Merge branch 'master' into rittik/multiclassf1_topk

.github/assistant.py

@@ -16,7 +16,7 @@
 import os
 import re
 import sys
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import fire
 from packaging.version import parse
@@ -83,7 +83,7 @@ def _replace_requirement(fpath: str, old_str: str = "", new_str: str = "") -> No
             fp.write(req)
 
     @staticmethod
-    def replace_str_requirements(old_str: str, new_str: str, req_files: List[str] = REQUIREMENTS_FILES) -> None:
+    def replace_str_requirements(old_str: str, new_str: str, req_files: list[str] = REQUIREMENTS_FILES) -> None:
         """Replace a particular string in all requirements files."""
         if isinstance(req_files, str):
             req_files = [req_files]
@@ -96,7 +96,7 @@ def replace_min_requirements(fpath: str) -> None:
         AssistantCLI._replace_requirement(fpath, old_str=">=", new_str="==")
 
     @staticmethod
-    def set_oldest_versions(req_files: List[str] = REQUIREMENTS_FILES) -> None:
+    def set_oldest_versions(req_files: list[str] = REQUIREMENTS_FILES) -> None:
         """Set the oldest version for requirements."""
         AssistantCLI.set_min_torch_by_python()
         if isinstance(req_files, str):
@@ -109,8 +109,8 @@ def changed_domains(
         pr: Optional[int] = None,
         auth_token: Optional[str] = None,
         as_list: bool = False,
-        general_sub_pkgs: Tuple[str] = _PKG_WIDE_SUBPACKAGES,
-    ) -> Union[str, List[str]]:
+        general_sub_pkgs: tuple[str] = _PKG_WIDE_SUBPACKAGES,
+    ) -> Union[str, list[str]]:
         """Determine what domains were changed in particular PR."""
         import github
 
@@ -139,7 +139,7 @@ def changed_domains(
             return "unittests"
 
         # parse domains
-        def _crop_path(fname: str, paths: List[str]) -> str:
+        def _crop_path(fname: str, paths: list[str]) -> str:
             for p in paths:
                 fname = fname.replace(p, "")
             return fname

.github/workflows/_focus-diff.yml

@@ -18,8 +18,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
-        #with:
-        #  python-version: 3.8
 
       - name: Get PR diff
         id: diff-domains

.github/workflows/ci-checks.yml

@@ -31,7 +31,7 @@ jobs:
       testing-matrix: |
         {
           "os": ["ubuntu-22.04", "macos-13", "windows-2022"],
-          "python-version": ["3.8", "3.11"]
+          "python-version": ["3.9", "3.11"]
         }
 
   check-md-links:

.github/workflows/ci-tests.yml

@@ -42,7 +42,7 @@ jobs:
           - "2.5.0"
         include:
           # cover additional python and PT combinations
-          - { os: "ubuntu-20.04", python-version: "3.8", pytorch-version: "2.0.1", requires: "oldest" }
+          - { os: "ubuntu-20.04", python-version: "3.9", pytorch-version: "2.0.1", requires: "oldest" }
           - { os: "ubuntu-22.04", python-version: "3.11", pytorch-version: "2.4.1" }
           - { os: "ubuntu-22.04", python-version: "3.12", pytorch-version: "2.5.0" }
           # standard mac machine, not the M1

.github/workflows/publish-pkg.yml

@@ -20,7 +20,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: 3.8
+          python-version: "3.10"
 
       - name: Install dependencies
         run: >-

CHANGELOG.md

@@ -12,43 +12,56 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Added `NormalizedRootMeanSquaredError` metric to regression subpackage ([#2442](https://github.com/Lightning-AI/torchmetrics/pull/2442))
+-
 
 
-- Added `NegativePredictiveValue` to classification metrics ([#2433](https://github.com/Lightning-AI/torchmetrics/pull/2433))
+### Changed
 
