AutoML_Alex

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

AutoML_Alex 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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

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</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)

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)

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