Nameautoml-alex JSON
Version 1.6.10 PyPI version JSON
SummaryState-of-the art Automated Machine Learning python library for Tabular Data
upload_time2021-06-10 21:11:05
authorAlex Lekov
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
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.

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

<div align="center">

![PyPI - Python Version](



<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="" alt="bench">

The bigger, the better   
From [AutoML-Benchmark]( 

### Scheme
<img width=800 src="" 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

pip install automl-alex

# Docs

# 🚀 Examples

from automl_alex import AutoMLClassifier

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

from automl_alex import AutoMLRegressor

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

from automl_alex import DataPrepare

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

Simple Models Wrapper:
from automl_alex import LightGBMClassifier

model = LightGBMClassifier(), 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](  [![Open in Colab](](
- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](  [![Open in Colab](](
- [03_Models.ipynb](  [![Open in Colab](](
- [04_ModelsReview.ipynb](  [![Open in Colab](](
- [05_BestSingleModel.ipynb](  [![Open in Colab](](
- [Production Docker template](

# 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](

<img width=800 src="" alt="Dashboard">

$ 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](


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    "description": "\n\n<h3 align=\"center\">AutoML Alex</h3>\n\n<div align=\"center\">\n\n[![Downloads](](\n![PyPI - Python Version](\n![PyPI](\n[![CodeFactor](](\n[![Telegram](](\n[![License](](/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=\"\" alt=\"bench\">\n\nThe bigger, the better   \nFrom [AutoML-Benchmark]( \n\n### Scheme\n<img width=800 src=\"\" 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](\n\n# \ud83d\ude80 Examples\n\nClassifier:\n```python\nfrom automl_alex import AutoMLClassifier\n\nmodel = AutoMLClassifier()\, y_train, timeout=600)\npredicts = model.predict(X_test)\n```\n\nRegression:\n```python\nfrom automl_alex import AutoMLRegressor\n\nmodel = AutoMLRegressor()\, 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()\, 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](  [![Open in Colab](](\n- [02_Data_Cleaning_and_Encoding_(DataPrepare).ipynb](  [![Open in Colab](](\n- [03_Models.ipynb](  [![Open in Colab](](\n- [04_ModelsReview.ipynb](  [![Open in Colab](](\n- [05_BestSingleModel.ipynb](  [![Open in Colab](](\n- [Production Docker template](\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. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.\n\n\n# Realtime Dashboard\nWorks with [optuna-dashboard](\n\n<img width=800 src=\"\" alt=\"Dashboard\">\n\nRun\n```console\n$ optuna-dashboard sqlite:///db.sqlite3\n```\n\n# Road Map\n\n-   [x] Feature Generation\n\n-   [x] Save/Load and Predict on New Samples\n\n-   [x] Advanced Logging\n\n-   [x] Add opt Pruners\n\n-   [x] Docs Site\n\n-   [ ] DL Encoders\n\n-   [ ] Add More libs (NNs)\n\n-   [ ] Multiclass Classification\n\n-   [ ] Build pipelines\n\n\n# Contact\n\n[Telegram Group](\n\n\n\n",
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