threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_sentence_size,
is_snopes=is_snopes)
# del fasttext_model
h_sent_sizes = test_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
test_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
test_set['data']['h_sizes'] = h_sizes
test_set['data']['h_np'] = np.expand_dims(test_set['data']['h_np'], 1)
# test_set['data']['h_ft_np'] = np.expand_dims(test_set['data']['h_ft_np'], 1)
x_dict = {'X_test': test_set['data'], 'embedding': embeddings}
predictions = estimator.predict(x_dict)
generate_submission(predictions, Config.test_set_file,
Config.submission_file)
if 'label' in test_set:
print_metrics(test_set['label'], predictions, logger)
elif args.mode == 'test2':
estimator = get_estimator(Config.estimator_name, Config.ckpt_folder)
vocab, embeddings = load_whole_glove(Config.glove_path)
vocab = vocab_map(vocab)
# test_set, _, _, _, _, _ = embed_data_set_with_glove_and_fasttext(Config.test_set_file, Config.db_path,
# fasttext_model, vocab_dict=vocab,
# glove_embeddings=embeddings,
# threshold_b_sent_num=Config.max_sentences,
# threshold_b_sent_size=Config.max_sentence_size,
# threshold_h_sent_size=Config.max_sentence_size,
# is_snopes=is_snopes)
test_set, _, _, _, _ = embed_data_set_with_glove_2(
Config.test_set_file,
Config.db_path,
vocab_dict=vocab,
def main(mode, config, estimator=None):
LogHelper.setup()
logger = LogHelper.get_logger(os.path.splitext(os.path.basename(__file__))[0] + "_" + mode)
logger.info("model: " + mode + ", config: " + str(config))
logger.info("scorer type: " + Config.estimator_name)
logger.info("random seed: " + str(Config.seed))
logger.info("ESIM arguments: " + str(Config.esim_hyper_param))
# loading FastText takes a long time, so better pickle the loaded FastText model
if os.path.splitext(Config.fasttext_path)[1] == '.p':
with open(Config.fasttext_path, "rb") as ft_file:
fasttext_model = pickle.load(ft_file)
else:
fasttext_model = Config.fasttext_path
if mode == 'train':
# # training mode
training_set, fasttext_model, vocab, embeddings, _, _ = embed_data_set_with_glove_and_fasttext(
Config.training_set_file, Config.db_path(), fasttext_model, glove_path=Config.glove_path,
threshold_b_sent_num=Config.max_sentences, threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_claim_size)
h_sent_sizes = training_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
training_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
training_set['data']['h_sizes'] = h_sizes
training_set['data']['h_np'] = np.expand_dims(training_set['data']['h_np'], 1)
training_set['data']['h_ft_np'] = np.expand_dims(training_set['data']['h_ft_np'], 1)
valid_set, _, _, _, _, _ = embed_data_set_with_glove_and_fasttext(Config.dev_set_file, Config.db_path(),
fasttext_model, vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_b_sent_num=Config.max_sentences,
threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_claim_size)
del fasttext_model
h_sent_sizes = valid_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
valid_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
valid_set['data']['h_sizes'] = h_sizes
valid_set['data']['h_np'] = np.expand_dims(valid_set['data']['h_np'], 1)
valid_set['data']['h_ft_np'] = np.expand_dims(valid_set['data']['h_ft_np'], 1)
X_dict = {
'X_train': training_set['data'],
'X_valid': valid_set['data'],
'y_valid': valid_set['label'],
'embedding': embeddings
}
if estimator is None:
estimator = get_estimator(Config.estimator_name, Config.ckpt_folder)
estimator.fit(X_dict, training_set['label'])
save_model(estimator, Config.model_folder, Config.pickle_name, logger)
elif mode == 'test':
# testing mode
restore_param_required = estimator is None
