update ranking 2

deeprank/learn/rankingMetrics.py

@@ -24,29 +24,31 @@ def hitrate(rs):
 
         rs (array): binary relevance array
 
-    Returns:
-        hirate (array): [recall@1,recall@2,...]
+    Rets:
+        hirtate (array): [recall@1,recall@2,...]
     """
-    rs =np.array(rs)
-    nr = np.max((1,np.sum(rs)))
+    rs = np.array(rs)
+    nr = np.max((1, np.sum(rs)))
     return np.cumsum(rs) / nr
 
+
 def success(hitrate):
     '''
-    Input:  hitrate = [0,  0, 0.3, 0.4]
     Output: success = [0,  0,   1,   1]
     '''
 
     success = np.ones(len(hitrate))
-    idx = (hitrate ==0)
+    idx = (hitrate == 0)
     success[idx] = 0
 
     return success
 
+
 def avprec(rs):
-    return [average_precision(rs[:i]) for i in range(1,len(rs))]
+    return [average_precision(rs[:i]) for i in range(1, len(rs))]
 
-def recall(rs,nr):
+
+def recall(rs, nr):
     """recall rate
     First element is rank 1, Relevance is binray
 
@@ -68,6 +70,7 @@ def recall(rs,nr):
 
     return np.sum(rs)/nr
 
+
 def mean_reciprocal_rank(rs):
     """Score is reciprocal of the rank of the first relevant item