diff --git a/doc/api.rst b/doc/api.rst
index 07ac6413c..bdf85e0bb 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -215,6 +215,7 @@ Imbalance-learn provides some fast-prototyping tools.
metrics.sensitivity_score
metrics.specificity_score
metrics.geometric_mean_score
+ metrics.macro_averaged_mean_absolute_error
metrics.make_index_balanced_accuracy
.. _datasets_ref:
diff --git a/doc/bibtex/refs.bib b/doc/bibtex/refs.bib
index 87eb3e30c..63f8bc0d1 100644
--- a/doc/bibtex/refs.bib
+++ b/doc/bibtex/refs.bib
@@ -207,4 +207,20 @@ @article{torelli2014rose
issn = {1573-756X},
url = {https://doi.org/10.1007/s10618-012-0295-5},
doi = {10.1007/s10618-012-0295-5}
+}
+
+@article{esuli2009ordinal,
+ author = {A. Esuli and S. Baccianella and F. Sebastiani},
+ title = {Evaluation Measures for Ordinal Regression},
+ journal = {Intelligent Systems Design and Applications, International Conference on},
+ year = {2009},
+ volume = {1},
+ issn = {},
+ pages = {283-287},
+ keywords = {ordinal regression;ordinal classification;evaluation measures;class imbalance;product reviews},
+ doi = {10.1109/ISDA.2009.230},
+ url = {https://doi.ieeecomputersociety.org/10.1109/ISDA.2009.230},
+ publisher = {IEEE Computer Society},
+ address = {Los Alamitos, CA, USA},
+ month = {dec}
}
\ No newline at end of file
diff --git a/doc/metrics.rst b/doc/metrics.rst
index 8b9474ca3..98368a650 100644
--- a/doc/metrics.rst
+++ b/doc/metrics.rst
@@ -45,6 +45,18 @@ The :func:`make_index_balanced_accuracy` :cite:`garcia2012effectiveness` can
wrap any metric and give more importance to a specific class using the
parameter ``alpha``.
+.. _macro_averaged_mean_absolute_error:
+
+Macro-Averaged Mean Absolute Error (MA-MAE)
+-------------------------------------------
+
+Ordinal classification is used when there is a rank among classes, for example
+levels of functionality or movie ratings.
+
+The :func:`macro_averaged_mean_absolute_error` :cite:`esuli2009ordinal` is used
+for imbalanced ordinal classification. The mean absolute error is computed for
+each class and averaged over classes, giving an equal weight to each class.
+
.. _classification_report:
Summary of important metrics
diff --git a/doc/whats_new/v0.7.rst b/doc/whats_new/v0.7.rst
index 6cb266854..bca4a680d 100644
--- a/doc/whats_new/v0.7.rst
+++ b/doc/whats_new/v0.7.rst
@@ -76,6 +76,12 @@ Enhancements
dictionary instead of a string.
:pr:`770` by :user:`Guillaume Lemaitre <glemaitre>`.
+- Add the the function
+ :func:`imblearn.metrics.macro_averaged_mean_absolute_error` returning the
+ average across class of the MAE. This metric is used in ordinal
+ classification.
+ :pr:`780` by :user:`Aurélien Massiot <AurelienMassiot>`.
+
Deprecation
...........
diff --git a/imblearn/metrics/__init__.py b/imblearn/metrics/__init__.py
index 3097a292c..ffab645a1 100644
--- a/imblearn/metrics/__init__.py
+++ b/imblearn/metrics/__init__.py
@@ -9,6 +9,7 @@
from ._classification import geometric_mean_score
from ._classification import make_index_balanced_accuracy
from ._classification import classification_report_imbalanced
+from ._classification import macro_averaged_mean_absolute_error
__all__ = [
"sensitivity_specificity_support",
@@ -17,4 +18,5 @@
"geometric_mean_score",
"make_index_balanced_accuracy",
"classification_report_imbalanced",
+ "macro_averaged_mean_absolute_error",
]
diff --git a/imblearn/metrics/_classification.py b/imblearn/metrics/_classification.py
index 5ab8c3f23..90ac6d51a 100644
--- a/imblearn/metrics/_classification.py
+++ b/imblearn/metrics/_classification.py
@@ -18,12 +18,16 @@
import numpy as np
import scipy as sp
+from sklearn.metrics import mean_absolute_error
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics._classification import _check_targets
from sklearn.metrics._classification import _prf_divide
-
from sklearn.preprocessing import LabelEncoder
from sklearn.utils.multiclass import unique_labels
+from sklearn.utils.validation import (
+ check_consistent_length,
+ column_or_1d,
+)
try:
from inspect import signature
@@ -997,3 +1001,68 @@ class 2 1.00 0.67 1.00 0.80 0.82 0.64\
if output_dict:
return report_dict
return report
+
+
+def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
+ """Compute Macro-Averaged Mean Absolute Error (MA-MAE)
+ for imbalanced ordinal classification.
