def load_classification_data(path, delimiter_text_labels = '\t', delimiter_labels = '\t', line_start_skip = None): f = codecs.open(path,'r',encoding='utf8') lines = [[t.strip() for t in l.split(delimiter_text_labels)] for l in f.readlines()] instances = [] for i in range(len(lines)): if line_start_skip is not None and lines[i][0].startswith(line_start_skip): continue text = data_helper.clean_str(lines[i][0].strip()).split() if delimiter_text_labels == delimiter_labels: labels = lines[i][1:] else: labels = lines[i][1].strip().split(delimiter_labels) instances.append((text, labels)) return instances
def train_cnn(texts,
languages,
labels,
embeddings,
parameters,
model_serialization_path,
emb_lang='default'):
# preparing texts
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Preparing texts...',
flush=True)
texts_clean = [data_helper.clean_str(t.strip()).split() for t in texts]
# encoding languages (full name to abbreviation)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Encoding languages (full name to abbreviation)...',
flush=True)
langs = [map_lang(x) for x in languages]
# preparing training examples
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Preparing training examples...',
flush=True)
x_train, y_train, dist_labels = data_shaper.prep_classification(
texts_clean,
labels,
embeddings,
embeddings_language=emb_lang,
multilingual_langs=langs,
numbers_token='<NUM/>',
punct_token='<PUNC/>',
add_out_of_vocabulary_terms=False)
# defining the CNN model
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Defining the CNN model...',
flush=True)
cnn_classifier = cnn.CNN(embeddings=(embeddings.emb_sizes[emb_lang],
embeddings.lang_embeddings[emb_lang]),
num_conv_layers=parameters["num_convolutions"],
filters=parameters["filters"],
k_max_pools=parameters["k_max_pools"],
manual_features_size=0)
cnn_classifier.define_model(
len(x_train[0]),
len(dist_labels),
loss_functions.softmax_cross_entropy,
len(embeddings.lang_vocabularies[emb_lang]),
l2_reg_factor=parameters["reg_factor"],
update_embeddings=parameters["update_embeddings"])
cnn_classifier.define_optimization(
learning_rate=parameters["learning_rate"])
cnn_classifier.set_distinct_labels(dist_labels)
# initializing a Tensorflow session
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Initializing a Tensorflow session...',
flush=True)
session = tf.InteractiveSession()
session.run(tf.global_variables_initializer())
# training the model
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Training the model...',
flush=True)
simp_trainer = trainer.SimpleTrainer(cnn_classifier,
session,
build_feed_dict_func,
eval_func,
configuration_func=None)
simp_trainer.train(
list(zip(x_train, y_train)),
parameters["batch_size"],
parameters["num_epochs"],
num_epochs_not_better_end=5,
epoch_diff_smaller_end=parameters["epoch_diff_smaller_end"],
print_batch_losses=True,
eval_params={"dist_labels": dist_labels})
# storing the model
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Storing the model...',
flush=True)
cnn_classifier.serialize(session, model_serialization_path)
session.close()
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Training model is done!',
flush=True)
def topically_scale(filenames,
texts,
languages,
embeddings,
model_serialization_path,
predictions_file_path,
parameters,
emb_lang='default',
stopwords=[]):
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
" Loading classifier...",
flush=True)
cnn_classifier, session = cnn.load_model(
model_serialization_path,
embeddings.lang_embeddings[emb_lang],
loss_functions.softmax_cross_entropy,
just_predict=True)
simp_trainer = trainer.SimpleTrainer(cnn_classifier,
session,
build_feed_dict_func,
None,
configuration_func=None)
classified_texts = {}
items = list(zip(filenames, texts, [map_lang(x) for x in languages]))
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
" Topically classifying texts...",
flush=True)
for item in items:
fn, text, lang = item
print(fn, flush=True)
# split text in sentences
sentences = nltk.sent_tokenize(text)
sents_clean = [
data_helper.clean_str(s.strip()).split() for s in sentences
]
langs = [lang] * len(sentences)
# preparing training examples
x_test = data_shaper.prep_classification(
sents_clean,
None,
embeddings,
embeddings_language=emb_lang,
multilingual_langs=langs,
numbers_token='<NUM/>',
punct_token='<PUNC/>',
add_out_of_vocabulary_terms=False,
dist_labels=cnn_classifier.dist_labels,
max_seq_len=cnn_classifier.max_text_length)
results = simp_trainer.test(list(zip(x_test, [None] * len(x_test))),
parameters["batch_size"],
batch_size_irrelevant=True,
print_batches=True)
pred_labs = get_prediction_labels(results, cnn_classifier.dist_labels)
print("Predictions: ", flush=True)
print(pred_labs, flush=True)
classified_texts[fn] = list(zip(sentences, pred_labs, langs))
print("Languages: " + str(langs), flush=True)
print("Done with classifying: " + fn, flush=True)
lines_to_write = []
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
" Topical scaling...",
flush=True)
for l in cnn_classifier.dist_labels:
label_filtered = [(fn, classified_texts[fn][0][2], ' '.join([
sent_label[0] for sent_label in classified_texts[fn]
if sent_label[1] == l
])) for fn in classified_texts]
label_filtered = [x for x in label_filtered if len(x[2].strip()) > 50]
if len(label_filtered) > 3:
print("Topic: " + l, flush=True)
fns = [x[0] for x in label_filtered]
langs = [x[1] for x in label_filtered]
filt_texts = [x[2] for x in label_filtered]
for i in range(len(fns)):
io_helper.write_list(
os.path.dirname(predictions_file_path) + "/" +
fns[i].split(".")[0] + "_" + l.replace(" ", "-") + ".txt",
[filt_texts[i]])
label_scale = scale_efficient(fns,
filt_texts,
[inverse_map_lang(x) for x in langs],
embeddings,
None,
parameters,
emb_lang=emb_lang,
stopwords=stopwords)
lines_to_write.append("Scaling for class: " + l)
lines_to_write.extend(
[k + " " + str(label_scale[k]) for k in label_scale])
lines_to_write.append("\n")
else:
lines_to_write.append(
"Topic: " + l +
": Insufficient number of files contains text of this topic (i.e., class) in order to allow for scaling for the topic."
