platform/aiml/etl/quantum_preprocessing_pipeline.py

#!/usr/bin/env python3

"""
QUANTUM-GRADE DATA PREPROCESSING PIPELINE
Bleeding Edge Deduplication, Normalization & Tokenization
Aurora - ETL Systems Specialist
"""

import os
import re
import json
import hashlib
import numpy as np
from datasketch import MinHash, MinHashLSH
from bs4 import BeautifulSoup
import html2text
import ftfy
from unidecode import unidecode
from langdetect import detect
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import spacy
from transformers import AutoTokenizer
import concurrent.futures
from tqdm import tqdm
from pathlib import Path

# Download NLTK data
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)

class QuantumPreprocessor:
    def __init__(self):
        # Load bleeding edge models
        self.nlp = spacy.load("en_core_web_sm", disable=['parser', 'ner'])
        self.tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
        self.stemmer = PorterStemmer()
        self.stop_words = set(stopwords.words('english'))
        
        # MinHash LSH for deduplication
        self.lsh = MinHashLSH(threshold=0.8, num_perm=128)
        self.minhashes = {}
        
        # Quality thresholds
        self.min_length = 50  # characters
        self.max_length = 10000
        self.min_quality_score = 0.7
    
    def advanced_html_cleaning(self, text):
        """Ultra-aggressive HTML/XML cleaning"""
        if not text:
            return ""
        
        # Convert HTML to clean text
        h = html2text.HTML2Text()
        h.ignore_links = False
        h.ignore_images = True
        h.ignore_emphasis = False
        h.body_width = 0
        
        cleaned = h.handle(text)
        
        # Remove residual markup
        cleaned = re.sub(r'\[.*?\]\(.*?\)', '', cleaned)  # Markdown links
        cleaned = re.sub(r'\*\*.*?\*\*', '', cleaned)  # Bold
        cleaned = re.sub(r'\*.*?\*', '', cleaned)  # Italic
        cleaned = re.sub(r'`.*?`', '', cleaned)  # Code
        
        return cleaned.strip()
    
    def unicode_normalization(self, text):
        """Fix all unicode issues"""
        text = ftfy.fix_text(text)
        text = unidecode(text)  # Convert to ASCII
        return text
    
    def aggressive_cleaning(self, text):
        """Bleeding edge text cleaning"""
        # Remove emails
        text = re.sub(r'\S*@\S*\s?', '', text)
        
        # Remove URLs
        text = re.sub(r'http\S+|www\.\S+', '', text)
        
        # Remove special characters but keep basic punctuation
        text = re.sub(r'[^\w\s.,!?;:\-\'\"()]', '', text)
        
        # Normalize whitespace
        text = re.sub(r'\s+', ' ', text)
        
        return text.strip()
    
    def language_detection(self, text):
        """Detect and filter non-English content"""
        try:
            lang = detect(text[:500])
            return lang == 'en'
        except:
            return False
    
    def quality_scoring(self, text):
        """Compute text quality score"""
        if len(text) < self.min_length:
            return 0.0
        
        # Sentence length variety
        sentences = sent_tokenize(text)
        if len(sentences) < 2:
            return 0.3
        
        # Word diversity
        words = word_tokenize(text.lower())
        unique_words = len(set(words))
        diversity = unique_words / len(words) if words else 0
        
        # Stopword ratio (should be reasonable)
        stopword_count = sum(1 for word in words if word in self.stop_words)
        stopword_ratio = stopword_count / len(words) if words else 0
        
        # Length score
        length_score = min(1.0, len(text) / 1000)
        
        # Composite score
        score = (diversity * 0.3 + 
                (0.5 - abs(stopword_ratio - 0.3)) * 0.3 + 
                length_score * 0.4)
        
        return max(0.0, min(1.0, score))
    
    def minhash_signature(self, text):
        """Create MinHash signature for deduplication"""
        words = text.lower().split()
        m = MinHash(num_perm=128)
        
        for word in words:
            m.update(word.encode('utf8'))
        
        return m
    
    def is_duplicate(self, text, doc_id):
        """Check if text is duplicate using MinHash LSH"""
        if len(text) < 100:  # Too short for reliable deduplication
            return False
        
        m = self.minhash_signature(text)
        
