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https://github.com/alex-lekov/automl_alex
State-of-the art Automated Machine Learning python library for Tabular Data
https://github.com/alex-lekov/automl_alex
auto-ml automatic-machine-learning automl cross-validation data-science data-science-projects hyperparameter-optimization hyperparameter-tuning machine-learning machine-learning-library machine-learning-models ml model-selection optimisation python sklearn stacking stacking-ensemble xgboost
Last synced: about 1 month ago
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State-of-the art Automated Machine Learning python library for Tabular Data
- Host: GitHub
- URL: https://github.com/alex-lekov/automl_alex
- Owner: Alex-Lekov
- License: mit
- Created: 2020-05-09T06:14:20.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-10-04T01:15:37.000Z (about 1 year ago)
- Last Synced: 2024-09-27T20:22:41.618Z (about 2 months ago)
- Topics: auto-ml, automatic-machine-learning, automl, cross-validation, data-science, data-science-projects, hyperparameter-optimization, hyperparameter-tuning, machine-learning, machine-learning-library, machine-learning-models, ml, model-selection, optimisation, python, sklearn, stacking, stacking-ensemble, xgboost
- Language: Python
- Homepage:
- Size: 25.9 MB
- Stars: 224
- Watchers: 7
- Forks: 42
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
AutoML Alex
[](https://pepy.tech/project/automl-alex)
[](https://www.codefactor.io/repository/github/alex-lekov/automl_alex)
[](https://t.me/automlalex)
[](/LICENSE)
---
State-of-the art Automated Machine Learning python library for Tabular Data
## Works with Tasks:
- [x] Binary Classification
- [x] Regression
- [ ] Multiclass Classification (in progress...)
### Benchmark Results
The bigger, the better
From [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/)
### 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) [](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) [](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) [](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) [](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) [](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)