Implement Projected Stochastic Gradient Shapley

As introduced in _Simon, Grah, and Thouvenot Vincent. ‘A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance’. In Machine Learning and Knowledge Extraction, edited by Andreas Holzinger, Peter Kieseberg, A Min Tjoa, and Edgar Weippl, 12279:97–115. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-57321-8_6._
Code available [here](https://github.com/ThalesGroup/shapkit)

The paper focuses on feature valuation, but it's just another approximation for Shapley Values based on convex optimization. It's also roughly on-par with Monte Carlo and sometimes worse, so maybe not so important. But may be worth checking.


阅读链接的论文，并检查 ThalesGroup/shapkit 仓库中的参考实现。了解用于近似 Shapley 值的投影随机梯度算法。确定在 pyDVL 代码库中添加新的近似方法的位置，可能是在类似 `pydvl.value.shapley` 的模块中。实现该算法，确保它与现有的数据估值 API 集成。编写测试，将其性能与 Monte Carlo 方法进行比较。

由索引模型根据 Issue 内容生成。