[Predator] Move ``SingleFeatureScore`` to LLM.

appengine/findit/crash/loglinear/test/model_test.py

Index: appengine/findit/crash/loglinear/test/model_test.py
diff --git a/appengine/findit/crash/loglinear/test/model_test.py b/appengine/findit/crash/loglinear/test/model_test.py
index 17d8e6e4b85a3b0a2cf9bb9e235042f119b6f750..9bf3b431f8850346872d693097006d26876605d9 100644
--- a/appengine/findit/crash/loglinear/test/model_test.py
+++ b/appengine/findit/crash/loglinear/test/model_test.py
@@ -5,20 +5,26 @@
 import math
 import numpy as np
 
-from crash.loglinear.model import ToFeatureFunction
+from crash.loglinear.feature import FeatureFunction
 from crash.loglinear.model import LogLinearModel
+from crash.loglinear.model import UnnormalizedLogLinearModel
 from crash.loglinear.test.loglinear_testcase import LoglinearTestCase
 
 
-class LoglinearTest(LoglinearTestCase):
+class UnnormalizedLogLinearModelTest(LoglinearTestCase):
 
-  def testToFeatureFunction(self):
-    """Test that ``ToFeatureFunction`` obeys the equality its docstring says."""
-    for x in self._X:
-      for y in self._Y(x):
-        for i in xrange(self._qty_features):
-          self.assertEqual(self._feature_list[i](x)(y),
-                           self._feature_function(x)(y)[i])
+  def testSingleFeatureScore(self):
+    """Test that ``SingleFeatureScore`` returns weighted feature score."""
+    model = UnnormalizedLogLinearModel(self._feature_function, self._weights,
+                                       0.1)
+    for feature in self._feature_list:
+      feature_value = feature(5)(True)
+      self.assertEqual(
+          model.SingleFeatureScore(feature_value),
+          feature_value.value * model._weights.get(feature_value.name, 0.))
+
+
+class LoglinearTest(LoglinearTestCase):
 
   def testLogLinearModel(self):
     """An arbitrary test to get 100% code coverage.
@@ -37,7 +43,7 @@ class LoglinearTest(LoglinearTestCase):
     model = LogLinearModel(self._Y, self._feature_function, self._weights, 0.1)
     model.ClearAllMemos()
     model = LogLinearModel(self._Y, self._feature_function, self._weights)
-    self.assertListEqual(self._weights, model.weights.tolist())
+    self.assertDictEqual(self._weights, model.weights)
     self.assertEqual(math.sqrt(model.quadrance), model.l2)
 
     for x in self._X: