Added MSE, from Marc Feger's PR 225

README.md

@@ -188,10 +188,10 @@ Contributors
 The following persons have contributed to [Surprise](http://surpriselib.com):
 
 caoyi, Олег Демиденко, Charles-Emmanuel Dias, dmamylin, Lauriane Ducasse,
-franckjay, Lukas Galke, Pierre-François Gimenez, Zachary Glassman, Nicolas
-Hug, Janniks, Doruk Kilitcioglu, Ravi Raju Krishna, Hengji Liu, Maher
-Malaeb, Manoj K, Naturale0, nju-luke, Jay Qi, Skywhat, David Stevens, Victor
-Wang, Mike Lee Williams, Jay Wong, Chenchen Xu, YaoZh1918.
+Marc Feger, franckjay, Lukas Galke, Pierre-François Gimenez, Zachary
+Glassman, Nicolas Hug, Janniks, Doruk Kilitcioglu, Ravi Raju Krishna, Hengji
+Liu, Maher Malaeb, Manoj K, Naturale0, nju-luke, Jay Qi, Skywhat, David
+Stevens, Victor Wang, Mike Lee Williams, Jay Wong, Chenchen Xu, YaoZh1918.
 
 Thanks a lot :) !
 

surprise/accuracy.py

@@ -8,6 +8,7 @@
     :nosignatures:
 
     rmse
+    mse
     mae
     fcp
 """
@@ -54,6 +55,40 @@ def rmse(predictions, verbose=True):
     return rmse_
 
 
+def mse(predictions, verbose=True):
+    """Compute MSE (Mean Squared Error).
+
+    .. math::
+        \\text{RMSE} = \\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \in
+        \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2.
+
+    Args:
+        predictions (:obj:`list` of :obj:`Prediction\
+            <surprise.prediction_algorithms.predictions.Prediction>`):
+            A list of predictions, as returned by the :meth:`test()
+            <surprise.prediction_algorithms.algo_base.AlgoBase.test>` method.
+        verbose: If True, will print computed value. Default is ``True``.
+
+
+    Returns:
+        The Mean Squared Error of predictions.
+
+    Raises:
+        ValueError: When ``predictions`` is empty.
+    """
+
+    if not predictions:
+        raise ValueError('Prediction list is empty.')
+
+    mse_ = np.mean([float((true_r - est)**2)
+                    for (_, _, true_r, est, _) in predictions])
+
+    if verbose:
+        print('MSE: {0:1.4f}'.format(mse_))
+
+    return mse_
+
+
 def mae(predictions, verbose=True):
     """Compute MAE (Mean Absolute Error).
 

tests/test_accuracy.py

@@ -6,7 +6,7 @@
 
 import pytest
 
-from surprise.accuracy import mae, rmse, fcp
+from surprise.accuracy import mae, rmse, fcp, mse
 
 
 def pred(true_r, est, u0=None):
@@ -67,3 +67,19 @@ def test_fcp():
 
     with pytest.raises(ValueError):
         fcp([])
+
+
+def test_mse():
+    """Tests for the MSE function."""
+
+    predictions = [pred(0, 0), pred(1, 1), pred(2, 2), pred(100, 100)]
+    assert mse(predictions) == 0
+
+    predictions = [pred(0, 0), pred(0, 2)]
+    assert mse(predictions) == ((0 - 2) ** 2) / 2
+
+    predictions = [pred(2, 0), pred(3, 4)]
+    assert mse(predictions) == ((2 - 0) ** 2 + (3 - 4) ** 2) / 2
+
+    with pytest.raises(ValueError):
+        mse([])

tests/test_evaluate.py

@@ -30,9 +30,10 @@ def test_performances():
     algo = NormalPredictor()
     tmp_dir = tempfile.mkdtemp()  # create tmp dir
     with pytest.warns(UserWarning):
-        performances = evaluate(algo, data, measures=['RmSe', 'Mae'],
+        performances = evaluate(algo, data, measures=['RmSe', 'Mae', 'mse'],
                                 with_dump=True, dump_dir=tmp_dir, verbose=2)
     shutil.rmtree(tmp_dir)  # remove tmp dir
 
     assert performances['RMSE'] is performances['rmse']
     assert performances['MaE'] is performances['mae']
+    assert performances['MsE'] is performances['mse']