用于表格数据的最先进的自动机器学习python库___.zip

<h3 align="center">AutoML Alex</h3> <div align="center"> [](https://pepy.tech/project/automl-alex)   [](https://www.codefactor.io/repository/github/alex-lekov/automl_alex) [](https://t.me/automlalex) [](/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 ``` # 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) <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 - [ ] Docs Site - [ ] DL Encoders - [ ] Add More libs (NNs) - [ ] Multiclass Classification - [ ] Build pipelines # Contact [Telegram Group](https://t.me/automlalex)