From 8a3532f57dc9804c00ec73abe93d7a843de0a84a Mon Sep 17 00:00:00 2001
From: martincousi <martincousi@gmail.com>
Date: Mon, 26 Mar 2018 16:45:55 -0400
Subject: [PATCH 1/2] added asym_rmse and asym_mae

---
 surprise/accuracy.py | 75 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)

diff --git a/surprise/accuracy.py b/surprise/accuracy.py
index 1e9e4855..04ffca0f 100644
--- a/surprise/accuracy.py
+++ b/surprise/accuracy.py
@@ -88,6 +88,81 @@ def mae(predictions, verbose=True):
     return mae_
 
 
+def asym_rmse(predictions, weight=0.5, verbose=True):
+    """Compute Asymmetric RMSE (Root Mean Squared Error).
+
+    .. math::
+        \\text{Asymmetric RMSE} = \\sqrt{\\frac{1}{|\\hat{R}|} 
+        \\sum_{\\hat{r}_{ui} \in \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2 |\\omega 
+        - 1_{r_{ui} - \\hat{r}_{ui} < 0}|}.
+
+    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.
+        weight (int): Weight used to characterize asymmetry.
+        verbose: If True, will print computed value. Default is ``True``.
+
+
+    Returns:
+        The Asymmetric Root Mean Squared Error of predictions.
+
+    Raises:
+        ValueError: When ``predictions`` is empty.
+    """
+
+    if not predictions:
+        raise ValueError('Prediction list is empty.')
+
+    res = np.array([float(true_r - est)
+                    for (_, _, true_r, est, _) in predictions])
+    asym_rmse_ = np.sqrt(np.mean(res**2 * np.abs(weight - 
+                         (res<0).astype(int))))
+
+    if verbose:
+        print('Asymmetric RMSE: {0:1.4f}'.format(asym_rmse_))
+
+    return asym_rmse_
+
+
+def asym_mae(predictions, weight=0.5, verbose=True):
+    """Compute Asymmetric MAE (Mean Absolute Error).
+
+    .. math::
+        \\text{Asymmetric MAE} = \\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \in
+        \\hat{R}}|r_{ui} - \\hat{r}_{ui}| |\\omega - 1_{r_{ui} - \\hat{r}_{ui} 
+        < 0}|.
+
+    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.
+        weight (int): Weight used to characterize asymmetry.
+        verbose: If True, will print computed value. Default is ``True``.
+
+
+    Returns:
+        The Asymmetric Mean Absolute Error of predictions.
+
+    Raises:
+        ValueError: When ``predictions`` is empty.
+    """
+
+    if not predictions:
+        raise ValueError('Prediction list is empty.')
+
+    res = np.array([float(true_r - est)
+                    for (_, _, true_r, est, _) in predictions])
+    asym_mae_ = np.mean(np.abs(res) * np.abs(weight - (res<0).astype(int)))
+
+    if verbose:
+        print('Asymmetric MAE: {0:1.4f}'.format(asym_mae_))
+
+    return asym_mae_
+
+
 def fcp(predictions, verbose=True):
     """Compute FCP (Fraction of Concordant Pairs).
 

From 93690015a0c4ec40a0c544c465fc42c05a4f85e3 Mon Sep 17 00:00:00 2001
From: martincousi <martincousi@gmail.com>
Date: Thu, 29 Mar 2018 07:31:15 -0400
Subject: [PATCH 2/2] pep8 issues

---
 surprise/accuracy.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/surprise/accuracy.py b/surprise/accuracy.py
index 04ffca0f..99a7bc53 100644
--- a/surprise/accuracy.py
+++ b/surprise/accuracy.py
@@ -92,8 +92,8 @@ def asym_rmse(predictions, weight=0.5, verbose=True):
     """Compute Asymmetric RMSE (Root Mean Squared Error).
 
     .. math::
-        \\text{Asymmetric RMSE} = \\sqrt{\\frac{1}{|\\hat{R}|} 
-        \\sum_{\\hat{r}_{ui} \in \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2 |\\omega 
+        \\text{Asymmetric RMSE} = \\sqrt{\\frac{1}{|\\hat{R}|}
+        \\sum_{\\hat{r}_{ui} \in \\hat{R}}(r_{ui} - \\hat{r}_{ui})^2 |\\omega
         - 1_{r_{ui} - \\hat{r}_{ui} < 0}|}.
 
     Args:
@@ -117,8 +117,8 @@ def asym_rmse(predictions, weight=0.5, verbose=True):
 
     res = np.array([float(true_r - est)
                     for (_, _, true_r, est, _) in predictions])
-    asym_rmse_ = np.sqrt(np.mean(res**2 * np.abs(weight - 
-                         (res<0).astype(int))))
+    asym_rmse_ = np.sqrt(np.mean(res**2 * np.abs(weight -
+                                                 (res < 0).astype(int))))
 
     if verbose:
         print('Asymmetric RMSE: {0:1.4f}'.format(asym_rmse_))
@@ -131,7 +131,7 @@ def asym_mae(predictions, weight=0.5, verbose=True):
 
     .. math::
         \\text{Asymmetric MAE} = \\frac{1}{|\\hat{R}|} \\sum_{\\hat{r}_{ui} \in
-        \\hat{R}}|r_{ui} - \\hat{r}_{ui}| |\\omega - 1_{r_{ui} - \\hat{r}_{ui} 
+        \\hat{R}}|r_{ui} - \\hat{r}_{ui}| |\\omega - 1_{r_{ui} - \\hat{r}_{ui}
         < 0}|.
 
     Args:
@@ -155,7 +155,7 @@ def asym_mae(predictions, weight=0.5, verbose=True):
 
     res = np.array([float(true_r - est)
                     for (_, _, true_r, est, _) in predictions])
-    asym_mae_ = np.mean(np.abs(res) * np.abs(weight - (res<0).astype(int)))
+    asym_mae_ = np.mean(np.abs(res) * np.abs(weight - (res < 0).astype(int)))
 
     if verbose:
         print('Asymmetric MAE: {0:1.4f}'.format(asym_mae_))