def do_OCR(df, path_txt_files, min_size, list_files=None):
"""
NEEDS imagemagick and tesseract-ocr
:param df: I need the complete df to get the language cause the program that does the OCR needs the supposed language
of the image
:param path_txt_files:
:param min_size:
:return:
"""
lang_map = {"en": "eng", "fr": "fra"}
import os
from subprocess import call
if list_files:
text_files = list_files
else:
text_files = get_texts(path_txt_files, "txt")
for f in text_files:
if os.path.getsize(f) < min_size or list_files:
lang = df.loc[df["id"] == int(os.path.basename(f)[:-4])]["lang_title"].tolist()[0]
call(
"convert -density 300 {0}[0] -depth 8 -background white -alpha remove -flatten +matte {0}.tiff".format(
f.replace(".txt", ".pdf")), shell=True)
call("tesseract -l {0} {1}.tiff {2}".format(lang_map[lang], f.replace(".txt", ".pdf"), f[:-4]), shell=True)
call("rm {0}.tiff".format(f.replace(".txt", ".pdf")), shell=True)
def do_OCR(df, path_txt_files, min_size, list_files=None):
"""
NEEDS imagemagick and tesseract-ocr
:param df: I need the complete df to get the language cause the program that does the OCR needs the supposed language
of the image
:param path_txt_files:
:param min_size:
:return:
"""
lang_map = {"en": "eng", "fr": "fra"}
import os
from subprocess import call
if list_files:
text_files = list_files
else:
text_files = get_texts(path_txt_files, "txt")
for f in text_files:
if os.path.getsize(f) < min_size or list_files:
lang = df.loc[df["id"] == int(os.path.basename(f)
[:-4])]["lang_title"].tolist()[0]
call(
"convert -density 300 {0}[0] -depth 8 -background white -alpha remove -flatten +matte {0}.tiff"
.format(f.replace(".txt", ".pdf")),
shell=True)
call("tesseract -l {0} {1}.tiff {2}".format(
lang_map[lang], f.replace(".txt", ".pdf"), f[:-4]),
shell=True)
call("rm {0}.tiff".format(f.replace(".txt", ".pdf")), shell=True)
def pdf2txt(pdf_folder):
from subprocess import call
pdf_files = get_texts(pdf_folder, "pdf")
for f in pdf_files:
try:
call("java -jar ../input/pdfs/pdfbox-app-1.8.5.jar ExtractText {0}".format(f), shell=True)
logging.info("Extracted text from {}".format(f))
except:
logging.warning("Could not extract text from {}".format(f))
pass
def pdf2txt(pdf_folder):
from subprocess import call
pdf_files = get_texts(pdf_folder, "pdf")
for f in pdf_files:
try:
call(
"java -jar ../input/pdfs/pdfbox-app-1.8.5.jar ExtractText {0}".
format(f),
shell=True)
logging.info("Extracted text from {}".format(f))
except:
logging.warning("Could not extract text from {}".format(f))
pass
def detect_garbage_text(text_path, min_chars=2):
import re
garbage_files = []
txt_files = get_texts(text_path, "txt")
# txt_files = ["../input/pdfs/1page/1630.txt"]
stop_words = set([l.strip("\n").decode("utf-8") for l in open("ngrams/stopwords.txt").readlines() if l])
for t in txt_files:
with open(t, "r") as f:
content = f.read().replace("\n", "")
search_re = "\w{{{min},}}".format(min=str(min_chars))
words = re.findall(search_re, content)
all_words = re.findall("\w+", content)
if len(words) < 10 or len(stop_words.intersection(all_words)) < 5:
logging.info("Garbage text!! File {}".format(t))
logging.debug(content)
garbage_files.append(t)
return garbage_files
pass
def detect_garbage_text(text_path, min_chars=2):
import re
garbage_files = []
txt_files = get_texts(text_path, "txt")
# txt_files = ["../input/pdfs/1page/1630.txt"]
stop_words = set([
l.strip("\n").decode("utf-8")
for l in open("ngrams/stopwords.txt").readlines() if l
])
for t in txt_files:
with open(t, "r") as f:
content = f.read().replace("\n", "")
search_re = "\w{{{min},}}".format(min=str(min_chars))
words = re.findall(search_re, content)
all_words = re.findall("\w+", content)
if len(words) < 10 or len(stop_words.intersection(all_words)) < 5:
logging.info("Garbage text!! File {}".format(t))
logging.debug(content)
garbage_files.append(t)
return garbage_files
pass
def make_input_file(folder, test_ids=[]):