+
+ This function computes each MAE for each class and average them,
+ giving an equal weight to each class.
+
+ Read more in the :ref:`User Guide <macro_averaged_mean_absolute_error>`.
+
+ Parameters
+ ----------
+ y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
+ Ground truth (correct) target values.
+
+ y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
+ Estimated targets as returned by a classifier.
+
+ sample_weight : array-like of shape (n_samples,), default=None
+ Sample weights.
+
+ Returns
+ -------
+ loss : float or ndarray of floats
+ Macro-Averaged MAE output is non-negative floating point.
+ The best value is 0.0.
+
+ Examples
+ --------
+ >>> import numpy as np
+ >>> from sklearn.metrics import mean_absolute_error
+ >>> from imblearn.metrics import macro_averaged_mean_absolute_error
+ >>> y_true_balanced = [1, 1, 2, 2]
+ >>> y_true_imbalanced = [1, 2, 2, 2]
+ >>> y_pred = [1, 2, 1, 2]
+ >>> mean_absolute_error(y_true_balanced, y_pred)
+ 0.5
+ >>> mean_absolute_error(y_true_imbalanced, y_pred)
+ 0.25
+ >>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
+ 0.5
+ >>> macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred)
+ 0.16666666666666666
+ """
+ _, y_true, y_pred = _check_targets(y_true, y_pred)
+ if sample_weight is not None:
+ sample_weight = column_or_1d(sample_weight)
+ else:
+ sample_weight = np.ones(y_true.shape)
+ check_consistent_length(y_true, y_pred, sample_weight)
+ labels = unique_labels(y_true, y_pred)
+ mae = []
+ for possible_class in labels:
+ indices = np.flatnonzero(y_true == possible_class)
+
+ mae.append(
+ mean_absolute_error(
+ y_true[indices],
+ y_pred[indices],
+ sample_weight=sample_weight[indices],
+ )
+ )
+
+ return np.sum(mae) / len(mae)
diff --git a/imblearn/metrics/tests/test_classification.py b/imblearn/metrics/tests/test_classification.py
index b6db641ce..3bab9b044 100644
--- a/imblearn/metrics/tests/test_classification.py
+++ b/imblearn/metrics/tests/test_classification.py
@@ -29,6 +29,7 @@
from imblearn.metrics import geometric_mean_score
from imblearn.metrics import make_index_balanced_accuracy
from imblearn.metrics import classification_report_imbalanced
+from imblearn.metrics import macro_averaged_mean_absolute_error
from imblearn.utils.testing import warns
@@ -498,3 +499,32 @@ def test_classification_report_imbalanced_dict():
assert outer_keys == expected_outer_keys
assert inner_keys == expected_inner_keys
+
+
+@pytest.mark.parametrize(
+ "y_true, y_pred, expected_ma_mae",
+ [
+ ([1, 1, 1, 2, 2, 2], [1, 2, 1, 2, 1, 2], 0.333),
+ ([1, 1, 1, 1, 1, 2], [1, 2, 1, 2, 1, 2], 0.2),
+ ([1, 1, 1, 2, 2, 2, 3, 3, 3], [1, 3, 1, 2, 1, 1, 2, 3, 3], 0.555),
+ ([1, 1, 1, 1, 1, 1, 2, 3, 3], [1, 3, 1, 2, 1, 1, 2, 3, 3], 0.166),
+
+ ],
+)
+def test_macro_averaged_mean_absolute_error(y_true, y_pred, expected_ma_mae):
+ ma_mae = macro_averaged_mean_absolute_error(y_true, y_pred)
+ assert ma_mae == pytest.approx(expected_ma_mae, rel=R_TOL)
+
+
+def test_macro_averaged_mean_absolute_error_sample_weight():
+ y_true = [1, 1, 1, 2, 2, 2]
+ y_pred = [1, 2, 1, 2, 1, 2]
+
+ ma_mae_no_weights = macro_averaged_mean_absolute_error(y_true, y_pred)
+
+ sample_weight = [1, 1, 1, 1, 1, 1]
+ ma_mae_unit_weights = macro_averaged_mean_absolute_error(
+ y_true, y_pred, sample_weight=sample_weight,
+ )
+
+ assert ma_mae_unit_weights == pytest.approx(ma_mae_no_weights)