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run_crf.py
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run_crf.py
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import os
import logging
from typing import List
import scipy.stats
import sklearn_crfsuite
from sklearn.externals import joblib
from pymongo import MongoClient
from sacred.observers import MongoObserver
from sacred import Experiment
from sacred.run import Run
from ingredients.crf_utils import get_tagged_sents_and_words
from ingredients.crf_utils import sent2features
from ingredients.crf_utils import sent2labels
from ingredients.crf_utils import evaluate
SECRET = os.environ.get('SACRED_KEY', None)
MONGOL = f'mongodb://<user>:<SECRET>@<uri>:<port>/<dbname>'
ex = Experiment('run_crf')
client = MongoClient(MONGOL)
ex.observers.append(MongoObserver.create(
url=MONGOL, db_name='dbname'))
db = client['<dbname>']
runs = db['runs']
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(levelname)s - %(name)s - %(message)s'))
logger.addHandler(handler)
ex.logger = logger
@ex.config
def default_config():
train_files = ['train.conll']
dev_files = ['dev.conll']
test_files = ['test.conll']
dirpath = '/home/<user>/workspace/dbpedia/dee'
num_experiments = 30
num_window_sizes = 4
retry_limit = 10
@ex.named_config
def cross_val_config():
train_files = [f'train-{i}.conll' for i in range(5)]
dev_files = [f'test-{i}.conll' for i in range(5)]
dirpath = '/home/<user>/workspace/dbpedia/dee'
num_experiments = 30
num_window_sizes = 5
retry_limit = 10
@ex.command
def train(train_corpus: str, dev_corpus: str,
c1: float = 0.0, c2: float = 0.0, algorithm: str = 'lbfgs',
max_iterations: int = 100, all_possible_transitions: bool = False,
window_size: int = 1, model_filename: str=None,
_run: Run = None, _log: logger = None):
"""
running crf experiment
"""
_run.add_resource(train_corpus)
_run.add_resource(dev_corpus)
train_sents, _ = get_tagged_sents_and_words(train_corpus)
dev_sents, _ = get_tagged_sents_and_words(dev_corpus)
X_train = [sent2features(s, window_size) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_dev = [sent2features(s, window_size) for s in dev_sents]
y_dev = [sent2labels(s) for s in dev_sents]
crf = sklearn_crfsuite.CRF(
algorithm = algorithm,
c1 = c1,
c2 = c2,
max_iterations = max_iterations,
all_possible_transitions= all_possible_transitions,
model_filename=model_filename,
)
crf.fit(X_train, y_train)
y_pred = crf.predict(X_dev)
overall, by_type = evaluate(y_dev, y_pred)
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')
if model_filename is not None:
_log.info(f'saving to: {model_filename}.pkl')
joblib.dump(crf, f'{model_filename}.pkl')
_run.add_artifact(f'{model_filename}.pkl')
@ex.command
def test(model_filename: str, test_corpus: str, window_size: int = 5,
_run: Run = None, _log: logger = None):
_run.add_resource(test_corpus)
_run.add_resource(f'{model_filename}.pkl')
test_sents, _ = get_tagged_sents_and_words(test_corpus)
X_test = [sent2features(s, window_size) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
_log.info(f'load from: {model_filename}.pkl')
crf = sklearn_crfsuite.CRF(
model_filename=model_filename
)
y_pred = crf.predict(X_test)
overall, by_type = evaluate(y_test, y_pred)
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')
@ex.command
def hyperparams(train_files: List[str],
dev_files: List[str],
dirpath: str, num_experiments: int = 30,
num_window_sizes: int = 5,
retry_limit: int = 100,
_run: Run = None, _log: logger = None):
"""
run hyperparameter optimization experiments
"""
# absolute paths for all training sets
train_corpora = [os.path.join(dirpath, t) for t in train_files]
# absolute paths for all dev sets
dev_corpora = [os.path.join(dirpath, t) for t in dev_files]
# absolute paths for all test sets
for i, _ in enumerate(train_corpora):
configs = []
c1_space = scipy.stats.expon(scale=0.5)
c2_space = scipy.stats.expon(scale=0.05)
for _ in range(num_experiments):
c1 = c1_space.rvs()
c2 = c2_space.rvs()
for window_size in range(0, num_window_sizes):
configs.append({
'train_corpus': train_corpora[i],
'dev_corpus': dev_corpora[i],
'c1': c1,
'c2': c2,
'window_size': window_size
})
current_idx = 0
current_ret = 0
while current_idx < len(configs) and current_ret < retry_limit:
try:
logger.info(f'Run {current_idx + 1} of {len(configs)}')
logger.info(f'Config: {configs[current_idx]}')
r = ex.run_command(command_name='train', config_updates=configs[current_idx])
current_idx += 1
except KeyboardInterrupt:
logger.info('Experiment aborted')
exit()
except RuntimeError:
if current_ret == retry_limit - 1:
logging.error('RETRY LIMIT EXCEEDED!, Experiment Failed')
break
else:
logger.warning(f'Run failed, will keep retrying {current_ret} of {retry_limit}')
current_ret += 1
except Exception:
break
@ex.automain
def main():
print('crf experiment main command.')