feat: first commit

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+test.csv filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text
+ag_news.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+ag_news.csv
+newsSpace

Makefile

@@ -0,0 +1,4 @@
+newsSpace:
+	wget http://groups.di.unipi.it/~gulli/newsSpace.bz2
+	bzip2 -d newsSpace.bz2
+	

convert.py

@@ -0,0 +1,142 @@
+"""
+This script converts the data from the raw data to CSV files.
+
+Usage:
+    make newsSpace
+    python convert.py
+"""
+
+import csv
+import html
+import sys
+
+import pandas as pd
+
+from bs4 import BeautifulSoup
+
+from sklearn.model_selection import train_test_split
+
+HEADER = [
+    "source",
+    "url",
+    "title",
+    "image",
+    "category",
+    "description",
+    "rank",
+    "pubdate",
+]
+
+OUTPUT_FILE = "ag_news.csv"
+TRAIN_OUTPUT_FILE = "train.csv"
+TEST_OUTPUT_FILE = "test.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, "lxml")
+    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")]
+
+    return "\n".join(lines)
+
+
+def _clean_image(image):
+    if image == "none":
+        return None
+    return image
+
+
+def _clean_rank(rank):
+    return int(rank)
+
+
+def run():
+    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),
+        }
+
+        rows.append(obj)
+
+    # Add a label to each row
+    _categories = list(categories)
+    _categories.sort()
+
+    for row in rows:
+        row["label"] = _categories.index(row["category"])
+
+    save_csv(rows)
+    split_csv_train_test(test_size=0.2, random_state=42)
+
+
+def save_csv(rows, fname=OUTPUT_FILE):
+    """
+    Save the processed data into a CSV file.
+    """
+    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 split_csv_train_test(**kwargs):
+    """
+    Split the data into training and testing sets.
+    """
+    df = pd.read_csv(OUTPUT_FILE)
+    train_df, test_df = train_test_split(df, **kwargs)
+    train_df.to_csv(TRAIN_OUTPUT_FILE, index=False)
+    test_df.to_csv(TEST_OUTPUT_FILE, index=False)
+
+
+if __name__ == "__main__":
+    run()

test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da30f3d131924df14ef5280f93ef45343a81a0592460a64e0847dca225f5ce70
+size 169955948

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b16743cfa3c595c010695b2ca80899fb6e52f1e91a736110d60b005e7e7b493
+size 679730930