        # Check for similar documents
        results = self.lsh.query(m)
        
        if results:
            return True
        
        # Add to index if not duplicate
        self.lsh.insert(doc_id, m)
        self.minhashes[doc_id] = m
        return False
    
    def advanced_tokenization(self, text):
        """Bleeding edge tokenization with multiple strategies"""
        # GPT-style tokenization
        gpt_tokens = self.tokenizer.tokenize(text)
        
        # SpaCy linguistic tokenization
        doc = self.nlp(text)
        spacy_tokens = [token.text for token in doc]
        
        # NLTK tokenization
        nltk_tokens = word_tokenize(text)
        
        # Return all for ensemble processing
        return {
            'gpt': gpt_tokens,
            'spacy': spacy_tokens,
            'nltk': nltk_tokens,
            'raw_text': text
        }
    
    def process_document(self, text, doc_id):
        """Full quantum-grade processing pipeline"""
        if not text or len(text.strip()) < self.min_length:
            return None
        
        # Step 1: HTML cleaning
        cleaned = self.advanced_html_cleaning(text)
        
        # Step 2: Unicode normalization
        cleaned = self.unicode_normalization(cleaned)
        
        # Step 3: Aggressive cleaning
        cleaned = self.aggressive_cleaning(cleaned)
        
        # Step 4: Language detection
        if not self.language_detection(cleaned):
            return None
        
        # Step 5: Quality scoring
        quality_score = self.quality_scoring(cleaned)
        if quality_score < self.min_quality_score:
            return None
        
        # Step 6: Deduplication
        if self.is_duplicate(cleaned, doc_id):
            return None
        
        # Step 7: Tokenization
        tokens = self.advanced_tokenization(cleaned)
        
        return {
            'id': doc_id,
            'original_length': len(text),
            'cleaned_length': len(cleaned),
            'quality_score': quality_score,
            'tokens': tokens,
            'cleaned_text': cleaned,
            'token_count': len(tokens['gpt'])
        }
    
    def process_batch(self, texts, doc_ids):
        """Process batch of documents in parallel"""
        results = []
        
        with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
            futures = []
            for text, doc_id in zip(texts, doc_ids):
                futures.append(executor.submit(self.process_document, text, doc_id))
            
            for future in tqdm(concurrent.futures.as_completed(futures), 
                             total=len(futures), desc="Processing documents"):
                result = future.result()
                if result:
                    results.append(result)
        
        return results

def main():
    print("🚀 QUANTUM-GRADE PREPROCESSING PIPELINE INITIALIZED")
    print("=" * 60)
    
    # Initialize preprocessor
    preprocessor = QuantumPreprocessor()
    
    # Test with sample data
    test_texts = [
        "Quantum computing represents the next frontier in computational power.",
        "Machine learning models require massive datasets for effective training.",
        "The quick brown fox jumps over the lazy dog.",
        "Quantum computing represents the next frontier in computational power."  # Duplicate
    ]
    
    test_ids = [f"test_{i}" for i in range(len(test_texts))]
    
    results = preprocessor.process_batch(test_texts, test_ids)
    
    print(f"\n✅ Processed {len(results)}/{len(test_texts)} documents")
    print(f"📊 Deduplication removed {len(test_texts) - len(results)} duplicates")
    
    for result in results:
        print(f"\n📄 Document {result['id']}:")
        print(f"   Quality: {result['quality_score']:.3f}")
        print(f"   Tokens: {result['token_count']}")
        print(f"   Preview: {result['cleaned_text'][:100]}...")

if __name__ == "__main__":
    main()