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CONTRIBUTE

中文版本

Guidance

Thank you for your interest in contributing to EduCDM! Before you begin writing code, it is important that you share your intention to contribute with the team, based on the type of contribution:

  1. You want to propose a new feature and implement it.
    • Post about your intended feature in an issue, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
  2. You want to implement a feature or bug-fix for an outstanding issue.
    • Search for your issue in the EduCDM issue list.
    • Pick an issue and comment that you'd like to work on the feature or bug-fix.
    • If you need more context on a particular issue, please ask and we shall provide.

Once you implement and test your feature or bug-fix, please submit a Pull Request to EduCDM:

  1. Fork this repository to your branch.
  2. Modify the code. Note that we strongly recommend that you comply with our commit format specifications.
  3. Pass code tests and make the test coverage reach 100%. An example.
  4. Submit a Pull Request to EduCDM. Note that we provide a standard template of Pull Request here. Please fill in the information carefully.

The followings are some helpful guidelines for different types contribution:

Add new dataset

If you want to add the data analysis or a new dataset, please submit a Pull Request to EduData.

Add new CDM model

The newly implemented CDM model requires:

  1. Dataset processing.
  2. Inherit the class CDM in EduCDM/meta.py and implement four methods in it.
  3. Write the corresponding example code for the model (This refers to a demo that can be tested by others). It should include at least notebook and script. An example.
  4. Write the corresponding test code for the model and make sure that the test coverage is 100%. An example.

Dataset Processing

As for the dataset preprocessing, we suggest:

  1. Write a script, and make sure that:
    • Processing and converting of the raw dataset.
    • Partitioning Training/validation/test dataset.
  2. Provide or use CDBD dataset (which is already divided into training/validation/test datasets).

Module

All modules are inherited from Class CDM, it will raise NotImplementedError if the functions are not implemented.

Note that we do not constrain your neural network or algorithms (for example, the network construction, optimizer, loss function definitions, etc.).

  • Train module

This module is a training module, which is used to train model.

    def train(self, *args, **kwargs) -> ...:
        raise NotImplementedError
  • Eval module

This module is an evaluation module, which is used to verify and test the model.

    def eval(self, *args, **kwargs) -> ...:
        raise NotImplementedError
  • Save module

This module is a model saving module, which is used to save the trained model.

    def save(self, *args, **kwargs) -> ...:
        raise NotImplementedError
  • Load module

This module is a model loading module, which is used to load the saved model.

    def load(self, *args, **kwargs) -> ...:
        raise NotImplementedError

Demo

Make sure you make a demo for your model. An example.

Docs Format

Numpy docs format is used:

function

    Parameters
    ----------
    Variable 1: type <int, float>, optional or not
       description
    Variable 2: type <int, float>, optional or not
       description
    ...

    Returns
    -------
    Variable: type <int, float>
       description

    See Also (Optional)
    --------
    Similar to function():

    Examples (Optional)
    --------
    >>> For example:
        ...

About Commit

commit format

[<type>](<scope>) <subject>

type

  • feat：New feature。
  • fix/to：Fix bugs, either found in Q&A or found in your own use.
    • fix：Generating diff and fixes the problem automatically. Suitable for one submit to fix the problem directly.
    • to：Generating only diff but does not automatically fix the problem. Suitable for multiple submissions. Use fix when the final fix problem is committed.
  • docs：Documentation.
  • style：Format (do not affect code execution).
  • refactor：Refactoring (not new features or bug fix).
  • perf：Optimize related issues, such as code performance, user experience.
  • test：Add test unit.
  • chore：Build process or auxiliary tools change.
  • revert：Roll back to the previous version.
  • merge：Code merge.
  • sync：Synchronizing the bug of main or branch。
  • arch: Engineering documents or tools change.
scope (optional)

Scope is used to describe the impact of the commit, such as the data layer, the control layer, the view layer, and so on, depending on the project.

For example, in Angular, it can be location, browser, compile, compile, rootScope, ngHref, ngClick, ngView, and so on. If your changes affect more than one scope, you can use * instead.

subject (mandatory)

A subject is a short description of the purpose of the commit, not more than 50 characters.

There is no period or other punctuation at the end.

Example

  • [docs] update the README.md
git commit -m "[docs] update the README.md"

FAQ

Q: I have carefully tested the code in my local system (all testing passed) but still failed in online CI?

A: There are two possible reasons:

  1. the online CI system is different from your local system;
  2. there are some network error causing the downloading test failed, which you can find in the CI log.

For the second reason, all you need to do is to retry the test.