Upload clean.py

This file contains the preprocessing applied to the original datasets taken from Kaggle's [Toxic Comment Classification Challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) in order to create the train, evaluation, and test datasets.

clean.py

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+import re
+import math
+
+import pandas as pd
+
+from tqdm import tqdm
+
+seed = 7497
+
+TOXIC_COLUMNS = [
+    "toxic",
+    "severe_toxic",
+    "obscene",
+    "threat",
+    "insult",
+    "identity_hate",
+]
+
+# Time and date regexes
+TIME = r"([0-9]{1,2}:[0-9]{2}( (am|AM|pm|PM))?)"
+DAY = r"([23]?(1(st)?|2(nd)?|3(rd)?|[4-9](th)?)|1[0-9](th)?)"
+MONTH = r"(January|February|March|April|May|June|July|August|September|October|November|December|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Nov|Dec)"
+YEAR = r"('?[0-9]{2}|[0-9]{4})"
+DATE = rf"(({DAY} {MONTH}|{MONTH} {DAY})(,? {YEAR})?)"
+TIMESTAMP = rf"((({TIME},? (\(UTC\) )?)?{DATE}|({DATE},? )?{TIME})(\s+\(UTC\))?)"
+
+# The 'talk' part at the end of a signature
+TALK = r"((\|\s*|\(\s*)?[tT]alk((\s*[-|•, ]\s*|\s+)[cC]ontribs)?(\s*[-|)])?)"
+
+# IP addresses
+IP = r"([0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})"
+
+# Username and the username part of a the signature
+USERNAME = r"([^#<>[\]|{}/@\s]+)"
+USER_SIG = rf"((((?:\s)[-–—]\s*)?(\((User:)?{USERNAME}\)|User:{USERNAME})|(?:\s)[-–—]\s*{USERNAME})(\s+{TALK})?)"
+
+# A full signature
+SIGNATURE = rf"(((([-–—]\s*)?{IP}(\s+{USER_SIG})?|(?:\s)[-–—]\s*[uU]nsigned|{TALK}|{USER_SIG})(\s+{TIMESTAMP})?)|{TIMESTAMP}(\s+{TALK})?)"
+
+# List of the patterns to remove
+REGEX_REMOVE = [
+    r"^(\"+|'+)",  # Initial quotation marks
+    r"(\"+|'+)$",  # Final quotation marks
+    r"^REDIRECT.*$",  # The whole comment is a redirect
+    rf"^\s*{SIGNATURE}",  # Initial signature
+    rf"{SIGNATURE}\s*$",  # Final signature
+    r" \[[0-9]+\]|\[[0-9]+\] ",  # Citations
+    r"‖\s+[tT]alk - [-a-zA-Z0-9._()\s]+‖",
+    r"==[^=]+==",
+    r"^::+",
+    r"^\s*\(UTC\)",
+    rf"Unblock {IP}",
+    r"2nd Unblock Request",
+    r":Category:",
+    r"File:[^\s]+",
+    r"\{\|.+\|\}",  # Embedded code
+    # r"\{\{.+\s.+\}\}", # Embedded code
+    r"^\s+",  # Initial whitespace
+    r"\s+$",  # Trailing whitespace
+]
+
+# List of patterns to replaces
+REGEX_REPLACE = {
+    "\n+": "\n",
+    "\\'": "'",
+    '""+': '"',
+    "''+": "'",
+    # r"(WP|Wikipedia):[^\s]+": "URL", # Wikipedia internal links
+    r"[^\s]+#[^\s]+": "URL",  # Wikipedia internal links
+    r"https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,4}\b([-a-zA-Z0-9@:%_\+.~#?&//=]*)": "URL",  # ULRs
+    r"([uU]ser_[tT]alk|[tT]alk):[^\s]+": "URL",  # Talk links
+}
+
+
+def clean_sentence(sentence):
+    """Preprocess a sentence using the regex rules"""
+    for pattern in REGEX_REMOVE:
+        sentence = re.sub(pattern, "", sentence)
+    for pattern, repl in REGEX_REPLACE.items():
+        sentence = re.sub(pattern, repl, sentence)
+    return sentence
+
+
+def make_binary_label(row):
+    """Make a row label binary by combining all toxicity types"""
+    for column in TOXIC_COLUMNS:
+        if row[column] == 1:
+            return 1
+    return 0
+
+
+print("Loading original data...")
+
+# Load up the original data
+train_df = pd.read_csv("orig_train.csv").set_index("id")
+test_text_df = pd.read_csv("orig_test.csv").set_index("id")
+test_labels_df = pd.read_csv("orig_test_labels.csv").set_index("id")
+
+# Remove the datapoints which have no label
+test_text_df = test_text_df.loc[test_labels_df["toxic"] != -1]
+test_labels_df = test_labels_df.loc[test_labels_df["toxic"] != -1]
+
+# Join the test text and labels to make a complete dataset
+test_df = test_text_df.join(test_labels_df)
+
+print("Cleaning train split...")
+for index, row in tqdm(train_df.iterrows(), total=len(train_df)):
+    row["comment_text"] = clean_sentence(row["comment_text"])
+
+print("Cleaning test split...")
+for index, row in tqdm(test_df.iterrows(), total=len(test_df)):
+    row["comment_text"] = clean_sentence(row["comment_text"])
+
+
+# Some texts will get reduced to the empty string. Let's remove them first
+print("Removing empty texts...")
+train_df = train_df.loc[train_df["comment_text"] != ""]
+test_df = test_df.loc[test_df["comment_text"] != ""]
+
+# Get rid of any duplicates we made
+print("Removing duplicate entries...")
+train_df = train_df.drop_duplicates(subset=["comment_text"])
+test_df = test_df.drop_duplicates(subset=["comment_text"])
+
+print("Creating binary column...")
+
+# Make the new binary column
+train_df["label"] = train_df.apply(make_binary_label, axis=1)
+test_df["label"] = test_df.apply(make_binary_label, axis=1)
+
+# Remove all other classification columns
+train_df = train_df.drop(columns=TOXIC_COLUMNS)
+test_df = test_df.drop(columns=TOXIC_COLUMNS)
+
+print("Creating eval split...")
+
+# Shuffle the current train split
+train_df = train_df.sample(frac=1, random_state=seed)
+
+# The new size of the train split
+train_size = math.floor(len(train_df) * 0.8)
+
+# Separate into train and eval splits
+eval_df = train_df[train_size:]
+train_df = train_df[:train_size]
+
+# print("Saving to disk...")
+with open("train.csv", "w") as f:
+    train_df.to_csv(f)
+with open("eval.csv", "w") as f:
+    eval_df.to_csv(f)
+with open("test.csv", "w") as f:
+    test_df.to_csv(f)