if estimator is None:
estimator = load_model(Config.model_folder, Config.pickle_name)
if estimator is None:
estimator = get_estimator(Config.estimator_name, Config.ckpt_folder)
vocab, embeddings = load_whole_glove(Config.glove_path)
vocab = vocab_map(vocab)
test_set, _, _, _, _, _ = embed_data_set_with_glove_and_fasttext(Config.test_set_file, Config.db_path(),
fasttext_model, vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_b_sent_num=Config.max_sentences,
threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_claim_size)
del fasttext_model
h_sent_sizes = test_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
test_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
test_set['data']['h_sizes'] = h_sizes
test_set['data']['h_np'] = np.expand_dims(test_set['data']['h_np'], 1)
test_set['data']['h_ft_np'] = np.expand_dims(test_set['data']['h_ft_np'], 1)
x_dict = {
'X_test': test_set['data'],
'embedding': embeddings
}
predictions = estimator.predict(x_dict, restore_param_required)
generate_submission(predictions, test_set['id'], Config.test_set_file, Config.submission_file())
if 'label' in test_set:
print_metrics(test_set['label'], predictions, logger)
else:
logger.error("Invalid argument --mode: " + mode + " Argument --mode should be either 'train’ or ’test’")
def main(mode: RTERunPhase, config=None, estimator=None):
LogHelper.setup()
logger = LogHelper.get_logger(
os.path.splitext(os.path.basename(__file__))[0] + "_" + str(mode))
if config is not None and isinstance(config, str):
logger.info("model: " + str(mode) + ", config: " + str(config))
Config.load_config(config)
logger.info("scorer type: " + Config.estimator_name)
logger.info("random seed: " + str(Config.seed))
logger.info("ESIM arguments: " + str(Config.esim_hyper_param))
# loading FastText takes long time, so better pickle the loaded FastText model
if os.path.splitext(Config.fasttext_path)[1] == '.p':
with open(Config.fasttext_path, "rb") as ft_file:
fasttext_model = pickle.load(ft_file)
else:
fasttext_model = Config.fasttext_path
if mode == RTERunPhase.train:
# # training mode
training_set, fasttext_model, vocab, embeddings = embed_data_set_with_glove_and_fasttext_claim_only(
Config.training_set_file,
fasttext_model,
glove_path=Config.glove_path,
threshold_h_sent_size=Config.max_sentence_size)
h_sent_sizes = training_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
training_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
training_set['data']['h_sizes'] = h_sizes
training_set['data']['h_np'] = np.expand_dims(
training_set['data']['h_np'], 1)
training_set['data']['h_ft_np'] = np.expand_dims(
training_set['data']['h_ft_np'], 1)
valid_set, _, _, _ = embed_data_set_with_glove_and_fasttext_claim_only(
Config.dev_set_file,
fasttext_model,
vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_h_sent_size=Config.max_sentence_size)
del fasttext_model
h_sent_sizes = valid_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
valid_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
valid_set['data']['h_sizes'] = h_sizes
valid_set['data']['h_np'] = np.expand_dims(valid_set['data']['h_np'],
1)
valid_set['data']['h_ft_np'] = np.expand_dims(
valid_set['data']['h_ft_np'], 1)
X_dict = {
'X_train': training_set['data'],
'X_valid': valid_set['data'],
'y_valid': valid_set['label'],
'embedding': embeddings
}
if estimator is None:
estimator = get_estimator(Config.estimator_name,
Config.ckpt_folder)
if 'CUDA_VISIBLE_DEVICES' not in os.environ or not str(
os.environ['CUDA_VISIBLE_DEVICES']).strip():
os.environ['CUDA_VISIBLE_DEVICES'] = str(
GPUtil.getFirstAvailable(maxLoad=1.0,
maxMemory=1.0 -
Config.max_gpu_memory)[0])
estimator.fit(X_dict, training_set['label'])
