Use ftrl training, to learn compressed model.
@@ -124,7 +124,7 @@ 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.model.l1_penalty = cfg.get('L1', 0.0)
self.cfg = cfg
@@ -263,10 +263,10 @@ cdef class Parser:
self.model.time += 1
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
if eg.c.costs[guess] > 0:
- best = VecVec.arg_max_if_zero(eg.c.scores, eg.c.costs, eg.c.nr_class)
+ best = arg_max_if_gold(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])
+ self.model.update_weight_ftrl(feat.key, best, -feat.value * eg.c.costs[guess])
+ self.model.update_weight_ftrl(feat.key, guess, feat.value * eg.c.costs[guess])
action = self.moves.c[guess]
action.do(stcls.c, action.label)
@@ -392,6 +392,14 @@ class ParserStateError(ValueError):
"Please include the text that the parser failed on, which is:\n"
"%s" % repr(doc.text))
+cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, int n) nogil:
+ cdef int best = -1
+ for i in range(n):
+ if costs[i] <= 0:
+ if best == -1 or scores[i] > scores[best]:
+ best = i
+ return best
+
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
int nr_class) except -1:
