import numpy as np
from sklearn.ensemble import RandomForestRegressor

# 模拟多源数据融合
climate_data = np.random.rand(1000, 5)  # 温度、降水、湿度等
epidemic_data = np.random.rand(1000, 3) # 发病率、死亡率、传播速度
socio_data = np.random.rand(1000, 4)   # 人口密度、经济指标、卫生支出

# 构建特征矩阵
X = np.hstack((climate_data, epidemic_data, socio_data))
y = np.random.rand(1000)  # 目标变量：疾病传播风险指数

model = RandomForestRegressor()
model.fit(X, y)

from transformers import CLIPProcessor, CLIPModel

processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")

# 处理文本和图像数据
inputs = processor(text=["dengue fever symptoms", "malaria vector"], 
                  images=image_list, 
                  return_tensors="pt", 
                  padding=True)
outputs = model(**inputs)

class DomainAdapter(nn.Module):
    def __init__(self, base_model, adapter_dim=64):
        super().__init__()
        self.base = base_model
        self.adapter = nn.Sequential(
            nn.Linear(adapter_dim, 128),
            nn.ReLU(),
            nn.Linear(128, adapter_dim)
        )

    def forward(self, x):
        base_out = self.base(x)
        adapted = self.adapter(base_out)
        return adapted

import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)

# 可视化重要特征
shap.summary_plot(shap_values, X, feature_names=feature_labels)