def process_literature_files():
print("---------------------------------------------------------------------------------------")
print("BRAZILIAN LITERATURE")
# walk and merge texts to one file
count_prev = 0
count_pos = 0
lines = list()
count_files = 0
count_authors = 0
for dir_path, dirs, files in os.walk(DATASET_PT_CORPUS_PATH + "corpus-of-brazilian-portuguese-literature/"):
print(dir_path)
count_authors += 1
for filename in tqdm.tqdm([f for f in files if f.endswith(".txt")]):
file_path = os.path.join(dir_path, filename)
count_files += 1
# print(file_path)
try:
with open(file_path, "r", encoding="windows-1252") as text_file:
line = text_file.read()
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
lines.append(line)
count_pos += len(line.split())
except:
try:
with open(file_path, "r", encoding="utf-8") as text_file:
line = text_file.read()
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
lines.append(line)
count_pos += len(line.split())
except:
with open(file_path, "r", errors="ignore") as text_file:
line = text_file.read()
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
lines.append(line)
count_pos += len(line.split())
print("Writing to output")
with open(DATASET_PT_CORPUS_PATH + "corpus-of-brazilian-portuguese-literature/literature_proc.txt", "w+") as out_file:
for line in tqdm.tqdm(lines):
out_file.write(line + "\n")
print("Prev: ", count_prev)
print("Pos: ", count_pos)
print("Authors: ", count_authors)
print("Files: ", count_files)
def process_hc():
print("---------------------------------------------------------------------------------------")
print("HC")
hc_df = pd.read_csv(DATASET_PT_CORPUS_PATH + "hc_corpus/hc_corpus.tsv", sep="\t")
texts = hc_df["content"].values
count_prev = 0
count_pos = 0
count_posts = 0
with open(DATASET_PT_CORPUS_PATH + "hc_corpus/hc_corpus_proc.txt", "w+") as output_file:
for line in tqdm.tqdm(texts):
line = str(line)
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
splits = line.split()
line_len = len(splits)
if line_len >= 25:
count_posts += 1
output_file.write(line + "\n")
count_pos += line_len
print("Prev: ", str(format(count_prev, ',d')))
print("Pos: ", str(format(count_pos, ',d')))
print("News: ", str(format(count_posts, ',d')))
def process_old_news():
print("---------------------------------------------------------------------------------------")
print("OLD NEWS")
# with open(DATASET_PT_CORPUS_PATH + "old-newspaper/old-newspaper.tsv") as f, \
# open(DATASET_PT_CORPUS_PATH + "old-newspaper/pt_old_news.tsv", "w+") as out:
# out.write("language\tnewspaper\tdate\ttext")
# for line in tqdm.tqdm(f):
# if line[0:25].find("Portuguese") != -1:
# out.write(line)
dataset_df = pd.read_csv(DATASET_PT_CORPUS_PATH + "old-newspapers/pt_old_news.tsv", sep="\t")
text_values = dataset_df["text"].values
count_prev = 0
count_pos = 0
with open(DATASET_PT_CORPUS_PATH + "old-newspapers/pt_old_news_proc.txt", "w+") as f:
for line in tqdm.tqdm(text_values):
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
f.write(line + "\n")
count_pos += len(line.split())
print("Prev: ", count_prev)
print("Pos: ", count_pos)
def process_folha():
print("---------------------------------------------------------------------------------------")
print("FOLHA UOL")
count_prev = 0
count_pos = 0
count_posts = 0
folha_df = pd.read_csv(DATASET_PT_CORPUS_PATH + "news-of-the-site-folhauol/articles.csv")
texts = folha_df["text"].values
with open(DATASET_PT_CORPUS_PATH + "news-of-the-site-folhauol/folha_proc.txt", "w+") as output_file:
for line in tqdm.tqdm(texts):
line = str(line)
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
splits = line.split()
line_len = len(splits)
if line_len >= 10:
count_posts += 1
output_file.write(line + "\n")
count_pos += line_len
print("Prev: ", str(format(count_prev, ',d')))
print("Pos: ", str(format(count_pos, ',d')))
print("News: ", str(format(count_posts, ',d')))
def process_instructions():
print("---------------------------------------------------------------------------------------")
print("INSTRUCTIONS")
count_prev = 0
count_pos = 0
count_posts = 0
files = glob.glob(DATASET_PT_CORPUS_PATH + "human-instructions-portuguese-wikihow/*.ttl")
for i in range(len(files)):
f = files[i]
with open(f) as input_file, \
open(DATASET_PT_CORPUS_PATH + "human-instructions-portuguese-wikihow/[email protected]".replace("@", str(i + 1).zfill(3)), "w+") as output_file:
print(f)
print("Reading file")
text = input_file.read()
