Delete clean.py

Testing to see if deleting clean.py will make load_dataset immediately able to parse the files.

clean.py

@@ -1,153 +0,0 @@
-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("evaluation.csv", "w") as f:
-    eval_df.to_csv(f)
-with open("test.csv", "w") as f:
-    test_df.to_csv(f)