def train_test(df_train, df_valid, df_test, args, stemmer, sp, folder=None):
print('Producing dataset...')
corpus = Corpus(df_train, df_valid, df_test, args)
print()
print('Batchifying')
if not args.classification:
train_data = batchify(corpus.train, args.batch_size, args.n_ctx)
val_data = batchify(corpus.valid, args.batch_size, args.n_ctx)
test_data = batchify(corpus.test, args.batch_size, args.n_ctx)
if args.POS_tags:
train_pos = batchify(corpus.train_pos, args.batch_size, args.n_ctx)
val_pos = batchify(corpus.valid_pos, args.batch_size, args.n_ctx)
test_pos = batchify(corpus.test_pos, args.batch_size, args.n_ctx)
val_target = None
valid_keywords = None
test_target = None
test_keywords = None
else:
valid_keywords = corpus.valid_keywords
test_keywords = corpus.test_keywords
train_data, train_target = batchify_docs(corpus.train,
corpus.train_target,
args.batch_size)
val_data, val_target = batchify_docs(corpus.valid, corpus.valid_target,
args.batch_size)
test_data, test_target = batchify_docs(corpus.test, corpus.test_target,
1)
if args.POS_tags:
train_pos, _ = batchify_docs(corpus.train_pos, corpus.train_target,
args.batch_size)
val_pos, _ = batchify_docs(corpus.valid_pos, corpus.valid_target,
args.batch_size)
test_pos, _ = batchify_docs(corpus.test_pos, corpus.test_target, 1)
ntokens = len(corpus.dictionary)
print('Vocabulary size: ', ntokens)
args.vocab_size = ntokens
# adaptive softmax / embedding
cutoffs, tie_projs = [], [False]
print("Adaptive softmax: ", args.adaptive)
if args.adaptive:
if not args.bpe:
cutoffs = [20000, 40000, 200000]
else:
cutoffs = [20000, 30000]
tie_projs += [True] * len(cutoffs)
args.cutoffs = cutoffs
args.tie_projs = tie_projs
if args.classification and args.transfer_learning:
model = torch.load(args.language_model_path)
model.head = TransformerHead(model.wte, args)
model.config = args
lm_embeddings = model.wte(torch.arange(
0, args.vocab_size).cuda()).contiguous().detach()
elif args.transfer_learning:
print('Domain adaptation language modelling')
model = torch.load(args.language_model_path)
model.config = args
lm_embeddings = None
else:
model = TransformerModel(args)
lm_embeddings = None
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,
args.max_step,
eta_min=args.eta_min)
best_loss = 9999
best_f = 0
best_model_path = ''
train_step = 0
for epoch in range(args.num_epoch):
print()
print("Epoch: ", epoch + 1, "Num. train batches: ", train_data.size(1))
print()
model.train()
total_loss = 0
total_seq = 0
i = 0
cut = 0
if not args.classification:
cut = args.n_ctx
all_steps = train_data.size(1)
else:
all_steps = train_data.size(0)
while i < all_steps - cut:
if not args.classification:
encoder_words, batch_labels, mask = get_batch(
train_data, i, args, corpus.dictionary.word2idx)
if args.POS_tags:
encoder_pos, _, _ = get_batch(train_pos, i, args,
corpus.dictionary.word2idx,
mask)
#print("iNPUT SIZE: ", encoder_words.size(), decoder_words.size())
else:
encoder_words, batch_labels = get_batch_docs(
train_data,
train_target,
i,
)
#batch_labels[batch_labels > 1] = 2
if args.POS_tags:
encoder_pos, _ = get_batch_docs(
train_pos,
train_target,
i,
)
#print("iNPUT SIZE: ", encoder_words.size(), decoder_words.size())
mask = None
if not args.POS_tags:
encoder_pos = None
optimizer.zero_grad()
loss = model(encoder_words,
input_pos=encoder_pos,
lm_labels=batch_labels,
embeddings=None,
masked_idx=mask)
loss = loss.float().mean().type_as(loss)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
train_step += 1
scheduler.step()
if args.classification:
report_step = 32
else:
report_step = 10240
if train_step % report_step == 0:
print("Learning rate: ", optimizer.param_groups[0]['lr'])
if not args.classification:
i += args.n_ctx
total_loss += batch_labels.size(0) * loss.item()
total_seq += batch_labels.size(0)
else:
i += 1
total_loss += loss.item()
total_seq += 1
if i % report_step == 0:
print('Step: ', i, ' loss: ', total_loss / total_seq)
#Validation
print()
print('Validating')
print()
if not args.POS_tags:
val_pos = None
total_loss, total_seq, total_pred, total_true = test(
model, val_data, val_pos, val_target, corpus, args, stemmer,
valid_keywords, lm_embeddings, sp)
total_loss = total_loss / total_seq
print("Total loss, total seq: ", total_loss, total_seq)
print("Val shape: ", val_data.size())
if args.classification:
print('Validating on ', folder)
#with open('russian_preds.pickle', 'wb') as file:
# pickle.dump((total_pred, total_true), file)
p_5, r_5, f_5, p_10, r_10, f_10, p_k, r_k, f_k, p_M, r_M, f_M = eval(
total_pred, total_true, lang=args.lang)
score = str(total_loss)
else:
perplexity = math.exp(total_loss)
score = str(perplexity)[:6]
print("Validation loss: ", total_loss)
print("Validation set perplexity: ", perplexity)
if not args.classification:
if total_loss < best_loss:
path = os.path.join(
args.trained_language_models_dir, args.lm_id + "_perp_" +
score + "_epoch_" + str(epoch + 1) + ".pt")
with open(path, 'wb') as f:
print('Saving model')
torch.save(model, f)
#delete all models but the best
if best_model_path:
if os.path.isfile(best_model_path):
os.remove(best_model_path)
best_model_path = path
best_loss = total_loss
else:
if f_10 > best_f:
path = os.path.join(
args.trained_classification_models_dir,
args.output_path + "_folder_" + folder + "_loss_" + score +
"_epoch_" + str(epoch + 1) + ".pt")
#if folder not in ['duc', 'nus']:
with open(path, 'wb') as f:
print('Saving model')
torch.save(model, f)
# delete all models but the best
if best_model_path:
if os.path.isfile(best_model_path):
os.remove(best_model_path)
best_model_path = path
best_f = f_10
gc.collect()
del model
del optimizer
del scheduler
model = torch.load(best_model_path)
num_parameters = str(count_parameters(model))
print()
print('Testing on test set')
print()
if not args.POS_tags:
test_pos = None
total_loss, total_seq, total_pred, total_true = test(
model, test_data, test_pos, test_target, corpus, args, stemmer,
test_keywords, lm_embeddings, sp)
total_loss = total_loss / total_seq
gc.collect()
del model
if not args.classification:
perplexity = math.exp(total_loss)
print("Test loss: ", total_loss)
print("Test set perplexity: ", perplexity)
return None
else:
print()
print(
'------------------------------------------------------------------------------------------------------------------'
)
print()
print('Testing on ', folder)
#classification_models = os.listdir(args.trained_classification_models_dir)
#for m in classification_models:
# clas_model_path = os.path.join(args.trained_classification_models_dir, m)
# os.remove(clas_model_path)
return total_pred, total_true, num_parameters
return ";".join(preprocessed_kws)
except:
return ''
if __name__ == '__main__':
df = file_to_df('data/croatian/croatian_test.json')
df_preds = pd.read_csv('predictions/croatian_5_lm+bpe+rnn_croatian_big.csv', sep=',', encoding='utf8')
df_all = pd.concat([df, df_preds], axis=1)
df_all = df_all.rename(columns={"True": "keywords_in_text", "Predicted": "predicted"})
df = df.applymap(str)
df_all['keywords_in_text'] = df_all['keywords_in_text'].map(lambda x: preprocess(x))
df_all['keywords'] = df_all['keywords'].map(lambda x: preprocess(x))
df_all['predicted'] = df_all['predicted'].map(lambda x: preprocess(x))
df_all = df_all[['keywords', 'keywords_in_text', 'predicted', "title", "abstract"]]
true = df_all['keywords_in_text'].tolist()
true = [x.split(';') for x in true]