save_model(estimator, Config.model_folder, Config.pickle_name, logger)
elif mode == 'test':
# testing mode
restore_param_required = estimator is None
if estimator is None:
estimator = load_model(Config.model_folder, Config.pickle_name)
if estimator is None:
estimator = get_estimator(Config.estimator_name,
Config.ckpt_folder)
vocab, embeddings = load_whole_glove(Config.glove_path)
vocab = vocab_map(vocab)
test_set, _, _, _ = embed_data_set_with_glove_and_fasttext_claim_only(
Config.test_set_file,
fasttext_model,
vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_h_sent_size=Config.max_sentence_size)
del fasttext_model
h_sent_sizes = test_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
test_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
test_set['data']['h_sizes'] = h_sizes
test_set['data']['h_np'] = np.expand_dims(test_set['data']['h_np'], 1)
test_set['data']['h_ft_np'] = np.expand_dims(
test_set['data']['h_ft_np'], 1)
x_dict = {'X_test': test_set['data'], 'embedding': embeddings}
if 'CUDA_VISIBLE_DEVICES' not in os.environ or not str(
os.environ['CUDA_VISIBLE_DEVICES']).strip():
os.environ['CUDA_VISIBLE_DEVICES'] = str(
GPUtil.getFirstAvailable(maxLoad=1.0,
maxMemory=1.0 -
Config.max_gpu_memory)[0])
predictions = estimator.predict(x_dict, restore_param_required)
generate_submission(predictions, test_set['id'], Config.test_set_file,
Config.submission_file)
if 'label' in test_set:
print_metrics(test_set['label'], predictions, logger)
return estimator
logger.debug("features: " + str(features))
feats_train, feats_indices = generate_features(X_train, scorer_type + "_training", features,
myConstants.features_dir)
feats_valid, _ = generate_features(X_valid, scorer_type + "_valid", features, myConstants.features_dir)
clf = final_clf_training(feats_train, Y_labels_train, feats_valid, Y_labels_valid, scorer_type,
model_parent_folder)
else:
# testing mode
assert args.test is not None, "--test test set should be provided in test mode"
X_train, Y_labels_train = read_data_set_from_jsonl(args.train, args.db, is_snopes=args.is_snopes)
X_test, Y_labels_test = read_data_set_from_jsonl(args.test, args.db, is_snopes=args.is_snopes)
X_train['b'] = _concat_evidences(X_train['b'])
X_test['b'] = _concat_evidences(X_test['b'])
myConstants.d = {'data': X_train, 'label': Y_labels_train}
myConstants.testdataset = {'data': X_test, 'label': Y_labels_test}
for scorer_type, features, _ in myConstants.feature_list:
logger.info("Predict with scorer type " + scorer_type)
predictions = final_clf_prediction(X_test, features, myConstants.features_dir, scorer_type,
model_parent_folder)
os.makedirs(args.save_result, exist_ok=True)
test_result_path = os.path.join(args.save_result,
"predicted.{}.s{}.jsonl".format(scorer_type, args.max_sent))
with open(test_result_path, "w") as result_file:
for i, prediction in enumerate(predictions):
data = {'id': X_test['id'][i], 'predicted': prediction_2_label(prediction)}
if Y_labels_test is not None and len(Y_labels_test) == len(predictions):
data['label'] = prediction_2_label(Y_labels_test[i])
result_file.write(json.dumps(data) + "\n")
if Y_labels_test is not None and len(Y_labels_test) == len(predictions):
print_metrics(Y_labels_test, predictions, logger)
def main(mode, config, estimator=None):
LogHelper.setup()
logger = LogHelper.get_logger(
os.path.splitext(os.path.basename(__file__))[0] + "_" + mode)
logger.info("model: " + mode + ", config: " + str(config))
if hasattr(Config, 'use_inter_evidence_comparison'):
use_inter_evidence_comparison = Config.use_inter_evidence_comparison
else:
use_inter_evidence_comparison = False
if hasattr(Config, 'use_claim_evidences_comparison'):
