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AutoML_Alex

(★ 237)

State-of-the art Automated Machine Learning python library for Tabular Data

파일 탐색기

최종 버전 다운로드 (.zip)

- - __init__.py
  - model_catboost.py
  - model_lightgbm.py
  - model_xgboost.py
  - sklearn_models.py
- __init__.py
- _base.py
- _encoders.py
- _logger.py
- automl_alex.py
- cross_validation.py
- data_prepare.py
- optimizer.py
- - boxplot.png
  - cv.png
  - dashboard.gif
  - dashboard_2.gif
  - data-cleaning.png
  - magic.gif
  - shema.png
- - docker-compose.yaml
  - Dockerfile
  - fit_model.ipynb
  - main.py
  - requirements.txt
- 01_Quick_Start.ipynb
- 02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb
- 03_Models.ipynb
- 04_ModelsReview.ipynb
- 05_BestSingleModel.ipynb
- __init__.py
- test_alexautoml.py
- test_automlalex_benchmark.py
- test_cross_validation.py
- test_data_prepare.py
- test_models.py
- test_optimizer.py
.gitignore
_config.yml
CHANGELOG.md
Dockerfile
LICENSE
poetry.lock
pyproject.toml
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/Alex-Lekov/AutoML_Alex

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd AutoML_Alex

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker build -t automl-alex .

Dockerfile을 기반으로 실행 가능한 이미지를 빌드합니다.

docker run -p 8080:80 automl-alex

빌드된 이미지를 실제 컨테이너로 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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<h3 align="center">AutoML Alex</h3>

<div align="center">

[![Downloads](https://pepy.tech/badge/automl-alex)](https://pepy.tech/project/automl-alex)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/automl-alex)
![PyPI](https://img.shields.io/pypi/v/automl-alex)
[![CodeFactor](https://www.codefactor.io/repository/github/alex-lekov/automl_alex/badge)](https://www.codefactor.io/repository/github/alex-lekov/automl_alex)
[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/automlalex)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/Alex-Lekov/AutoML_Alex/master/LICENSE)

</div>

---

<p align="center"> State-of-the art Automated Machine Learning python library for Tabular Data</p>

## Works with Tasks:

-   [x] Binary Classification

-   [x] Regression

-   [ ] Multiclass Classification (in progress...)

### Benchmark Results
<img width=800 src="https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png" alt="bench">

The bigger, the better   
From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)

### Scheme
<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png" alt="scheme">

# Features

- Automated Data Clean (Auto Clean)
- Automated **Feature Engineering** (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Optimization Timelimit and EarlyStoping
- Save and Load (Predict new data)

# Installation

```python
pip install automl-alex
```

# Docs
[DocPage](https://alex-lekov.github.io/AutoML_Alex/)

# 🚀 Examples

Classifier:
```python
from automl_alex import AutoMLClassifier

model = AutoMLClassifier()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```

Regression:
```python
from automl_alex import AutoMLRegressor

model = AutoMLRegressor()
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
```

DataPrepare:
```python
from automl_alex import DataPrepare

de = DataPrepare()
X_train = de.fit_transform(X_train)
X_test = de.transform(X_test)
```

Simple Models Wrapper:
```python
from automl_alex import LightGBMClassifier

model = LightGBMClassifier()
model.fit(X_train, y_train)
predicts = model.predict_proba(X_test)

model.opt(X_train, y_train,
    timeout=600, # optimization time in seconds,
    )
predicts = model.predict_proba(X_test)
```

More examples in the folder ./examples:

- [01_Quick_Start.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/01_Quick_Start.ipynb)
- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb)
- [03_Models.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/03_Models.ipynb)
- [04_ModelsReview.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/04_ModelsReview.ipynb)
- [05_BestSingleModel.ipynb](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb)  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Alex-Lekov/AutoML_Alex/blob/master/examples/05_BestSingleModel.ipynb)
- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample)

# What's inside

It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...

# Works with Features

-   [x] Categorical Features

-   [x] Numerical Features

-   [x] Binary Features

-   [ ] Text

-   [ ] Datetime

-   [ ] Timeseries

-   [ ] Image

# Note

- **With a large dataset, a lot of memory is required!**
Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.

# Realtime Dashboard
Works with [optuna-dashboard](https://github.com/optuna/optuna-dashboard)

<img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard.gif" alt="Dashboard">

Run
```console
$ optuna-dashboard sqlite:///db.sqlite3
```

# Road Map

-   [x] Feature Generation

-   [x] Save/Load and Predict on New Samples

-   [x] Advanced Logging

-   [x] Add opt Pruners

-   [ ] Docs Site

-   [ ] DL Encoders

-   [ ] Add More libs (NNs)

-   [ ] Multiclass Classification

-   [ ] Build pipelines

# Contact

[Telegram Group](https://t.me/automlalex)