def baseline_predict(instances):
predictions = []
with open(Config.raw_test_set(), "w") as fp:
for instance in instances:
fp.write("%s\n" % json.dumps(instance))
pipeline.main()
with open(Config.submission_file(), "r") as fp:
for line in fp:
predictions.append(json.loads(line.strip()))
return predictions
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, 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_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)
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_claim_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)
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_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_claim_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, 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’")
return estimator