+-
 
-- Added method `merge_state` to `Metric` ([#2786](https://github.com/Lightning-AI/torchmetrics/pull/2786))
 
+### Removed
 
-- Added a new audio metric `NISQA` ([#2792](https://github.com/PyTorchLightning/metrics/pull/2792))
+-
 
 
-- Added `Dice` metric to segmentation metrics ([#2725](https://github.com/Lightning-AI/torchmetrics/pull/2725))
+### Fixed
 
+-
 
-- Added support for propagation of the autograd graph in ddp setting ([#2754](https://github.com/Lightning-AI/torchmetrics/pull/2754))
 
+---
+
+## [1.6.0] - 2024-11-12
+
+### Added
+
+- Added audio metric `NISQA` ([#2792](https://github.com/PyTorchLightning/metrics/pull/2792))
+- Added classification metric `LogAUC` ([#2377](https://github.com/Lightning-AI/torchmetrics/pull/2377))
+- Added classification metric `NegativePredictiveValue` ([#2433](https://github.com/Lightning-AI/torchmetrics/pull/2433))
+- Added regression metric `NormalizedRootMeanSquaredError` ([#2442](https://github.com/Lightning-AI/torchmetrics/pull/2442))
+- Added segmentation metric `Dice` ([#2725](https://github.com/Lightning-AI/torchmetrics/pull/2725))
+- Added method `merge_state` to `Metric` ([#2786](https://github.com/Lightning-AI/torchmetrics/pull/2786))
+- Added support for propagation of the autograd graph in ddp setting ([#2754](https://github.com/Lightning-AI/torchmetrics/pull/2754))
 
 ### Changed
 
 - Changed naming and input order arguments in `KLDivergence` ([#2800](https://github.com/Lightning-AI/torchmetrics/pull/2800))
 
-
 ### Deprecated
 
 - Deprecated Dice from classification metrics ([#2725](https://github.com/Lightning-AI/torchmetrics/pull/2725))
 
-
 ### Removed
 
 - Changed minimum supported Pytorch version to 2.0 ([#2671](https://github.com/Lightning-AI/torchmetrics/pull/2671))
-
-
+- Dropped support for Python 3.8 ([#2827](https://github.com/Lightning-AI/torchmetrics/pull/2827))
 - Removed `num_outputs` in `R2Score` ([#2800](https://github.com/Lightning-AI/torchmetrics/pull/2800))
 
 ### Fixed
 
+
+- Fixed segmentation `Dice` + `GeneralizedDice` for 2d index tensors ([#2832](https://github.com/Lightning-AI/torchmetrics/pull/2832))
 - Fixed mixed results of `rouge_score` with `accumulate='best'` ([#2830](https://github.com/Lightning-AI/torchmetrics/pull/2830))
 
 

_samples/bert_score-own_model.py

@@ -18,7 +18,7 @@
 """
 
 from pprint import pprint
-from typing import Dict, List, Union
+from typing import Union
 
 import torch
 from torch import Tensor, nn
@@ -53,7 +53,7 @@ def __init__(self) -> None:
             self.PAD_TOKEN: torch.zeros(1, _MODEL_DIM),
         }
 
-    def __call__(self, sentences: Union[str, List[str]], max_len: int = _MAX_LEN) -> Dict[str, Tensor]:
+    def __call__(self, sentences: Union[str, list[str]], max_len: int = _MAX_LEN) -> dict[str, Tensor]:
         """Call method to tokenize user input.
 
         The `__call__` method must be defined for this class. To ensure the functionality, the `__call__` method
@@ -69,7 +69,7 @@ def __call__(self, sentences: Union[str, List[str]], max_len: int = _MAX_LEN) ->
             Python dictionary containing the keys `input_ids` and `attention_mask` with corresponding values.
 
