Marcio Monteiro
fix: removing train/test split (leaving this responsiblity to the data loader)
9288bfa
""" | |
This script converts the data from the raw data to CSV files. | |
Usage: | |
make newsSpace | |
python convert.py | |
""" | |
import csv | |
import html | |
import os | |
import sys | |
import pandas as pd | |
from bs4 import BeautifulSoup | |
HEADER = [ | |
"source", | |
"url", | |
"title", | |
"image", | |
"category", | |
"description", | |
"rank", | |
"pubdate", | |
] | |
OUTPUT_FILE_PATH = os.path.join("data", "all", "train.csv") | |
def _clean_text(text): | |
text = text.replace("\\\n", "\n") | |
text = html.unescape(text) | |
if text == "\\N": | |
return "" | |
return text | |
def _clean_html(text): | |
html_code = _clean_text(text) | |
html_code.replace("</p>", "\n\n</p>") | |
html_code.replace("<br>", "\n") | |
soup = BeautifulSoup(html_code, "html.parser") | |
text = soup.get_text(separator=" ") | |
text = text.replace(" \n", "\n").replace("\n ", "\n") | |
# remove extra spaces at the beginning of the text | |
lines = [line.strip() for line in text.split("\n")] | |
output = "\n".join(lines) | |
output = output.strip() | |
if output == "null": | |
return "" | |
return output | |
def _clean_image(image): | |
if image == "none": | |
return None | |
return image | |
def _clean_rank(rank): | |
return int(rank) | |
def run(): | |
""" | |
Run the conversion process. | |
""" | |
rows = [] | |
categories = set() | |
with open("newsSpace", encoding="ISO-8859-15") as f: | |
doc = f.read() | |
for row in doc.split("\t\\N\n"): | |
if not row: | |
continue | |
row = row.replace("\\\t", "") | |
try: | |
source, url, title, image, category, description, rank, pubdate = row.split( | |
"\t" | |
) | |
except ValueError: | |
print(repr(row)) | |
sys.exit(1) | |
categories.add(category) | |
obj = { | |
"source": source, | |
"url": url, | |
"title": _clean_text(title), | |
"image": _clean_image(image), | |
"category": category, | |
"description": _clean_text(description), | |
"rank": _clean_rank(rank), | |
"pubdate": pubdate, | |
"text": _clean_html(description), | |
} | |
if obj["text"]: | |
rows.append(obj) | |
# Add a label to each row | |
_categories = list(categories) | |
_categories.sort() | |
save_categories(_categories) | |
for row in rows: | |
row["label"] = _categories.index(row["category"]) | |
save_csv(rows) | |
save_csv_categories(["World", "Sports", "Business", "Sci/Tech"], "top4-balanced", is_balanced=True) | |
def save_csv(rows, fname=OUTPUT_FILE_PATH): | |
""" | |
Save the processed data into a CSV file. | |
""" | |
os.makedirs(os.path.join("data", "all"), exist_ok=True) | |
with open(fname, "w", encoding="utf8") as f: | |
writer = csv.DictWriter(f, fieldnames=rows[0].keys()) | |
writer.writeheader() | |
for row in rows: | |
writer.writerow(row) | |
def save_csv_categories(categories, config_name, is_balanced=True, **kwargs): | |
""" | |
Filter the data by categories and split the data into training and testing | |
sets. If is_balanced is True, the data will be balanced to size of the | |
class with fewer examples. | |
""" | |
df = pd.read_csv(OUTPUT_FILE_PATH) | |
if is_balanced: | |
dfs = [] | |
for category in categories: | |
_df = df[df["category"] == category] | |
dfs.append(_df) | |
min_size = min([len(_df) for _df in dfs]) | |
dfs = [df.sample(min_size) for df in dfs] | |
df = pd.concat(dfs) | |
else: | |
df = df[df["category"].isin(categories)] | |
os.makedirs(f"data/{config_name}", exist_ok=True) | |
df.to_csv(os.path.join("data", config_name, "train.csv"), index=False) | |
def save_categories(categories, fname="categories.txt"): | |
""" | |
Save the categories into a text file. | |
""" | |
with open(fname, "w") as f: | |
for category in categories: | |
f.write(category + os.linesep) | |
if __name__ == "__main__": | |
run() | |