vectorize pearson rank and make it symmetric

diffir/measure/unsupervised.py

@@ -92,16 +92,21 @@ def pearson_rank(self, x, y):
         indices = sorted(list(range(len(x))), key=lambda idx : x[idx], reverse=True)
         x = x[indices]
         y = y[indices]
+        x_diff = x.reshape(1,-1) - x.reshape(-1,1)
+        y_diff = y.reshape(1,-1) - y.reshape(-1,1)
         den = x[1:].sum()
         pr = 0
-        for i in range(1,len(x)):
-            num_i = sum([(x[j]-x[i])*(y[j]-y[i]) for j in range(0,i)])
-            den_i = math.sqrt(sum([(x[j]-x[i])*(x[j]-x[i]) for j in range(0,i)])) * math.sqrt(sum([(y[j]-y[i])*(y[j]-y[i]) for j in range(0,i)]))
-            if den_i == 0:
-                den_i = 1e-5
-            pr += x[i]*num_i/den_i
-        pr = pr/den
-        return pr
+        mask = np.tril(np.ones((len(x),len(x))),k=-1)
+        xy = x_diff*y_diff*mask
+        xx = x_diff*x_diff*mask
+        yy = y_diff*y_diff*mask 
+        xy = xy.sum(axis=1)[1:]
+        xx = xx.sum(axis=1)[1:]
+        yy = yy.sum(axis=1)[1:]
+        den_i = np.sqrt(xx)*np.sqrt(yy)
+        den_i[den_i==0]=1e-5
+        res = (xy*x[1:]/den_i).sum()/den 
+        return res
 
     def _query_differences(self, run1, run2, *args, **kwargs):
         """
@@ -137,7 +142,7 @@ def _query_differences(self, run1, run2, *args, **kwargs):
             elif metric == "spearmanr":
                 tau, p_value = stats.spearmanr(union_score1, union_score2)
             elif metric == "pearsonr":
-                tau = self.pearson_rank(union_score1, union_score2)
+                tau = (self.pearson_rank(union_score1, union_score2)+self.pearson_rank(union_score2, union_score1))/2
             else:
                 raise ValueError("Metric {} not supported for the measure {}".format(self.config["metric"], self.module_name))
             id2measure[qid] = tau