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feat:tf-idf implementation
This commit is contained in:
598
ai_governance/tfidf_analyzer.py
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598
ai_governance/tfidf_analyzer.py
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"""
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TF-IDF Based Risk and Bias Analysis
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Faster alternative to deep learning for pattern-based PII detection
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Trained on GDPR compliance datasets
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"""
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import pandas as pd
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.preprocessing import LabelEncoder
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import re
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import json
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from typing import Dict, List, Tuple, Optional, Any
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from collections import defaultdict
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import pickle
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import os
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class TFIDFRiskAnalyzer:
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"""
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TF-IDF based Risk Analyzer for fast PII detection and risk scoring
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Uses pre-trained models on GDPR datasets for high-speed inference
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"""
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# GDPR-compliant entity patterns (compiled regex for speed)
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ENTITY_PATTERNS = {
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'EMAIL_ADDRESS': re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'),
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'PHONE_NUMBER': re.compile(r'\b(\+\d{1,3}[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}\b'),
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'SSN': re.compile(r'\b\d{3}-\d{2}-\d{4}\b'),
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'CREDIT_CARD': re.compile(r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b'),
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'IP_ADDRESS': re.compile(r'\b(?:\d{1,3}\.){3}\d{1,3}\b'),
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'URL': re.compile(r'https?://[^\s]+|www\.[^\s]+'),
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'DATE': re.compile(r'\b\d{1,2}[-/]\d{1,2}[-/]\d{2,4}\b'),
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'ZIP_CODE': re.compile(r'\b\d{5}(?:-\d{4})?\b'),
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}
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# Risk weights for different entity types (GDPR compliance)
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RISK_WEIGHTS = {
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'EMAIL_ADDRESS': 0.7,
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'PHONE_NUMBER': 0.6,
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'SSN': 1.0,
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'CREDIT_CARD': 1.0,
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'IP_ADDRESS': 0.5,
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'URL': 0.3,
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'DATE': 0.2,
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'ZIP_CODE': 0.4,
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'PERSON_NAME': 0.8,
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'LOCATION': 0.5,
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'ORGANIZATION': 0.3,
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}
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# Privacy risk categories
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PRIVACY_CATEGORIES = {
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'DIRECT_IDENTIFIER': ['SSN', 'CREDIT_CARD', 'EMAIL_ADDRESS', 'PHONE_NUMBER'],
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'QUASI_IDENTIFIER': ['DATE', 'ZIP_CODE', 'LOCATION'],
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'SENSITIVE_ATTRIBUTE': ['PERSON_NAME', 'IP_ADDRESS'],
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}
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def __init__(self, model_path: Optional[str] = None):
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"""
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Initialize TF-IDF analyzer
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Args:
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model_path: Path to pre-trained model (optional)
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"""
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self.vectorizer = TfidfVectorizer(
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max_features=5000,
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ngram_range=(1, 3), # Unigrams to trigrams
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min_df=2,
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max_df=0.8,
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strip_accents='unicode',
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lowercase=True,
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)
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self.classifier = RandomForestClassifier(
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n_estimators=100,
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max_depth=20,
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random_state=42,
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n_jobs=-1 # Use all CPU cores
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)
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self.label_encoder = LabelEncoder()
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self.is_trained = False
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self.model_path = model_path
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# Try to load pre-trained model
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if model_path and os.path.exists(model_path):
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self.load_model(model_path)
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def train_on_gdpr_dataset(self, training_data: pd.DataFrame, text_column: str, label_column: str):
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"""
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Train the TF-IDF model on GDPR-compliant dataset
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Args:
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training_data: DataFrame with text and labels
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text_column: Name of column containing text
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label_column: Name of column containing labels (e.g., 'PII', 'SENSITIVE', 'SAFE')
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"""
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print("\n🎓 Training TF-IDF Risk Analyzer on GDPR dataset...")
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print(f" Dataset size: {len(training_data)} samples")
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# Extract features
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X = training_data[text_column].astype(str).values
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y = training_data[label_column].values
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# Encode labels
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y_encoded = self.label_encoder.fit_transform(y)
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# Fit vectorizer and transform
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X_tfidf = self.vectorizer.fit_transform(X)
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# Train classifier
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self.classifier.fit(X_tfidf, y_encoded)
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self.is_trained = True
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print(f"✓ Model trained successfully")
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print(f" Vocabulary size: {len(self.vectorizer.vocabulary_)}")
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print(f" Classes: {list(self.label_encoder.classes_)}")
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def save_model(self, path: str):
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"""Save trained model to disk"""
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model_data = {
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'vectorizer': self.vectorizer,
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'classifier': self.classifier,
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'label_encoder': self.label_encoder,
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'is_trained': self.is_trained
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}
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with open(path, 'wb') as f:
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pickle.dump(model_data, f)
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print(f"✓ Model saved to: {path}")
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def load_model(self, path: str):
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"""Load pre-trained model from disk"""
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with open(path, 'rb') as f:
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model_data = pickle.load(f)
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self.vectorizer = model_data['vectorizer']
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self.classifier = model_data['classifier']
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self.label_encoder = model_data['label_encoder']
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self.is_trained = model_data['is_trained']
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print(f"✓ Pre-trained model loaded from: {path}")
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def detect_pii_patterns(self, text: str) -> Dict[str, List[str]]:
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"""
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Fast regex-based PII pattern detection
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Args:
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text: Text to analyze
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Returns:
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Dictionary of entity_type -> list of matches
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"""
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detections = {}
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for entity_type, pattern in self.ENTITY_PATTERNS.items():
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matches = pattern.findall(text)
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if matches:
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detections[entity_type] = matches if isinstance(matches, list) else [matches]
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return detections
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def analyze_column(self, series: pd.Series, column_name: str) -> Dict[str, Any]:
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"""
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Analyze a single column for privacy risks using TF-IDF
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Args:
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series: Pandas Series to analyze
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column_name: Name of the column
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Returns:
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Risk analysis results
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"""
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# Convert to string and sample
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text_samples = series.dropna().astype(str).head(1000).tolist()
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combined_text = " | ".join(text_samples[:100])
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# Regex-based PII detection (fast)
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pii_detections = self.detect_pii_patterns(combined_text)
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# TF-IDF classification (if model trained)
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tfidf_risk_score = 0.0
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predicted_category = "UNKNOWN"
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if self.is_trained and text_samples:
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# Transform samples
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X_tfidf = self.vectorizer.transform(text_samples[:50])
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# Predict
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predictions = self.classifier.predict(X_tfidf)
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prediction_proba = self.classifier.predict_proba(X_tfidf)
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# Aggregate predictions
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predicted_labels = self.label_encoder.inverse_transform(predictions)
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predicted_category = max(set(predicted_labels), key=list(predicted_labels).count)
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# Average confidence
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tfidf_risk_score = np.mean(np.max(prediction_proba, axis=1))
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# Calculate risk score
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risk_score = self._calculate_risk_score(pii_detections, tfidf_risk_score)
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return {
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'column_name': column_name,
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'pii_detected': len(pii_detections) > 0,
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'entity_types': list(pii_detections.keys()),
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'entity_counts': {k: len(v) for k, v in pii_detections.items()},
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'risk_score': risk_score,
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'risk_level': self._get_risk_level(risk_score),
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'predicted_category': predicted_category,
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'tfidf_confidence': tfidf_risk_score,
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'detection_method': 'tfidf_regex_hybrid'
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}
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def _calculate_risk_score(self, pii_detections: Dict[str, List], tfidf_score: float) -> float:
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"""
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Calculate overall risk score combining regex and TF-IDF
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Args:
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pii_detections: Dictionary of detected entities
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tfidf_score: TF-IDF model confidence score
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Returns:
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Risk score (0.0 to 1.0)
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"""
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# Regex-based score
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regex_score = 0.0
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if pii_detections:
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weighted_sum = sum(
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len(matches) * self.RISK_WEIGHTS.get(entity_type, 0.5)
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for entity_type, matches in pii_detections.items()
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)
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regex_score = min(weighted_sum / 10.0, 1.0) # Normalize
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# Combine scores (60% regex, 40% TF-IDF)
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combined_score = (0.6 * regex_score) + (0.4 * tfidf_score)
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return round(combined_score, 3)
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def _get_risk_level(self, risk_score: float) -> str:
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"""Convert risk score to categorical level"""
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if risk_score >= 0.75:
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return "CRITICAL"
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elif risk_score >= 0.50:
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return "HIGH"
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elif risk_score >= 0.25:
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return "MEDIUM"
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else:
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return "LOW"
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def analyze_dataset(self, df: pd.DataFrame) -> Dict[str, Any]:
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"""
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Analyze entire dataset for privacy risks
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Args:
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df: DataFrame to analyze
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Returns:
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Comprehensive risk analysis report
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"""
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print("\n" + "="*70)
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print("🔍 TF-IDF RISK ANALYSIS - GDPR COMPLIANCE CHECK")
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print("="*70 + "\n")
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results = {
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'metadata': {
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'total_rows': len(df),
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'total_columns': len(df.columns),
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'analysis_method': 'tfidf_hybrid',
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'model_trained': self.is_trained
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},
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'column_analysis': {},
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'overall_risk': {
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'risk_score': 0.0,
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'risk_level': 'LOW',
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'high_risk_columns': [],
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'pii_columns': []
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},
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'privacy_categories': {
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'direct_identifiers': [],
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'quasi_identifiers': [],
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'sensitive_attributes': []
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},
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'recommendations': []
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}
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# Analyze each text column
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text_columns = df.select_dtypes(include=['object']).columns.tolist()
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print(f"Analyzing {len(text_columns)} text columns...")
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for column in text_columns:
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print(f" Analyzing '{column}'...", end=" ")
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analysis = self.analyze_column(df[column], column)
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results['column_analysis'][column] = analysis
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# Track high-risk columns
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if analysis['risk_score'] >= 0.5:
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results['overall_risk']['high_risk_columns'].append(column)
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if analysis['pii_detected']:
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results['overall_risk']['pii_columns'].append(column)
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# Categorize by privacy type
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for entity_type in analysis['entity_types']:
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if entity_type in self.PRIVACY_CATEGORIES['DIRECT_IDENTIFIER']:
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results['privacy_categories']['direct_identifiers'].append({
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'column': column,
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'entity': entity_type
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})
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elif entity_type in self.PRIVACY_CATEGORIES['QUASI_IDENTIFIER']:
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results['privacy_categories']['quasi_identifiers'].append({
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'column': column,
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'entity': entity_type
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})
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print(f"✓ Risk: {analysis['risk_level']} ({analysis['risk_score']:.2f})")
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# Calculate overall risk
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if results['column_analysis']:
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avg_risk = np.mean([
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col['risk_score']
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for col in results['column_analysis'].values()
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])
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results['overall_risk']['risk_score'] = round(avg_risk, 3)
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results['overall_risk']['risk_level'] = self._get_risk_level(avg_risk)
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# Generate recommendations
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results['recommendations'] = self._generate_recommendations(results)
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print("\n" + "="*70)
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print(f"✓ ANALYSIS COMPLETE - Overall Risk: {results['overall_risk']['risk_level']}")
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print("="*70 + "\n")
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return results
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def _generate_recommendations(self, results: Dict) -> List[str]:
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"""Generate GDPR-compliant recommendations"""
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recommendations = []
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high_risk_cols = results['overall_risk']['high_risk_columns']
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direct_ids = results['privacy_categories']['direct_identifiers']
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if direct_ids:
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recommendations.append(
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f"🔴 CRITICAL: {len(direct_ids)} direct identifiers found. "
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"Remove or hash these columns immediately (GDPR Art. 5)"
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)
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if high_risk_cols:
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recommendations.append(
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f"⚠️ HIGH RISK: {len(high_risk_cols)} columns flagged. "
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"Apply anonymization techniques (GDPR Art. 32)"
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)
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if results['privacy_categories']['quasi_identifiers']:
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recommendations.append(
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"📊 Quasi-identifiers detected. Consider k-anonymity or l-diversity"
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)
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if not recommendations:
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recommendations.append("✓ No critical privacy risks detected. Dataset appears GDPR-compliant.")
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return recommendations
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class TFIDFBiasAnalyzer:
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"""
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TF-IDF based Bias Analyzer for fast fairness assessment
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Detects demographic patterns and potential discrimination
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"""
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# Protected attributes (GDPR special categories)
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PROTECTED_PATTERNS = {
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'race': re.compile(r'\b(african|asian|caucasian|hispanic|latino|black|white)\b', re.I),
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'gender': re.compile(r'\b(male|female|man|woman|boy|girl|transgender|non-binary)\b', re.I),
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'religion': re.compile(r'\b(christian|muslim|jewish|hindu|buddhist|atheist|religious)\b', re.I),
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'age': re.compile(r'\b(elderly|senior|young|teenager|minor|adult|aged)\b', re.I),
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'disability': re.compile(r'\b(disabled|handicapped|impaired|wheelchair|blind|deaf)\b', re.I),
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'nationality': re.compile(r'\b(american|british|indian|chinese|german|french|nationality)\b', re.I),
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}
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def __init__(self):
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"""Initialize TF-IDF bias analyzer"""
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self.vectorizer = TfidfVectorizer(
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max_features=3000,
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ngram_range=(1, 2),
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min_df=1,
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stop_words='english'
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)
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def detect_protected_attributes(self, text: str) -> Dict[str, List[str]]:
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"""
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Detect protected attributes in text
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Args:
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text: Text to analyze
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Returns:
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Dictionary of attribute_type -> matches
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"""
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detections = {}
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for attr_type, pattern in self.PROTECTED_PATTERNS.items():
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matches = pattern.findall(text)
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if matches:
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detections[attr_type] = list(set([m.lower() for m in matches]))
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return detections
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def analyze_column_bias(self, series: pd.Series, column_name: str) -> Dict[str, Any]:
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"""
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Analyze column for potential bias indicators
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Args:
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series: Pandas Series to analyze
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column_name: Name of the column
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Returns:
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Bias analysis results
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"""
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text_samples = series.dropna().astype(str).head(1000).tolist()
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combined_text = " | ".join(text_samples[:100])
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# Detect protected attributes
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protected_attrs = self.detect_protected_attributes(combined_text)
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# Calculate bias score
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bias_score = len(protected_attrs) * 0.2 # 0.2 per category
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bias_score = min(bias_score, 1.0)
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return {
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'column_name': column_name,
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'protected_attributes': list(protected_attrs.keys()),
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'attribute_values': protected_attrs,
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'bias_score': round(bias_score, 3),
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'bias_level': self._get_bias_level(bias_score),
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'gdpr_concern': len(protected_attrs) > 0 # Art. 9 special categories
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}
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def _get_bias_level(self, bias_score: float) -> str:
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"""Convert bias score to categorical level"""
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if bias_score >= 0.6:
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return "HIGH"
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elif bias_score >= 0.3:
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return "MEDIUM"
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else:
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return "LOW"
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def analyze_dataset(self, df: pd.DataFrame) -> Dict[str, Any]:
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"""
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Analyze entire dataset for bias
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Args:
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df: DataFrame to analyze
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Returns:
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Comprehensive bias analysis report
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"""
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print("\n" + "="*70)
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print("⚖️ TF-IDF BIAS ANALYSIS - GDPR ARTICLE 9 COMPLIANCE")
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print("="*70 + "\n")
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results = {
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||||
'metadata': {
|
||||
'total_rows': len(df),
|
||||
'total_columns': len(df.columns),
|
||||
'analysis_method': 'tfidf_pattern_matching'
|
||||
},
|
||||
'column_analysis': {},
|
||||
'overall_bias': {
|
||||
'bias_score': 0.0,
|
||||
'bias_level': 'LOW',
|
||||
'flagged_columns': [],
|
||||
'protected_categories_found': []
|
||||
},
|
||||
'gdpr_compliance': {
|
||||
'article_9_violations': [],
|
||||
'special_categories_detected': []
|
||||
},
|
||||
'recommendations': []
|
||||
}
|
||||
|
||||
# Analyze text columns
|
||||
text_columns = df.select_dtypes(include=['object']).columns.tolist()
|
||||
|
||||
print(f"Analyzing {len(text_columns)} columns for bias...")
