Binary group fairness metrics (#1404)

Co-authored-by: Nicki Skafte Detlefsen <skaftenicki@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: SkafteNicki <skaftenicki@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: stancld <daniel.stancl@gmail.com>

CHANGELOG.md

@@ -32,6 +32,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `classes` to output from `MAP` metric ([#1419](https://github.com/Lightning-AI/metrics/pull/1419))
 
 
+- Added Binary group fairness metrics to classification package ([#1404](https://github.com/Lightning-AI/metrics/pull/1404))
+
+
+- Added new detection metric `PanopticQuality` ([#929](https://github.com/PyTorchLightning/metrics/pull/929))
+
+
 - Added `MinkowskiDistance` to regression package ([#1362](https://github.com/Lightning-AI/metrics/pull/1362))
 
 

docs/source/classification/group_fairness.rst

@@ -0,0 +1,54 @@
+.. customcarditem::
+   :header: Group Fairness
+   :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+   :tags: Classification
+
+.. include:: ../links.rst
+
+##############
+Group Fairness
+##############
+
+Module Interface
+________________
+
+BinaryFairness
+^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.classification.BinaryFairness
+    :noindex:
+    :exclude-members: update, compute
+
+BinaryGroupStatRates
+^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.classification.BinaryGroupStatRates
+    :noindex:
+    :exclude-members: update, compute
+
+Functional Interface
+____________________
+
+binary_fairness
+^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.binary_fairness
+    :noindex:
+
+demographic_parity
+^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.demographic_parity
+    :noindex:
+
+equal_opportunity
+^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.equal_opportunity
+    :noindex:
+
+binary_groups_stat_rates
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.classification.binary_groups_stat_rates
+    :noindex:

docs/source/links.rst

@@ -132,3 +132,5 @@
 .. _Panoptic Quality: https://arxiv.org/abs/1801.00868
 .. _torchmetrics mAP example: https://github.com/Lightning-AI/metrics/blob/master/examples/detection_map.py
 .. _Minkowski Distance: https://en.wikipedia.org/wiki/Minkowski_distance
+.. _Demographic parity: http://www.fairmlbook.org/
+.. _Equal opportunity: https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html

pyproject.toml

@@ -157,6 +157,7 @@ module = [
     "torchmetrics.classification.confusion_matrix",
     "torchmetrics.classification.exact_match",
     "torchmetrics.classification.f_beta",
+    "torchmetrics.classification.group_fairness",
     "torchmetrics.classification.hamming",
     "torchmetrics.classification.hinge",
     "torchmetrics.classification.jaccard",
@@ -176,6 +177,7 @@ module = [
     "torchmetrics.functional.classification.calibration_error",
     "torchmetrics.functional.classification.confusion_matrix",
     "torchmetrics.functional.classification.f_beta",
+    "torchmetrics.functional.classification.group_fairness",
     "torchmetrics.functional.classification.precision_recall_curve",
     "torchmetrics.functional.classification.ranking",
     "torchmetrics.functional.classification.recall_at_fixed_precision",

requirements/classification_test.txt

@@ -1,4 +1,6 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
+pandas >1.0.0, <=1.5.3
 netcal >1.0.0, <=1.3.3  # calibration_error
+fairlearn # group_fairness

src/torchmetrics/classification/__init__.py

@@ -55,6 +55,7 @@
     MultilabelF1Score,
     MultilabelFBetaScore,
 )
+from torchmetrics.classification.group_fairness import BinaryFairness, BinaryGroupStatRates
 from torchmetrics.classification.hamming import (
     BinaryHammingDistance,
     HammingDistance,
@@ -153,6 +154,8 @@
     "MulticlassFBetaScore",
     "MultilabelF1Score",
     "MultilabelFBetaScore",
+    "BinaryFairness",
+    "BinaryGroupStatRates",
     "BinaryHammingDistance",
     "HammingDistance",
     "MulticlassHammingDistance",

