def generic_predict(p_or_n, tokenized_sents, vocab, clf, use_lstm):
'''
generic_predict()
Train a model that works for both prose and nonprose
@param p_or_n. A string that indicates "prose", "nonprose",
or "all"
@param tokenized_sents. A list of sentences, where each sentence is
tokenized
into words
@param vocab. A dictionary mapping word tokens to numeric indices.
@param clf. An encoding of the trained keras model.
@param use_lstm. Bool indicating whether clf is a CRF or LSTM.
'''
# use_lstm=self._use_lstm
if use_lstm:
parameters = hd.load_parameters_from_file("LSTM_parameters.txt")
parameters['use_pretrained_model'] = True
# model_folder="./models/NN_models"
predictions = []
sys.stdout.write('\n use_lstm \n')
dataset = Exp.Dataset()
fictional_labels = copy.deepcopy(tokenized_sents)
for idx, x in enumerate(fictional_labels):
for val_id, _ in enumerate(x):
fictional_labels[idx][val_id] = 'O'
Datasets_tokens = {}
Datasets_labels = {}
Datasets_tokens['deploy'] = tokenized_sents
Datasets_labels['deploy'] = fictional_labels
token_to_vector = dataset.load_dataset(Datasets_tokens,
Datasets_labels,
"",
parameters,
token_to_vector=None,
pretrained_dataset=None)
print(dataset.token_indices.keys())
parameters['Feature_vector_length'] = dataset.feature_vector_size
parameters['use_features_before_final_lstm'] = False
dataset.update_dataset("", ['deploy'], Datasets_tokens,
Datasets_labels)
del Datasets_tokens
del Datasets_labels
# model=current_model
model = entity_model.EntityLSTM(dataset, parameters)
os.mkdir(parameters['conll_like_result_folder'])
test_temp = os.path.join(parameters['conll_like_result_folder'],
'test/')
train_temp = os.path.join(parameters['conll_like_result_folder'],
'train/')
valid_temp = os.path.join(parameters['conll_like_result_folder'],
'valid/')
os.mkdir(test_temp)
os.mkdir(train_temp)
os.mkdir(valid_temp)
sess = tf.Session()
with sess.as_default():
# model=entity_model.EntityLSTM(dataset,parameters)
transition_params_trained = model.restore_from_pretrained_model(
parameters,
dataset,
sess,
token_to_vector=token_to_vector,
pretrained_dataset=None)
del token_to_vector
predictions = training_predict_LSTM.prediction_step(
sess, dataset, "deploy", model, 0,
parameters['conll_like_result_folder'],
transition_params_trained)
sess.close()
tf.reset_default_graph()
shutil.rmtree(parameters['conll_like_result_folder'])
return predictions, model
# If nothing to predict, skip actual prediction
if len(tokenized_sents) == 0:
sys.stdout.write('\tnothing to predict %s\n' % p_or_n)
return []
sys.stdout.write('\tvectorizing words %s\n' % p_or_n)
if use_lstm:
print('todo: incorporate lstm')
# vectorize tokenized sentences
# X = []
# for sent in tokenized_sents:
# id_seq = []
# for w in sent:
# if w in vocab:
# id_seq.append(vocab[w])
# else:
# id_seq.append(vocab['oov'])
# X.append(id_seq)
else:
# vectorize validation X
text_features = extract_features(tokenized_sents)
flat_X_feats = vocab.transform(flatten(text_features))
X = reconstruct_list(flat_X_feats, save_list_structure(text_features))
sys.stdout.write('\tpredicting labels %s\n' % p_or_n)
# Predict labels
if use_lstm:
print("TEST_PREDICT")
sys.exit()
else:
predictions = crf.predict(clf, X)
# Format labels from output
return predictions
def predict(self):