predicted = df_all['predicted'].tolist()
predicted = [x.split(';') for x in predicted]
print(true[:500])
p_5, r_5, f_5, p_10, r_10, f_10, p_k, r_k, f_k, p_M, r_M, f_M = eval(predicted, true, lang='croatian')
df_all.to_csv("croatian_predictions.csv", sep=',', encoding="utf8", index=False)
'''df = pd.read_csv('predictions/croatian_predictions_check.csv', sep=',', encoding='utf8')
df = df.applymap(str)
predicted = df['predicted'].tolist()
predicted = [x.split(';') for x in predicted]
true = df['keywords_in_text'].tolist()
true = [x.split(';') for x in true]
p_5, r_5, f_5, p_10, r_10, f_10, p_k, r_k, f_k, p_M, r_M, f_M = eval(predicted, true, lang='croatian')'''
def run_model(batch_size, learning_rate, n_ctx, n_head, n_embd, n_layer,
adaptive, bpe, masked_lm, classification, bpe_model_path,
datasets, lm_corpus_file, transfer_learning, pos_tags, dict_path,
rnn, crf, lm_id, output_path):
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=batch_size)
parser.add_argument("--length", type=int, default=-1)
parser.add_argument("--temperature", type=int, default=1)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument('--unconditional',
action='store_true',
help='If true, unconditional generation.')
parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
parser.add_argument('--lr_warmup', type=float, default=0.002)
parser.add_argument('--lr', type=float, default=learning_rate)
parser.add_argument('--b1', type=float, default=0.9)
parser.add_argument('--b2', type=float, default=0.999)
parser.add_argument('--e', type=float, default=1e-8)
parser.add_argument('--l2', type=float, default=0.01)
parser.add_argument('--vector_l2', action='store_true')
parser.add_argument('--max_grad_norm', type=int, default=1)
parser.add_argument("--initializer_range", type=float, default=0.02)
parser.add_argument("--layer_norm_epsilon", type=float, default=1e-6)
parser.add_argument("--n_ctx", type=int, default=n_ctx)
parser.add_argument("--n_positions", type=int, default=n_ctx)
parser.add_argument("--n_embd", type=int, default=n_embd)
parser.add_argument("--n_head", type=int, default=n_head)
parser.add_argument("--n_layer", type=int, default=n_layer)
parser.add_argument("--max_vocab_size",
type=int,
default=0,
help='Zero means no limit.')
parser.add_argument('--max_step',
type=int,
default=100000,
help='upper epoch limit')
parser.add_argument('--eta_min',
type=float,
default=0.0,
help='min learning rate for cosine scheduler')
parser.add_argument('--clip',
type=float,
default=0.25,
help='gradient clipping')
parser.add_argument('--kw_cut',
type=int,
default=10,
help='Precison and recall @')
parser.add_argument("--num_epoch", type=int, default=10)
parser.add_argument('--data_path', type=str, default='data')
parser.add_argument('--result_path',
type=str,
default='results_512_sorted_big.txt')
parser.add_argument('--adaptive',
action='store_true',
help='If true, use adaptive softmax.')
parser.add_argument('--bpe',
action='store_true',
help='If true, use byte pair encoding.')
parser.add_argument(
'--masked_lm',
action='store_true',
help=
'If true, use masked language model objective for pretraining instead of regular language model.'
)
parser.add_argument('--transfer_learning',
action='store_true',
help='If true, use a pretrained language model.')
parser.add_argument('--POS_tags', action='store_true', help='POS tags')
parser.add_argument('--classification',
action='store_true',
help='If true, train a classifier.')
parser.add_argument(
'--rnn',
action='store_true',
help='If true, use a RNN with attention in classification head.')
parser.add_argument(
'--crf',
action='store_true',
help=
'If true, use CRF instead of costum loss function in classification head.'
