Metadata-Version: 2.1
Name: automl-alex
Version: 1.3.1
Summary: State-of-the art Automated Machine Learning python library for Tabular Data
Home-page: UNKNOWN
Author: Alex Lekov
Author-email: itslek@yandex.ru
License: MIT
Project-URL: Bug Reports, https://github.com/Alex-Lekov/AutoML_Alex/issues
Project-URL: Source, https://github.com/Alex-Lekov/AutoML_Alex/
Description: 
        
        <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)](/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
        ```
        
        
        # 🚀 Examples
        
        Classifier:
        ```python
        from automl_alex import AutoMLClassifier
        
        model = AutoMLClassifier()
        model = model.fit(X_train, y_train, timeout=600)
        predicts = model.predict(X_test)
        ```
        
        Regression:
        ```python
        from automl_alex import AutoMLRegressor
        
        model = AutoMLRegressor()
        model = 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">
            
        <img width=800 src="https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/dashboard_2.gif" alt="Dashboard_2">
        
        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
        
        -   [ ] DL Encoders
        
        -   [ ] Add More libs (NNs)
        
        -   [ ] Multiclass Classification
        
        -   [ ] Build pipelines
        
        -   [ ] Docs Site
        
        
        # Contact
        
        [Telegram Group](https://t.me/automlalex)
        
        
Keywords: machine learning,data science,automated machine learning,automl,hyperparameter optimization,artificial intelligence,ensembling,stacking,blending,deep learning,tensorflow,deeplearning,lightgbm,gradient boosting,gbm,keras
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7.*
Description-Content-Type: text/markdown