# Put it here to make sure the existance of the file for glob.
# pylint: disable=attribute-defined-outside-init
self.files = glob.glob(self.txt)
# Must specify output format
if self.format not in ['i2b2']:
sys.stderr.write('\n\tError: Must specify output format\n')
sys.stderr.write('\tAvailable formats: i2b2\n')
sys.stderr.write('\n')
sys.exit()
# Load model
# if use_lstm==False:
with open(self.model_path, 'rb') as f:
Model = pickle.load(f, encoding='latin1')
# pylint: disable=protected-access
if Model._use_lstm:
parameters = helper_dataset.load_parameters_from_file(
"LSTM_parameters.txt")
parameters['use_pretrained_model'] = True
temp_pretrained_dataset_adress = parameters[
'model_folder'] + os.sep + \
"dataset.pickle"
# pylint: disable=protected-access
Model._pretrained_dataset = pickle.load(
open(temp_pretrained_dataset_adress, 'rb'))
Model._pretrained_wordvector = \
helper_dataset.load_pretrained_token_embeddings(
parameters)
Model._current_model = None
print("END TEST")
# exit()
# model.parameters=None
# Tell user if not predicting
if not self.files:
sys.stderr.write(
"\n\tNote: You did not supply any input files\n\n")
sys.exit()
n = len(self.files)
for i, txt in enumerate(sorted(self.files)):
note = Document(txt)
# Output file
fname = os.path.splitext(os.path.basename(txt))[0] + '.' + 'con'
out_path = os.path.join(self.output, fname)
# '''
if os.path.exists(out_path):
print('\tWARNING: prediction file already exists (%s)' %
out_path)
# continue
# '''
sys.stdout.write('%s\n' % ('-' * 30))
sys.stdout.write('\n\t%d of %d\n' % (i + 1, n))
sys.stdout.write('\t%s\n\n' % txt)
# Predict concept labels
labels = Model.predict_classes_from_document(note)
# Get predictions in proper format
output = note.write(labels)
# Output the concept predictions
sys.stdout.write('\n\nwriting to: %s\n' % out_path)
with open(out_path, 'w') as f:
f.write('%s\n' % output)
sys.stdout.write('\n')
def generic_train(p_or_n,
train_sents,
train_labels,
use_lstm,
val_sents=None,
val_labels=None,
test_sents=None,
test_labels=None,
dev_split=None):
'''
generic_train()
Train a model that works for both prose and nonprose
@param p_or_n. A string that indicates "prose", "nonprose", or "all"
@param train_sents. A list of sentences; each sentence is tokenized
into words
@param train_labels. Parallel to `train_sents`, 7-way labels for
concept spans
@param use_lstm Bool indicating whether to train CRF or LSTM.
@param val_sents. Validation data. Same format as train_sents
@param val_labels. Validation data. Same format as train_labels
@param dev_split. A real number from 0 to 1
'''
# Must have data to train on:
if len(train_sents) == 0:
raise Exception('Training must have %s training examples' % p_or_n)
# if you should split the data into train/dev yourself
if (not val_sents) and (dev_split > 0.0) and (len(train_sents) > 10):
p = int(dev_split * 100)
sys.stdout.write('\tCreating %d/%d train/dev split\n' % (100 - p, p))
perm = list(range(len(train_sents)))
random.shuffle(perm)
train_sents = [train_sents[i] for i in perm]
train_labels = [train_labels[i] for i in perm]
ind = int(dev_split * len(train_sents))
val_sents = train_sents[:ind]
train_sents = train_sents[ind:]
val_labels = train_labels[:ind]
train_labels = train_labels[ind:]
else:
sys.stdout.write('\tUsing existing validation data\n')
sys.stdout.write('\tvectorizing words %s\n' % p_or_n)
if use_lstm:
print("TESTING NEW DATSET OBJECT")
dataset = Exp.Dataset()
parameters = hd.load_parameters_from_file("LSTM_parameters.txt")
parameters['use_pretrained_model'] = False
Datasets_tokens = {}
Datasets_labels = {}
Datasets_tokens['train'] = train_sents
Datasets_labels['train'] = train_labels
if val_sents:
Datasets_tokens['valid'] = val_sents
Datasets_labels['valid'] = val_labels
if test_sents:
Datasets_tokens['test'] = test_sents
Datasets_labels['test'] = test_labels
dataset.load_dataset(Datasets_tokens, Datasets_labels, "", parameters)
pickle.dump(
dataset,
open(os.path.join(parameters['model_folder'], 'dataset.pickle'),
'wb'))
print(Datasets_tokens['valid'][0])
print(Datasets_tokens['test'][0])
parameters['Feature_vector_length'] = dataset.feature_vector_size
parameters['use_features_before_final_lstm'] = False
parameters['learning_rate'] = 0.005
sess = tf.Session()
number_of_sent = list(range(len(dataset.token_indices['train'])))
with sess.as_default():
model = entity_model.EntityLSTM(dataset, parameters)
sess.run(tf.global_variables_initializer())
model.load_pretrained_token_embeddings(sess, dataset, parameters)
epoch_number = -1
transition_params_trained = np.random.rand(5 + 2, 5 + 2)
values = {}
values["best"] = 0
f1_dictionary = {}
f1_dictionary['best'] = 0
model_saver = tf.train.Saver(max_to_keep=100)
print("START TRAINING")
eval_dir = os.path.join(