)
parser.add_argument('--bpe_model_path', type=str, default=bpe_model_path)
parser.add_argument('--datasets', type=str, default=datasets)
parser.add_argument('--lm_corpus_file', type=str, default=lm_corpus_file)
parser.add_argument('--trained_language_models_dir',
type=str,
default='trained_language_models')
parser.add_argument('--trained_classification_models_dir',
type=str,
default='trained_classification_models')
parser.add_argument('--dict_path',
type=str,
default=dict_path,
help='Path to dictionary')
parser.add_argument('--lang',
type=str,
default='english',
help='Path to dictionary')
parser.add_argument('--lm_id',
type=str,
default=lm_id,
help='Path to language model')
parser.add_argument('--output_path',
type=str,
default=output_path,
help='Output designator')
parser.add_argument('--cuda',
action='store_false',
help='If true, use gpu.')
args = parser.parse_args()
args.adaptive = adaptive
args.classification = classification
args.transfer_learning = transfer_learning
args.POS_tags = pos_tags
args.bpe = bpe
args.masked_lm = masked_lm
args.rnn = rnn
args.crf = crf
args.cuda = True
if not os.path.exists(args.trained_classification_models_dir):
os.makedirs(args.trained_classification_models_dir)
if not os.path.exists(args.trained_language_models_dir):
os.makedirs(args.trained_language_models_dir)
if args.bpe:
sp = spm.SentencePieceProcessor()
sp.Load(args.bpe_model_path)
else:
sp = None
if args.crf:
assert not args.rnn
if args.rnn:
assert not args.crf
if args.classification:
assert args.trained_classification_models_dir != args.trained_language_models_dir
assert not args.adaptive
if args.transfer_learning:
l_models = os.listdir(args.trained_language_models_dir)
for l_model in l_models:
if args.lm_id in l_model:
args.language_model_path = os.path.join(
args.trained_language_models_dir, l_model)
print('Classification, using language model: ',
args.language_model_path)
print()
if not args.transfer_learning:
assert not os.path.exists(args.dict_path)
print(args)
if args.lang == 'english':
stemmer = PorterStemmer()
elif args.lang == 'estonian':
stemmer = Lemmatizer('et')
elif args.lang == 'croatian':
stemmer = Lemmatizer('hr')
elif args.lang == 'russian':
stemmer = Lemmatizer('ru')
np.random.seed(args.seed)
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
if not args.classification:
df_data = file_to_df(os.path.join(args.data_path, args.lm_corpus_file),
classification=False)
df_data = df_data.sample(frac=1, random_state=2019)
val_idx = int(0.8 * df_data.shape[0])
test_idx = int(0.9 * df_data.shape[0])
df_train = df_data[:val_idx]
df_valid = df_data[val_idx:test_idx]
df_test = df_data[test_idx:]
print(
'------------------------------------------------------------------------------------------------------'
)
print('Training language model on all data')
print("Train size: ", df_train.shape, "Valid size: ", df_valid.shape,
"Test size: ", df_test.shape)
print(
'------------------------------------------------------------------------------------------------------'
)
print()
train_test(df_train, df_valid, df_test, args, stemmer, sp)
else:
result_file = open(args.result_path, 'a', encoding='utf8')
result_file.write("Classification results using language model " +
args.lm_id + " and config " + args.output_path +
":\n\n")
result_file.write("Parameters:\n")
result_file.write(
str(args) + '\n------------------------------------------------\n')
for folder in args.datasets.split(';'):
print(
'------------------------------------------------------------------------------------------------------'
)
