Merge branch 'main' into xai-inverse-estimation

README.md

@@ -23,6 +23,7 @@ _Quantus is currently under active development so carefully note the Quantus rel
 
 ## News and Highlights! :rocket:
 
+- If you want to contribute/ improve/ extend Quantus, join our [Discord](https://discord.gg/HB77krUE)!
 - 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) 
 - Released a new version [here](https://github.com/understandable-machine-intelligence-lab/Quantus/releases)
 - Accepted to Journal of Machine Learning Research (MLOSS), read the [paper](https://jmlr.org/papers/v24/22-0142.html)

pyproject.toml

@@ -56,7 +56,7 @@ tests = [
     "coverage>=7.2.3",
     "flake8<=4.0.1; python_version == '3.7'",
     "flake8>=6.0.0; python_version > '3.7'",
-    "pytest>=7.3.1",
+    "pytest<=7.4.4",
     "pytest-cov>=4.0.0",
     "pytest-lazy-fixture>=0.6.3",
     "pytest-mock==3.10.0",

quantus/helpers/asserts.py

@@ -286,6 +286,12 @@ def assert_value_smaller_than_input_size(
     -------
     None
     """
+    if len(x.shape) == 2:
+        if value >= np.prod(x.shape[1:]):
+            raise ValueError(
+                f"'{value_name}' must be smaller than input size."
+                f" [{value} >= {np.prod(x.shape[1:])}]"
+            )
     if value >= np.prod(x.shape[2:]):
         raise ValueError(
             f"'{value_name}' must be smaller than input size."

quantus/metrics/faithfulness/infidelity.py

@@ -60,7 +60,7 @@ class Infidelity(Metric[List[float]]):
     """
 
     name = "Infidelity"
-    data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR}
+    data_applicability = {DataType.IMAGE}
     model_applicability = {ModelType.TORCH, ModelType.TF}
     score_direction = ScoreDirection.LOWER
     evaluation_category = EvaluationCategory.FAITHFULNESS

quantus/metrics/randomisation/efficient_mprt.py

@@ -127,7 +127,7 @@ def __init__(
             Indicates whether normalise operation is applied on the attribution, default=True.
         normalise_func: callable
             Attribution normalisation function applied in case normalise=True.
-            If normalise_func=None, the default value is used, default=normalise_by_max.
+            If normalise_func=None, the default value is used, default=normalise_by_average_second_moment_estimate.
         normalise_func_kwargs: dict
             Keyword arguments to be passed to normalise_func on call, default={}.
         return_aggregate: boolean

quantus/metrics/randomisation/smooth_mprt.py

@@ -29,6 +29,7 @@
 from scipy import stats
 
 from quantus.functions.similarity_func import correlation_spearman
+from quantus.functions.normalise_func import normalise_by_average_second_moment_estimate
 from quantus.helpers import asserts, warn, utils
 from quantus.helpers.enums import (
     DataType,
@@ -130,7 +131,7 @@ def __init__(
             Indicates whether normalise operation is applied on the attribution, default=True.
         normalise_func: callable
             Attribution normalisation function applied in case normalise=True.
-            If normalise_func=None, the default value is used, default=normalise_by_max.
+            If normalise_func=None, the default value is used, default=normalise_by_average_second_moment_estimate.
         normalise_func_kwargs: dict
             Keyword arguments to be passed to normalise_func on call, default={}.
         return_aggregate: boolean
@@ -163,7 +164,16 @@ def __init__(
         # Save metric-specific attributes.
         if similarity_func is None:
             similarity_func = correlation_spearman
+        if normalise_func is None:
+            normalise_func = normalise_by_average_second_moment_estimate
+
+        if normalise_func_kwargs is None:
+            normalise_func_kwargs = {}
+
         self.similarity_func = similarity_func
+        self.normalise_func = normalise_func
+        self.abs = abs
+        self.normalise_func_kwargs = normalise_func_kwargs
         self.layer_order = layer_order
         self.seed = seed
         self.nr_samples = nr_samples