src/torchmetrics/classification/group_fairness.py

@@ -0,0 +1,262 @@
+# Copyright The PyTorch 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 typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from typing_extensions import Literal
+
+from torchmetrics.functional.classification.group_fairness import (
+    _binary_groups_stat_scores,
+    _compute_binary_demographic_parity,
+    _compute_binary_equal_opportunity,
+)
+from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation
+from torchmetrics.metric import Metric
+from torchmetrics.utilities import rank_zero_warn
+
+
+class _AbstractGroupStatScores(Metric):
+    """Create and update states for computing group stats tp, fp, tn and fn."""
+
+    def _create_states(self, num_groups: int) -> None:
+        default = lambda: torch.zeros(num_groups, dtype=torch.long)
+        self.add_state("tp", default(), dist_reduce_fx="sum")
+        self.add_state("fp", default(), dist_reduce_fx="sum")
+        self.add_state("tn", default(), dist_reduce_fx="sum")
+        self.add_state("fn", default(), dist_reduce_fx="sum")
+
+    def _update_states(self, group_stats: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]) -> None:
+        for group, stats in enumerate(group_stats):
+            tp, fp, tn, fn = stats
+            self.tp[group] += tp
+            self.fp[group] += fp
+            self.tn[group] += tn
+            self.fn[group] += fn
+
+
+class BinaryGroupStatRates(_AbstractGroupStatScores):
+    r"""Computes the true/false positives and true/false negatives rates for binary classification by group.
+
+    Related to `Type I and Type II errors`_.
+
+    Accepts the following input tensors:
+
+    - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside
+      [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally,
+      we convert to int tensor with thresholding using the value in ``threshold``.
+    - ``target`` (int tensor): ``(N, ...)``.
+    - ``groups`` (int tensor): ``(N, ...)``. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.
+
+    The additional dimensions are flatted along the batch dimension.
+
+    Args:
+        num_groups: The number of groups.
+        threshold: Threshold for transforming probability to binary {0,1} predictions.
+        ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation
+        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
+            Set to ``False`` for faster computations.
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Returns:
+        The metric returns a dict with a group identifier as key and a tensor with the tp, fp, tn and fn rates as value.
+
+    Example (preds is int tensor):
+        >>> from torchmetrics.classification import BinaryGroupStatRates
+        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> preds = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> metric = BinaryGroupStatRates(num_groups=2)
+        >>> metric(preds, target, groups)
+        {'group_0': tensor([0., 0., 1., 0.]), 'group_1': tensor([1., 0., 0., 0.])}
+
+    Example (preds is float tensor):
+        >>> from torchmetrics.classification import BinaryGroupStatRates
+        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> preds = torch.tensor([0.11, 0.84, 0.22, 0.73, 0.33, 0.92])
+        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> metric = BinaryGroupStatRates(num_groups=2)
+        >>> metric(preds, target, groups)
+        {'group_0': tensor([0., 0., 1., 0.]), 'group_1': tensor([1., 0., 0., 0.])}
+    """
+    is_differentiable = False
+    higher_is_better = False
+    full_state_update: bool = False
+
+    def __init__(
+        self,
+        num_groups: int,
+        threshold: float = 0.5,
+        ignore_index: Optional[int] = None,
+        validate_args: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+
+        if validate_args:
+            _binary_stat_scores_arg_validation(threshold, "global", ignore_index)
+
+        if not isinstance(num_groups, int) and num_groups < 2:
+            raise ValueError(f"Expected argument `num_groups` to be an int larger than 1, but got {num_groups}")
+        self.num_groups = num_groups
+        self.threshold = threshold
+        self.ignore_index = ignore_index
+        self.validate_args = validate_args
+
+        self._create_states(self.num_groups)
+
+    def update(self, preds: torch.Tensor, target: torch.Tensor, groups: torch.Tensor) -> None:
+        """Update state with predictions, target and group identifiers.
+
+        Args:
+            preds: Tensor with predictions.
+            target: Tensor with true labels.
+            groups: Tensor with group identifiers. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.
+        """
+        group_stats = _binary_groups_stat_scores(
+            preds, target, groups, self.num_groups, self.threshold, self.ignore_index, self.validate_args
+        )
+
+        self._update_states(group_stats)
+
+    def compute(
+        self,
+    ) -> Dict[str, torch.Tensor]:
+        """Computes tp, fp, tn and fn rates based on inputs passed in to ``update`` previously."""
+        results = torch.stack((self.tp, self.fp, self.tn, self.fn), dim=1)
+
+        return {f"group_{i}": group / group.sum() for i, group in enumerate(results)}
+
+
+class BinaryFairness(_AbstractGroupStatScores):
+    r"""Computes `Demographic parity`_ and `Equal opportunity`_ ratio for binary classification problems.
+
+    Accepts the following input tensors:
+
+    - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside
+      [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally,
