Contributing

Thank you for your interest in contributing to carefree-learn! 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 carefree-learn 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 include some unittests and submit a Pull Request to https://github.com/carefree0910/carefree-learn.

Developing

To develop carefree-learn on your machine, here are some tips:

  1. Uninstall all existing carefree-learn installs:
conda uninstall carefree-learn
pip uninstall carefree-learn

  1. Follow Installation Guide to install carefree-learn. Remember to choose the GitHub tab in the pip installation section.

  2. Follow Style Guide and happy coding!

Style Guide

carefree-learn adopted black and mypy to stylize its codes, so you may need to check the format, coding style and type hint with them before your codes could actually be merged.

Besides, there are a few more principles that I'm using for sorting imports:

  • From short to long (for both naming and path).
  • From a to z (alphabetically).
  • Divided into four sections:
    1. import ...
    2. import ... as ...
    3. from ... import ...
    4. relative imports
  • From general to specific (a * will always appear at the top of each section)

Here's an example to illustrate these (source code):

import os
import json
import torch
import optuna
import numpy as np
import optuna.visualization as vis
from typing import *
from functools import partial
from tqdm.autonotebook import tqdm
from cftool.misc import shallow_copy_dict
from cftool.misc import lock_manager
from cftool.misc import Saving
from cftool.ml.utils import scoring_fn_type
from cfdata.tabular import task_type_type
from cfdata.tabular import parse_task_type
from cfdata.tabular import TabularData
from optuna.trial import TrialState
from optuna.trial import FrozenTrial
from optuna.importance import BaseImportanceEvaluator
from plotly.graph_objects import Figure
from .basic import *
from .ensemble import *
from .hpo import optuna_tune
from .hpo import default_scoring
from .hpo import optuna_params_type
from .hpo import OptunaPresetParams
from .production import Pack
from .production import Predictor
from ..types import data_type

But after all, this is not a strict constraint so everything will be fine as long as it 'looks good'🤣

Creating a Pull Request

When you are ready to create a pull request, please try to keep the following in mind.

Title

The title of your pull request should

  • briefly describe and reflect the changes
  • wrap any code with backticks

Description

The description of your pull request should

  • describe the motivation
  • describe the changes
  • if still work-in-progress, describe remaining tasks
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