Added test for combining preference data types

tests/models/test_preference_model.py

@@ -14,6 +14,9 @@
 def f_sin(x, data=None):
     return 2 * np.cos(np.pi * (x-2)) * np.exp(-(0.9*x))
 
+def f_sq(x, data=None):
+    return (x/10.0)**2
+
 
 def test_preference_model_add_pairs():
     pm = lop.PreferenceModel()
@@ -90,10 +93,10 @@ def test_adding_multiple_types():
     pm = lop.PreferenceModel(other_probits={'abs': lop.AbsBoundProbit()})
 
     X_pref = np.array([0,1,2,3])
-    y_pref = lop.generate_fake_pairs(X_pref, f_sin, 0) + lop.generate_fake_pairs(X_pref, f_sin, 1)
+    y_pref = lop.generate_fake_pairs(X_pref, f_sq, 0) + lop.generate_fake_pairs(X_pref, f_sq, 1)
 
     X_abs = np.array([1.5, 2.5])
-    y_abs = f_sin(X_abs)
+    y_abs = f_sq(X_abs)
 
     pm.add(X_pref, y_pref)
 
@@ -104,6 +107,30 @@ def test_adding_multiple_types():
     assert pm is not None
 
 
+def test_preference_model_likelyhood_multiple():
+    pm = lop.PreferenceModel()
+
+    X_train = np.array([0,1,2,3,4.2,6,7])
+    pairs = lop.generate_fake_pairs(X_train, f_sq, 0) + \
+            lop.generate_fake_pairs(X_train, f_sq, 1) + \
+            lop.generate_fake_pairs(X_train, f_sq, 2) + \
+            lop.generate_fake_pairs(X_train, f_sq, 3) + \
+            lop.generate_fake_pairs(X_train, f_sq, 4)
+
+
+    pm.add(X_train, pairs)
+
+    X_train = np.array([0.2,1.5,2.3,3.2,4.2,6.2,7.3])
+    y_train = f_sq(X_train)
+
+    pm.add(X_train, y_train, type='abs')
+
+
+    with pytest.raises(TypeError):
+        log_like = pm.log_likelyhood_training()
+    log_like = pm.log_likelyhood_training(np.array([0,0,0,1,1,1,0.5,0.1,0.05,0.2,0.8,0.9,0.9,0.5]))
+
+    assert not np.isnan(log_like)