print("Find patterns")
lines = re.findall(r'"""(.*?)"""', text)
print("Processing")
for line in tqdm.tqdm(lines):
try:
if line.find("Main Steps") != -1 or line.find("Requirements") != -1:
continue
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
splits = line.split()
line_len = len(splits)
if line_len >= 10 and line.find("div class") == -1:
count_posts += 1
output_file.write(line + "\n")
count_pos += line_len
except:
pass
print("Prev: ", str(format(count_prev, ',d')))
print("Pos: ", str(format(count_pos, ',d')))
print("News: ", str(format(count_posts, ',d')))
def process_wiki_files():
print("---------------------------------------------------------------------------------------")
print("WIKI PROCESS")
with open(DATASET_PT_CORPUS_PATH + "wikipedia_ptbr/ptwiki.txt", "r") as wiki_file, \
open(DATASET_PT_CORPUS_PATH + "wikipedia_ptbr/wiki_proc.txt", "w+") as wiki_proc_file:
count_prev = 0
count_pos = 0
for line in tqdm.tqdm(wiki_file):
if (not line.startswith('<') and not line.startswith('</')) and (not line.endswith(">")) and \
len(line.replace("\n", "").split()) >= 1:
count_prev += len(line.split())
line = line.replace("Section::::", "").replace("BULLET::::", "").replace("\n\n", "\n")
line = clear_text(line)
line = clean_text_ret(line)
count_pos += len(line.split())
wiki_proc_file.write(line + "\n")
print("Prev: ", count_prev)
print("Pos: ", count_pos)
return None
def process_blog():
print("---------------------------------------------------------------------------------------")
print("BLOG POSTS")
count_prev = 0
count_pos = 0
count_posts = 0
i = 0
for chunk in pd.read_csv(DATASET_PT_CORPUS_PATH + "blogspot-posts/blogposts.csv", chunksize=10 ** 6):
i += 1
with open(DATASET_PT_CORPUS_PATH + "blogspot-posts/[email protected]".replace("@", str(i).zfill(6)), "w+") as output_file:
contents = chunk["content"].values
print("Processing chunk ", i)
for line in tqdm.tqdm(contents):
line = str(line)
splits = line.split()
if len(splits) < 25:
continue
count_prev += len(line.split())
line = clear_text(line)
line = clean_text_ret(line)
splits = line.split()
line_len = len(splits)
if line_len >= 25 and line.find("if gte mso") == -1:
count_posts += 1
output_file.write(line + "\n")
count_pos += line_len
print("Prev: ", str(format(count_prev, ',d')))
print("Pos: ", str(format(count_pos, ',d')))
print("Posts:", str(format(count_posts, ',d')))
def setup_data():
# Load "procedentes"
print("Loading text files...")
data = []
dict_classes = {
PATH_LAB_PROC: PROCEDENTE,
PATH_LAB_INPROC: IMPROCEDENTE,
PATH_LAB_EXT: EXTINCAO,
PATH_LAB_PARC_PROC: PARCIALMENTE_PROCEDENTE
}
regression_df = pd.read_csv("data/attributes.csv")
print(regression_df.describe())
for path_class in dict_classes.keys():
folder = JEC_DATASET_PATH + path_class
file_paths = glob.glob(folder + "*.txt")
for file_path in file_paths:
with open(file_path) as f:
raw_content = f.read()
file_name = file_path.replace(folder, "").replace(".txt", "")
found_df = np.array(regression_df.loc[regression_df['sentenca'] == int(file_name)])
# print(np.array(found_df))
if len(found_df) == 0:
arr = [file_name, raw_content, dict_classes[path_class], 0.0]
data.append(arr)
else:
for fdf in found_df:
arr = [file_name, raw_content, dict_classes[path_class], float(str(fdf[1]).replace("R$ ", "").replace(".", "").replace(",", "."))]
data.append(arr)
print("Pre-processing...")
processed_data = []
for file_name, content, label, valor in tqdm.tqdm(data):
clean_text = text_preprocessing.clear_text(content)
processed_text = text_preprocessing.pre_process(clean_text)
processed_data.append([file_name, processed_text, label, valor])
df = pd.DataFrame(data=processed_data, columns=["file_name", "content", "label", "indenizacao"])
df.to_csv(PROJECT_PATH + JEC_DEST_PATH + JEC_FINAL_PATH + "jec_ufsc_dataset.csv")
print(df.describe())
x = df[["file_name", "content"]]
y = df["label"]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=(int(time.time()) % 2 ** 32), stratify=y)
x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.5, random_state=(int(time.time()) % 2 ** 32), stratify=y_test)
print(x_train.shape, x_test.shape, x_val.shape, y_train.shape, y_test.shape, y_val.shape)
_path = PROJECT_PATH + JEC_DEST_PATH + JEC_FINAL_PATH + "jec_ufsc_train.csv"
# save_data(x_train, y_train, _path)
_path = PROJECT_PATH + JEC_DEST_PATH + JEC_FINAL_PATH + "jec_ufsc_test.csv"
# save_data(x_test, y_test, _path)
_path = PROJECT_PATH + JEC_DEST_PATH + JEC_FINAL_PATH + "jec_ufsc_val.csv"