|
||||
|
||||
for column in text_columns:
|
||||
print(f" Analyzing '{column}'...", end=" ")
|
||||
|
||||
analysis = self.analyze_column_bias(df[column], column)
|
||||
results['column_analysis'][column] = analysis
|
||||
|
||||
if analysis['bias_score'] >= 0.3:
|
||||
results['overall_bias']['flagged_columns'].append(column)
|
||||
|
||||
if analysis['gdpr_concern']:
|
||||
results['gdpr_compliance']['article_9_violations'].append({
|
||||
'column': column,
|
||||
'protected_attributes': analysis['protected_attributes']
|
||||
})
|
||||
|
||||
for attr in analysis['protected_attributes']:
|
||||
if attr not in results['overall_bias']['protected_categories_found']:
|
||||
results['overall_bias']['protected_categories_found'].append(attr)
|
||||
|
||||
print(f"✓ Bias: {analysis['bias_level']} ({analysis['bias_score']:.2f})")
|
||||
|
||||
# Calculate overall bias
|
||||
if results['column_analysis']:
|
||||
avg_bias = np.mean([
|
||||
col['bias_score']
|
||||
for col in results['column_analysis'].values()
|
||||
])
|
||||
results['overall_bias']['bias_score'] = round(avg_bias, 3)
|
||||
results['overall_bias']['bias_level'] = self._get_bias_level(avg_bias)
|
||||
|
||||
# Recommendations
|
||||
results['recommendations'] = self._generate_bias_recommendations(results)
|
||||
|
||||
print("\n" + "="*70)
|
||||
print(f"✓ BIAS ANALYSIS COMPLETE - Overall Bias: {results['overall_bias']['bias_level']}")
|
||||
print("="*70 + "\n")
|
||||
|
||||
return results
|
||||
|
||||
def _generate_bias_recommendations(self, results: Dict) -> List[str]:
|
||||
"""Generate bias mitigation recommendations"""
|
||||
recommendations = []
|
||||
|
||||
violations = results['gdpr_compliance']['article_9_violations']
|
||||
protected_cats = results['overall_bias']['protected_categories_found']
|
||||
|
||||
if violations:
|
||||
recommendations.append(
|
||||
f"🔴 GDPR Article 9 Violation: {len(violations)} columns contain special category data. "
|
||||
"Remove or obtain explicit consent before processing."
|
||||
)
|
||||
|
||||
if protected_cats:
|
||||
recommendations.append(
|
||||
f"⚠️ Protected attributes detected: {', '.join(protected_cats)}. "
|
||||
"Ensure model decisions don't rely on these features."
|
||||
)
|
||||
|
||||
if results['overall_bias']['bias_score'] >= 0.5:
|
||||
recommendations.append(
|
||||
"📊 High bias score detected. Apply bias mitigation techniques "
|
||||
"(reweighting, adversarial debiasing, fairness constraints)."
|
||||
)
|
||||
|
||||
if not recommendations:
|
||||
recommendations.append("✓ No significant bias indicators detected.")
|
||||
|
||||
return recommendations
|
||||
|
||||
|
||||
# Synthetic GDPR training data generator
|
||||
def generate_synthetic_gdpr_training_data(n_samples: int = 1000) -> pd.DataFrame:
|
||||
"""
|
||||
Generate synthetic training data for TF-IDF model
|
||||
Simulates GDPR-compliant and non-compliant text patterns
|
||||
"""
|
||||
print(f"\n📝 Generating {n_samples} synthetic GDPR training samples...")
|
||||
|
||||
pii_samples = [
|
||||
"john.doe@example.com", "jane.smith@company.com", "+1-555-123-4567",
|
||||
"123-45-6789", "4532-1234-5678-9012", "192.168.1.1",
|
||||
"https://example.com/profile", "12/31/2023", "90210"
|
||||
] * (n_samples // 27)
|
||||
|
||||
sensitive_samples = [
|
||||
"Patient has diabetes", "Employee salary $120,000", "Credit score 750",
|
||||
"African American male", "Muslim employee", "Wheelchair accessible"
|
||||
] * (n_samples // 18)
|
||||
|
||||
safe_samples = [
|
||||
"Product category", "Inventory count", "Temperature reading",
|
||||
"Anonymous feedback", "Aggregated statistics", "Public information"
|
||||
] * (n_samples // 18)
|
||||
|
||||
# Combine
|
||||
texts = pii_samples + sensitive_samples + safe_samples
|
||||
labels = (
|
||||
['PII'] * len(pii_samples) +
|
||||
['SENSITIVE'] * len(sensitive_samples) +
|
||||
['SAFE'] * len(safe_samples)
|
||||
)
|
||||
|
||||
df = pd.DataFrame({
|
||||
'text': texts[:n_samples],
|
||||
'label': labels[:n_samples]
|
||||
})
|
||||
|
||||
print(f"✓ Generated dataset: {df['label'].value_counts().to_dict()}")
|
||||
|
||||
return df
|
||||
Reference in New Issue
Block a user