 +143,11 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             :scale: 75
 
             >>> # Example plotting multiple values
-            >>> import torch
+            >>> from torch import randn
             >>> from torchmetrics.audio import ShortTimeObjectiveIntelligibility
             >>> metric = ShortTimeObjectiveIntelligibility(8000, False)
-            >>> g = torch.manual_seed(1)
-            >>> preds = torch.randn(8000)
-            >>> target = torch.randn(8000)
+            >>> preds = randn(8000)
+            >>> target = randn(8000)
             >>> values = [ ]
             >>> for _ in range(10):
             ...     values.append(metric(preds, target))

src/torchmetrics/classification/group_fairness.py

@@ -302,25 +302,23 @@ def plot(
         .. plot::
             :scale: 75
 
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import rand, randint
             >>> # Example plotting a single value
             >>> from torchmetrics.classification import BinaryFairness
             >>> metric = BinaryFairness(2)
-            >>> metric.update(torch.rand(20), torch.randint(2,(20,)), torch.randint(2,(20,)))
+            >>> metric.update(rand(20), randint(2, (20,)), randint(2, (20,)))
             >>> fig_, ax_ = metric.plot()
 
         .. plot::
             :scale: 75
 
-            >>> import torch
-            >>> _ = torch.manual_seed(42)
+            >>> from torch import ones, rand, randint
             >>> # Example plotting multiple values
             >>> from torchmetrics.classification import BinaryFairness
             >>> metric = BinaryFairness(2)
             >>> values = [ ]
             >>> for _ in range(10):
-            ...     values.append(metric(torch.rand(20), torch.randint(2,(20,)), torch.ones(20).long()))
+            ...     values.append(metric(rand(20), randint(2, (20,) ), ones(20).long()))
             >>> fig_, ax_ = metric.plot(values)
 
         """

src/torchmetrics/classification/ranking.py

@@ -66,10 +66,10 @@ class MultilabelCoverageError(Metric):
             Set to ``False`` for faster computations.
 
     Example:
+        >>> from torch import rand, randint
         >>> from torchmetrics.classification import MultilabelCoverageError
-        >>> _ = torch.manual_seed(42)
-        >>> preds = torch.rand(10, 5)
-        >>> target = torch.randint(2, (10, 5))
+        >>> preds = rand(10, 5)
+        >>> target = randint(2, (10, 5))
         >>> mlce = MultilabelCoverageError(num_labels=5)
         >>> mlce(preds, target)
         tensor(3.9000)
@@ -186,10 +186,10 @@ class MultilabelRankingAveragePrecision(Metric):
             Set to ``False`` for faster computations.
 
     Example:
+        >>> from torch import rand, randint
         >>> from torchmetrics.classification import MultilabelRankingAveragePrecision
-        >>> _ = torch.manual_seed(42)
-        >>> preds = torch.rand(10, 5)
-        >>> target = torch.randint(2, (10, 5))
+        >>> preds = rand(10, 5)
+        >>> target = randint(2, (10, 5))
         >>> mlrap = MultilabelRankingAveragePrecision(num_labels=5)
         >>> mlrap(preds, target)
         tensor(0.7744)
@@ -308,10 +308,10 @@ class MultilabelRankingLoss(Metric):
             Set to ``False`` for faster computations.
 
     Example:
+        >>> from torch import rand, randint
         >>> from torchmetrics.classification import MultilabelRankingLoss
-        >>> _ = torch.manual_seed(42)
-        >>> preds = torch.rand(10, 5)
-        >>> target = torch.randint(2, (10, 5))
+        >>> preds = rand(10, 5)
+        >>> target = randint(2, (10, 5))
         >>> mlrl = MultilabelRankingLoss(num_labels=5)
         >>> mlrl(preds, target)
         tensor(0.4167)

src/torchmetrics/clustering/calinski_harabasz_score.py

@@ -54,11 +54,10 @@ class CalinskiHarabaszScore(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example::
-        >>> import torch
+        >>> from torch import randn, randint
         >>> from torchmetrics.clustering import CalinskiHarabaszScore
-        >>> _ = torch.manual_seed(42)
-        >>> data = torch.randn(10, 3)
-        >>> labels = torch.randint(3, (10,))
+        >>> data = randn(10, 3)
+        >>> labels = randint(3, (10,))
         >>> metric = CalinskiHarabaszScore()
         >>> metric(data, labels)
         tensor(3.0053)

src/torchmetrics/clustering/davies_bouldin_score.py

@@ -64,11 +64,10 @@ class DaviesBouldinScore(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Example::
-        >>> import torch
+        >>> from torch import randn, randint
         >>> from torchmetrics.clustering import DaviesBouldinScore
-        >>> _ = torch.manual_seed(42)
-        >>> data = torch.randn(10, 3)
-        >>> labels = torch.randint(3, (10,))
+        >>> data = randn(10, 3)
+        >>> labels = randint(3, (10,))
         >>> metric = DaviesBouldinScore()
         >>> metric(data, labels)
         tensor(1.2540)