import numpy as np
import pandas as pd
import logging
from typing import List, Dict, Any, Union, Optional
from sklearn.base import ClassifierMixin # For classification models
from sklearn.metrics import accuracy_score
from scipy.stats import pearsonr

# Set up basic logging for this module
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
logger = logging.getLogger(__name__)

class PrivacyAuditor:
    """
    A class to assess potential privacy risks in machine learning models,
    including conceptual membership inference attacks and feature leakage detection.
    """
    def __init__(self):
        logger.info("PrivacyAuditor initialized.")

    def conduct_membership_inference_attack(self,
                                            model: ClassifierMixin,
                                            X_train: Union[np.ndarray, pd.DataFrame],
                                            y_train: np.ndarray,
                                            X_test: Union[np.ndarray, pd.DataFrame],
                                            y_test: np.ndarray,
                                            confidence_threshold: float = 0.9,
                                            random_state: Optional[int] = None) -> Dict[str, Any]:
        """
        Conducts a conceptual membership inference attack.
        This simplified version infers membership based on the model's confidence
        in its predictions for training vs. non-training data. Models tend to be
        more confident on data they've seen during training.

        A true membership inference attack (MIA) often involves training a shadow model
        or requires access to model logits/gradients and specific attack models.
        This implementation provides a basic heuristic using prediction confidence.

        Args:
            model (ClassifierMixin): The trained classifier model.
            X_train (Union[np.ndarray, pd.DataFrame]): Training features.
            y_train (np.ndarray): Training labels.
            X_test (Union[np.ndarray, pd.DataFrame]): Test/non-training features.
            y_test (np.ndarray): Test/non-training labels.
            confidence_threshold (float): Probability threshold above which a prediction
                                          is considered "high confidence".
            random_state (Optional[int]): Seed for reproducibility.

        Returns:
            Dict[str, Any]: A report on the attack's findings, including accuracy of inference.
        """
        logger.info("Starting conceptual membership inference attack.")
        np.random.seed(random_state)

        if not hasattr(model, 'predict_proba'):
            logger.error("Model does not support `predict_proba`. Cannot conduct confidence-based membership inference.")
            return {"status": "Error", "message": "Model must have `predict_proba` method."}

        X_train_np = X_train.to_numpy() if isinstance(X_train, pd.DataFrame) else X_train
        X_test_np = X_test.to_numpy() if isinstance(X_test, pd.DataFrame) else X_test

        # Get confidence scores for training data
        train_probs = model.predict_proba(X_train_np)
        train_preds = np.argmax(train_probs, axis=1)
        train_confidences = train_probs[np.arange(len(train_preds)), train_preds] # Confidence for predicted class

        # Get confidence scores for non-training data
        test_probs = model.predict_proba(X_test_np)
        test_preds = np.argmax(test_probs, axis=1)
        test_confidences = test_probs[np.arange(len(test_preds)), test_preds] # Confidence for predicted class

        # Heuristic: If confidence is high AND prediction is correct, it's more likely to be a member
        # A simpler heuristic for MIA: samples with high confidence AND correct prediction are "inferred members"
        
        # Infer members:
        # A sample is inferred as a member if the model predicts it correctly with high confidence.
        # This is a strong simplification for MIA; real MIAs use attack models.
        
        # Let's refine the heuristic for a *conceptual* attack:
        # Compare distribution of confidences between training and test sets.
        # If training set has significantly higher average confidence (or a different distribution tail),
        # it suggests potential leakage.
        
        avg_train_confidence = np.mean(train_confidences)
        avg_test_confidence = np.mean(test_confidences)

        # For the conceptual "attack", let's classify a sample as "member" if its confidence is above threshold.
        # Then, we evaluate how accurate this "attack" is on true members vs. non-members.
        
        # Create labels for the "attack" model's evaluation:
        # 1 for actual training members, 0 for actual non-members (test set)
        
        # Predicted labels for the "attack" model:
        # 1 if inferred as member (confidence > threshold), 0 otherwise
        
        inferred_train_members = (train_confidences > confidence_threshold).astype(int)
        inferred_test_non_members = (test_confidences <= confidence_threshold).astype(int) # Infer non-members if confidence is not high

        # Combine results to evaluate the inference attack accuracy
        # True labels for inference attack: 1 for training, 0 for test
        y_attack_true = np.concatenate([np.ones(len(X_train_np)), np.zeros(len(X_test_np))])
        # Predicted labels for inference attack: based on confidence heuristic
        y_attack_pred = np.concatenate([inferred_train_members, (test_confidences > confidence_threshold).astype(int)]) # Corrected this part to include test set inference too

        inference_accuracy = accuracy_score(y_attack_true, y_attack_pred)

        report = {
            "status": "success",
            "message": "Conceptual membership inference attack completed.",
            "confidence_threshold": confidence_threshold,
            "avg_train_confidence": float(avg_train_confidence),
            "avg_test_confidence": float(avg_test_confidence),
            "inference_accuracy": float(inference_accuracy),
            "inferred_members_count_train_set": int(np.sum(inferred_train_members)),
            "inferred_members_count_test_set": int(np.sum((test_confidences > confidence_threshold).astype(int))),
            "recommendations": []
        }

        if avg_train_confidence > avg_test_confidence * 1.1: # Heuristic for significant difference
            report['recommendations'].append("Training set samples tend to have notably higher confidence, suggesting potential for membership inference.")
            report['recommendations'].append("Consider techniques like differential privacy or more careful data splitting/augmenation.")
        elif inference_accuracy > 0.6: # If the simple inference attack is somewhat accurate
            report['recommendations'].append(f"Simple confidence-based inference attack achieved {inference_accuracy:.2f} accuracy, indicating some privacy vulnerability.")
            report['recommendations'].append("Further investigation with advanced MIA methods (e.g., black-box attacks) is recommended.")
        else:
            report['recommendations'].append("No strong indicators of membership leakage detected by this conceptual attack.")

        logger.info(f"Membership inference attack accuracy: {inference_accuracy:.4f}")
        return report

    def detect_feature_leakage(self,
                               df: pd.DataFrame,
                               target_column: str,
                               feature_columns: Optional[List[str]] = None,
                               correlation_threshold: float = 0.9) -> Dict[str, Any]:
        """
        Detects potential feature leakage by identifying features that are highly
        correlated with the target column (or other features) in a way that might
        not be known at inference time.

        This is a heuristic method, primarily looking at Pearson correlation.
        True leakage detection often requires domain expertise and understanding
        of data collection processes.

        Args:
            df (pd.DataFrame): The input DataFrame.
            target_column (str): The name of the target column (e.g., 'y_true', 'label').
            feature_columns (Optional[List[str]]): List of feature columns to check.
                                                    If None, all numerical columns except target are checked.
            correlation_threshold (float): Absolute correlation value above which
                                           a feature is flagged as potentially leaking.

        Returns:
            Dict[str, Any]: A report on detected leakage, including highly correlated features.
        """
        logger.info(f"Starting feature leakage detection with correlation threshold={correlation_threshold}.")

        if target_column not in df.columns:
            logger.error(f"Target column '{target_column}' not found in DataFrame.")
            return {"status": "Error", "message": f"Target column '{target_column}' missing."}

        numerical_cols = df.select_dtypes(include=np.number).columns.tolist()
        if target_column in numerical_cols:
            numerical_cols.remove(target_column) # Exclude target from features if it's numerical

        if feature_columns is None:
            cols_to_check = numerical_cols
        else:
            cols_to_check = [col for col in feature_columns if col in numerical_cols]

        if not cols_to_check:
            logger.warning("No numerical features to check for leakage. Skipping.")
            return {"status": "No numerical features", "message": "No numerical features found for correlation check."}

        potential_leakage = []

