dc decision criteria fixed

test_benchmark.py

@@ -66,6 +66,13 @@ def load_tubingen_pairs():
         yield load_tubingen_pair(pair)
 
 
+def preprocess_dc_style(X, scale_factor=20):
+    if abs(max(X)) < 1:
+        X = np.multiply(X, scale_factor)
+    X = map(round, X)
+    return X
+
+
 def test_tubingen_pairs():
     epsilon = 0.0
     level = 0.05
@@ -86,6 +93,10 @@ def test_tubingen_pairs():
             continue
 
         X, Y = data
+        # if i+1 in [65, 66, 67]:
+        #     X, Y = preprocess_dc_style(X, 100), preprocess_dc_style(Y, 100)
+        # else:
+        #     X, Y = preprocess_dc_style(X), preprocess_dc_style(Y)
         # X, Y = normalise(X), normalise(Y)
         # discretizer = UnivariateIPDiscretizer(X, Y)
         # aX, Xd, aY, Yd = discretizer.discretize()
@@ -106,9 +117,9 @@ def test_tubingen_pairs():
         else:
             cause_cisc = ""
 
-        if dc_score[0] > dc_score[1]:
+        if dc_score[0] < dc_score[1]:
             cause_dc = "X"
-        elif dc_score[0] < dc_score[1]:
+        elif dc_score[0] > dc_score[1]:
             cause_dc = "Y"
         else:
             cause_dc = ""
@@ -170,7 +181,7 @@ def test_tubingen_pairs():
         accs_cisc.append(sum(rcisc) / len(rcisc))
         accs_dc.append(sum(rdc) / len(rdc))
         accs_dr.append(sum(rdr) / len(rdr))
-        fp.write("%.2f %.2f %.2f %.2f" % (r, sum(rdc) / len(rdc),
+        fp.write("%.2f %.2f %.2f %.2f\n" % (r, sum(rdc) / len(rdc),
                                           sum(rdr) / len(rdr), sum(rcisc) / len(rcisc)))
     fp.close()
     plot_multiline([accs_cisc, accs_dc, accs_dr], dec_rate, [