import lime
import lime.lime_image
from skimage.segmentation import mark_boundaries
import matplotlib.pyplot as plt

# LIME Explainer 초기화
explainer = lime.lime_image.LimeImageExplainer()

# 설명 생성
explanation = explainer.explain_instance(
    image,                    # 설명할 이미지
    model.predict,           # 예측 함수
    top_labels=5,            # 상위 5개 클래스
    hide_color=0,            # 숨길 영역 색상
    num_samples=1000         # 샘플링 횟수
)

# 시각화
temp, mask = explanation.get_image_and_mask(
    explanation.top_labels[0],
    positive_only=True,
    num_features=5,
    hide_rest=False
)

plt.imshow(mark_boundaries(temp, mask))
plt.title('LIME Explanation: Important Regions')
plt.show()

import shap
import xgboost as xgb

# 모델 학습
model = xgb.XGBClassifier()
model.fit(X_train, y_train)

# SHAP Explainer 생성
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# 전역적 특징 중요도 시각화
shap.summary_plot(shap_values, X_test, plot_type="bar")

# 개별 예측 설명
shap.force_plot(
    explainer.expected_value,
    shap_values[0],
    X_test.iloc[0]
)

# 의존성 플롯 (특징 간 상호작용)
shap.dependence_plot(
    "feature_name",
    shap_values,
    X_test
)

import torch
import torch.nn as nn

class AttentionLayer(nn.Module):
    def __init__(self, hidden_dim):
        super(AttentionLayer, self).__init__()
        self.attention = nn.Linear(hidden_dim, 1)
    
    def forward(self, lstm_output):
        # Attention 가중치 계산
        attention_weights = torch.softmax(
            self.attention(lstm_output), 
            dim=1
        )
        
        # 가중 합 계산
        context_vector = torch.sum(
            attention_weights * lstm_output, 
            dim=1
        )
        
        return context_vector, attention_weights

# 사용 예시
model = AttentionLayer(hidden_dim=256)
output, attention = model(lstm_output)

# Attention 가중치 시각화
import matplotlib.pyplot as plt
plt.bar(range(len(attention[0])), attention[0].detach().numpy())
plt.xlabel('Token Position')
plt.ylabel('Attention Weight')
plt.title('Attention Visualization')
plt.show()

# 모델 드리프트 감지 예시
from scipy.stats import ks_2samp

def detect_drift(reference_data, current_data, threshold=0.05):
    """
    Kolmogorov-Smirnov 테스트로 데이터 분포 변화 감지
    """
    statistic, p_value = ks_2samp(reference_data, current_data)
    
    if p_value < threshold:
        print(f"⚠️ 드리프트 감지! p-value: {p_value}")
        return True
    else:
        print(f"✅ 정상 범위. p-value: {p_value}")
        return False

# 주기적으로 실행
drift_detected = detect_drift(
    train_data['feature_1'],
    production_data['feature_1']
)

if drift_detected:
    # 재학습 트리거 또는 알림 발송
    trigger_retraining()