scikit-learn · larsmans · Oct 12, 2014 · Oct 10, 2014 · Oct 10, 2014 · Oct 10, 2014
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -770,6 +770,7 @@ details.
    metrics.explained_variance_score
    metrics.mean_absolute_error
    metrics.mean_squared_error
+   metrics.median_absolute_error
    metrics.r2_score
 
 Multilabel ranking metrics

diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
@@ -72,6 +72,7 @@ Scoring                    Function
 **Regression**
 'mean_absolute_error'      :func:`metrics.mean_absolute_error`
 'mean_squared_error'       :func:`metrics.mean_squared_error`
+'median_absolute_error'    :func:`metrics.median_absolute_error`
 'r2'                       :func:`metrics.r2_score`
 ======================     =======================================     ==================================
 
@@ -83,7 +84,7 @@ Usage examples:
     >>> model = svm.SVC()
     >>> cross_validation.cross_val_score(model, X, y, scoring='wrong_choice')
     Traceback (most recent call last):
-    ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'precision', 'r2', 'recall', 'roc_auc']
+    ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'precision', 'r2', 'recall', 'roc_auc']
     >>> clf = svm.SVC(probability=True, random_state=0)
     >>> cross_validation.cross_val_score(clf, X, y, scoring='log_loss') # doctest: +ELLIPSIS
     array([-0.07..., -0.16..., -0.06...])
@@ -1087,6 +1088,34 @@ function::
     for an example of mean squared error usage to
     evaluate gradient boosting regression.
 
+Median absolute error
+---------------------
+
+The :func:`median_absolute_error` is particularly interesting because it is
+robust to outliers. The loss is calculated by taking the median of all absolute
+differences between the target and the prediction.
+
+If :math:`\hat{y}_i` is the predicted value of the :math:`i`-th sample
+and :math:`y_i` is the corresponding true value, then the median absolute error
+(MedAE) estimated over :math:`n_{\text{samples}}` is defined as
+
+.. math::
+
+  \text{MedAE}(y, \hat{y}) = \text{median}(\mid y_1 - \hat{y}_1 \mid, \ldots, \mid y_n - \hat{y}_n \mid).
+
+Here is a small example of usage of the :func:`median_absolute_error`
+function::
+
+  >>> from sklearn.metrics import median_absolute_error
+  >>> y_true = [3, -0.5, 2, 7]
+  >>> y_pred = [2.5, 0.0, 2, 8]
+  >>> median_absolute_error(y_true, y_pred)
+  0.5
+  >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
+  >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
+  >>> median_absolute_error(y_true, y_pred)
+  1.0
+
 R² score, the coefficient of determination
 -------------------------------------------
 

diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py
@@ -47,6 +47,7 @@
 from .regression import explained_variance_score
 from .regression import mean_absolute_error
 from .regression import mean_squared_error
+from .regression import median_absolute_error
 from .regression import r2_score
 
 from .scorer import make_scorer
@@ -81,6 +82,7 @@
     'matthews_corrcoef',
     'mean_absolute_error',
     'mean_squared_error',
+    'median_absolute_error',
     'mutual_info_score',
     'normalized_mutual_info_score',
     'pairwise_distances',

diff --git a/sklearn/metrics/metrics.py b/sklearn/metrics/metrics.py
@@ -29,6 +29,7 @@
 from .regression import explained_variance_score
 from .regression import mean_absolute_error
 from .regression import mean_squared_error
+from .regression import median_absolute_error
 from .regression import r2_score
 
 # Deprecated in 0.16

diff --git a/sklearn/metrics/regression.py b/sklearn/metrics/regression.py
@@ -27,6 +27,7 @@
 __ALL__ = [
     "mean_absolute_error",
     "mean_squared_error",
+    "median_absolute_error",
     "r2_score",
     "explained_variance_score"
 ]
@@ -177,6 +178,39 @@ def mean_squared_error(y_true, y_pred, sample_weight=None):
                       weights=sample_weight)
 
 
+def median_absolute_error(y_true, y_pred):
+    """Median absolute error regression loss
+
+    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 target values.
+
+    Returns
+    -------
+    loss : float
+        A positive floating point value (the best value is 0.0).
+
+    Examples
+    --------
+    >>> from sklearn.metrics import median_absolute_error
+    >>> y_true = [3, -0.5, 2, 7]
+    >>> y_pred = [2.5, 0.0, 2, 8]
+    >>> median_absolute_error(y_true, y_pred)
+    0.5
+    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
+    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
+    >>> median_absolute_error(y_true, y_pred)
+    1.0
+
+    """
+    y_type, y_true, y_pred = _check_reg_targets(y_true, y_pred)
+    return np.median(np.abs(y_pred - y_true))
+
+
 def explained_variance_score(y_true, y_pred, sample_weight=None):
     """Explained variance regression score function
 

diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
@@ -22,9 +22,9 @@
 
 import numpy as np
 
-from . import (r2_score, mean_absolute_error, mean_squared_error,
-               accuracy_score, f1_score, roc_auc_score,
-               average_precision_score,
+from . import (r2_score, median_absolute_error, mean_absolute_error,
+               mean_squared_error, accuracy_score, f1_score,
+               roc_auc_score, average_precision_score,
                precision_score, recall_score, log_loss)
 from .cluster import adjusted_rand_score
 from ..utils.multiclass import type_of_target
@@ -86,7 +86,7 @@ def __call__(self, estimator, X, y_true, sample_weight=None):
         else:
             return self._sign * self._score_func(y_true, y_pred,
                                                  **self._kwargs)
-                
+
 
 class _ProbaScorer(_BaseScorer):
     def __call__(self, clf, X, y, sample_weight=None):
@@ -316,6 +316,8 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
                                         greater_is_better=False)
 mean_absolute_error_scorer = make_scorer(mean_absolute_error,
                                          greater_is_better=False)
+median_absolute_error_scorer = make_scorer(median_absolute_error,
+                                           greater_is_better=False)
 
 # Standard Classification Scores
 accuracy_scorer = make_scorer(accuracy_score)
@@ -337,6 +339,7 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
 adjusted_rand_scorer = make_scorer(adjusted_rand_score)
 
 SCORERS = dict(r2=r2_scorer,
+               median_absolute_error=median_absolute_error_scorer,
                mean_absolute_error=mean_absolute_error_scorer,
                mean_squared_error=mean_squared_error_scorer,
                accuracy=accuracy_scorer, f1=f1_scorer, roc_auc=roc_auc_scorer,

diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
@@ -37,6 +37,7 @@
 from sklearn.metrics import matthews_corrcoef
 from sklearn.metrics import mean_absolute_error
 from sklearn.metrics import mean_squared_error
+from sklearn.metrics import median_absolute_error
 from sklearn.metrics import precision_score
 from sklearn.metrics import r2_score
 from sklearn.metrics import recall_score
@@ -87,6 +88,7 @@
 REGRESSION_METRICS = {
     "mean_absolute_error": mean_absolute_error,
     "mean_squared_error": mean_squared_error,
+    "median_absolute_error": median_absolute_error,
     "explained_variance_score": explained_variance_score,
     "r2_score": r2_score,
 }
@@ -278,7 +280,8 @@
 
 # Regression metrics with "multioutput-continuous" format support
 MULTIOUTPUT_METRICS = [
-    "mean_absolute_error", "mean_squared_error", "r2_score",
+    "mean_absolute_error", "mean_squared_error", "median_absolute_error",
+    "r2_score",
 ]
 
 # Symmetric with respect to their input arguments y_true and y_pred
@@ -291,7 +294,8 @@
 
     "f1_score", "weighted_f1_score", "micro_f1_score", "macro_f1_score",
 
-    "matthews_corrcoef_score", "mean_absolute_error", "mean_squared_error"
+    "matthews_corrcoef_score", "mean_absolute_error", "mean_squared_error",
+    "median_absolute_error"
 
 ]
 
@@ -321,6 +325,7 @@
     "hamming_loss",
     "hinge_loss",
     "matthews_corrcoef_score",
+    "median_absolute_error",
 ]
 
 

diff --git a/sklearn/metrics/tests/test_regression.py b/sklearn/metrics/tests/test_regression.py
@@ -11,6 +11,7 @@
 from sklearn.metrics import explained_variance_score
 from sklearn.metrics import mean_absolute_error
 from sklearn.metrics import mean_squared_error
+from sklearn.metrics import median_absolute_error
 from sklearn.metrics import r2_score
 
 from sklearn.metrics.regression import _check_reg_targets
@@ -22,6 +23,7 @@ def test_regression_metrics(n_samples=50):
 
     assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
     assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
+    assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
     assert_almost_equal(r2_score(y_true, y_pred),  0.995, 2)
     assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
 
@@ -45,6 +47,7 @@ def test_multioutput_regression():
 def test_regression_metrics_at_limits():
     assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
     assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
+    assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
     assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
     assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
 

diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py
@@ -28,7 +28,8 @@
 from sklearn.multiclass import OneVsRestClassifier
 
 
-REGRESSION_SCORERS = ['r2', 'mean_absolute_error', 'mean_squared_error']
+REGRESSION_SCORERS = ['r2', 'mean_absolute_error', 'mean_squared_error',
+                      'median_absolute_error']
 CLF_SCORERS = ['accuracy', 'f1', 'roc_auc', 'average_precision', 'precision',
                'recall', 'log_loss',
                'adjusted_rand_score'  # not really, but works
@@ -273,9 +274,10 @@ def test_scorer_sample_weight():
                              "called with sample weights: {1} vs "
                              "{2}".format(name, weighted, unweighted))
             assert_almost_equal(weighted, ignored,
-                         err_msg="scorer {0} behaves differently when "
-                         "ignoring samples and setting sample_weight to 0: "
-                         "{1} vs {2}".format(name, weighted, ignored))
+                                err_msg="scorer {0} behaves differently when "
+                                "ignoring samples and setting sample_weight to"
+                                " 0: {1} vs {2}".format(name, weighted,
+                                                        ignored))
 
         except TypeError as e:
             assert_true("sample_weight" in str(e),