         """
-        output_dict: Dict[str, Tensor] = {}
+        output_dict: dict[str, Tensor] = {}
         if isinstance(sentences, str):
             sentences = [sentences]
         # Add special tokens
@@ -96,7 +96,7 @@ def get_user_model_encoder(num_layers: int = _NUM_LAYERS, d_model: int = _MODEL_
     return nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
 
 
-def user_forward_fn(model: Module, batch: Dict[str, Tensor]) -> Tensor:
+def user_forward_fn(model: Module, batch: dict[str, Tensor]) -> Tensor:
     """User forward function used for the computation of model embeddings.
 
     This function might be arbitrarily complicated inside. However, to ensure functionality, it should obey the

_samples/rouge_score-own_normalizer_and_tokenizer.py

@@ -18,8 +18,8 @@
 """
 
 import re
+from collections.abc import Sequence
 from pprint import pprint
-from typing import Sequence
 
 from torchmetrics.text.rouge import ROUGEScore
 

docs/source/classification/logauc.rst

@@ -0,0 +1,55 @@
+.. customcarditem::
+   :header: Log Area Receiver Operating Characteristic (LogAUC)
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+   :tags: Classification
+
+.. include:: ../links.rst
+
+#######
+Log AUC
+#######
+
+Module Interface
+________________
+
+.. autoclass:: torchmetrics.LogAUC
+    :exclude-members: update, compute
+    :special-members: __new__
+
+BinaryLogAUC
+^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.classification.BinaryLogAUC
+    :exclude-members: update, compute
+
+MulticlassLogAUC
+^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.classification.MulticlassLogAUC
+    :exclude-members: update, compute
+
+MultilabelLogAUC
+^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.classification.MultilabelLogAUC
+    :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+.. autofunction:: torchmetrics.functional.logauc
+
+binary_logauc
+^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.binary_logauc
+
+multiclass_logauc
+^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.multiclass_logauc
+
+multilabel_logauc
+^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.multilabel_logauc

docs/source/links.rst

@@ -177,4 +177,5 @@
 .. _Hausdorff Distance: https://en.wikipedia.org/wiki/Hausdorff_distance
 .. _averaging curve objects: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
 .. _Procrustes Disparity: https://en.wikipedia.org/wiki/Procrustes_analysis
+.. _Log AUC: https://pubmed.ncbi.nlm.nih.gov/20735049/
 .. _Negative Predictive Value: https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values

examples/audio/signal_to_noise_ratio.py

@@ -8,7 +8,6 @@
 
 # %%
 # Import necessary libraries
-from typing import Tuple
 
 import matplotlib.animation as animation
 import matplotlib.pyplot as plt
@@ -20,7 +19,7 @@
 # Generate a clean signal (simulating a high-quality recording)
 
 
-def generate_clean_signal(length: int = 1000) -> Tuple[np.ndarray, np.ndarray]:
+def generate_clean_signal(length: int = 1000) -> tuple[np.ndarray, np.ndarray]:
     """Generate a clean signal (sine wave)"""
     t = np.linspace(0, 1, length)
     signal = np.sin(2 * np.pi * 10 * t)  # 10 Hz sine wave, representing the clean recording

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
 ]
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 #[tool.ruff.pycodestyle]
@@ -68,6 +68,8 @@ lint.per-file-ignores."setup.py" = [
 lint.per-file-ignores."src/**" = [
   "ANN401",
   "S310",   # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.
+  "UP006",  # todo: Use `list` instead of `List` for type annotation
+  "UP035",  # todo: `typing.List` is deprecated, use `list` instead
 ]
 lint.per-file-ignores."tests/**" = [
   "ANN001",
@@ -77,9 +79,6 @@ lint.per-file-ignores."tests/**" = [
   "S101",
   "S301",   # todo: `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue
 ]
-lint.unfixable = [
-  "F401",
-]
 # Unlike Flake8, default to a complexity level of 10.
 lint.mccabe.max-complexity = 10
 # Use Google-style docstrings.

