explosion/spaCy
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This commit is contained in:
Ines Montani 2019-04-22 14:31:11 +02:00
parent 1d567913f9
commit 9767427669
1 changed files with 20 additions and 12 deletions
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32
spacy/cli/train.py
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@ -226,7 +226,7 @@ def train(
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
@ -335,8 +335,8 @@ def train(
gpu_wps=gpu_wps,
)
msg.row(progress, **row_settings)
# early stopping
if early_stopping_iter is not None:
# Early stopping
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
@ -344,8 +344,14 @@ def train(
iter_since_best = 0
best_score = current_score
if iter_since_best >= early_stopping_iter:
msg.text("Early stopping, best iteration is: {}".format(i-iter_since_best))
msg.text("Best score = {}; Final iteration score = {}".format(best_score, current_score))
msg.text(
"Early stopping, best iteration "
"is: {}".format(i - iter_since_best)
)
msg.text(
"Best score = {}; Final iteration "
"score = {}".format(best_score, current_score)
)
break
finally:
with nlp.use_params(optimizer.averages):
@ -356,19 +362,21 @@ def train(
best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
msg.good("Created best model", best_model_path)
def _score_for_model(meta):
""" Returns mean score between tasks in pipeline that can be used for early stopping. """
mean_acc = list()
pipes = meta['pipeline']
acc = meta['accuracy']
if 'tagger' in pipes:
mean_acc.append(acc['tags_acc'])
if 'parser' in pipes:
mean_acc.append((acc['uas']+acc['las']) / 2)
if 'ner' in pipes:
mean_acc.append((acc['ents_p']+acc['ents_r']+acc['ents_f']) / 3)
pipes = meta["pipeline"]
acc = meta["accuracy"]
if "tagger" in pipes:
mean_acc.append(acc["tags_acc"])
if "parser" in pipes:
mean_acc.append((acc["uas"] + acc["las"]) / 2)
if "ner" in pipes:
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
return sum(mean_acc) / len(mean_acc)
@contextlib.contextmanager
def _create_progress_bar(total):
if int(os.environ.get("LOG_FRIENDLY", 0)):