use_claim_evidences_comparison = Config.use_claim_evidences_comparison
else:
use_claim_evidences_comparison = False
if hasattr(Config, 'use_extra_features'):
use_extra_features = Config.use_extra_features
else:
use_extra_features = False
if hasattr(Config, 'use_numeric_feature'):
use_numeric_feature = Config.use_numeric_feature
else:
use_numeric_feature = False
logger.info("scorer type: " + Config.estimator_name)
logger.info("random seed: " + str(Config.seed))
logger.info("ESIM arguments: " + str(Config.esim_end_2_end_hyper_param))
logger.info("use_inter_sentence_comparison: " +
str(use_inter_evidence_comparison))
logger.info("use_extra_features: " + str(use_extra_features))
logger.info("use_numeric_feature: " + str(use_numeric_feature))
logger.info("use_claim_evidences_comparison: " +
str(use_claim_evidences_comparison))
if mode == 'train':
# # training mode
if hasattr(Config, 'training_dump') and os.path.exists(
Config.training_dump):
with open(Config.training_dump, 'rb') as f:
(X_dict, y_train) = pickle.load(f)
else:
training_set, vocab, embeddings, _, _ = embed_data_set_with_glove_2(
Config.training_set_file,
Config.db_path,
glove_path=Config.glove_path,
threshold_b_sent_num=Config.max_sentences,
threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_sentence_size)
h_sent_sizes = training_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
training_set['data']['h_sent_sizes'] = np.expand_dims(
h_sent_sizes, 1)
training_set['data']['h_sizes'] = h_sizes
training_set['data']['h_np'] = np.expand_dims(
training_set['data']['h_np'], 1)
valid_set, _, _, _, _ = embed_data_set_with_glove_2(
Config.dev_set_file,
Config.db_path,
vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_b_sent_num=Config.max_sentences,
threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_sentence_size)
h_sent_sizes = valid_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
valid_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
valid_set['data']['h_sizes'] = h_sizes
valid_set['data']['h_np'] = np.expand_dims(
valid_set['data']['h_np'], 1)
if use_extra_features:
assert hasattr(
Config, 'feature_path'
), "Config should has feature_path if Config.use_feature is True"
training_claim_features, training_evidence_features = load_feature_by_data_set(
Config.training_set_file, Config.feature_path,
Config.max_sentences)
valid_claim_features, valid_evidence_features = load_feature_by_data_set(
Config.dev_set_file, Config.feature_path,
Config.max_sentences)
training_set['data']['h_feats'] = training_claim_features
training_set['data']['b_feats'] = training_evidence_features
valid_set['data']['h_feats'] = valid_claim_features
valid_set['data']['b_feats'] = valid_evidence_features
if use_numeric_feature:
training_num_feat = number_feature(Config.training_set_file,
Config.db_path,
Config.max_sentences)
valid_num_feat = number_feature(Config.dev_set_file,
Config.db_path,
Config.max_sentences)
training_set['data']['num_feat'] = training_num_feat
valid_set['data']['num_feat'] = valid_num_feat
if use_inter_evidence_comparison:
training_concat_sent_indices, training_concat_sent_sizes = generate_concat_indices_for_inter_evidence(
training_set['data']['b_np'],
training_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
training_set['data'][
'b_concat_indices'] = training_concat_sent_indices
training_set['data'][
'b_concat_sizes'] = training_concat_sent_sizes
valid_concat_sent_indices, valid_concat_sent_sizes = generate_concat_indices_for_inter_evidence(
valid_set['data']['b_np'],
valid_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
valid_set['data'][
'b_concat_indices'] = valid_concat_sent_indices
valid_set['data']['b_concat_sizes'] = valid_concat_sent_sizes
