* Add support for tag dictionary, and fix error-code for predict method

spacy/tagger.pxd

@@ -3,6 +3,7 @@ from cymem.cymem cimport Pool
 from thinc.learner cimport LinearModel
 from thinc.features cimport Extractor
 from thinc.typedefs cimport atom_t, feat_t, weight_t, class_t
+from preshed.maps cimport PreshMap
 
 from .typedefs cimport hash_t
 from .tokens cimport Tokens
@@ -15,7 +16,7 @@ cpdef enum TagType:
 
 cdef class Tagger:
     cpdef int set_tags(self, Tokens tokens) except -1
-    cpdef class_t predict(self, int i, Tokens tokens, object golds=*) except 0
+    cpdef class_t predict(self, int i, Tokens tokens, object golds=*) except *
  
     cpdef readonly Pool mem
     cpdef readonly Extractor extractor
@@ -23,3 +24,4 @@ cdef class Tagger:
 
     cpdef readonly TagType tag_type
     cpdef readonly list tag_names
+    cdef dict tagdict

spacy/tagger.pyx

@@ -18,7 +18,7 @@ from thinc.features cimport Feature, count_feats
 NULL_TAG = 0
 
 
-def setup_model_dir(tag_type, tag_names, templates, model_dir):
+def setup_model_dir(tag_type, tag_names, tag_counts, templates, model_dir):
     if path.exists(model_dir):
         shutil.rmtree(model_dir)
     os.mkdir(model_dir)
@@ -26,6 +26,7 @@ def setup_model_dir(tag_type, tag_names, templates, model_dir):
         'tag_type': tag_type,
         'templates': templates,
         'tag_names': tag_names,
+        'tag_counts': tag_counts,
     }
     with open(path.join(model_dir, 'config.json'), 'w') as file_:
         json.dump(config, file_)
@@ -35,24 +36,19 @@ def train(train_sents, model_dir, nr_iter=10):
     cdef Tokens tokens
     cdef Tagger tagger = Tagger(model_dir)
     cdef int i
+    cdef class_t guess = 0
+    cdef class_t gold
     for _ in range(nr_iter):
         n_corr = 0
         total = 0
         for tokens, golds in train_sents:
             assert len(tokens) == len(golds), [t.string for t in tokens]
             for i in range(tokens.length):
-                if tagger.tag_type == POS:
+                gold = golds[i]
-                    gold = _get_gold_pos(i, golds)
+                guess = tagger.predict(i, tokens, [gold])
-                else:
-                    raise StandardError
-
-                guess = tagger.predict(i, tokens)
                 tokens.set_tag(i, tagger.tag_type, guess)
-                if gold is not None:
-                    tagger.tell_answer(gold)
                 total += 1
-                    n_corr += guess in gold
+                n_corr += guess == gold
-                #print('%s\t%d\t%d' % (tokens[i].string, guess, gold))
         print('%.4f' % ((n_corr / total) * 100))
         random.shuffle(train_sents)
     tagger.model.end_training()
@@ -96,8 +92,9 @@ cdef class Tagger:
         templates = cfg['templates']
         self.tag_names = cfg['tag_names']
         self.tag_type = cfg['tag_type']
+        self.tagdict = _make_tag_dict(cfg['tag_counts'])
         self.extractor = Extractor(templates)
-        self.model = LinearModel(len(self.tag_names))
+        self.model = LinearModel(len(self.tag_names), self.extractor.n_templ+2)
         if path.exists(path.join(model_dir, 'model')):
             self.model.load(path.join(model_dir, 'model'))
 
@@ -113,7 +110,7 @@ cdef class Tagger:
         for i in range(tokens.length):
             tokens.set_tag(i, self.tag_type, self.predict(i, tokens))
 
-    cpdef class_t predict(self, int i, Tokens tokens, object golds=None) except 0:
+    cpdef class_t predict(self, int i, Tokens tokens, object golds=None) except *:
         """Predict the tag of tokens[i].  The tagger remembers the features and
         prediction, in case you later call tell_answer.
 
@@ -121,16 +118,18 @@ cdef class Tagger:
         >>> tag = EN.pos_tagger.predict(0, tokens)
         >>> assert tag == EN.pos_tagger.tag_id('DT') == 5
         """
-        cdef int n_feats
+        cdef atom_t sic = tokens.data[i].lex.sic
+        if sic in self.tagdict:
+            return self.tagdict[sic]
         cdef atom_t[N_FIELDS] context
-        print sizeof(context)
         fill_context(context, i, tokens.data)
+        cdef int n_feats
         cdef Feature* feats = self.extractor.get_feats(context, &n_feats)
         cdef weight_t* scores = self.model.get_scores(feats, n_feats)
-        cdef class_t guess = _arg_max(scores, self.nr_class)
+        guess = _arg_max(scores, self.model.nr_class)
         if golds is not None and guess not in golds:
             best = _arg_max_among(scores, golds)
-            counts = {}
+            counts = {guess: {}, best: {}}
             count_feats(counts[guess], feats, n_feats, -1)
             count_feats(counts[best], feats, n_feats, 1)
             self.model.update(counts)
@@ -145,12 +144,28 @@ cdef class Tagger:
         return tag_id
 
 
-cdef class_t _arg_max(weight_t* scores, int n_classes):
+def _make_tag_dict(counts):
+    freq_thresh = 50
+    ambiguity_thresh = 0.98
+    tagdict = {}
+    cdef atom_t word
+    cdef atom_t tag
+    for word_str, tag_freqs in counts.items():
+        tag_str, mode = max(tag_freqs.items(), key=lambda item: item[1])
+        n = sum(tag_freqs.values())
+        word = int(word_str)
+        tag = int(tag_str)
+        if n >= freq_thresh and (float(mode) / n) >= ambiguity_thresh:
+            tagdict[word] = tag
+    return tagdict
+
+
+cdef class_t _arg_max(weight_t* scores, int n_classes) except 9000:
     cdef int best = 0
     cdef weight_t score = scores[best]
     cdef int i
     for i in range(1, n_classes):
-        if scores[i] > score:
+        if scores[i] >= score:
             score = scores[i]
             best = i
     return best