- data source,
skat.rwrite theSKAT.haplotypesdata inhp.csvfile. - data generation,
datagen.pycreate train/dev/test data for torch model. - pytorch model: utils see
dataloader.py,model.pyandutils.py; training process seetrain.py
To run the whole process, see main.py
- torch model part finished
- datagen finished (some methods of generating have not been realized)
main.pyalmost finished:- considering better design of simulation study;
- some questions concerning assessment metrics.
训练中使用的loss是（负的）后验概率 $$ \mathcal{L}(y, \pi, \sigma^2,\mu)= \log \sum_{i=1}^\mathrm{KMIX} \frac{\pi_i}{\sigma_i}\exp\left[ -\frac{(y-\mu_i)^2}{2\sigma_i^2} \right] $$
（今天晚上梯子好像挂掉了，push不上来= =所以只能先更新一下readme, sry)
- 跑通了整个代码（终于）
- python里算GMM的KL散度没有现成的工具，无奈目前用数值积分凑合一下，testset跑得很慢[500item/3min].
- 计算结果正确性在极端情况下存疑，和
R::FNN::KL.divergence给出的结果有一定出入（大体还是相近
- 计算结果正确性在极端情况下存疑，和
- 暂时没能复现出来之前的结果，目前训练出来的testset KLloss稳定大于1，还在调查是算KL散度的numerical问题/训练框架的问题
- wandb.ai可以在上面看到训练参数&结果，还没开始做超参搜索的工作因为想先把之前的结果复现出来= =
数据为$\mathcal{D}=(G,X)$，实际生成过程为$\mathbb{P}(E|G,X)$，模型的部分为$\mathbb{P}(E|G)$（e.g.用一个更大的pre-training set训练出$\mathbb{P}(E|G)$，然后将其应用在$\mathcal{D}$上) ，得到 $$ {G_i,X_i}\mapsto { \mathbb{P}(E_i|G_i),X_i } $$ 然后在这个经过处理的$\mathcal{D}$上尝试用conformal prediction方法，得到一些检验结果。
i.e.在$\mathbb{P}(E|G)$其实包含了一些$X$的信息，所以我们在训练模型的时候原则上还是应该让模型去预测/学习那个ground truth, say
- 后面再说，先把GMM预测模型搞好罢（哭）
主要解决了一些工程问题；有一些初步的结果，完成了接下来的框架，在研究搬到服务器上的事。 detailed online report
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? Use the template at the bottom!
- Create or upload files
- Add files using the command line or push an existing Git repository with the following command:
cd existing_repo
git remote add origin https://gitlab.com/gmm_with_datagen/test.git
git branch -M main
git push -uf origin main
- Invite team members and collaborators
- Create a new merge request
- Automatically close issues from merge requests
- Enable merge request approvals
- Automatically merge when pipeline succeeds
Use the built-in continuous integration in GitLab.
- Get started with GitLab CI/CD
- Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)
- Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy
- Use pull-based deployments for improved Kubernetes management
- Set up protected environments
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to makeareadme.com for this template.
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
Choose a self-explaining name for your project.
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
If you have ideas for releases in the future, it is a good idea to list them in the README.
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
Show your appreciation to those who have contributed to the project.
For open source projects, say how it is licensed.
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.