# Create folder if necessary
if not os.path.exists(folder):
os.mkdir(folder)
# Sample some publications and create the structure to
pubs = PubTexts(n=100000)
pub_ids = set(pubs.ids())
# Maps string id to incremental integer cited_ids_map
# for pid in pubs.cited_ids_map():
# cited_ids_map[str(pid)] = len(cited_ids_map)
# Ignore these cited_ids_map in the training files so they can be used for testing
test_ids_set = set(test_ids)
# Make citation file
cited_ids_map = {}
citations = defaultdict(list)
rows = db.select(fields=["citing", "cited"], table="graph")
for citing, cited in rows:
# Convert to ascii string
citing, cited = str(citing), str(cited)
# Only include it if it's on sample set and it's not on test set
if (citing in pub_ids) and (cited in pub_ids) and \
(citing not in test_ids_set) and (cited not in test_ids_set):
# print "Adding"
if cited not in cited_ids_map:
cited_ids_map[str(cited)] = len(cited_ids_map)
citations[str(citing)].append(cited_ids_map[str(cited)])
cits_per_pub = np.mean([len(c) for c in citations.values()])
print "%d citing pubs with %.2f citations per pub." % (len(citations),
cits_per_pub)
print "%d cited pubs." % len(cited_ids_map)
citing = citations.keys()
# citing_mask = [(pid in citing) for pid in pubs.ids()]
vec = CountVectorizer(stop_words='english',
ngram_range=(1, 2),
max_df=0.5,
min_df=5,
max_features=20000)
texts = vec.fit_transform(pubs.texts(citing, use_title=True, use_abs=True))
# Write training file. Every row in the citing_tr.txt is the vector
# representation of the text in the file.
citations_file = open(os.path.join(folder, "citations_tr.txt"), "w")
citing_file = open(os.path.join(folder, "citing_tr.txt"), "w")
citing_ids_file = open(os.path.join(folder, "citing_tr_ids.txt"), "w")
for i, pid in enumerate(citing):
# Writing citing ids to map back on the search
print >> citing_ids_file, str(pid)
# Writing citations
cited = sorted(citations[str(pid)])
print >> citations_file, len(cited), ' '.join(map(str, cited))
print_row(citing_file, texts[i])
citations_file.close()
citing_file.close()
citing_ids_file.close()
# Release some memory
del citations, citing, texts
# Find vector representation for texts in the test set. This one we can't
# fit, only transform, because it's test data (therefore unseen).
texts = vec.transform(get_texts(test_ids, use_title=True, use_abs=True))
# Write the test files now. The citing contains term frequencies and the citations
# is basically zeros, since it's actually what we are trying to predict.
citations_file = open(os.path.join(folder, "citations_ts.txt"), "w")
citing_file = open(os.path.join(folder, "citing_ts.txt"), "w")
citing_ids_file = open(os.path.join(folder, "citing_ts_ids.txt"), "w")
for i in xrange(len(test_ids)):
print >> citing_ids_file, str(test_ids[i])
print >> citations_file, '0'
print_row(citing_file, texts[i])
citations_file.close()
citing_file.close()
# Sort numeric cited_ids_map so that each line x corresponds to pub x
str_ids, num_ids = zip(*cited_ids_map.items())
cited = np.asarray(str_ids)[list(num_ids)]
texts = vec.fit_transform(pubs.texts(cited, use_title=True, use_abs=True))
cited_file = open(os.path.join(folder, "cited.txt"), "w")
cited_ids_file = open(os.path.join(folder, "cited_ids.txt"), "w")
# Writing terms frequency
for i in xrange(len(cited)):
# Writing cited ids to map back the searches
print >> cited_ids_file, str(cited[i])
print_row(cited_file, texts[i])
cited_file.close()
cited_ids_file.close()
print "Done!"
def main(feature_type: str, language: str, domain: str, main_dir: str, seq_len: int,
batch_size: int, lstm_dim: int, character_level: bool = False):
"""
Parameters
----------
feature_type: the name of the feature
language: language of the text.
main_dir: base directory
seq_len: sequence length
batch_size: batch size
lstm_dim: lstm hidden dimension
character_level: whether tokenizer should be on character level.