requirements/_tests.txt

@@ -5,15 +5,15 @@ codecov ==2.1.13
 coverage ==7.6.*
 codecov ==2.1.13
 pytest ==8.3.*
-pytest-cov ==5.0.0
+pytest-cov ==6.0.0
 pytest-doctestplus ==1.2.1
 pytest-rerunfailures ==14.0
 pytest-timeout ==2.3.1
 pytest-xdist ==3.6.1
 phmdoctest ==1.4.0
 
 psutil ==6.*
-pyGithub >2.0.0, <2.5.0
+pyGithub >2.0.0, <2.6.0
 fire ==0.7.*
 
 cloudpickle >1.3, <=3.1.0

requirements/classification_test.txt

@@ -5,3 +5,4 @@ pandas >1.4.0, <=2.2.3
 netcal >1.0.0, <1.4.0 # calibration_error
 numpy <2.2.0
 fairlearn # group_fairness
+PyTDC ==0.4.1 ; python_version <"3.12"  # locauc, temporal_dependency

requirements/text.txt

@@ -2,8 +2,8 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 nltk >3.8.1, <=3.9.1
-tqdm <4.67.0
-regex >=2021.9.24, <=2024.9.11
+tqdm <4.68.0
+regex >=2021.9.24, <=2024.11.6
 transformers >4.4.0, <4.47.0
 mecab-python3 >=1.0.6, <1.1.0
 ipadic >=1.0.0, <1.1.0

setup.py

@@ -2,11 +2,12 @@
 import glob
 import os
 import re
+from collections.abc import Iterable, Iterator
 from functools import partial
 from importlib.util import module_from_spec, spec_from_file_location
 from itertools import chain
 from pathlib import Path
-from typing import Any, Iterable, Iterator, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 from pkg_resources import Requirement, yield_lines
 from setuptools import find_packages, setup
@@ -97,7 +98,7 @@ def _parse_requirements(strs: Union[str, Iterable[str]]) -> Iterator[_Requiremen
 
 def _load_requirements(
     path_dir: str, file_name: str = "base.txt", unfreeze: bool = not _FREEZE_REQUIREMENTS
-) -> List[str]:
+) -> list[str]:
     """Load requirements from a file.
 
     >>> _load_requirements(_PATH_REQUIRE)
@@ -161,7 +162,7 @@ def _load_py_module(fname: str, pkg: str = "torchmetrics"):
 BASE_REQUIREMENTS = _load_requirements(path_dir=_PATH_REQUIRE, file_name="base.txt")
 
 
-def _prepare_extras(skip_pattern: str = "^_", skip_files: Tuple[str] = ("base.txt",)) -> dict:
+def _prepare_extras(skip_pattern: str = "^_", skip_files: tuple[str] = ("base.txt",)) -> dict:
     """Preparing extras for the package listing requirements.
 
     Args:
@@ -215,7 +216,7 @@ def _prepare_extras(skip_pattern: str = "^_", skip_files: Tuple[str] = ("base.tx
         include_package_data=True,
         zip_safe=False,
         keywords=["deep learning", "machine learning", "pytorch", "metrics", "AI"],
-        python_requires=">=3.8",
+        python_requires=">=3.9",
         setup_requires=[],
         install_requires=BASE_REQUIREMENTS,
         extras_require=_prepare_extras(),

src/torchmetrics/__about__.py

@@ -1,8 +1,8 @@
-__version__ = "1.6.0dev"
+__version__ = "1.7.0dev"
 __author__ = "Lightning-AI et al."
 __author_email__ = "[email protected]"
 __license__ = "Apache-2.0"
-__copyright__ = f"Copyright (c) 2020-2023, {__author__}."
+__copyright__ = f"Copyright (c) 2020-2024, {__author__}."
 __homepage__ = "https://github.com/Lightning-AI/torchmetrics"
 __docs__ = "PyTorch native Metrics"
 __docs_url__ = "https://lightning.ai/docs/torchmetrics/stable/"