        for feature_col in cols_to_check:
            try:
                # Calculate Pearson correlation coefficient
                correlation, _ = pearsonr(df[feature_col].dropna(), df[target_column].dropna())
                
                if abs(correlation) >= correlation_threshold:
                    potential_leakage.append({
                        "feature": feature_col,
                        "target_correlation": float(correlation),
                        "reason": f"High correlation ({correlation:.4f}) with target '{target_column}'.",
                        "recommendation": "Investigate if this feature is available at inference time without data leakage. If not, remove it or process it differently."
                    })
            except Exception as e:
                logger.warning(f"Could not calculate correlation for '{feature_col}': {e}")
        
        report = {
            "status": "success",
            "message": "Feature leakage detection completed.",
            "correlation_threshold": correlation_threshold,
            "potential_leakage_features": potential_leakage,
            "summary": "No highly correlated features detected." if not potential_leakage else "Potential leakage features detected."
        }

        if potential_leakage:
            logger.warning(f"Detected {len(potential_leakage)} potential feature leakage issues.")
        else:
            logger.info("No high correlation potential feature leakage detected.")
        
        return report

# --- Example Usage ---
if __name__ == "__main__":
    print("--- Testing py_ai_trust.privacy ---")

    np.random.seed(42)

    # --- Setup: Dummy Classifier Model ---
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.datasets import make_classification

    # Generate a synthetic classification dataset
    X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0,
                               n_clusters_per_class=1, random_state=42, flip_y=0.1)
    
    # Split into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    # Train a simple Logistic Regression model
    model = LogisticRegression(solver='liblinear', random_state=42, max_iter=1000)
    model.fit(X_train, y_train)

    print(f"Model trained on {X_train.shape[0]} samples. Test accuracy: {accuracy_score(y_test, model.predict(X_test)):.4f}")
    print("-" * 50)

    auditor = PrivacyAuditor()

    # --- Test 1: Membership Inference Attack ---
    print("\n--- Test 1: Conducting Conceptual Membership Inference Attack ---")
    mia_report = auditor.conduct_membership_inference_attack(
        model=model,
        X_train=X_train,
        y_train=y_train,
        X_test=X_test,
        y_test=y_test,
        confidence_threshold=0.9, # Adjust this based on model behavior
        random_state=42
    )
    print("Membership Inference Attack Report:")
    print(json.dumps(mia_report, indent=4))
    print("-" * 50)

    # --- Test 2: Feature Leakage Detection ---
    print("\n--- Test 2: Detecting Feature Leakage ---")
    
    # Create a dummy DataFrame with a leaking feature
    df_leak = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
    df_leak['target'] = y # Add target column
    
    # Create a 'leaking' feature: highly correlated with the target
    # e.g., a feature that is literally a slightly noisy version of the target or a very strong predictor
    # Let's make 'feature_10' highly correlated with 'target'
    df_leak['leaky_feature'] = df_leak['target'] * 0.9 + np.random.normal(0, 0.1, num_samples)
    
    # Also, a feature that might accidentally leak part of another feature
    df_leak['derived_feature_from_f0'] = df_leak['feature_0'] * 0.95 + np.random.normal(0, 0.05, num_samples)

    leakage_report = auditor.detect_feature_leakage(
        df=df_leak,
        target_column='target',
        correlation_threshold=0.8 # Set a high threshold for clear leakage
    )
    print("Feature Leakage Detection Report:")
    print(json.dumps(leakage_report, indent=4))
    print("-" * 50)
    
    # Test with specific columns
    print("\n--- Test 2.1: Detecting Feature Leakage (Specific Columns) ---")
    leakage_report_specific = auditor.detect_feature_leakage(
        df=df_leak,
        target_column='target',
        feature_columns=['feature_0', 'leaky_feature'], # Only check these
        correlation_threshold=0.8
    )
    print("Feature Leakage Detection Report (Specific Columns):")
    print(json.dumps(leakage_report_specific, indent=4))
    print("-" * 50)

    print("\n--- py_ai_trust.privacy testing complete ---")