"""
texts = get_texts(main_dir, language, feature_type, character_level, domain)
tokenizer = Tokenizer(texts.values(), character_level=character_level)
samples = {}
for book in texts:
print(len(texts[book]))
len_text = len(texts[book]) if character_level else len(texts[book].split())
if len_text < seq_len:
logger.warn(f"Requested seq_len larger than text length: {len_text} / {seq_len} "
f"for {book} and feature type {feature_type}.")
continue
rand_idx = np.random.randint(0, len_text - seq_len, batch_size)
if character_level:
samples[book] = tokenizer.encode([texts[book][i: i + seq_len] for i in rand_idx])
else:
split_text = texts[book].split()
samples[book] = tokenizer.encode(
[" ".join(split_text[i: i + seq_len]) for i in rand_idx]
)
test_generator = DataGenerator(tokenizer,
tokenizer.full_text,
seq_len=seq_len,
batch_size=batch_size,
with_embedding=True,
train=False)
sample_batch = next(iter(test_generator))
logger.info(f"X batch shape: {sample_batch[0].shape}, y batch shape: {sample_batch[1].shape}")
logger.info(f"Sample batch text: {tokenizer.decode(sample_batch[0][0])}")
file_path = os.path.join(main_dir, 'models',
f'{feature_type}_{language}_lstm_{lstm_dim}')
if domain:
file_path += '_' + domain
if character_level:
file_path += '_character_level'
file_path += '.h5'
logger.info(f"Loading {file_path}")
prediction_model = lstm_model(num_words=tokenizer.num_words,
lstm_dim=lstm_dim,
seq_len=1,
batch_size=batch_size,
stateful=True,
return_state=True)
prediction_model.load_weights(file_path)
hiddens = {}
seeds = {}
predictions = {}
for book in samples:
seed = np.stack(samples[book])
print(seed.shape)
hf, preds = generate_text(prediction_model, tokenizer, seed, get_hidden=True)
print(hf.shape)
hiddens[book] = hf
seeds[book] = seed
preds = [tokenizer.ix_to_word[pred] for pred in preds]
predictions[book] = preds
file_name = f'{feature_type}_{language}_lstm_{lstm_dim}_seq_len_{seq_len}'
if domain:
file_name += '_' + domain
if character_level:
file_name += '_character-level'
file_name += '.pkl'
path_out = os.path.join('data', 'hidden_states', file_name)
with open(path_out, 'wb') as f:
pickle.dump(hiddens, f)
logger.info(f"Succesfully saved hidden dimensions to {path_out}")
path_out = os.path.join('data', 'seeds', file_name)
with open(path_out, 'wb') as f:
pickle.dump(seeds, f)
logger.info(f"Succesfully saved seeds to {path_out}")
path_out = os.path.join('data', 'predictions', file_name)
with open(path_out, 'wb') as f:
pickle.dump(predictions, f)
logger.info(f"Succesfully saved predictions to {path_out}")
def main(feature_type: str,
language: str,
domain: str,
main_dir: str,
seq_len: int,
batch_size: int,
test_batch_size: int,
lstm_dim: int,
character_level: bool = False):
"""
Parameters
----------
feature_type: the name of the feature
main_dir: base directory
language: language of corpus
seq_len: sequence length
batch_size: batch size
test_batch_size: test batch size
lstm_dim: lstm hidden dimension
character_level: whether tokenizer should be on character level.
"""
texts = get_texts(main_dir, language, feature_type, character_level,
domain)
tokenizer = Tokenizer(texts.values(), character_level=character_level)
train_generator = DataGenerator(tokenizer,
tokenizer.full_text,
seq_len=seq_len,
batch_size=batch_size,
with_embedding=True,
train=True)
test_generator = DataGenerator(tokenizer,
tokenizer.full_text,
seq_len=seq_len,
batch_size=test_batch_size,
with_embedding=True,
train=False)
sample_batch = next(iter(train_generator))
logger.info(
f"X batch shape: {sample_batch[0].shape}, y batch shape: {sample_batch[1].shape}"
)
logger.info(f"Sample batch text: {tokenizer.decode(sample_batch[0][0])}")
training_model = lstm_model(num_words=tokenizer.num_words,
seq_len=seq_len,
lstm_dim=lstm_dim,
stateful=False)
file_path = os.path.join(main_dir, 'models',
f'{feature_type}_{language}_lstm_{lstm_dim}')
if domain:
file_path += '_' + domain
if character_level:
file_path += '_character_level'
file_path += '.h5'
training_model.save_weights(file_path)
checkpoint = tf.keras.callbacks.ModelCheckpoint(file_path,
monitor='val_loss',
save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
patience=2)
generate_text = GenerateText(test_generator, tokenizer, file_path,
lstm_dim)
callbacks_list = [checkpoint, early_stopping, generate_text]
training_model.fit_generator(train_generator,
validation_data=test_generator,
callbacks=callbacks_list,
epochs=256)