)
print(
"Topic: " + l +
": Insufficient number of files contains text of this topic (i.e., class) in order to allow for scaling for the topic.",
flush=True)
io_helper.write_list(predictions_file_path, lines_to_write)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Topical Scaling is done!',
flush=True)
def test_cnn(texts,
languages,
labels,
embeddings,
model_serialization_path,
predictions_file_path,
parameters,
emb_lang='default'):
# loading the serialized
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Loading the serialized model...',
flush=True)
cnn_classifier, session = cnn.load_model(
model_serialization_path,
embeddings.lang_embeddings[emb_lang],
loss_functions.softmax_cross_entropy,
just_predict=(labels is None))
# preparing/cleaning the texts
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Preparing/cleaning the texts...',
flush=True)
texts_clean = [data_helper.clean_str(t.strip()).split() for t in texts]
# encoding languages (full name to abbreviation)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Encoding languages (full name to abbreviation)...',
flush=True)
langs = [map_lang(x) for x in languages]
# preparing testing examples
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Preparing training examples...',
flush=True)
if labels:
x_test, y_test, dist_labels = data_shaper.prep_classification(
texts_clean,
labels,
embeddings,
embeddings_language=emb_lang,
multilingual_langs=langs,
numbers_token='<NUM/>',
punct_token='<PUNC/>',
add_out_of_vocabulary_terms=False,
dist_labels=cnn_classifier.dist_labels,
max_seq_len=cnn_classifier.max_text_length)
else:
x_test = data_shaper.prep_classification(
texts_clean,
labels,
embeddings,
embeddings_language=emb_lang,
multilingual_langs=langs,
numbers_token='<NUM/>',
punct_token='<PUNC/>',
add_out_of_vocabulary_terms=False,
dist_labels=cnn_classifier.dist_labels,
max_seq_len=cnn_classifier.max_text_length)
simp_trainer = trainer.SimpleTrainer(cnn_classifier,
session,
build_feed_dict_func,
None if not labels else eval_func,
configuration_func=None)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') + ' Starting test...',
flush=True)
results = simp_trainer.test(
list(zip(x_test, y_test if labels else [None] * len(x_test))),
parameters["batch_size"],
eval_params={"dist_labels": cnn_classifier.dist_labels},
batch_size_irrelevant=True)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Getting prediction labels...',
flush=True)
pred_labs = get_prediction_labels(results[0] if labels else results,
cnn_classifier.dist_labels)
if labels is None:
io_helper.write_list(predictions_file_path, pred_labs)
else:
list_pairs = list(zip(pred_labs, labels))
list_pairs.insert(0, ("Prediction", "Real label"))
list_pairs.append(("Performance: ", str(results[1])))
io_helper.write_list_tuples_separated(predictions_file_path,
list_pairs)
print(datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
' Prediction is done!',
flush=True)
sent_index_de_test = {
i: sent_lines_de_test[i]
for i in range(len(sent_lines_de_test))
}
sent_index_en_test = {
i: sent_lines_en_test[i]
for i in range(len(sent_lines_en_test))
}
print("Length sent_index_de_test: " + str(len(sent_index_de_test)))
print("Length sent_index_en_test: " + str(len(sent_index_en_test)))
print("Cleaning dictionaries DE test...")
for k in sent_index_de_test:
sent_index_de_test[k] = data_helper.clean_str(
sent_index_de_test[k]).split()
print("Cleaning dictionaries EN test...")
for k in sent_index_en_test:
sent_index_en_test[k] = data_helper.clean_str(
sent_index_en_test[k]).split()
print("Embedding indices lookup DE test...")
sent_index_embedded_de_test = data_shaper.prep_embeddings_lookup(
sent_index_de_test,
t_embeddings,
stopwords=stopwords_de,
punctuation=punctuation,
lang='default',
text_lang_prefix='de__',
min_tokens=2,
num_token="<NUM/>")
