automl-alex


Nameautoml-alex JSON
Version 1.6.10 PyPI version JSON
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home_page
SummaryState-of-the art Automated Machine Learning python library for Tabular Data
upload_time2021-06-10 21:11:05
maintainer
docs_urlNone
authorAlex Lekov
requires_python>=3.7.*
licenseMIT
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
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requirements No requirements were recorded.
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<h3 align="center">AutoML Alex</h3>

<div align="center">

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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)

<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

-   [x] Docs Site

-   [ ] DL Encoders

-   [ ] Add More libs (NNs)

-   [ ] Multiclass Classification

-   [ ] Build pipelines


# Contact

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




            

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    "description": "\n\n<h3 align=\"center\">AutoML Alex</h3>\n\n<div align=\"center\">\n\n[![Downloads](https://pepy.tech/badge/automl-alex)](https://pepy.tech/project/automl-alex)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/automl-alex)\n![PyPI](https://img.shields.io/pypi/v/automl-alex)\n[![CodeFactor](https://www.codefactor.io/repository/github/alex-lekov/automl_alex/badge)](https://www.codefactor.io/repository/github/alex-lekov/automl_alex)\n[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/automlalex)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](/LICENSE)\n\n</div>\n\n---\n\n<p align=\"center\"> State-of-the art Automated Machine Learning python library for Tabular Data</p>\n\n## Works with Tasks:\n\n-   [x] Binary Classification\n\n-   [x] Regression\n\n-   [ ] Multiclass Classification (in progress...)\n\n### Benchmark Results\n<img width=800 src=\"https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png\" alt=\"bench\">\n\nThe bigger, the better   \nFrom [AutoML-Benchmark](https://github.com/Alex-Lekov/AutoML-Benchmark/) \n\n### Scheme\n<img width=800 src=\"https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png\" alt=\"scheme\">\n\n\n# Features\n\n- Automated Data Clean (Auto Clean)\n- Automated **Feature Engineering** (Auto FE)\n- Smart Hyperparameter Optimization (HPO)\n- Feature Generation\n- Feature Selection\n- Models Selection\n- Cross Validation\n- Optimization Timelimit and EarlyStoping\n- Save and Load (Predict new data)\n\n\n# Installation\n\n```python\npip install automl-alex\n```\n\n# Docs\n[DocPage](https://alex-lekov.github.io/AutoML_Alex/)\n\n# \ud83d\ude80 Examples\n\nClassifier:\n```python\nfrom automl_alex import AutoMLClassifier\n\nmodel = AutoMLClassifier()\nmodel.fit(X_train, y_train, timeout=600)\npredicts = model.predict(X_test)\n```\n\nRegression:\n```python\nfrom automl_alex import AutoMLRegressor\n\nmodel = AutoMLRegressor()\nmodel.fit(X_train, y_train, timeout=600)\npredicts = model.predict(X_test)\n```\n\nDataPrepare:\n```python\nfrom automl_alex import DataPrepare\n\nde = DataPrepare()\nX_train = de.fit_transform(X_train)\nX_test = de.transform(X_test)\n```\n\nSimple Models Wrapper:\n```python\nfrom automl_alex import LightGBMClassifier\n\nmodel = LightGBMClassifier()\nmodel.fit(X_train, y_train)\npredicts = model.predict_proba(X_test)\n\nmodel.opt(X_train, y_train,\n    timeout=600, # optimization time in seconds,\n    )\npredicts = model.predict_proba(X_test)\n```\n\nMore examples in the folder ./examples:\n\n- [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)\n- [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)\n- [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)\n- [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)\n- [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)\n- [Production Docker template](https://github.com/Alex-Lekov/AutoML_Alex/blob/master/examples/prod_sample)\n\n\n\n# What's inside\n\nIt integrates many popular frameworks:\n- scikit-learn\n- XGBoost\n- LightGBM\n- CatBoost\n- Optuna\n- ...\n\n\n# Works with Features\n\n-   [x] Categorical Features\n\n-   [x] Numerical Features\n\n-   [x] Binary Features\n\n-   [ ] Text\n\n-   [ ] Datetime\n\n-   [ ] Timeseries\n\n-   [ ] Image\n\n\n# Note\n\n- **With a large dataset, a lot of memory is required!**\nLibrary creates many new features. 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