adding documentation

model_interface.py

@@ -9,6 +9,7 @@
 
 
 class SidekitModel():
+    """This class is just an interface for my models to inherit"""
 
     def __init__(self, conf_filepath):
         self.conf = parse_yaml(conf_filepath)
@@ -19,6 +20,16 @@ def __init__(self, conf_filepath):
 
 
     def createFeatureServer(self, group=None):
+        """
+        This methos is used to crate FeatureServer object which is an object for
+        features management. It loads datasets from a HDF5 files ( produced by
+        a FeaturesExtractor object)
+        Args:
+            group (string): the group of features to manage. If None, then it 
+            will create a generic FeatureServer over the feat directory.
+        Returns:
+            server: which is the FeatureServer object
+        """
         if group:
             feat_dir = os.path.join(self.BASE_DIR, "feat", group)
         else:
@@ -52,24 +63,24 @@ def evaluate(self):
     def plotDETcurve(self):
         pass
 
-    def getAccuracy(self, speakers, test_files, scores, mode=2, threshold=0):
+    def getAccuracy(self, speakers, test_files, scores, threshold=0):
         """
-        This private method is used to get the accuracy of a model
-        given five pieces of information:
-        -> speakers: list of speakers
-        -> test_files: list of filenames that used to evaluate model
-        -> scores: score numpy matrix obtained by the model
-        -> mode: which is one of these values [0, 1, 2] where:
-            -> 0: means get verification accuracy.
-            -> 1: means get recognition accuracy.
-            -> 2: means get both accuracy, verification and recognition.
-        -> threshold: the value above which we will consider the verification
+        This method is used to get the accuracy of a model over a bunch of data
+        files. The accuracy is returned in percentage. 
+        NOTE: The file is considered to be correct if the correct speaker has the
+        max score (that's in case the speaker is in the training set). And also
+        if the max score is below threshold (that's in case the speaker is not
+        in the training set).
+        Args
+            speakers: list of speakers
+            test_files: list of filenames that used to evaluate model
+            scores: the score numpy matrix obtained by the model
+            threshold: the value above which we will consider the verification
                 is done correctly. In other words, if the score>threshold, then
                 the answer is considered; otherwise, the answer is not considered
-        And it should return the accuracy of the model in percentage
+        Returns
+            accuracy (float): accuracy of the model in percentage
         """
-        assert mode in [0, 1, 2],\
-            "The model variable must be one of these values[0, 1, 2]"
         assert scores.shape == (len(speakers), len(test_files)),\
             "The dimensions of the input don't match"
         accuracy = 0.
@@ -80,28 +91,11 @@ def getAccuracy(self, speakers, test_files, scores, mode=2, threshold=0):
             test_filename = test_filename.decode() #convert from byte to string
             actual_speaker = test_filename.split("/")[-1].split(".")[0]
             predicted_speaker = speakers[max_indices[idx]]
-            #TODO: 
-            ########## JUST VERIFICATION ##########
-            if mode == 0:
-                if max_scores[idx] < threshold:
-                    continue
-                else:
-                    accuracy += 1.
-            ########## JUST RECOGNITION ##########
-            elif mode == 1:
-                #skip speakers outside the training
+            #evaluate the test data file
+            if max_scores[idx] < threshold:
                 if actual_speaker not in speakers:
-                    continue
-                else:
-                    if predicted_speaker == actual_speaker:
-                        accuracy += 1.
-            ########## VERIFICATION & RECOGNITION ##########
-            elif mode == 2:
-                #skip speakers outside the training
-                if max_scores[idx] < threshold:
-                    continue
-                else:
-                    if predicted_speaker == actual_speaker:
-                        accuracy += 1.
-
-        return accuracy/len(test_files)
+                    accuracy += 1
+            else:
+                if predicted_speaker == actual_speaker:
+                    accuracy += 1.
+        return accuracy*100./len(test_files)