print('Training on: ', folder)
print(
'------------------------------------------------------------------------------------------------------'
)
if folder == 'duc' or folder == 'nus':
#cross validation
kf = model_selection.KFold(n_splits=10)
df_data = file_to_df(os.path.join(args.data_path, folder,
folder + '_test.json'),
classification=True)
df_data = df_data.sample(frac=1, random_state=2019)
print()
print('Cross validation on duc')
fold_counter = 0
total_pred = []
total_true = []
for train_index, test_index in kf.split(df_data):
fold_counter += 1
df_train, df_test = df_data.iloc[
train_index], df_data.iloc[test_index]
sep_idx = int(df_train.shape[0] / 10)
df_valid = df_train[:sep_idx]
df_train = df_train[sep_idx:]
print("Train fold ", fold_counter, "fold size: ",
df_train.shape, "Valid fold size: ", df_valid.shape,
"Test fold size: ", df_test.shape)
print()
fold_pred, fold_true, num_parameters = train_test(
df_train, df_valid, df_test, args, stemmer, sp, folder)
total_pred.extend(fold_pred)
total_true.extend(fold_true)
print()
print(
'--------------------------------------------------------------------'
)
print('Final CV results:')
print()
else:
df_train = file_to_df(os.path.join(args.data_path, folder,
folder + '_valid.json'),
classification=True)
df_train = df_train.sample(frac=1, random_state=2019)
val_idx = int(0.8 * df_train.shape[0])
df_valid = df_train[val_idx:]
df_train = df_train[:val_idx]
df_test = file_to_df(os.path.join(args.data_path, folder,
folder + '_test.json'),
classification=True)
print("Train size: ", df_train.shape, "Valid size: ",
df_valid.shape, "Test size: ", df_test.shape)
print()
total_pred, total_true, num_parameters = train_test(
df_train, df_valid, df_test, args, stemmer, sp, folder)
p_5, r_5, f_5, p_10, r_10, f_10, p_k, r_k, f_k, p_M, r_M, f_M = eval(
total_pred, total_true, lang=args.lang)
result_file.write("Dataset: " + folder + '\n')
result_file.write('Precision@5: ' + str(p_5) + ' Recall@5: ' +
str(r_5) + ' F1@5: ' + str(f_5) + '\n')
result_file.write('Precision@10: ' + str(p_10) + ' Recall@10: ' +
str(r_10) + ' F1@10: ' + str(f_10) + '\n')
result_file.write('Precision@k: ' + str(p_k) + ' Recall@k: ' +
str(r_k) + ' F1@k: ' + str(f_k) + '\n')
result_file.write('Precision@M: ' + str(p_M) + ' Recall@M: ' +
str(r_M) + ' F1@M: ' + str(f_M) + '\n')
result_file.write('Num. trainable parameters: ' +
str(num_parameters) + '\n')
outputs = []
for pred, true in zip(total_pred, total_true):
pred = ";".join(list(pred))
true = ";".join(list(true))
outputs.append((pred, true))
df_preds = pd.DataFrame(outputs, columns=['Predicted', 'True'])
df_preds.to_csv('predictions/' + folder + '_' + args.output_path +
'.csv',
sep=',',
encoding='utf8')
result_file.write(
"\n-----------------------------------------------------------\n")
result_file.write(
"\n-----------------------End of the run----------------------\n")
result_file.write(
"\n-----------------------------------------------------------\n")
result_file.close()
R@10: 0.004109407491250351
F1@10: 0.0015
P@k: 0.0001558337103503745
R@k: 7.346998751010212e-05
F1@k: 0.0001
P@M: 0.0009441583902449534
R@M: 0.004109407491250351
F1@M: 0.0015
textrank croatian
P@5: 0.00015658641612840087
R@5: 0.0002185685391792262
F1@5: 0.0002
P@10: 0.001990729089965731
R@10: 0.006108734353009162
F1@10: 0.003
P@k: 3.914660403210022e-05
R@k: 3.914660403210022e-05
F1@k: 0.0
P@M: 0.001990729089965731
R@M: 0.006108734353009162
F1@M: 0.003
-----------------------------------------------------------------------
'''
eval(all_preds, all_true, lang=args.lang)
