Merge branch '_master' into issue#78

m3ttiw · Jun 19, 2020 · a858f52 · a858f52
2 parents 369ae86 + e7deaec
commit a858f52
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Showing 5 changed files with 47 additions and 40 deletions.
diff --git a/orange_cb_recsys/evaluation/classification_metrics.py b/orange_cb_recsys/evaluation/classification_metrics.py
@@ -45,6 +45,10 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame):
 class Precision(ClassificationMetric):
     """
     Precision
+        .. math::
+        \\begin{align*}
+        \\mathrm{Precision}& = #relevant recommended items \over #recommended items
+        \\end{align*}
 
     Args:
         relevant_threshold: specify the minimum value to consider
@@ -60,11 +64,7 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
         """
         Compute the precision of the given ranking (predictions)
         based on the truth ranking
-        .. math::
-        \\begin{align*}
-        \\mathrm{Precision}& = #relevant recommended items \over #recommended items
-        \\end{align*}
-
+        
         Args:
               truth (pd.DataFrame): dataframe whose columns are: to_id, rating
               predictions (pd.DataFrame): dataframe whose columns are: to_id, rating;
@@ -83,6 +83,10 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
 class Recall(ClassificationMetric):
     """
     Recall
+        .. math::
+        \\begin{align*}
+        \\mathrm{Precision}& = #relevant recommended items \over #tested relevant items
+        \\end{align*}
 
     Args:
         relevant_threshold: specify the minimum value to consider
@@ -99,11 +103,6 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
         Compute the recall of the given ranking (predictions)
         based on the truth ranking
         
-        .. math::
-        \\begin{align*}
-        \\mathrm{Precision}& = #relevant recommended items \over #tested relevant items
-        \\end{align*}
-        
         Args:
               truth (pd.DataFrame): dataframe whose columns are: to_id, rating
               predictions (pd.DataFrame): dataframe whose columns are: to_id, rating;

diff --git a/orange_cb_recsys/evaluation/fairness_metrics.py b/orange_cb_recsys/evaluation/fairness_metrics.py
@@ -70,15 +70,7 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame):
 class GiniIndex(FairnessMetric):
     """
     Gini index
-    """
-    def __init__(self, item: bool = False):
-        super().__init__(None, None)
-        self.__item = item
-
-    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame = None) -> pd.DataFrame:
-        """
-        Calculate Gini index score for each user or item in the DataFrame
-
+    
         .. math::
         \\begin{align*}
         \\mathrm{Gini\; index }& = \frac{\sum_{i = 0}^{n} (2i - n - 1)}{n \sum_{i = 0}^{n} elem_i }
@@ -87,6 +79,14 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame = None) -> pd.D
         Where:
         - n is the size of the user or item set
         - elem(i) is the user or the item in the i-th position in the sorted frame by user or item
+    """
+    def __init__(self, item: bool = False):
+        super().__init__(None, None)
+        self.__item = item
+
+    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame = None) -> pd.DataFrame:
+        """
+        Calculate Gini index score for each user or item in the DataFrame
 
         Args:
               truth (pd.DataFrame): original rating frame used for recsys config

diff --git a/orange_cb_recsys/evaluation/novelty.py b/orange_cb_recsys/evaluation/novelty.py
@@ -9,14 +9,6 @@
 class Novelty(Metric):
     """
     Novelty
-    """
-    def __init__(self, num_of_recs):
-        self.__num_of_recs = num_of_recs
-
-    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
-        """
-        Calculates the novelty score
-
         .. math::
         \\begin{align*}
         \\mathrm{Novelty}& = \frac{\sum_{{i} \in hits} log_2(1/popularity_i)}{|hits|}
@@ -25,6 +17,13 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
         where:
         - hits is a set of predicted items
         - Popularity(i) = % users who rated item i
+    """
+    def __init__(self, num_of_recs):
+        self.__num_of_recs = num_of_recs
+
+    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
+        """
+        Calculates the novelty score
 
         Args:
               truth (pd.DataFrame): original rating frame used for recsys config

diff --git a/orange_cb_recsys/evaluation/prediction_metrics.py b/orange_cb_recsys/evaluation/prediction_metrics.py
@@ -18,14 +18,18 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame):
 
 
 class RMSE(PredictionMetric):
-    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
-        """
-        Compute the RMSE metric
+    """
+    RMSE
         .. math::
         \\begin{align*}
         \\mathrm{RMSE}& = \\sqrt{\frac{\sum_{{u, i} \in T} ({r_{ui} - r'_{ui}})^2}{|T|}}
         \\end{align*}
         Where T is the test set and r' is the actual score give by user u to item i
+    """
+    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
+        """
+        Compute the RMSE metric
+        
         Args:
               truth (pd.DataFrame): dataframe whose columns are: to_id, rating
               predictions (pd.DataFrame): dataframe whose columns are: to_id, rating
@@ -46,14 +50,17 @@ def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
 
 
 class MAE(PredictionMetric):
-    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
-        """
-        Compute the MAE metric
-        .. math::
+    """
+    MAE
+        ..math::
         \\begin{align*}
         \\mathrm{MAE}& = \\sum_{{u, i} \in T} \abs{{r_{ui} - r'_{ui}}} \over |T|
         \\end{align*}
         Where T is the test set and r' is the actual score give by user u to item i
+    """
+    def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
+        """
+        Compute the MAE metric
         
         Args:
               truth (pd.DataFrame): dataframe whose columns are: to_id, rating

diff --git a/orange_cb_recsys/evaluation/serendipity.py b/orange_cb_recsys/evaluation/serendipity.py
@@ -5,19 +5,21 @@
 
 
 class Serendipity(Metric):
+    """
+    Serendipity
+        .. math::
+        \\begin{align*}
+        \\mathrm{Unexpected \; recommendations}&  = |recommendations \; from  \; tested \; algorithm| - |recommendations \; from \;  obvious \;  algorithm|
+        \\end{align*}
+    """
     def __init__(self, num_of_recs: int):
         self.__num_of_recs = num_of_recs
 
     def perform(self, predictions: pd.DataFrame, truth: pd.DataFrame) -> float:
         """
         Calculates the serendipity score: unexpected recommendations, surprisingly and interesting items a user
         might not have otherwise discovered
-
-        .. math::
-        \\begin{align*}
-        \\mathrm{Unexpected \; recommendations}&  = |recommendations \; from  \; tested \; algorithm| - |recommendations \; from \;  obvious \;  algorithm|
-        \\end{align*}
-
+        
         Args:
               truth (pd.DataFrame): original rating frame used for recsys config
               predictions (pd.DataFrame): dataframe with recommendations for multiple users