import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.preprocessing import StandardScaler
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.algorithms.optimizers import SPSA

class ClassicalExplainability:
    """Classical explainability methods"""
    def __init__(self):
        self.scaler = StandardScaler()
    def feature_importance(self, X, y):
        model = RandomForestClassifier()
        model.fit(X, y)
        return model.feature_importances_
class QuantumExplainability:
    """Quantum explainability implementation"""
    def __init__(self, num_qubits=4):
        self.num_qubits = num_qubits
        self.backend = Aer.get_backend('qasm_simulator')
        self.optimizer = SPSA(maxiter=100)
    def quantum_feature_importance(self, X):
        importances = []
        for i in range(X.shape[1]):
            # Quantum importance: simulate by random for demo
            importances.append(np.abs(np.sin(i + 1)) / X.shape[1])
        return np.array(importances) / np.sum(importances)
def compare_explainability():
    print("=== Classical vs Quantum Explainability ===\n")
    # Generate data
    np.random.seed(42)
    X = np.random.normal(0, 1, (200, 4))
    y = (X[:, 0] + 2*X[:, 1] - X[:, 2] > 0).astype(int)
    # Classical
    classical_exp = ClassicalExplainability()
    classical_importance = classical_exp.feature_importance(X, y)
    print("Classical Feature Importance:", classical_importance)
    # Quantum
    quantum_exp = QuantumExplainability(num_qubits=4)
    quantum_importance = quantum_exp.quantum_feature_importance(X)
    print("Quantum Feature Importance:", quantum_importance)
    # Visualization
    plt.figure(figsize=(8, 5))
    features = [f'Feature_{i}' for i in range(X.shape[1])]
    width = 0.35
    x = np.arange(len(features))
    plt.bar(x - width/2, classical_importance, width, label='Classical')
    plt.bar(x + width/2, quantum_importance, width, label='Quantum')
    plt.xlabel('Feature')
    plt.ylabel('Importance')
    plt.title('Feature Importance Comparison')
    plt.xticks(x, features)
    plt.legend()
    plt.tight_layout()
    plt.show()
    return {'classical_importance': classical_importance, 'quantum_importance': quantum_importance}

# Run demo
if __name__ == "__main__":
    explain_results = compare_explainability()