Use FTRL training in parser

spacy/syntax/parser.pyx

@@ -124,6 +124,8 @@ cdef class Parser:
         elif 'features' not in cfg:
             cfg['features'] = self.feature_templates
         self.model = ParserModel(cfg['features'])
+        self.model.l1_penalty = 1e-7
+
         self.cfg = cfg
 
     def __reduce__(self):
@@ -258,15 +260,20 @@ cdef class Parser:
             self.model.set_featuresC(&eg.c, stcls.c)
             self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
             self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
-            self.model.updateC(&eg.c)
+            self.model.time += 1
             guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
-
-            action = self.moves.c[eg.guess]
+            if eg.c.costs[guess] > 0:
+                best = VecVec.arg_max_if_zero(eg.c.scores, eg.c.costs, eg.c.nr_class)
+                for feat in eg.c.features[:eg.c.nr_feat]:
+                    self.model.update_weight_ftrl(feat.key, best, -feat.value * eg.costs[guess])
+                    self.model.update_weight_ftrl(feat.key, guess, feat.value * eg.costs[guess])
+ 
+            action = self.moves.c[guess]
             action.do(stcls.c, action.label)
-            loss += eg.costs[eg.guess]
-            eg.fill_scores(0, eg.nr_class)
-            eg.fill_costs(0, eg.nr_class)
-            eg.fill_is_valid(1, eg.nr_class)
+            loss += eg.costs[guess]
+            eg.fill_scores(0, eg.c.nr_class)
+            eg.fill_costs(0, eg.c.nr_class)
+            eg.fill_is_valid(1, eg.c.nr_class)
         return loss
 
     def step_through(self, Doc doc):
@@ -296,7 +303,7 @@ cdef class Parser:
         # Doesn't set label into serializer -- subclasses override it to do that.
         for action in self.moves.action_types:
             self.moves.add_action(action, label)
-                
+
 
 cdef class StepwiseState:
     cdef readonly StateClass stcls