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Fix train command line args

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Matthew Honnibal 2017-05-22 10:41:39 -05:00
parent a7ee63c0ac
commit 6e8dce2c05
1 changed files with 17 additions and 30 deletions
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47
spacy/cli/train.py
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@ -53,17 +53,18 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
if no_entities and 'entities' in pipeline: pipeline.remove('entities') if no_entities and 'entities' in pipeline: pipeline.remove('entities')
nlp = lang_class(pipeline=pipeline) nlp = lang_class(pipeline=pipeline)
corpus = GoldCorpus(train_path, dev_path) corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
dropout = util.env_opt('dropout', 0.0) dropout = util.env_opt('dropout', 0.0)
dropout_decay = util.env_opt('dropout_decay', 0.0) dropout_decay = util.env_opt('dropout_decay', 0.0)
orig_dropout = dropout
optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu) optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu)
n_train_docs = corpus.count_train() n_train_docs = corpus.count_train()
batch_size = float(util.env_opt('min_batch_size', 4)) batch_size = float(util.env_opt('min_batch_size', 4))
max_batch_size = util.env_opt('max_batch_size', 64) max_batch_size = util.env_opt('max_batch_size', 64)
batch_accel = util.env_opt('batch_accel', 1.001) batch_accel = util.env_opt('batch_accel', 1.001)
print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %") print("Itn.\tDep. Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
for i in range(n_iter): for i in range(n_iter):
with tqdm.tqdm(total=n_train_docs) as pbar: with tqdm.tqdm(total=n_train_docs) as pbar:
train_docs = corpus.train_docs(nlp, shuffle=i, projectivize=True) train_docs = corpus.train_docs(nlp, shuffle=i, projectivize=True)
@ -77,8 +78,8 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
pbar.update(len(docs)) pbar.update(len(docs))
idx += len(docs) idx += len(docs)
batch_size *= batch_accel batch_size *= batch_accel
batch_size = min(int(batch_size), max_batch_size) batch_size = min(batch_size, max_batch_size)
dropout = linear_decay(dropout, dropout_decay, i*n_train_docs+idx) dropout = linear_decay(orig_dropout, dropout_decay, i*n_train_docs+idx)
with nlp.use_params(optimizer.averages): with nlp.use_params(optimizer.averages):
scorer = nlp.evaluate(corpus.dev_docs(nlp)) scorer = nlp.evaluate(corpus.dev_docs(nlp))
print_progress(i, {}, scorer.scores) print_progress(i, {}, scorer.scores)
@ -97,38 +98,24 @@ def _render_parses(i, to_render):
file_.write(html) file_.write(html)
def evaluate(Language, gold_tuples, path):
with (path / 'model.bin').open('rb') as file_:
nlp = dill.load(file_)
# TODO:
# 1. This code is duplicate with spacy.train.Trainer.evaluate
# 2. There's currently a semantic difference between pipe and
# not pipe! It matters whether we batch the inputs. Must fix!
all_docs = []
all_golds = []
for raw_text, paragraph_tuples in dev_sents:
if gold_preproc:
raw_text = None
else:
paragraph_tuples = merge_sents(paragraph_tuples)
docs = self.make_docs(raw_text, paragraph_tuples)
golds = self.make_golds(docs, paragraph_tuples)
all_docs.extend(docs)
all_golds.extend(golds)
scorer = Scorer()
for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
scorer.score(doc, gold)
return scorer
def print_progress(itn, losses, dev_scores): def print_progress(itn, losses, dev_scores):
# TODO: Fix! # TODO: Fix!
scores = {} scores = {}
for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']: for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
'ents_p', 'ents_r', 'ents_f']:
scores[col] = 0.0 scores[col] = 0.0
scores.update(losses) scores.update(losses)
scores.update(dev_scores) scores.update(dev_scores)
tpl = '{:d}\t{dep_loss:.3f}\t{tag_loss:.3f}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}' tpl = '\t'.join((
'{:d}',
'{dep_loss:.3f}',
'{tag_loss:.3f}',
'{uas:.3f}',
'{ents_p:.3f}',
'{ents_r:.3f}',
'{ents_f:.3f}',
'{tags_acc:.3f}',
'{token_acc:.3f}'))
print(tpl.format(itn, **scores)) print(tpl.format(itn, **scores))