From 35124b144a4b25f8377fcbbf0ab32fbffc3320eb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal@gmail.com>
Date: Thu, 9 Mar 2017 18:44:53 -0600
Subject: [PATCH] Add L1 penalty option to parser

---
 bin/parser/train.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/bin/parser/train.py b/bin/parser/train.py
index 24484f7cf..26b545b6d 100755
--- a/bin/parser/train.py
+++ b/bin/parser/train.py
@@ -66,7 +66,7 @@ def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
 
 def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
         n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
-    print("Itn.\tP.Loss\tN feats\tUAS\tNER F.\tTag %\tToken %")
+    print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
     format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
     with Language.train(model_dir, train_data,
             tagger_cfg, parser_cfg, entity_cfg) as trainer:
@@ -76,12 +76,13 @@ def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, ent
             for doc, gold in epoch:
                 trainer.update(doc, gold)
             dev_scores = trainer.evaluate(dev_data, gold_preproc=gold_preproc)
-            print(format_str.format(itn, loss,
+            print(format_str.format(itn, trainer.nlp.parser.model.nr_weight,
                 trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
 
 
 def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
              beam_width=None, cand_preproc=None):
+    print("Load parser", model_dir)
     nlp = Language(path=model_dir)
     if nlp.lang == 'de':
         nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
@@ -146,22 +147,25 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
     verbose=("Verbose error reporting", "flag", "v", bool),
     debug=("Debug mode", "flag", "d", bool),
     pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
+    L1=("L1 regularization penalty", "option", "L", float),
 )
 def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
-         debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False):
+         debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False,
+         L1=1e-6):
     parser_cfg = dict(locals())
     tagger_cfg = dict(locals())
     entity_cfg = dict(locals())
 
     lang = spacy.util.get_lang_class(language)
-    
+
     parser_cfg['features'] = lang.Defaults.parser_features
     entity_cfg['features'] = lang.Defaults.entity_features
 
     if not eval_only:
         gold_train = list(read_json_file(train_loc))
         gold_dev = list(read_json_file(dev_loc))
-        gold_train = gold_train[:n_sents]
+        if n_sents > 0:
+            gold_train = gold_train[:n_sents]
         train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
               n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
               n_iter=n_iter)