Generalised Explanation Faithfulness metric

README.md

@@ -27,7 +27,7 @@ If you want to contribute/ improve/ extend Quantus, join our [Discord](https://d
 
 - 🐼 For **training data attribution** evaluation, check out [quanda](https://github.com/dilyabareeva/quanda)!
 - New [batch implementation](https://github.com/understandable-machine-intelligence-lab/Quantus/pull/351) for 12X speedup of existing faithfulness metrics (!)
-- New metrics added: [EfficientMPRT](https://github.com/understandable-machine-intelligence-lab/Quantus/blob/main/quantus/metrics/randomisation/efficient_mprt.py) and [SmoothMPRT](https://github.com/understandable-machine-intelligence-lab/Quantus/blob/main/quantus/metrics/randomisation/smooth_mprt.py) by [Hedström et al., (2023)](https://openreview.net/pdf?id=vVpefYmnsG)
+- New metric added: [Generalised Explanation Faithfulness (GEF)](https://github.com/understandable-machine-intelligence-lab/Quantus/blob/main/quantus/metrics/unified/generalised_explanation_faithfulness.py) by [Hedström et al., (2025]https://openreview.net/pdf?id=ukLxqA8zXj)
 - Accepted to Journal of Machine Learning Research (MLOSS), read the [paper](https://jmlr.org/papers/v24/22-0142.html)
 - Offers more than **35+ metrics in 6 categories** for XAI evaluation
 - Supports different data types (image, time-series, tabular, NLP next up!) and models (PyTorch, TensorFlow)

quantus/helpers/constants.py

@@ -72,6 +72,9 @@
         "NonSensitivity": NonSensitivity,
         "InputInvariance": InputInvariance,
     },
+    "Unified": {
+        "Generalised Explanation Faithfulness": GeneralisedExplanationFaithfulness
+        }
 }
 
 

quantus/helpers/enums.py

@@ -58,4 +58,5 @@ class EvaluationCategory(Enum):
     COMPLEXITY = "Complexity"
     LOCALISATION = "Localisation"
     AXIOMATIC = "Axiomatic"
+    UNIFIED = "Unified"
     NONE = "None"

quantus/metrics/__init__.py

@@ -11,3 +11,4 @@
 from quantus.metrics.localisation import *
 from quantus.metrics.randomisation import *
 from quantus.metrics.robustness import *
+from quantus.metrics.unified import *

quantus/metrics/unified/generalised_explanation_faithfulness.py

@@ -0,0 +1,58 @@
+"""This module contains the implementation of the Completeness metric."""
+
+# This file is part of Quantus.
+# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
+# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see <https://www.gnu.org/licenses/>.
+# Quantus project URL: <https://github.com/understandable-machine-intelligence-lab/Quantus>.
+
+import sys
+from typing import Any, Callable, Dict, List, Optional
+
+import numpy as np
+
+from quantus.functions.perturb_func import baseline_replacement_by_indices
+from quantus.helpers import warn
+from quantus.helpers.enums import (
+    DataType,
+    EvaluationCategory,
+    ModelType,
+    ScoreDirection,
+)
+from quantus.helpers.model.model_interface import ModelInterface
+from quantus.helpers.perturbation_utils import make_perturb_func
+from quantus.metrics.base import Metric
+from quantus.helpers.utils import identity
+
+if sys.version_info >= (3, 8):
+    from typing import final
+else:
+    from typing_extensions import final
+
+
+@final
+class GeneralisedExplanationFaithfulness(Metric[List[float]]):
+    """
+    Implementation of Generalised Explanation Faithfulness test by Hedström et al., 2025.
+
+    Insert desription
+
+    References:
+        1) Hedström et al., "Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions"
+       Transactions of Machine Learning Research, 2025.
+
+    Attributes:
+        -  _name: The name of the metric.
+        - _data_applicability: The data types that the metric implementation currently supports.
+        - _models: The model types that this metric can work with.
+        - score_direction: How to interpret the scores, whether higher/ lower values are considered better.
+    """
+
+    name = "GEF"
+    data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR}
+    model_applicability = {ModelType.TORCH}
+    score_direction = ScoreDirection.HIGHER
+    evaluation_category = EvaluationCategory.UNIFIED
+
+    def __init__()
+        pass

tox.ini

@@ -27,14 +27,14 @@ commands =
 
 [testenv:build]
 description = Build environment
-base_python = py310
+base_python = python3.10
 deps =
     .
     build
     twine
 commands =
-    python3 -m build .
-    python3 -m twine check ./dist/* --strict
+    python3.10 -m build .
+    python3.10 -m twine check ./dist/* --strict
 
 [testenv:lint]
 description = Check the code style