tmp_dir, 'cliner_eval_%d' % random.randint(0, 256) + os.sep)
parameters['conll_like_result_folder'] = eval_dir
test_temp = os.path.join(parameters['conll_like_result_folder'],
'test/')
train_temp = os.path.join(parameters['conll_like_result_folder'],
'train/')
valid_temp = os.path.join(parameters['conll_like_result_folder'],
'valid/')
os.mkdir(parameters['conll_like_result_folder'])
os.mkdir(test_temp)
os.mkdir(train_temp)
os.mkdir(valid_temp)
while epoch_number < 90:
average_loss_per_phrase = 0
accuracy_per_phase = 0
step = 0
epoch_number += 1
if epoch_number:
sequence_numbers = list(
range(len(dataset.token_indices['train'])))
random.shuffle(sequence_numbers)
for sequence_number in sequence_numbers:
loss, accuracy, transition_params_trained = \
training_predict_LSTM.train_step(
sess, dataset,
sequence_number, model)
average_loss_per_phrase += loss
accuracy_per_phase += accuracy
step += 1
if step % 10 == 0:
print('Training {0:.2f}% done\n'.format(
step / len(sequence_numbers) * 100))
model_saver.save(
sess,
os.path.join(parameters['model_folder'],
'model_{0:05d}.ckpt'.format(epoch_number)))
_ = average_loss_per_phrase
_ = accuracy_per_phase
average_loss_per_phrase = average_loss_per_phrase / len(
number_of_sent)
accuracy_per_phase = accuracy_per_phase / len(number_of_sent)
if epoch_number > 0:
f1, _ = training_predict_LSTM.prediction_step(
sess, dataset, "test", model, epoch_number,
parameters['conll_like_result_folder'],
transition_params_trained)
f1_train, _ = training_predict_LSTM.prediction_step(
sess, dataset, "train", model, epoch_number,
parameters['conll_like_result_folder'],
transition_params_trained)
f1_valid, _ = training_predict_LSTM.prediction_step(
sess, dataset, "valid", model, epoch_number,
parameters['conll_like_result_folder'],
transition_params_trained)
correctly_predicted_tokens = \
training_predict_LSTM.compute_train_accuracy(
parameters[
'conll_like_result_folder'] + "valid" + os.sep +
"epoche_" + str(
epoch_number) + ".txt")
if f1_dictionary['best'] < float(f1_valid):
f1_dictionary['epoche'] = epoch_number
f1_dictionary['best'] = float(f1_valid)
if values["best"] < correctly_predicted_tokens:
values["epoche"] = epoch_number
values["best"] = correctly_predicted_tokens
# print ("Number of correctly predicted tokens -test "+str(
# correctly_predicted_tokens))
print("NEW EPOCHE" + " " + str(epoch_number))
print("Current F1 on train" + " " + str(f1_train))
print("Current F1 on valid" + " " + str(f1_valid))
print("Current F1 on test" + " " + str(f1))
print("Current F1 best (validation): ")
print(f1_dictionary)
shutil.rmtree(parameters['conll_like_result_folder'])
return parameters, dataset, f1_dictionary['best']
else:
########
# CRF
########
# vectorize tokenized sentences
text_features = extract_features(train_sents)
# type(text_features): <type 'list'>
# Collect list of feature types
enabled_features = set()
for sf in text_features:
for wf in sf:
for (feature_type, _), _ in wf.items():
if feature_type.startswith('prev'):
feature_type = 'PREV*'
if feature_type.startswith('next'):
feature_type = 'NEXT*'
enabled_features.add(feature_type)
enabled_features = sorted(enabled_features)
# Vectorize features
vocab = DictVectorizer()
flat_X_feats = vocab.fit_transform(flatten(text_features))
X_feats = reconstruct_list(flat_X_feats,
save_list_structure(text_features))
# vectorize IOB labels
Y_labels = [[tag2id[y] for y in y_seq] for y_seq in train_labels]
assert len(X_feats) == len(Y_labels)
for _, i in enumerate(range(len(X_feats))):
assert X_feats[i].shape[0] == len(Y_labels[i])
# if there is specified validation data, then vectorize it
if val_sents:
# vectorize validation X
val_text_features = extract_features(val_sents)
flat_val_X_feats = vocab.transform(flatten(val_text_features))
val_X = reconstruct_list(flat_val_X_feats,
save_list_structure(val_text_features))
# vectorize validation Y
val_Y = [[tag2id[y] for y in y_seq] for y_seq in val_labels]
# if there is specified test data, then vectorize it
if test_sents:
# vectorize test X
test_text_features = extract_features(test_sents)
flat_test_X_feats = vocab.transform(flatten(test_text_features))
test_X = reconstruct_list(flat_test_X_feats,
save_list_structure(test_text_features))
# vectorize test Y
test_Y = [[tag2id[y] for y in y_seq] for y_seq in test_labels]
else:
test_X = None
test_Y = None
sys.stdout.write('\ttraining classifiers %s\n' % p_or_n)
if use_lstm:
# train using lstm
# clf, dev_score = keras_ml.train(X_seq_ids, Y_labels, tag2id,
# len(vocab),
# val_X_ids=val_X, val_Y_ids=val_Y,
# test_X_ids=test_X, test_Y_ids=test_Y)
clf, dev_score = None, None
else:
# train using crf
clf, dev_score = crf.train(X_feats,
Y_labels,
val_X=val_X,
val_Y=val_Y,
test_X=test_X,
test_Y=test_Y)
return vocab, clf, dev_score, enabled_features