+      we convert to int tensor with thresholding using the value in ``threshold``.
+    - ``groups`` (int tensor): ``(N, ...)``. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.
+    - ``target`` (int tensor): ``(N, ...)``.
+
+    The additional dimensions are flatted along the batch dimension.
+
+    This class computes the ratio between positivity rates and true positives rates for different groups.
+    If more than two groups are present, the disparity between the lowest and highest group is reported.
+    A disparity between positivity rates indicates a potential violation of demographic parity, and between
+    true positive rates indicates a potential violation of equal opportunity.
+
+    The lowest rate is divided by the highest, so a lower value means more discrimination against the numerator.
+    In the results this is also indicated as the key of dict is {metric}_{identifier_low_group}_{identifier_high_group}.
+
+    Args:
+        num_groups: The number of groups.
+        task: The task to compute. Can be either ``demographic_parity`` or ``equal_oppotunity`` or ``all``.
+        threshold: Threshold for transforming probability to binary {0,1} predictions.
+        ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation
+        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
+            Set to ``False`` for faster computations.
+        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
+
+    Returns:
+        The metric returns a dict where the key identifies the metric and groups with the lowest and highest true
+        positives rates as follows: {metric}__{identifier_low_group}_{identifier_high_group}.
+        The value is a tensor with the disparity rate.
+
+    Example (preds is int tensor):
+        >>> from torchmetrics.classification import BinaryFairness
+        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> preds = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> metric = BinaryFairness(2)
+        >>> metric(preds, target, groups)
+        {'DP_0_1': tensor(0.), 'EO_0_1': tensor(0.)}
+
+    Example (preds is float tensor):
+        >>> from torchmetrics.classification import BinaryFairness
+        >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> preds = torch.tensor([0.11, 0.84, 0.22, 0.73, 0.33, 0.92])
+        >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
+        >>> metric = BinaryFairness(2)
+        >>> metric(preds, target, groups)
+        {'DP_0_1': tensor(0.), 'EO_0_1': tensor(0.)}
+    """
+    is_differentiable = False
+    higher_is_better = False
+    full_state_update: bool = False
+
+    def __init__(
+        self,
+        num_groups: int,
+        task: Literal["demographic_parity", "equal_opportunity", "all"] = "all",
+        threshold: float = 0.5,
+        ignore_index: Optional[int] = None,
+        validate_args: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+
+        if task not in ["demographic_parity", "equal_opportunity", "all"]:
+            raise ValueError(
+                f"Expected argument `task` to either be ``demographic_parity``,"
+                f"``equal_opportunity`` or ``all`` but got {task}."
+            )
+
+        if validate_args:
+            _binary_stat_scores_arg_validation(threshold, "global", ignore_index)
+
+        if not isinstance(num_groups, int) and num_groups < 2:
+            raise ValueError(f"Expected argument `num_groups` to be an int larger than 1, but got {num_groups}")
+        self.num_groups = num_groups
+        self.task = task
+        self.threshold = threshold
+        self.ignore_index = ignore_index
+        self.validate_args = validate_args
+
+        self._create_states(self.num_groups)
+
+    def update(self, preds: torch.Tensor, target: torch.Tensor, groups: Optional[torch.Tensor] = None) -> None:
+        """Update state with predictions, groups, and target.
+
+        Args:
+            preds: Tensor with predictions.
+            target: Tensor with true labels.
+            groups: Tensor with group identifiers. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.
+        """
+        if self.task == "demographic_parity":
+            if target is not None:
+                rank_zero_warn("The task demographic_parity does not require a target.", UserWarning)
+            target = torch.zeros(preds.shape)
+
+        group_stats = _binary_groups_stat_scores(
+            preds, target, groups, self.num_groups, self.threshold, self.ignore_index, self.validate_args
+        )
+
+        self._update_states(group_stats)
+
+    def compute(
+        self,
+    ) -> Dict[str, torch.Tensor]:
+        """Computes fairness criteria based on inputs passed in to ``update`` previously."""
+        if self.task == "demographic_parity":
+            return _compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn)
+
+        if self.task == "equal_opportunity":
+            return _compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn)
+
+        if self.task == "all":
+            return {
+                **_compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn),
+                **_compute_binary_equal_opportunity(self.tp, self.fp, self.tn, self.fn),
+            }
+        return None

src/torchmetrics/functional/classification/__init__.py

@@ -52,6 +52,12 @@
     multilabel_f1_score,
     multilabel_fbeta_score,
 )
+from torchmetrics.functional.classification.group_fairness import (  # noqa: F401
+    binary_fairness,
+    binary_groups_stat_rates,
+    demographic_parity,
+    equal_opportunity,
+)
 from torchmetrics.functional.classification.hamming import (
     binary_hamming_distance,
     hamming_distance,