if use_claim_evidences_comparison:
training_all_evidences_indices, training_all_evidences_sizes = generate_concat_indices_for_claim(
training_set['data']['b_np'],
training_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
training_set['data'][
'b_concat_indices_for_h'] = training_all_evidences_indices
training_set['data'][
'b_concat_sizes_for_h'] = training_all_evidences_sizes
valid_all_evidences_indices, valid_all_evidences_sizes = generate_concat_indices_for_claim(
valid_set['data']['b_np'],
valid_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
valid_set['data'][
'b_concat_indices_for_h'] = valid_all_evidences_indices
valid_set['data'][
'b_concat_sizes_for_h'] = valid_all_evidences_sizes
X_dict = {
'X_train': training_set['data'],
'X_valid': valid_set['data'],
'y_valid': valid_set['label'],
'embedding': embeddings
}
y_train = training_set['label']
if hasattr(Config, 'training_dump'):
with open(Config.training_dump, 'wb') as f:
pickle.dump((X_dict, y_train),
f,
protocol=pickle.HIGHEST_PROTOCOL)
if estimator is None:
estimator = get_estimator(Config.estimator_name,
Config.ckpt_folder)
estimator.fit(X_dict, y_train)
save_model(estimator, Config.model_folder, Config.pickle_name, logger)
elif mode == 'test':
# testing mode
restore_param_required = estimator is None
if estimator is None:
estimator = load_model(Config.model_folder, Config.pickle_name)
vocab, embeddings = load_whole_glove(Config.glove_path)
vocab = vocab_map(vocab)
test_set, _, _, _, _ = embed_data_set_with_glove_2(
Config.test_set_file,
Config.db_path,
vocab_dict=vocab,
glove_embeddings=embeddings,
threshold_b_sent_num=Config.max_sentences,
threshold_b_sent_size=Config.max_sentence_size,
threshold_h_sent_size=Config.max_sentence_size)
h_sent_sizes = test_set['data']['h_sent_sizes']
h_sizes = np.ones(len(h_sent_sizes), np.int32)
test_set['data']['h_sent_sizes'] = np.expand_dims(h_sent_sizes, 1)
test_set['data']['h_sizes'] = h_sizes
test_set['data']['h_np'] = np.expand_dims(test_set['data']['h_np'], 1)
if use_extra_features:
assert hasattr(
Config, 'feature_path'
), "Config should has feature_path if Config.use_feature is True"
test_claim_features, test_evidence_features = load_feature_by_data_set(
Config.test_set_file, Config.feature_path,
Config.max_sentences)
test_set['data']['h_feats'] = test_claim_features
test_set['data']['b_feats'] = test_evidence_features
if use_numeric_feature:
test_num_feat = number_feature(Config.test_set_file,
Config.db_path,
Config.max_sentences)
test_set['data']['num_feat'] = test_num_feat
x_dict = {'X_test': test_set['data'], 'embedding': embeddings}
if use_inter_evidence_comparison:
test_concat_sent_indices, test_concat_sent_sizes = generate_concat_indices_for_inter_evidence(
test_set['data']['b_np'], test_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
test_set['data']['b_concat_indices'] = test_concat_sent_indices
test_set['data']['b_concat_sizes'] = test_concat_sent_sizes
if use_claim_evidences_comparison:
test_all_evidences_indices, test_all_evidences_sizes = generate_concat_indices_for_claim(
test_set['data']['b_np'], test_set['data']['b_sent_sizes'],
Config.max_sentence_size, Config.max_sentences)
test_set['data'][
'b_concat_indices_for_h'] = test_all_evidences_indices
test_set['data']['b_concat_sizes_for_h'] = test_all_evidences_sizes
predictions = estimator.predict(x_dict, restore_param_required)
generate_submission(predictions, Config.test_set_file,
Config.submission_file)
if 'label' in test_set:
print_metrics(test_set['label'], predictions, logger)
else:
logger.error("Invalid argument --mode: " + mode +
" Argument --mode should be either 'train’ or ’test’")
return estimator